Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review

Reconocimiento de actividades humanas por medio de extracción de características y técnicas de inteligencia artificial: una revisión

Autores/as

Palabras clave:

reconocimiento de la actividad humana, detección de caídas, tipos de actividades, extracción de características, redes neuronales convolucionales (es).

Palabras clave:

human activity recognition, fall detection, type of activities, feature extraction, convolutional neural networks (en).

Biografía del autor/a

José Camilo Eraso Guerrero, Universidad del Cauca

Ingeniero electrónico, candidato a Magíster en Automática. Universidad del Cauca

Elena Muñoz España, Universidad del Cauca

Ingeniero en Electrónica y Telecomunicaciones, especialista en Informática Industrial, especialista en Redes y Servicios Telemáticos, Magíster en Electrónica y Telecomunicaciones. Profesor de la Universidad del Cauca

Mariela Muñoz Añasco, Universidad del Cauca

Ingeniero Industrial, Magíster en Administración de Empresas, Magíster en Automática, Doctorado en Automática, Robótica e Informática Industrial. Profesor de la Universidad del Cauca.

Referencias

Adhikari, K., Bouchachia, H., & Nait-Charif, H. (2017, May 8-12). Activity recognition for indoor fall detection using convolutional neural network [Conference presentation]. 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA). Nagoya, Japan. https://doi.org/10.23919/MVA.2017.7986795 DOI: https://doi.org/10.23919/MVA.2017.7986795

Akhavian, R., & Behzadan, A. H. (2016). Smartphone-based construction workers’ activity recognition and classification. Automation in Construction, 71(Part 2), 198-209. https://doi.org/10.1016/j.autcon.2016.08.015 DOI: https://doi.org/10.1016/j.autcon.2016.08.015

Amiri, S. M., Pourazad, M. T., Nasiopoulos, P., & Leung, V. C. M. (2014). Improved human action recognition in a smart home environment setting. IRBM, 35(6), 321-328. https://doi.org/10.1016/j.irbm.2014.10.005 DOI: https://doi.org/10.1016/j.irbm.2014.10.005

Auvinet, E., Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J. (n.d.). Multiple cameras fall dataset. http://www.iro.umontreal.ca/~labimage/Dataset/

Auvinet, E., Multon, F., Saint-Arnaud, A., Rousseau, J., & Meunier, J. (2011). Fall detection with multiple cameras: An occlusion-resistant method based on 3-D silhouette vertical distribution. IEEE Transactions on Information Technology in Biomedicine, 15(2), 290-300. https://doi.org/10.1109/TITB.2010.2087385 DOI: https://doi.org/10.1109/TITB.2010.2087385

Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., & Havinga, P. (2010, February 22-25). Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey [Conference presentation]. 23th International Conference on Architecture of Computing Systems, Hannover, Germany. https://ieeexplore.ieee.org/document/5759000

Banos, O., Damas, M., Pomares, H., Prieto, A., & Rojas, I. (2012). Daily living activity recognition based on statistical feature quality group selection. Expert Systems with Applications, 39(9), 8013-8021. https://doi.org/10.1016/j.eswa.2012.01.164 DOI: https://doi.org/10.1016/j.eswa.2012.01.164

Barbosa-Chacón, J. W., Barbosa-Herrera, J. C., & Rodríguez-Villabona, M. (2013). Revision y análisis documental para estado del arte: una propuesta metodológica desde el contexto de la sistematización de experiencias educativas. Scielo Analytics, 27, 83-105. https://doi.org/10.1016/S0187-358X(13)72555-3 DOI: https://doi.org/10.1016/S0187-358X(13)72555-3

Ben Mabrouk, A., & Zagrouba, E. (2018). Abnormal behavior recognition for intelligent video surveillance systems: A review. Expert Systems with Applications, 91, 480-491. https://doi.org/10.1016/j.eswa.2017.09.029 DOI: https://doi.org/10.1016/j.eswa.2017.09.029

Berlin, S. J., & John, M. (2016, October 24-27). Human interaction recognition through deep learning network [Conference presentation]. 2016 IEEE International Carnahan Conference on Security Technology (ICCST), Orlando, FL, USA. https://doi.org/10.1109/CCST.2016.7815695 DOI: https://doi.org/10.1109/CCST.2016.7815695

Brophy, E., Domínguez-Veiga, J. J., Wang, Z., & Ward, T. E. (2018, June 21-22). A machine vision approach to human activity recognition using photoplethysmograph sensor data [Conference presentation]. 2018 29th Irish Signals and Systems Conference (ISSC), Belfast, UK. https://doi.org/10.1109/ISSC.2018.8585372 DOI: https://doi.org/10.1109/ISSC.2018.8585372

Cai, X., Liu, X., Li, S., & Han, G. (2019, October 16-19). Fall detection based on colorization coded MHI combining with convolutional neural network [Conference presentation]. 2019 IEEE 19th International Conference on Communication Technology (ICCT), Xi'an, China. https://doi.org/10.1109/ICCT46805.2019.8947223 DOI: https://doi.org/10.1109/ICCT46805.2019.8947223

Chakraborty, B., Holte, M. B., Moeslund, T. B., and González, J. (2012). Selective spatio-temporal interest points. Computer Vision and Image Understanding, 116(3), 396-410. https://doi.org/10.1016/j.cviu.2011.09.010 DOI: https://doi.org/10.1016/j.cviu.2011.09.010

Charfi, I., Miteran, J., Dubois, J., Atri, M., & Tourki, R. (2013). Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification. Journal of Electronic Imaging, 22(4), 041106. https://doi.org/10.1117/1.JEI.22.4.041106 DOI: https://doi.org/10.1117/1.JEI.22.4.041106

Chen, L., Nugent, C. D., & Wang, H. (2012). A knowledge-driven approach to activity recognition in smart homes. IEEE Transactions on Knowledge and Data Engineering, 24(6), 961-974. https://doi.org/10.1109/TKDE.2011.51 DOI: https://doi.org/10.1109/TKDE.2011.51

Computer Vision Department of the MICA International Research Institute & Posts & Telecommunications Institute of Technology (COMVIS-PTIT) (n.d.). Continuous multimodal multi-view dataset of human fall (CMDFALL). https://www.mica.edu.vn/perso/Tran-Thi-Thanh-Hai/CMDFALL.html

Concone, F., Re, G. Lo, & Morana, M. (2019). A fog-based application for human activity recognition using personal smart devices. ACM Transactions on Internet Technology, 19(2), 1-20. https://doi.org/10.1145/3266142 DOI: https://doi.org/10.1145/3266142

Contreras-Contreras, G. F., Medina-Delgado, B., Acevedo-Jaimes, B. R., & Guevara-Ibarra, D. (2022). Metodología de desarrollo de técnicas de agrupamiento de datos usando aprendizaje automático. Tecnura, 26(72), 42-58. https://doi.org/10.14483/22487638.17246 DOI: https://doi.org/10.14483/22487638.17246

Cosar, S., Donatiello, G., Bogorny, V., Garate, C., Alvares, L. O., & Bremond, F. (2017). Toward abnormal trajectory and event detection in video surveillance. IEEE Transactions on Circuits and Systems for Video Technology, 27(3), 683-695. https://doi.org/10.1109/TCSVT.2016.2589859 DOI: https://doi.org/10.1109/TCSVT.2016.2589859

Das Dawn, D., & Shaikh, S. H. (2016). A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector. The Visual Computer, 32(3), 289-306. https://doi.org/10.1007/s00371-015-1066-2 DOI: https://doi.org/10.1007/s00371-015-1066-2

Debard, G., Mertens, M., Deschodt, M., Vlaeyen, E., Devriendt, E., Dejaeger, E., Milisen, K., Tournoy, J., Croonenborghs, T., Goedemé, T. Tuytelaars, T., & Vanrumste, B. (2016). Camera-based fall detection using real-world versus simulated data: How far are we from the solution? Journal of Ambient Intelligence and Smart Environments, 8(2) 149-168. https://doi.org/10.3233/AIS-160369 DOI: https://doi.org/10.3233/AIS-160369

Durrant-Whyte, H., Roy, N., & Abbeel, P. (2012). Robotics: Science and Systems VII. MIT Press. DOI: https://doi.org/10.7551/mitpress/9481.001.0001

Efros, Berg, Mori, & Malik. (2003, October 13-16). Recognizing action at a distance [Conference presentation]. 9th IEEE International Conference on Computer Vision, Nice, France. https://doi.org/10.1109/ICCV.2003.1238420 DOI: https://doi.org/10.1109/ICCV.2003.1238420

El Kaid, A., Baïna, K., & Baïna, J. (2019). Reduce false positive alerts for elderly person fall video-detection algorithm by convolutional neural network model. Procedia Computer Science, 148, 2-11. https://doi.org/10.1016/j.procs.2019.01.004 DOI: https://doi.org/10.1016/j.procs.2019.01.004

Elbasiony, R., & Gomaa, W. (2020). A survey on human activity recognition based on temporal signals of portable inertial sensors. In A. E. Hassanien, A. T. Azar, T. Gaber, R. Bhatnagar, & M. F. Tolba (Eds.), The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) (pp. 734-745). Springer. https://doi.org/10.1007/978-3-030-14118-9_72 DOI: https://doi.org/10.1007/978-3-030-14118-9_72

Espinosa, R., Ponce, H., Gutiérrez, S., Martínez-Villaseñor, L., Brieva, J., & Moya-Albor, E. (2019). A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset. Computers in Biology and Medicine, 115, 103520. https://doi.org/10.1016/j.compbiomed.2019.103520 DOI: https://doi.org/10.1016/j.compbiomed.2019.103520

Fan, Y., Levine, M. D., Wen, G., & Qiu, S. (2017). A deep neural network for real-time detection of falling humans in naturally occurring scenes. Neurocomputing, 260, 43-58. https://doi.org/10.1016/j.neucom.2017.02.082 DOI: https://doi.org/10.1016/j.neucom.2017.02.082

Foroughi, H., Aski, B. S., & Pourreza, H. (2008). Intelligent video surveillance for monitoring fall detection of elderly in home environments [Conference presentation]. 2008 11th International Conference on Computer and Information Technology, Khulna, Bangladesh. https://doi.org/10.1109/ICCITECHN.2008.4803020 DOI: https://doi.org/10.1109/ICCITECHN.2008.4803020

Goudelis, G., Tsatiris, G., Karpouzis, K., & Kollias, S. (2015). Fall detection using history triple features. In ACM (Eds.), Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments - PETRA ’15 (art. 81). ACM Press. https://doi.org/10.1145/2769493.2769562 DOI: https://doi.org/10.1145/2769493.2769562

Han, J., Shao, L., Xu, D., & Shotton, J. (2013). Enhanced computer vision with Microsoft Kinect sensor: A review. IEEE Transactions on Cybernetics, 43(5), 1318-1334. https://doi.org/10.1109/TCYB.2013.2265378 DOI: https://doi.org/10.1109/TCYB.2013.2265378

Harris, C., & Stephens, M. (1988). A combined edge and corner detector. In C. J. Taylor (Ed.), Proceedings of the Alvey Vision Conference (pp. 23.1-23.6). Alvey Vision Club. DOI: https://doi.org/10.5244/C.2.23

Hassan, M. M., Uddin, M. Z., Mohamed, A., & Almogren, A. (2018). A robust human activity recognition system using smartphone sensors and deep learning. Future Generation Computer Systems, 81, 303-313. https://doi.org/10.1016/j.future.2017.11.029 DOI: https://doi.org/10.1016/j.future.2017.11.029

Hbali, Y., Hbali, S., Ballihi, L., & Sadgal, M. (2018). Skeleton‐based human activity recognition for elderly monitoring systems. IET Computer Vision, 12(1), 16-26. https://doi.org/10.1049/iet-cvi.2017.0062 DOI: https://doi.org/10.1049/iet-cvi.2017.0062

He, K., Zhang, X., Ren, S., & Sun, J. (2016, June 27-30). Deep residual learning for image recognition [Conference presentation]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA. https://doi.org/10.1109/CVPR.2016.90 DOI: https://doi.org/10.1109/CVPR.2016.90

Hsieh, J.-W., Chuang, C.-H., Alghyaline, S., Chiang, H.-F., & Chiang, C.-H. (2014). Abnormal scene change detection from a moving camera using bags of patches and spider-web map. IEEE Sensors Journal, 15(5), 2866-2881. https://doi.org/10.1109/JSEN.2014.2381257 DOI: https://doi.org/10.1109/JSEN.2014.2381257

Hsieh, Y.-Z., & Jeng, Y.-L. (2018). Development of home intelligent fall detection iot system based on feedback optical flow convolutional neural network. IEEE Access, 6, 6048-6057. https://doi.org/10.1109/ACCESS.2017.2771389 DOI: https://doi.org/10.1109/ACCESS.2017.2771389

Ismail, S. J., Rahman, M. A. A., Mazlan, S. A., & Zamzuri, H. (2015, October 18-20). Human gesture recognition using a low cost stereo vision in rehab activities [Conference presentation]. 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), Langkawi, Malaysia. https://doi.org/10.1109/IRIS.2015.7451615 DOI: https://doi.org/10.1109/IRIS.2015.7451615

Jalal, A., Kim, Y.-H., Kim, Y.-J., Kamal, S., & Kim, D. (2017). Robust human activity recognition from depth video using spatiotemporal multi-fused features. Pattern Recognition, 61, 295-308. https://doi.org/10.1016/j.patcog.2016.08.003 DOI: https://doi.org/10.1016/j.patcog.2016.08.003

Jalal, A., Uddin, M. Z., Kim, J. T., & Kim, T.-S. (2012). Recognition of human home activities via depth silhouettes and ℜ transformation for smart homes. Indoor and Built Environment, 21(1), 184-190. https://doi.org/10.1177/1420326X11423163 DOI: https://doi.org/10.1177/1420326X11423163

Kahani, R., Talebpour, A., & Mahmoudi-Aznaveh, A. (2019). A correlation based feature representation for first-person activity recognition. Multimedia Tools and Applications, 78, 21673-21694. https://doi.org/10.1007/s11042-019-7429-3 DOI: https://doi.org/10.1007/s11042-019-7429-3

Keceli, A. S., & Burak Can, A. (2013, April 24-26). Recognition of human actions by using depth information [Conference presentation]. 2013 21st Signal Processing and Communications Applications Conference (SIU), Haspolat, Turkey. https://doi.org/10.1109/SIU.2013.6531211 DOI: https://doi.org/10.1109/SIU.2013.6531211

Khan, Z. A., & Sohn, W. (2011). Abnormal human activity recognition system based on R-transform and kernel discriminant technique for elderly home care. IEEE Transactions on Consumer Electronics, 57(4), 1843-1850. https://doi.org/10.1109/TCE.2011.6131162 DOI: https://doi.org/10.1109/TCE.2011.6131162

Khan, Z. A., & Sohn, W. (2013). A hierarchical abnormal human activity recognition system based on R-transform and kernel discriminant analysis for elderly health care. Computing, 95(2), 109-127. https://doi.org/10.1007/s00607-012-0216-x DOI: https://doi.org/10.1007/s00607-012-0216-x

Khraief, C., Benzarti, F., & Amiri, H. (2019). Convolutional Neural network based on dynamic motion and shape variations for elderly fall detection. International Journal of Machine Learning and Computing, 9(6), 814-820. https://doi.org/10.18178/ijmlc.2019.9.6.878 DOI: https://doi.org/10.18178/ijmlc.2019.9.6.878

Khraief, C., Benzarti, F., & Amiri, H. (2020). Elderly fall detection based on multi-stream deep convolutional networks. Multimedia Tools and Applications, 79, 19537-19560. https://doi.org/10.1007/s11042-020-08812-x DOI: https://doi.org/10.1007/s11042-020-08812-x

Kim, E., Helal, S., & Cook, D. (2010). Human activity recognition and pattern discovery. IEEE Pervasive Computing, 9(1), 48-53. https://doi.org/10.1109/MPRV.2010.7 DOI: https://doi.org/10.1109/MPRV.2010.7

Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet classification with deep convolutional neural networks [Conference presentation]. 26th Annual Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA. https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf

Kwolek, B., & Kepski, M. (2014). Human fall detection on embedded platform using depth maps and wireless accelerometer. Computer Methods and Programs in Biomedicine, 117(3), 489-501. https://doi.org/10.1016/j.cmpb.2014.09.005 DOI: https://doi.org/10.1016/j.cmpb.2014.09.005

Laptev, I., & Lindeberg, T. (2003, October 13-16). Space-time interest points [Conference presentation]. Ninth IEEE International Conference on Computer Vision, Nice, France. https://doi.org/10.1109/ICCV.2003.1238378 DOI: https://doi.org/10.1109/ICCV.2003.1238378

Laptev, I. (2005). On space-time interest points. International Journal of Computer Vision, 64, 107-123. https://doi.org/10.1007/s11263-005-1838-7 DOI: https://doi.org/10.1007/s11263-005-1838-7

Lawrence, E., Sax, C., Navarro, K. F., & Qiao, M. (2010, February 10-16). Interactive games to improve quality of life for the elderly: Towards integration into a WSN monitoring system [Conference presentation]. 2010 Second International Conference on EHealth, Telemedicine, and Social Medicine, Saint Marteen, Netherlands Antilles. https://doi.org/10.1109/eTELEMED.2010.21 DOI: https://doi.org/10.1109/eTELEMED.2010.21

Li, H., Shrestha, A., Fioranelli, F., Kernec, J. Le, & Heidari, H. (2018, October 28-31). Hierarchical classification on multimodal sensing for human activity recogintion and fall detection [Conference presentation]. 2018 IEEE SENSORS, New Delhi, India. https://doi.org/10.1109/ICSENS.2018.8589797 DOI: https://doi.org/10.1109/ICSENS.2018.8589797

Li, X., Pang, T., Liu, W., & Wang, T. (2017, October 14-16). Fall detection for elderly person care using convolutional neural networks [Conference presentation]. 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China. https://doi.org/10.1109/CISP-BMEI.2017.8302004 DOI: https://doi.org/10.1109/CISP-BMEI.2017.8302004

Liu, L., & Shao, L. (2013). Learning discriminative representations from RGB-D video data. In F. Rossi (Ed.), IJCAI '13: Proceedings of the Twenty-Third international joint conference on Artificial Intelligence (pp. 1493-1500). ACM https://dl.acm.org/doi/10.5555/2540128.2540343

Liu, Y., Li, X., & Jia, L. (2014, June 29 - July 4). Abnormal crowd behavior detection based on optical flow and dynamic threshold [Conference presentation]. 11th World Congress on Intelligent Control and Automation, Shenyang, China. https://doi.org/10.1109/WCICA.2014.7053189 DOI: https://doi.org/10.1109/WCICA.2014.7053189

Lohit, S., Bansal, A., Shroff, N., Pillai, J., Turaga, P., & Chellappa, R. (2018, June 18-22). Predicting dynamical evolution of human activities from a single image [Conference presentation]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA. https://doi.org/10.1109/CVPRW.2018.00079 DOI: https://doi.org/10.1109/CVPRW.2018.00079

Lu, N., Ren, X., Song, J., & Wu, Y. (2017, August 20-23). Visual guided deep learning scheme for fall detection [Conference presentation]. 2017 13th IEEE Conference on Automation Science and Engineering (CASE), Xi'an, China. https://doi.org/10.1109/COASE.2017.8256202 DOI: https://doi.org/10.1109/COASE.2017.8256202

Ma, C., Shimada, A., Uchiyama, H., Nagahara, H., & Taniguchi, R. (2019). Fall detection using optical level anonymous image sensing system. Optics & Laser Technology, 110, 44-61. https://doi.org/10.1016/j.optlastec.2018.07.013 DOI: https://doi.org/10.1016/j.optlastec.2018.07.013

Ma, X., Wang, H., Xue, B., Zhou, M., Ji, B., & Li, Y. (2014). Depth-based human fall detection via shape features and improved extreme learning machine. IEEE Journal of Biomedical and Health Informatics, 18(6), 1915-1922. https://doi.org/10.1109/JBHI.2014.2304357 DOI: https://doi.org/10.1109/JBHI.2014.2304357

Martínez-Villaseñor, L., Ponce, H., Brieva, J., Moya-Albor, E., Núñez-Martínez, J., & Peñafort-Asturiano, C. (2019). UP-Fall detection dataset: A multimodal approach. Sensors, 19(9), 1988. https://doi.org/10.3390/s19091988 DOI: https://doi.org/10.3390/s19091988

Mastorakis, G., & Makris, D. (2014). Fall detection system using Kinect’s infrared sensor. Journal of Real-Time Image Processing, 9(4), 635-646. https://doi.org/10.1007/s11554-012-0246-9 DOI: https://doi.org/10.1007/s11554-012-0246-9

Nguyen, T. V., Song, Z., & Yan, S. (2015). STAP: Spatial-Temporal Attention-Aware Pooling for action recognition. IEEE Transactions on Circuits and Systems for Video Technology, 25(1), 77-86. https://doi.org/10.1109/TCSVT.2014.2333151 DOI: https://doi.org/10.1109/TCSVT.2014.2333151

Nguyen, V. A., Le, T. H., & Nguyen, T. T. (2016). Single camera based fall detection using motion and human shape features. In ACM (Eds.), Proceedings of the Seventh Symposium on Information and Communication Technology - SoICT ’16. (pp. 339-344) ACM Press. https://doi.org/10.1145/3011077.3011103 DOI: https://doi.org/10.1145/3011077.3011103

Ni, B., Pei, Y., Moulin, P., & Yan, S. (2013). Multilevel depth and image fusion for human activity detection. IEEE Transactions on Cybernetics, 43(5), 1383-1394. https://doi.org/10.1109/TCYB.2013.2276433 DOI: https://doi.org/10.1109/TCYB.2013.2276433

Nivia-Vargas, A. M., & Jaramillo-Jaramillo, I. (2018). La industria de sensores en Colombia. Tecnura, 22(57), 44-54. https://doi.org/10.14483/22487638.13518 DOI: https://doi.org/10.14483/22487638.13518

Nizam, Y., Mohd, M. N. H., & Jamil, M. M. A. (2017). Human fall detection from depth images using position and velocity of subject. Procedia Computer Science, 105, 131-137. https://doi.org/10.1016/j.procs.2017.01.191 DOI: https://doi.org/10.1016/j.procs.2017.01.191

Núñez-Marcos, A., Azkune, G., & Arganda-Carreras, I. (2017). Vision-based fall detection with convolutional neural networks. Wireless Communications and Mobile Computing, 2017, 9474806. https://doi.org/10.1155/2017/9474806 DOI: https://doi.org/10.1155/2017/9474806

OMS (WHO) (2015). Datos interesantes acerca del envejecimiento. http://www.who.int/ageing/about/facts/es/

Panahi, L., & Ghods, V. (2018). Human fall detection using machine vision techniques on RGB-D images. Biomedical Signal Processing and Control, 44, 146-153. https://doi.org/10.1016/j.bspc.2018.04.014 DOI: https://doi.org/10.1016/j.bspc.2018.04.014

Pava, R., Pérez-Castillo, J. N., & Niño-Vásquez, L. F. (2021). Perspectiva para el uso del modelo P6 de atención en salud bajo un escenario soportado en IoT y blockchain. Tecnura, 25(67), 112-130. https://doi.org/10.14483/22487638.16159 DOI: https://doi.org/10.14483/22487638.16159

Pazhoumand-Dar, H., Lam, C.-P., & Masek, M. (2015). Joint movement similarities for robust 3D action recognition using skeletal data. Journal of Visual Communication and Image Representation, 30, 10-21. https://doi.org/10.1016/j.jvcir.2015.03.002 DOI: https://doi.org/10.1016/j.jvcir.2015.03.002

Peng, X., Wang, L., Wang, X., & Qiao, Y. (2016). Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice. Computer Vision and Image Understanding, 150, 109-125. https://doi.org/10.1016/j.cviu.2016.03.013 DOI: https://doi.org/10.1016/j.cviu.2016.03.013

Planinc, R., & Kampel, M. (2013). Introducing the use of depth data for fall detection. Personal and Ubiquitous Computing, 17(6), 1063-1072. https://doi.org/10.1007/s00779-012-0552-z DOI: https://doi.org/10.1007/s00779-012-0552-z

Preis, J., Kessel, M., Werner, M., & Linnhoff-Popien, C. (2012). Gait Recognition with Kinect. https://www.researchgate.net/publication/239862819_Gait_Recognition_with_Kinect/citations

Rafferty, J., Nugent, C. D., Liu, J., & Chen, L. (2017). From activity recognition to intention recognition for assisted living within smart homes. IEEE Transactions on Human-Machine Systems, 47(3), 368-379. https://doi.org/10.1109/THMS.2016.2641388 DOI: https://doi.org/10.1109/THMS.2016.2641388

Rahnemoonfar, M., & Alkittawi, H. (2018, December 10-13). Spatio-temporal convolutional neural network for elderly fall detection in depth video cameras [Conference presentation]. 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA. https://doi.org/10.1109/BigData.2018.8622342 DOI: https://doi.org/10.1109/BigData.2018.8622342

Rosati, S., Balestra, G., & Knaflitz, M. (2018). Comparison of different sets of features for human activity recognition by wearable sensors. Sensors, 18(12), 4189. https://doi.org/10.3390/s18124189 DOI: https://doi.org/10.3390/s18124189

Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J. (2007, May 21-23). Fall detection from human shape and motion history using video surveillance [Conference presentation]. 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW’07), Niagara Falls, ON, Canada. https://doi.org/10.1109/AINAW.2007.181 DOI: https://doi.org/10.1109/AINAW.2007.181

Ryoo, M. S. (2011, November 6-13). Human activity prediction: Early recognition of ongoing activities from streaming videos [Conference presentation]. 2011 International Conference on Computer Vision, Barcelona, Spain. https://doi.org/10.1109/ICCV.2011.6126349 DOI: https://doi.org/10.1109/ICCV.2011.6126349

Saini, R., Kumar, P., Roy, P. P., & Dogra, D. P. (2018). A novel framework of continuous human-activity recognition using Kinect. Neurocomputing, 311, 99-111. https://doi.org/10.1016/j.neucom.2018.05.042 DOI: https://doi.org/10.1016/j.neucom.2018.05.042

Sazonov, E., Metcalfe, K., Lopez-Meyer, P., & Tiffany, S. (2011, November 28 - December 1). RF hand gesture sensor for monitoring of cigarette smoking [Conference presentation]. 2011 Fifth International Conference on Sensing Technology, Palmerson North, New Zealand. https://doi.org/10.1109/ICSensT.2011.6137014 DOI: https://doi.org/10.1109/ICSensT.2011.6137014

Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., & Blake, A. (2011, June 20-25). Real-time human pose recognition in parts from single depth images [Conference presentation]. CVPR 2011, Colorado Springs, CO, USA. https://doi.org/10.1109/CVPR.2011.5995316 DOI: https://doi.org/10.1109/CVPR.2011.5995316

Soomro, K., Roshan, A., & Shah, M. (2012). UCF101: A Dataset of 101 human actions classes from videos in the wild. arXiv preprint. https://doi.org/10.48550/arXiv.1212.0402

Sreenidhi, I. (2020). Real-time human fall detection and emotion recognition using embedded device and deep learning. International Journal of Emerging Trends in Engineering Research, 8(3), 780-786. https://doi.org/10.30534/ijeter/2020/28832020 DOI: https://doi.org/10.30534/ijeter/2020/28832020

Suto, J., & Oniga, S. (2019). Efficiency investigation from shallow to deep neural network techniques in human activity recognition. Cognitive Systems Research, 54, 37-49. https://doi.org/10.1016/j.cogsys.2018.11.009 DOI: https://doi.org/10.1016/j.cogsys.2018.11.009

Uzunovic, T., Golubovic, E., Tucakovic, Z., Acikmese, Y., & Sabanovic, A. (2018, October 21-23). Task-based control and human activity recognition for human-robot collaboration [Conference presentation]. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington DC, USA. https://doi.org/10.1109/IECON.2018.8591206 DOI: https://doi.org/10.1109/IECON.2018.8591206

Venkatesha, S., & Turk, M. (2010, August 23-26). Human activity recognition using local shape descriptors [Conference presentation]. 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey. https://doi.org/10.1109/ICPR.2010.902 DOI: https://doi.org/10.1109/ICPR.2010.902

Vrigkas, M., Nikou, C., & Kakadiaris, I. A. (2015). A review of human activity recognition methods. Frontiers in Robotics and AI, 2, 28. https://doi.org/10.3389/frobt.2015.00028 DOI: https://doi.org/10.3389/frobt.2015.00028

Wang, L., Qiao, Y., & Tang, X. (2015, June 7-12). Action recognition with trajectory-pooled deep-convolutional descriptors [Conference presentation]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA. DOI: https://doi.org/10.1109/CVPR.2015.7299059

Xu, Q., Huang, G., Yu, M., & Guo, Y. (2020). Fall prediction based on key points of human bones. Physica A: Statistical Mechanics and Its Applications, 540, 123205. https://doi.org/10.1016/j.physa.2019.123205 DOI: https://doi.org/10.1016/j.physa.2019.123205

Yan, S., Xiong, Y., & Lin, D. (2018). Spatial temporal graph convolutional networks for skeleton-based action recognition. Computer Vision and Pattern Recognition, 32(1). 12328. https://doi.org/10.1609/aaai.v32i1.12328 DOI: https://doi.org/10.1609/aaai.v32i1.12328

Yang, L., Ren, Y., & Zhang, W. (2016). 3D depth image analysis for indoor fall detection of elderly people. Digital Communications and Networks, 2(1), 24-34. https://doi.org/10.1016/j.dcan.2015.12.001 DOI: https://doi.org/10.1016/j.dcan.2015.12.001

Yang, X., & Tian, Y. (2014). Effective 3D action recognition using EigenJoints. Journal of Visual Communication and Image Representation, 25(1), 2-11. https://doi.org/10.1016/j.jvcir.2013.03.001 DOI: https://doi.org/10.1016/j.jvcir.2013.03.001

Yang, Y., Hou, C., Lang, Y., Guan, D., Huang, D., & Xu, J. (2019). Open-set human activity recognition based on micro-Doppler signatures. Pattern Recognition, 85, 60-69. https://doi.org/10.1016/j.patcog.2018.07.030 DOI: https://doi.org/10.1016/j.patcog.2018.07.030

Yao, L., Min, W., & Lu, K. (2017). A new approach to fall detection based on the human torso motion model. Applied Sciences, 7(10), 993. https://doi.org/10.3390/app7100993 DOI: https://doi.org/10.3390/app7100993

Yong Du, Wang, W., & Wang, L. (2015, June 7-12). Hierarchical recurrent neural network for skeleton based action recognition [Conference presentation]. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA. https://doi.org/10.1109/CVPR.2015.7298714 DOI: https://doi.org/10.1109/CVPR.2015.7298714

Yu, M., Naqvi, S. M., Rhuma, A., & Chambers, J. (2012). One class boundary method classifiers for application in a video-based fall detection system. IET Computer Vision, 6(2), 90-100. https://doi.org/10.1049/iet-cvi.2011.0046 DOI: https://doi.org/10.1049/iet-cvi.2011.0046

Yu, M., Yu, Y., Rhuma, A., Naqvi, S. M. R., Wang, L., & Chambers, J. A. (2013). An online one class support vector machine-based person-specific fall detection system for monitoring an elderly individual in a room environment. IEEE Journal of Biomedical and Health Informatics, 17(6), 1002-1014. https://doi.org/10.1109/JBHI.2013.2274479 DOI: https://doi.org/10.1109/JBHI.2013.2274479

Zhang, H.-B., Zhang, Y.-X., Zhong, B., Lei, Q., Yang, L., Du, J.-X., & Chen, D.-S. (2019). A comprehensive survey of vision-based human action recognition methods. Sensors, 19(5), 1005. https://doi.org/10.3390/s19051005 DOI: https://doi.org/10.3390/s19051005

Zhang, S., Wei, Z., Nie, J., Huang, L., Wang, S., & Li, Z. (2017). A review on human activity recognition using vision-based method. Journal of Healthcare Engineering, 2017, 3090343. https://doi.org/10.1155/2017/3090343 DOI: https://doi.org/10.1155/2017/3090343

Zhu, Y., Zhao, X., Fu, Y., & Liu, Y. (2011). Sparse coding on local spatial-temporal volumes for human action recognition. In R. Kimmel, R. Klette, & A. Sugimoto (Eds.), Computer Vision - ACCV 2010 (pp. 660-671). Springer. https://doi.org/10.1007/978-3-642-19309-5_51 DOI: https://doi.org/10.1007/978-3-642-19309-5_51

Cómo citar

APA

Eraso Guerrero, J. C., Muñoz España, E. ., y Muñoz Añasco, M. (2022). Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review. Tecnura, 26(74), 213–236. https://doi.org/10.14483/22487638.17413

ACM

[1]
Eraso Guerrero, J.C. et al. 2022. Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review. Tecnura. 26, 74 (oct. 2022), 213–236. DOI:https://doi.org/10.14483/22487638.17413.

ACS

(1)
Eraso Guerrero, J. C.; Muñoz España, E. .; Muñoz Añasco, M. Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review. Tecnura 2022, 26, 213-236.

ABNT

ERASO GUERRERO, José Camilo; MUÑOZ ESPAÑA, Elena; MUÑOZ AÑASCO, Mariela. Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review. Tecnura, [S. l.], v. 26, n. 74, p. 213–236, 2022. DOI: 10.14483/22487638.17413. Disponível em: https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/17413. Acesso em: 2 mar. 2024.

Chicago

Eraso Guerrero, José Camilo, Elena Muñoz España, y Mariela Muñoz Añasco. 2022. «Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review». Tecnura 26 (74):213-36. https://doi.org/10.14483/22487638.17413.

Harvard

Eraso Guerrero, J. C., Muñoz España, E. . y Muñoz Añasco, M. (2022) «Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review», Tecnura, 26(74), pp. 213–236. doi: 10.14483/22487638.17413.

IEEE

[1]
J. C. Eraso Guerrero, E. . Muñoz España, y M. Muñoz Añasco, «Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review», Tecnura, vol. 26, n.º 74, pp. 213–236, oct. 2022.

MLA

Eraso Guerrero, José Camilo, et al. «Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review». Tecnura, vol. 26, n.º 74, octubre de 2022, pp. 213-36, doi:10.14483/22487638.17413.

Turabian

Eraso Guerrero, José Camilo, Elena Muñoz España, y Mariela Muñoz Añasco. «Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review». Tecnura 26, no. 74 (octubre 1, 2022): 213–236. Accedido marzo 2, 2024. https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/17413.

Vancouver

1.
Eraso Guerrero JC, Muñoz España E, Muñoz Añasco M. Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review. Tecnura [Internet]. 1 de octubre de 2022 [citado 2 de marzo de 2024];26(74):213-36. Disponible en: https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/17413

Descargar cita

Visitas

84

Dimensions


PlumX


Descargas

Los datos de descargas todavía no están disponibles.

Recibido: 3 de enero de 2022; Aceptado: 4 de julio de 2022

RESUMEN

Contexto:

En los últimos años, el reconocimiento de actividades humanas se ha convertido en un área de constante exploración en diferentes campos. Este artículo presenta una revisión de la literatura enfocada en diferentes tipos de actividades humanas y dispositivos de adquisición de información para el reconocimiento de actividades, y profundiza en la detección de caídas de personas de tercera edad por medio de visión computacional, utilizando métodos de extracción de características y técnicas de inteligencia artificial.

Metodología:

Este manuscrito se elaboró con criterios de la metodología de revisión y análisis documental (RAD), dividiendo el proceso de investigación en heurística y hermenéutica de las fuentes de información. Finalmente, se referenciaron 102 investigaciones que permitieron dar a conocer la actualidad del reconocimiento de actividades humanas.

Resultados:

El análisis de las técnicas propuestas para el reconocimiento de actividades humanas muestra la importancia de la detección eficiente de caídas. Si bien es cierto en la actualidad se obtienen resultados positivos con las técnicas descritas en este artículo, sus entornos de estudio son controlados, lo cual no contribuye al verdadero avance de las investigaciones.

Conclusiones:

Sería de gran impacto presentar resultados de estudios en entornos semejantes a la realidad, por lo que es primordial centrar el trabajo de investigación en la elaboración de bases de datos con caídas reales de personas adultas o en entornos no controlados.

Palabras clave:

reconocimiento de la actividad humana, detección de caídas, tipos de actividades, extracción de características, redes neuronales convolucionales.

ABSTRACT

Context:

In recent years, the recognition of human activities has become an area of constant exploration in different fields. This article presents a literature review focused on the different types of human activities and information acquisition devices for the recognition of activities. It also delves into elderly fall detection via computer vision using feature extraction methods and artificial intelligence techniques.

Methodology:

This manuscript was elaborated following the criteria of the document review and analysis methodology (RAD), dividing the research process into the heuristics and hermeneutics of the information sources. Finally, 102 research works were referenced, which made it possible to provide information on current state of the recognition of human activities.

Results:

The analysis of the proposed techniques for the recognition of human activities shows the importance of efficient fall detection. Although it is true that, at present, positive results are obtained with the techniques described in this article, their study environments are controlled, which does not contribute to the real advancement of research.

Conclusions:

It would be of great impact to present the results of studies in environments similar to reality, which is why it is essential to focus research on the development of databases with real falls of adults or in uncontrolled environments.

Keywords:

human activity recognition, fall detection, type of activities, feature extraction, convolutional neural networks.

INTRODUCTION

Human activity recognition (HAR) aims to model user behavior and automatically identify the tasks they perform by observing and analyzing human behavior (Saini et al., 2018; Uzunovic et al., 2018; Brophy et al., 2018), which results in the recognition of people’s activities, identities, personalities, and psychological state (Vrigkas et al., 2015).

In recent years, HAR has become an area of constant exploration in different fields; its applications are a current research subject, as it helps automate processes and activities that may go unnoticed by the human eye or may constitute tedious tasks. For Lohit et al. (2018), a human pose transmits the configuration of the body parts and implicit predictive information on people’s subsequent movement, dynamic information that may be utilized in various applications. By reviewing the literature, it can be found that HAR exhibits a growing demand in the fields of entertainment (Lawrence et al., 2010; Han et al., 2013; Akhavian & Behzadan, 2016); video surveillance systems (Ryoo, 2011; Preis et al., 2012; Y. Liu et al., 2014; Ben Mabrouk & Zagrouba, 2018; Cosar et al., 2017; J.-W. Hsieh et al., 2014); emergency rescue and emergency robotics (Durrant-Whyte et al., 2012); smart cities, sports performance, military applications, medical monitoring for caring of the elderly, and diverse health care (Banos et al., 2012; Chen et al., 2012; Avci et al., 2010; Kim et al., 2010; Sazonov et al., 2011; Ismail et al., 2015; Rafferty et al., 2017); among others (Elbasiony & Gomaa, 2020). The common factor of research on HAR is the set of problems under study, which involve recognizing a specific activity such as the weather, object protection, and lighting conditions, among others. Moreover, an activity may vary from one person to another (Y. Yang et al., 2019; Kahani et al., 2019). Thus, it is essential to find different ways to optimize the recognition of human activities.

Modern and efficient methods for healthcare are now being proposed, such as the use of blockchain and the Internet of Things (Pava et al., 2021). However, this review prioritizes the works and advances on HAR, specifically elderly fall detection since, according to the World Health Organization (WHO), the proportion of the planet's population over 60 years will double from 11 to 22% between 2000 and 2050 (2015). In huge numbers, this age group will grow from 605 million to 2 billion in the course of half a century. As a consequence, caring for and monitoring the health of the elderly will become an essential and daunting task. Li et al. (2018) state that approximately 58% of elderly over 80 years old have passed away after a severe fall due to physical trauma, mild traumatic brain injury, hip fracture, among others, which may discourage this population from working out. A sedentary lifestyle in the elderly is another problem that entails other health consequences, such as obesity and cardiovascular diseases (Suto & Oniga, 2019).

As workout and movement are vital for the elderly, monitoring and recognizing daily activities is essential to provide them with proper healthcare. According to Li et al., (2018), the automatic detection of falls or movements that may affect health can significantly reduce the consequences of the incident. It may also allow tracking and reporting anomalies from patterns of normal daily behaviors by adults with high risk of falling.

This document presents a review of the state of the art regarding 1) the classification of human activities, 2) the methods for HAR information acquisition and 3) the methods that have been used for feature extraction from videos and images in order to recognize the activities of the elderly.

This research was conducted following the criteria for review methodology and document analysis (Barbosa-Chacón et al., 2013), dividing the research process into the heuristics and hermeneutics of the different information sources.

CLASSIFICATION OF HUMAN ACTIVITIES

Human activity recognition is a current research topic due to its various applications in the entertainment industry, video surveillance, healthcare, robotics, smart cities, sports performance, and military applications. Therefore, the main objective of this work is to review the field of HAR with a focus on elderly care.

Human activities are classified depending on their complexity and duration. For Hassan et al. (2018), activities are divided into short-term activities and simple and complex tasks: firstly, short-term activities such as the transition between sitting and standing up; secondly, basic activities such as walking and reading; and complex activities, which involve scenarios where there is an interaction with objects or people. On the other hand, Vrigkas et al. (2015) propose another mechanism to classify activities which also considers their complexity. A summary of this classification is shown in Table 1.

Table 1: Classification of human activities

Classification Description
Gestures Primitive movements of a person’s body parts, which correspond to a particular action.
Atomic actions A person’s movements that are part of more complex activities.
Human-object interaction or human-human interaction Human activities that involve two or more people or objects.
Activities in group Activities carried out by groups of people.
Behaviors Physical activities associated with feelings, personality, and the psychological state of an individual.
Events High-level activities that describe social actions between individuals and indicate a person’s social roles.

Research related to recognizing activities carried out by the elderly is focused on identifying short-term and basic-specific activities. An example of this is the work carried out by Khan and Sohn (2011), which aimed to detect six specific activities (forward falls, backward falls, chest pain, faints, vomiting, and headaches). On the other hand, Ma et al. (2014) attempted to recognize other six activities (people falling, flexing, sitting, squatting, or lying down). In turn, Amiri et al. (2014) increased the number of activities to be recognized (a person cleaning a table, drinking a drink, taking or dropping an object, reading, sitting, standing up, writing, using a phone, and falling), which also expanded the difficulty and vagueness of the system due to occlusion issues and the similarity between actions (Yu et al., 2013).

INFORMATION ACQUISITION METHODS

The first step to recognize a determined human activity is obtaining information for subsequent processing. This process may be carried out in different ways. The first method is based on environmental sensors, such as pressure, acoustic, electromyography, and different sensors that may be integrated and distributed around the environment where the identification of different activities is required (L. Yang et al., 2016). The use of different sensors may entail high costs and could be an intrusive method. Aspects such as the arrangement and generation of different types of sensors should also be taken into account, especially in underdeveloped territories, as discussed by Nivia-Vargas and Jaramillo-Jaramillo (2018).

The second method also receives information through portable sensors such as contact sensors, gyroscopes, and accelerometers (Rosati et al., 2018). Using methods based on sensors has a specific set of difficulties when recognizing elderly activities. According to Khan and Sohn (2013), the elderly often forget to wear portable sensors, and their use in different parts of the body causes frustration since it limits their movement.

On the other hand, Kwolek and Kepski (2014) argue that the vast majority of elderly people do not enjoy using sensors, as they generate excessive false alarms. Some daily activities are wrongly detected as falls, which may also frustrate users.

This article delves into the third information acquisition method: incorporating computer vision using cameras, depth sensors, and image processing techniques. According to Yu et al., (2013) and Amiri et al. (2014), this is a non-intrusive method that can extract a large amount of information in comparison with portable sensor methods. Furthermore, this method is not easily affected by noise in the environment. On that premise, Panahi and Ghods (2018) highlight the technological progress of extracting images from video using RGB (red, green, and blue) cameras or using depth map images to determine the different distances of objects or people. L. Yang et al. (2016) divide the vision-based method into three categories: methods using standard RGB cameras, 3D-based methods using multiple cameras, and 3D-based methods using depth cameras.

The vision-based method also has its limitations, which include a lack of privacy, as it implies having a camera in the environment at all times. Moreover, Concone et al. (2019) criticize its computational cost, since this method may rarely run in real-time, and they highlight the fact that the performance of the method strongly depends on the position of the cameras.

FEATURE EXTRACTION

Although HAR has been a continuous topic of research in the last decade, there are still different aspects that hinder the accurate recognition of elderly activities. With the computer vision method, this includes features such as the weather, object protection or occlusion, lighting conditions, the similarity between some activities, clutter in the background of the image, privacy problems, and other specific difficulties that may cause false detections. For this reason, it is vital to study the different methods in order to optimize the recognition of these activities, especially regarding fall detection.

Some studies (Yu et al., 2013; Goudelis et al., 2015) argue that the most essential step for successful activity recognition is to select a method for feature extraction from an image or a video. Different methods have been proposed whose purpose is to effectively distinguish non-intentional actions such as falls from other daily activities. This review of the state of the art focuses on the classification of feature extraction methods presented by S. Zhang et al. (2017), which classifies them in terms of their approach: local characteristics, global characteristics, and depth-based representation. Also, the current method based on convolutional neural networks is attached.

For Das Dawn and Shaikh, (2016), the shape or edge of an object are relevant data that can be used to determine local characteristics. However, global information involves flow description or movement in a video.

Global feature extraction

This method allows extracting global descriptions from videos and images, which, according to Zhang et al. (2017), allows localizing the human subject and isolating them from the background, using subtraction methods to acquire their silhouette and shape. Other global representation methods are 3D space-time volumes, which monitor a human being's silhouette for a determined period of time. There is also the Fourier Transform method, which is based on monitoring the frequency of a silhouette for activity recognition.

Various other studies use global feature extraction to recognize human actions, especially for elderly fall detection. In general, research in this field takes advantage of the silhouette of the human body to reach its objective.

Elderly fall detection is the main objective of several works (Khan & Sohn, 2011, 2013; Yu et al., 2012, 2013; Foroughi et al., 2008), which focus on extracting the human silhouette for subsequent processing. These studies have several differences. Khan and Sohn (2011) use the human silhouette to extract information from the elderly using R-transform, invariant scale, rotation characteristics, and Kernel discriminant analysis (KDA) as they attempt to detect human falls while considering the different distances of people in front of the camera. On the other hand, the works by Yu et al. (2012, 2013) have several common factors: both techniques detect falls in the elderly, extract the adult's silhouette, and calculate the human figure's center of mass. Nonetheless, Yu et al. (2013) employ the method presented by Rougier et al. (2007) to extract and delimit people's silhouettes via ellipse features and look for the structural characteristics and shape of human actions, locating its centroid as a fall detection method. Meanwhile, Yu et al. (2012) calculate the centroid of the human silhouette and identify the person's orientation. To this effect, at least two cameras are needed, both of them synchronized in order to minimize occlusion. Finally, Foroughi et al. (2008) use the human silhouette as captured from videos or images to identify histograms of its segmented projection, analyzing temporary changes in an elderly person's head in order to recognize a possible fall.

As these systems were implemented in different contexts, they reported different performances. However, the study areas of these works were controlled environments such as small apartments with multiple cameras, few lighting changes, and high computational costs. For instance, Auvinet et al. (2011) used various cameras to extract 3D images, aiming to detect and analyze the volume in the elderly silhouette's vertical space, activating a falling alarm when the volume distribution was abnormally close to the floor. For an extended period, this method reached a recognition effectiveness of 99,7%, albeit using eight simultaneous cameras, having a high computational cost with regard to synchronization and performance, and making the system challenging to implement on a daily basis.

On the other hand, V. A. Nguyen et al. (2016) aimed to recognize indoor human actions tested in different environments with natural lighting and different shadows, as well as involving diverse daily activities, which caused several failures. Nonetheless, as their method is based on the use of a single RGB camera, it is easy to implement and entails a low computational cost. Falls in the elderly are detected by analyzing movement orientation and magnitude, changes in the human shape, and movement in the image's histogram. The authors suggest using additional techniques in future research, which includes detecting the head and the inactivity zone.

Optical flow is a global extraction technique used to extract and describe silhouettes on moving or dynamic backgrounds. Efros et al. (2003) used this method to recognize actions performed by soccer and tennis players and ballet dancers in TV broadcasts. The authors suggest applying this technique to extract the dynamic background, focusing only on the sportsmen's silhouette.

Despite the fact that systems using global feature extraction have performed well in controlled environments, Zhang et al. (2017) have exposed the difficulties of these systems given their noise sensitivity and viewpoint changes. Furthermore, authors such as Goudelis et al. (2015) have indicated that methods based on silhouettes and figures lack solidity and generalization, as they depend on an accurate extraction of the human silhouette and the different geometric transformations, which may be distorted by the distance and position of the subject.

Local feature extraction

Zhang et al. (2017) explain that this method focuses on specific local patches determined by interest point detectors or dense sampling, which densely cover the content of a video or an image. The first interest point detector was proposed by Harris and Stephens (1988) and is known for being an excellent corner detector, giving rise to further research works such as the one by Laptev and Lindeberg (2003), who proposed 3D space-time interest points (STIP). The latter would become the main interest point detectors and inspire even further research (Chakraborty et al., 2012; Laptev, 2005; T. V. Nguyen et al., 2015), which aimed to optimize these techniques.

According to Das Dawn and Shaikh (2016), STIP is an essential technique for robust interest points extraction from a video or image in the space-time domain, such as a corner point or an isolated point where the intensity is maximum or minimum -even endpoints of lines and curves.

Amiri et al. (2014) focused on simulating a smart home environment using two cameras and a Kinect sensor placed between them. Local feature extraction with space-time techniques was implemented using the Hariss3D algorithm as a feature detector and STIP as a feature descriptor. The system's main difficulties are occlusion problems and clutter in the background, since tracking the human body is a challenging and an error-prone task. The capacity of the Kinect sensor to recognize skeletal information only for objects in the range of 1,2 to 3,5 m may also have caused recognition problems. On the other hand, Berlin and John, (2016) used Harris's corner point detectors differently, including the histogram form of the diverse images in order to recognize different activities performed in two sets with controlled environments. The results showed 95 and 88% recognition rates for Set1 and Set2, respectively.

Conversely, Venkatesha and Turk (2010) attempted online human activity recognition, that is to say, without storing any video. The systems immediately learned the actions in the scene and classified them, considering the shape of human actions. They also used interest point extraction techniques while analyzing the histogram of the image in order to identify the action performed. This method showed a recognition effectiveness of 87% in non-complex actions. Meanwhile, Peng et al. (2016) obtained a similar recognition rate, albeit combining local space-time characteristics and the construction of a visual dictionary, proposing a hybrid super vector.

Zhu et al., (2011) presented another technique based on recognizing an action through feature coding of local 3D space-time gradients within the framework of scattered code. By doing so, each space-time characteristic is transformed into a linear combination of some ‘atoms’ in a dictionary trained to detect local movement and appearance features. This method provides an increase in scale invariance, achieving the recognition of some basic activities.

Considering the above studies and that proposed by H.-B. Zhang et al. (2019), local feature extraction does not require pre-processing activities such as background segmentation or human detection. It also offers scale invariance and rotation and is stable under lighting changes and more resistant to occlusion than global feature extraction.

S. Zhang et al. (2017) highlighted the fact that, although these detectors achieve satisfactory results in HAR, they have a significant deficiency: the calculation of stable interest points is often inadequate, as "discriminative" and "correct" interest points are difficult to identify.

Similarly, H.-B. Zhang et al. (2019) faced some difficulties with the current local feature extraction method, as it is easily affected by changes in camera view, background movement, and camera movement.

Depth-based feature extraction

The development of depth sensors such as the Microsoft Kinect (Shotton et al., 2011) has allowed higher access to depth maps and the real-time position of skeletal joints, thus contributing to HAR via computer vision.

Various studies (X. Ma et al., 2014; Planinc & Kampel, 2013; Bogdan Kwolek & Kepski, 2014; Nizam et al., 2017; Mastorakis & Makris, 2014; Yao et al., 2017; Jalal et al., 2012) have used the Kinect sensor as an information acquisition instrument and employed its depth images for HAR. The difference lies in the characteristics that each researcher wanted to extract. For example, Ma et al. (2014) conducted a complex study aiming to recognize six human actions (people falling, bending, sitting down, squatting, walking, and lying down) while combining global extraction techniques from depth images and analyzing changes in the human shape in short periods of time. On the other hand, Nizam et al. (2017) focused on extracting the elderly center of mass and added the angle between the human body and the floor plan. If this data is below specific thresholds, then a fall is detected.

On the other hand, Kwolek and Kepski (2014) complemented the use of depth images and calculated the distance from the human center of mass to the ground using an accelerometer for elderly fall detection. In this approach, if the acceleration exceeds a threshold value, it means that the person is in motion. At that moment, the depth sensor begins to extract information in order to detect a possible fall. However, the process requires calibrating the cameras and accelerometers, which increases its computational cost. Nizam et al. (2017) also used a Kinect sensor to study the speed and position of a person. Thus, if a high speed in a short time is detected, it is assumed that a fall has occurred. The fall is confirmed or discarded by analyzing the position of the body. This system has an average precision of 93,94%.

Mastorakis and Makris (2014) attempted elderly fall detection by using a Kinect sensor to extract the environment's 3D image, aiming to obtain a 3D bounding box surrounding the older person. Here, when the bounding box changes its width, height, and depth, the speed is analyzed. When the speed is higher than a certain threshold, it is considered that a fall has occurred. In turn, Yao et al. (2017) used depth images to extract information such as the movement of the human torso, the 3D positions of the central hip joint, the central shoulder joint, and the height of a person's centroid. With this method, a fall can be identified when the rates of the aforementioned characteristics reach threshold values. Despite this robust method, using only a Kinect makes the system dependent on the distance at which the sensor is working.

Unlike the aforementioned studies, Jalal et al. (2012) did not calculate the distance to the ground of any part of the human body. Instead, they combined the extraction of some global features such as depth data in order to recognize the elderly's daily activities. To this effect, R-transform was used to extract depth silhouettes of elderly body parts, and a hidden Markov model was subsequently used to train and recognize daily household activities. The results showed an average recognition rate of 96,55%.

According to Ma et al. (2014), light is not a problem when extracting silhouettes, since the Kinect sensor uses infrared light. This is very advantageous, as the sensor can also recognize human silhouettes in the dark and extract information from the human skeleton for HAR (Yong Du et al., 2015). This technique has been widely applied in different studies (Keceli & Burak Can, 2013; Pazhoumand-Dar et al., 2015; X. Yang & Tian, 2014; Hbali et al., 2018). However, occlusion represents a problem with this approach, as recognition can be affected if the human body is occluded by any object. Therefore, several studies (Ni et al., 2013; Jalal et al., 2017; Liu & Shao, 2013) have merged spatial-temporal features with RGB cameras and depth data to reduce the occlusion problem. Data merging makes the processing volume larger, which increases feature dimensions. These factors increase the computational complexity of the algorithm for activity recognition.

Convolutional neural networks

Finally, the current state of the art highlights the growing importance and impact of using convolutional neural networks (CNN) for HAR, as well as their classification and optimization in recent years. Different authors have adopted the use of CNN as a recognition method. For instance, Y.-Z. Hsieh and Jeng (2018) applied a feedback CNN of optical flow to video transmission incorporating point estimation histograms, the limit of the object in motion, and limits of the subject in order to detect falls. Moreover, Yan et al. (2018) proposed a novel model of dynamic skeletons called Spatial Temporal Graph Convolutional Networks. This model automatically learns the spatial and temporal patterns of data, which allows for a higher generalization capacity. Xu et al. (2020) also based their research on mapping the human skeleton to predict falls using OPENPOSE, thus obtaining a skeletal map and transforming it into a dataset to then feed the CNN. Other studies involving CNNs are based on the movement of a person. Wang et al. (2015) extracted the trajectory in a determined scenario while attempting to recognize and classify different activities. Núñez-Marcos et al. (2017) used optical flow images as the neural network's input, followed by a training phase to detect falls. Similarly, Espinosa et al. (2019) incorporates optical flow to a CNN that not only learns static information. CNNs have also been used in studies that incorporate depth maps from a Kinect sensor for fall detection (Rahnemoonfar & Alkittawi, 2018; Adhikari et al., 2017). Adhikari et al. (2017) concludes that combining RGB image background subtraction and depth images with CNNs provides a possible solution to monitor falls based on indoor videos.

Lu et al. (2017) used a three-dimensional convolutional neural network (3D-CNN) to extract the spatial characteristics of 2D images. It also incorporated video motion information to detect falls, thus reducing the failures caused by image noise, lighting variations, and occlusion. Similarly, C. Ma et al., (2019) incorporated a 3D-CNN, albeit hiding the facial regions optically perceivable in the video capture phase, thus helping to protect privacy while using surveillance cameras. Khraief et al. (2020) used a CNN with its own characteristics. In this study, the authors created a multi-stream CNN -a CNN with multiple flows. That is to say, four CNNs fed by the same images but extracting different features from them (color, texture, depth, shape, movement). Finally, they concatenated the four CNNs in order to obtain a unique classification of activities for fall detection.

A different CNN-based method to detect falls was proposed by Sreenidhi (2020), in which feature extraction from images of people falling was carried out. The CNN employed facial recognition because the author manifests that human expression when falling is highly distinguishable.

When working with CNNs, a large amount of data is required for training, which may be disadvantageous. For that reason, some authors (Cai et al., 2019; Khraief et al., 2019; X. Li et al., 2017) have used networks based on pre-trained architectures such as AlexNet, VGG16 (Krizhevsky et al., 2012), and ResNet (He et al., 2016).

According to El Kaid et al. (2019), although the application of CNNs in activity recognition is successful, it has been done in very restricted environments. None of these networks are flexible enough to work well outside their domain. In this vein, the studies by Debard et al. (2016) and Fan et al. (2017) are concerned with the functioning of algorithms for detecting human actions, considering real falls, global and local feature extraction, and feature extraction through CNN.

Accordingly, the vast majority of studies focus on HAR using short video data segments captured in artificial environments, optimal conditions, and simulated falls by actors. Thereupon, Debard et al. (2016) selected algorithms with a good percentage of activity recognition when used with databases created in controlled or acted scenarios in order to implement them in real environments and falls of real adults. The authors concluded that said algorithms did not have the same efficiency, since they do not consider image quality, overexposure problems, occlusion, and changes in lighting conditions, thus demonstrating that not all the specifications for a robust system in real-world situations are met. Modern clustering techniques, such as the one proposed by Contreras-Contreras et al. (2022) could be applied to such databases.

Table 2 shows popular databases used among the research community that studies human falls. They were created in real or poorly controlled environments.

Table 2: Databases of human falls

Data base Videos Data provided Environment Population Types of falls
URFD (Kwolek & Kepski, 2014) 70 videos, 30 falls, and 40 daily activities RGB depth images, images, and accelerometer signals Indoors Adult people People falling while standing and sitting on a chair
LE2I (Charfi et al., 2013) 191 videos: falls and daily activities RGB images Realistic indoors, home environments, and offices with variable lighting, occlusion, and cluttered and textured background Adult people Falls when walking, stumbling, and falling from chairs
CMDFALL (COMVIS-PTIT, n.d.) 600 videos with 20 human actions including falls RGB images, depth images, and accelerometer signals Home simulation indoors 30 men and 20 women between 21 and 40 years old Falling backwards and forwards, to the left, and to the right
FALL-UP (Martínez-Villaseñor et al., 2019) 361 videos including falls and daily activities RGB images, accelerometer signals and different indoor images Sensors 17 adults between 18 and 24 years old Different falls
Multiple Cameras Fall Dataset (Auvinet et al., n.d.) 192 videos including falls and daily activities RGB images Realistic and indoor home environments with occlusion, disorder, texture, variable lighting, and movement in the background Adults Backward falls, forward falls
UCF101 (Soomro et al., 2012) 13.000 clips, 27 hours of video data with 101 human actions RGB images Controlled and realistic environments, moving cameras, and cluttered background Adult people Different falls

Source: Authors

CONCLUSIONS

This work reviewed the progress made on human activity recognition with an emphasis on elderly falls, showing different devices and techniques to acquire data for subsequent processing and recognition. Furthermore, the main feature extraction methods used in the literature to detect human falls were presented.

Despite the fact that the vast majority of the proposed techniques have a high fall detection percentage and perform well in controlled environments, methods such as global feature extraction are highly sensitive to noise, occlusion, and viewpoint changes.

Similarly, local feature extraction shows a high deficiency when calculating correct interest points. Moreover, interest points are affected by changes in camera view, movement of the background, and camera movement. In addition, the main issue with extracting depth-based features is occlusion, as it may affect human skeleton extraction. Current applications of convolutional neural networks have been successful. However, they are placed in controlled and restricted environments, and their performance is not very good outside their domain.

This work demonstrates the importance of an efficient fall detection method, as well as the great potential of this research area going forward. Although good results are obtained by using the different techniques proposed by the authors mentioned in this paper, the environments where these techniques have been used are controlled, unrealistic, or use simulated falls, which does not contribute to the real advancement of this field. Therefore, presenting the results of these studies in environments similar to reality would have a positive impact, which is why studies that focus on elaborating databases with real falls of adults in non-controlled environments become essential.

Acknowledgements

ACKNOWLEDGEMENTS

The authors would like to express their sincere thanks to Universidad del Cauca for supporting this research process.

REFERENCES

Adhikari, K., Bouchachia, H., & Nait-Charif, H. (2017, May 8-12). Activity recognition for indoor fall detection using convolutional neural network [Conference presentation]. 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA). Nagoya, Japan. https://doi.org/10.23919/MVA.2017.7986795 [Link]

Akhavian, R., & Behzadan, A. H. (2016). Smartphone-based construction workers’ activity recognition and classification. Automation in Construction, 71(Part 2), 198-209. https://doi.org/10.1016/j.autcon.2016.08.015 [Link]

Amiri, S. M., Pourazad, M. T., Nasiopoulos, P., & Leung, V. C. M. (2014). Improved human action recognition in a smart home environment setting. IRBM, 35(6), 321-328. https://doi.org/10.1016/j.irbm.2014.10.005 [Link]

Auvinet, E., Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J. (n.d.). Multiple cameras fall dataset. http://www.iro.umontreal.ca/~labimage/Dataset/ [Link]

Auvinet, E., Multon, F., Saint-Arnaud, A., Rousseau, J., & Meunier, J. (2011). Fall detection with multiple cameras: An occlusion-resistant method based on 3-D silhouette vertical distribution. IEEE Transactions on Information Technology in Biomedicine, 15(2), 290-300. https://doi.org/10.1109/TITB.2010.2087385 [Link]

Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., & Havinga, P. (2010, February 22-25). Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey [Conference presentation]. 23th International Conference on Architecture of Computing Systems, Hannover, Germany. https://ieeexplore.ieee.org/document/5759000 [Link]

Banos, O., Damas, M., Pomares, H., Prieto, A., & Rojas, I. (2012). Daily living activity recognition based on statistical feature quality group selection. Expert Systems with Applications, 39(9), 8013-8021. https://doi.org/10.1016/j.eswa.2012.01.164 [Link]

Barbosa-Chacón, J. W., Barbosa-Herrera, J. C., & Rodríguez-Villabona, M. (2013). Revision y análisis documental para estado del arte: una propuesta metodológica desde el contexto de la sistematización de experiencias educativas. Scielo Analytics, 27, 83-105. https://doi.org/10.1016/S0187-358X(13)72555-3 [Link]

Ben Mabrouk, A., & Zagrouba, E. (2018). Abnormal behavior recognition for intelligent video surveillance systems: A review. Expert Systems with Applications, 91, 480-491. https://doi.org/10.1016/j.eswa.2017.09.029 [Link]

Berlin, S. J., & John, M. (2016, October 24-27). Human interaction recognition through deep learning network [Conference presentation]. 2016 IEEE International Carnahan Conference on Security Technology (ICCST), Orlando, FL, USA. https://doi.org/10.1109/CCST.2016.7815695 [Link]

Brophy, E., Domínguez-Veiga, J. J., Wang, Z., & Ward, T. E. (2018, June 21-22). A machine vision approach to human activity recognition using photoplethysmograph sensor data [Conference presentation]. 2018 29th Irish Signals and Systems Conference (ISSC), Belfast, UK. https://doi.org/10.1109/ISSC.2018.8585372 [Link]

Cai, X., Liu, X., Li, S., & Han, G. (2019, October 16-19). Fall detection based on colorization coded MHI combining with convolutional neural network [Conference presentation]. 2019 IEEE 19th International Conference on Communication Technology (ICCT), Xi'an, China. https://doi.org/10.1109/ICCT46805.2019.8947223 [Link]

Chakraborty, B., Holte, M. B., Moeslund, T. B., and González, J. (2012). Selective spatio-temporal interest points. Computer Vision and Image Understanding, 116(3), 396-410. https://doi.org/10.1016/j.cviu.2011.09.010 [Link]

Charfi, I., Miteran, J., Dubois, J., Atri, M., & Tourki, R. (2013). Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification. Journal of Electronic Imaging, 22(4), 041106. https://doi.org/10.1117/1.JEI.22.4.041106 [Link]

Chen, L., Nugent, C. D., & Wang, H. (2012). A knowledge-driven approach to activity recognition in smart homes. IEEE Transactions on Knowledge and Data Engineering, 24(6), 961-974. https://doi.org/10.1109/TKDE.2011.51 [Link]

Computer Vision Department of the MICA International Research Institute & Posts & Telecommunications Institute of Technology (COMVIS-PTIT) (n.d.). Continuous multimodal multi-view dataset of human fall (CMDFALL). https://www.mica.edu.vn/perso/Tran-Thi-Thanh-Hai/CMDFALL.html [Link]

Concone, F., Re, G. Lo, & Morana, M. (2019). A fog-based application for human activity recognition using personal smart devices. ACM Transactions on Internet Technology, 19(2), 1-20. https://doi.org/10.1145/3266142 [Link]

Contreras-Contreras, G. F., Medina-Delgado, B., Acevedo-Jaimes, B. R., & Guevara-Ibarra, D. (2022). Metodología de desarrollo de técnicas de agrupamiento de datos usando aprendizaje automático. Tecnura, 26(72), 42-58. https://doi.org/10.14483/22487638.17246 [Link]

Cosar, S., Donatiello, G., Bogorny, V., Garate, C., Alvares, L. O., & Bremond, F. (2017). Toward abnormal trajectory and event detection in video surveillance. IEEE Transactions on Circuits and Systems for Video Technology, 27(3), 683-695. https://doi.org/10.1109/TCSVT.2016.2589859 [Link]

Das Dawn, D., & Shaikh, S. H. (2016). A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector. The Visual Computer, 32(3), 289-306. https://doi.org/10.1007/s00371-015-1066-2 [Link]

Debard, G., Mertens, M., Deschodt, M., Vlaeyen, E., Devriendt, E., Dejaeger, E., Milisen, K., Tournoy, J., Croonenborghs, T., Goedemé, T. Tuytelaars, T., & Vanrumste, B. (2016). Camera-based fall detection using real-world versus simulated data: How far are we from the solution? Journal of Ambient Intelligence and Smart Environments, 8(2) 149-168. https://doi.org/10.3233/AIS-160369 [Link]

Durrant-Whyte, H., Roy, N., & Abbeel, P. (2012). Robotics: Science and Systems VII. MIT Press.

Efros, Berg, Mori, & Malik. (2003, October 13-16). Recognizing action at a distance [Conference presentation]. 9th IEEE International Conference on Computer Vision, Nice, France. https://doi.org/10.1109/ICCV.2003.1238420 [Link]

El Kaid, A., Baïna, K., & Baïna, J. (2019). Reduce false positive alerts for elderly person fall video-detection algorithm by convolutional neural network model. Procedia Computer Science, 148, 2-11. https://doi.org/10.1016/j.procs.2019.01.004 [Link]

Elbasiony, R., & Gomaa, W. (2020). A survey on human activity recognition based on temporal signals of portable inertial sensors. In A. E. Hassanien, A. T. Azar, T. Gaber, R. Bhatnagar, & M. F. Tolba (Eds.), The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) (pp. 734-745). Springer. https://doi.org/10.1007/978-3-030-14118-9_72 [Link]

Espinosa, R., Ponce, H., Gutiérrez, S., Martínez-Villaseñor, L., Brieva, J., & Moya-Albor, E. (2019). A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset. Computers in Biology and Medicine, 115, 103520. https://doi.org/10.1016/j.compbiomed.2019.103520 [Link]

Fan, Y., Levine, M. D., Wen, G., & Qiu, S. (2017). A deep neural network for real-time detection of falling humans in naturally occurring scenes. Neurocomputing, 260, 43-58. https://doi.org/10.1016/j.neucom.2017.02.082 [Link]

Foroughi, H., Aski, B. S., & Pourreza, H. (2008). Intelligent video surveillance for monitoring fall detection of elderly in home environments [Conference presentation]. 2008 11th International Conference on Computer and Information Technology, Khulna, Bangladesh. https://doi.org/10.1109/ICCITECHN.2008.4803020 [Link]

Goudelis, G., Tsatiris, G., Karpouzis, K., & Kollias, S. (2015). Fall detection using history triple features. In ACM (Eds.), Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments - PETRA ’15 (art. 81). ACM Press. https://doi.org/10.1145/2769493.2769562 [Link]

Han, J., Shao, L., Xu, D., & Shotton, J. (2013). Enhanced computer vision with Microsoft Kinect sensor: A review. IEEE Transactions on Cybernetics, 43(5), 1318-1334. https://doi.org/10.1109/TCYB.2013.2265378 [Link]

Harris, C., & Stephens, M. (1988). A combined edge and corner detector. In C. J. Taylor (Ed.), Proceedings of the Alvey Vision Conference (pp. 23.1-23.6). Alvey Vision Club.

Hassan, M. M., Uddin, M. Z., Mohamed, A., & Almogren, A. (2018). A robust human activity recognition system using smartphone sensors and deep learning. Future Generation Computer Systems, 81, 303-313. https://doi.org/10.1016/j.future.2017.11.029 [Link]

Hbali, Y., Hbali, S., Ballihi, L., & Sadgal, M. (2018). Skeleton‐based human activity recognition for elderly monitoring systems. IET Computer Vision, 12(1), 16-26. https://doi.org/10.1049/iet-cvi.2017.0062 [Link]

He, K., Zhang, X., Ren, S., & Sun, J. (2016, June 27-30). Deep residual learning for image recognition [Conference presentation]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA. https://doi.org/10.1109/CVPR.2016.90 [Link]

Hsieh, J.-W., Chuang, C.-H., Alghyaline, S., Chiang, H.-F., & Chiang, C.-H. (2014). Abnormal scene change detection from a moving camera using bags of patches and spider-web map. IEEE Sensors Journal, 15(5), 2866-2881. https://doi.org/10.1109/JSEN.2014.2381257 [Link]

Hsieh, Y.-Z., & Jeng, Y.-L. (2018). Development of home intelligent fall detection iot system based on feedback optical flow convolutional neural network. IEEE Access, 6, 6048-6057. https://doi.org/10.1109/ACCESS.2017.2771389 [Link]

Ismail, S. J., Rahman, M. A. A., Mazlan, S. A., & Zamzuri, H. (2015, October 18-20). Human gesture recognition using a low cost stereo vision in rehab activities [Conference presentation]. 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), Langkawi, Malaysia. https://doi.org/10.1109/IRIS.2015.7451615 [Link]

Jalal, A., Kim, Y.-H., Kim, Y.-J., Kamal, S., & Kim, D. (2017). Robust human activity recognition from depth video using spatiotemporal multi-fused features. Pattern Recognition, 61, 295-308. https://doi.org/10.1016/j.patcog.2016.08.003 [Link]

Jalal, A., Uddin, M. Z., Kim, J. T., & Kim, T.-S. (2012). Recognition of human home activities via depth silhouettes and ℜ transformation for smart homes. Indoor and Built Environment, 21(1), 184-190. https://doi.org/10.1177/1420326X11423163 [Link]

Kahani, R., Talebpour, A., & Mahmoudi-Aznaveh, A. (2019). A correlation based feature representation for first-person activity recognition. Multimedia Tools and Applications, 78, 21673-21694. https://doi.org/10.1007/s11042-019-7429-3 [Link]

Keceli, A. S., & Burak Can, A. (2013, April 24-26). Recognition of human actions by using depth information [Conference presentation]. 2013 21st Signal Processing and Communications Applications Conference (SIU), Haspolat, Turkey. https://doi.org/10.1109/SIU.2013.6531211 [Link]

Khan, Z. A., & Sohn, W. (2011). Abnormal human activity recognition system based on R-transform and kernel discriminant technique for elderly home care. IEEE Transactions on Consumer Electronics, 57(4), 1843-1850. https://doi.org/10.1109/TCE.2011.6131162 [Link]

Khan, Z. A., & Sohn, W. (2013). A hierarchical abnormal human activity recognition system based on R-transform and kernel discriminant analysis for elderly health care. Computing, 95(2), 109-127. https://doi.org/10.1007/s00607-012-0216-x [Link]

Khraief, C., Benzarti, F., & Amiri, H. (2019). Convolutional Neural network based on dynamic motion and shape variations for elderly fall detection. International Journal of Machine Learning and Computing, 9(6), 814-820. https://doi.org/10.18178/ijmlc.2019.9.6.878 [Link]

Khraief, C., Benzarti, F., & Amiri, H. (2020). Elderly fall detection based on multi-stream deep convolutional networks. Multimedia Tools and Applications, 79, 19537-19560. https://doi.org/10.1007/s11042-020-08812-x [Link]

Kim, E., Helal, S., & Cook, D. (2010). Human activity recognition and pattern discovery. IEEE Pervasive Computing, 9(1), 48-53. https://doi.org/10.1109/MPRV.2010.7 [Link]

Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet classification with deep convolutional neural networks [Conference presentation]. 26th Annual Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA. https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf [Link]

Kwolek, B., & Kepski, M. (2014). Human fall detection on embedded platform using depth maps and wireless accelerometer. Computer Methods and Programs in Biomedicine, 117(3), 489-501. https://doi.org/10.1016/j.cmpb.2014.09.005 [Link]

Laptev, I., & Lindeberg, T. (2003, October 13-16). Space-time interest points [Conference presentation]. Ninth IEEE International Conference on Computer Vision, Nice, France. https://doi.org/10.1109/ICCV.2003.1238378 [Link]

Laptev, I. (2005). On space-time interest points. International Journal of Computer Vision, 64, 107-123. https://doi.org/10.1007/s11263-005-1838-7 [Link]

Lawrence, E., Sax, C., Navarro, K. F., & Qiao, M. (2010, February 10-16). Interactive games to improve quality of life for the elderly: Towards integration into a WSN monitoring system [Conference presentation]. 2010 Second International Conference on EHealth, Telemedicine, and Social Medicine, Saint Marteen, Netherlands Antilles. https://doi.org/10.1109/eTELEMED.2010.21 [Link]

Li, H., Shrestha, A., Fioranelli, F., Kernec, J. Le, & Heidari, H. (2018, October 28-31). Hierarchical classification on multimodal sensing for human activity recogintion and fall detection [Conference presentation]. 2018 IEEE SENSORS, New Delhi, India. https://doi.org/10.1109/ICSENS.2018.8589797 [Link]

Li, X., Pang, T., Liu, W., & Wang, T. (2017, October 14-16). Fall detection for elderly person care using convolutional neural networks [Conference presentation]. 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China. https://doi.org/10.1109/CISP-BMEI.2017.8302004 [Link]

Liu, L., & Shao, L. (2013). Learning discriminative representations from RGB-D video data. In F. Rossi (Ed.), IJCAI '13: Proceedings of the Twenty-Third international joint conference on Artificial Intelligence (pp. 1493-1500). ACM https://dl.acm.org/doi/10.5555/2540128.2540343 [Link]

Liu, Y., Li, X., & Jia, L. (2014, June 29 - July 4). Abnormal crowd behavior detection based on optical flow and dynamic threshold [Conference presentation]. 11th World Congress on Intelligent Control and Automation, Shenyang, China. https://doi.org/10.1109/WCICA.2014.7053189 [Link]

Lohit, S., Bansal, A., Shroff, N., Pillai, J., Turaga, P., & Chellappa, R. (2018, June 18-22). Predicting dynamical evolution of human activities from a single image [Conference presentation]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA. https://doi.org/10.1109/CVPRW.2018.00079 [Link]

Lu, N., Ren, X., Song, J., & Wu, Y. (2017, August 20-23). Visual guided deep learning scheme for fall detection [Conference presentation]. 2017 13th IEEE Conference on Automation Science and Engineering (CASE), Xi'an, China. https://doi.org/10.1109/COASE.2017.8256202 [Link]

Ma, C., Shimada, A., Uchiyama, H., Nagahara, H., & Taniguchi, R. (2019). Fall detection using optical level anonymous image sensing system. Optics & Laser Technology, 110, 44-61. https://doi.org/10.1016/j.optlastec.2018.07.013 [Link]

Ma, X., Wang, H., Xue, B., Zhou, M., Ji, B., & Li, Y. (2014). Depth-based human fall detection via shape features and improved extreme learning machine. IEEE Journal of Biomedical and Health Informatics, 18(6), 1915-1922. https://doi.org/10.1109/JBHI.2014.2304357 [Link]

Martínez-Villaseñor, L., Ponce, H., Brieva, J., Moya-Albor, E., Núñez-Martínez, J., & Peñafort-Asturiano, C. (2019). UP-Fall detection dataset: A multimodal approach. Sensors, 19(9), 1988. https://doi.org/10.3390/s19091988 [Link]

Mastorakis, G., & Makris, D. (2014). Fall detection system using Kinect’s infrared sensor. Journal of Real-Time Image Processing, 9(4), 635-646. https://doi.org/10.1007/s11554-012-0246-9 [Link]

Nguyen, T. V., Song, Z., & Yan, S. (2015). STAP: Spatial-Temporal Attention-Aware Pooling for action recognition. IEEE Transactions on Circuits and Systems for Video Technology, 25(1), 77-86. https://doi.org/10.1109/TCSVT.2014.2333151 [Link]

Nguyen, V. A., Le, T. H., & Nguyen, T. T. (2016). Single camera based fall detection using motion and human shape features. In ACM (Eds.), Proceedings of the Seventh Symposium on Information and Communication Technology - SoICT ’16. (pp. 339-344) ACM Press. https://doi.org/10.1145/3011077.3011103 [Link]

Ni, B., Pei, Y., Moulin, P., & Yan, S. (2013). Multilevel depth and image fusion for human activity detection. IEEE Transactions on Cybernetics, 43(5), 1383-1394. https://doi.org/10.1109/TCYB.2013.2276433 [Link]

Nivia-Vargas, A. M., & Jaramillo-Jaramillo, I. (2018). La industria de sensores en Colombia. Tecnura, 22(57), 44-54. https://doi.org/10.14483/22487638.13518 [Link]

Nizam, Y., Mohd, M. N. H., & Jamil, M. M. A. (2017). Human fall detection from depth images using position and velocity of subject. Procedia Computer Science, 105, 131-137. https://doi.org/10.1016/j.procs.2017.01.191 [Link]

Núñez-Marcos, A., Azkune, G., & Arganda-Carreras, I. (2017). Vision-based fall detection with convolutional neural networks. Wireless Communications and Mobile Computing, 2017, 9474806. https://doi.org/10.1155/2017/9474806 [Link]

OMS (WHO) (2015). Datos interesantes acerca del envejecimiento. http://www.who.int/ageing/about/facts/es/ [Link]

Panahi, L., & Ghods, V. (2018). Human fall detection using machine vision techniques on RGB-D images. Biomedical Signal Processing and Control, 44, 146-153. https://doi.org/10.1016/j.bspc.2018.04.014 [Link]

Pava, R., Pérez-Castillo, J. N., & Niño-Vásquez, L. F. (2021). Perspectiva para el uso del modelo P6 de atención en salud bajo un escenario soportado en IoT y blockchain. Tecnura, 25(67), 112-130. https://doi.org/10.14483/22487638.16159 [Link]

Pazhoumand-Dar, H., Lam, C.-P., & Masek, M. (2015). Joint movement similarities for robust 3D action recognition using skeletal data. Journal of Visual Communication and Image Representation, 30, 10-21. https://doi.org/10.1016/j.jvcir.2015.03.002 [Link]

Peng, X., Wang, L., Wang, X., & Qiao, Y. (2016). Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice. Computer Vision and Image Understanding, 150, 109-125. https://doi.org/10.1016/j.cviu.2016.03.013 [Link]

Planinc, R., & Kampel, M. (2013). Introducing the use of depth data for fall detection. Personal and Ubiquitous Computing, 17(6), 1063-1072. https://doi.org/10.1007/s00779-012-0552-z [Link]

Preis, J., Kessel, M., Werner, M., & Linnhoff-Popien, C. (2012). Gait Recognition with Kinect. https://www.researchgate.net/publication/239862819_Gait_Recognition_with_Kinect/citations [Link]

Rafferty, J., Nugent, C. D., Liu, J., & Chen, L. (2017). From activity recognition to intention recognition for assisted living within smart homes. IEEE Transactions on Human-Machine Systems, 47(3), 368-379. https://doi.org/10.1109/THMS.2016.2641388 [Link]

Rahnemoonfar, M., & Alkittawi, H. (2018, December 10-13). Spatio-temporal convolutional neural network for elderly fall detection in depth video cameras [Conference presentation]. 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA. https://doi.org/10.1109/BigData.2018.8622342 [Link]

Rosati, S., Balestra, G., & Knaflitz, M. (2018). Comparison of different sets of features for human activity recognition by wearable sensors. Sensors, 18(12), 4189. https://doi.org/10.3390/s18124189 [Link]

Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J. (2007, May 21-23). Fall detection from human shape and motion history using video surveillance [Conference presentation]. 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW’07), Niagara Falls, ON, Canada. https://doi.org/10.1109/AINAW.2007.181 [Link]

Ryoo, M. S. (2011, November 6-13). Human activity prediction: Early recognition of ongoing activities from streaming videos [Conference presentation]. 2011 International Conference on Computer Vision, Barcelona, Spain. https://doi.org/10.1109/ICCV.2011.6126349 [Link]

Saini, R., Kumar, P., Roy, P. P., & Dogra, D. P. (2018). A novel framework of continuous human-activity recognition using Kinect. Neurocomputing, 311, 99-111. https://doi.org/10.1016/j.neucom.2018.05.042 [Link]

Sazonov, E., Metcalfe, K., Lopez-Meyer, P., & Tiffany, S. (2011, November 28 - December 1). RF hand gesture sensor for monitoring of cigarette smoking [Conference presentation]. 2011 Fifth International Conference on Sensing Technology, Palmerson North, New Zealand. https://doi.org/10.1109/ICSensT.2011.6137014 [Link]

Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., & Blake, A. (2011, June 20-25). Real-time human pose recognition in parts from single depth images [Conference presentation]. CVPR 2011, Colorado Springs, CO, USA. https://doi.org/10.1109/CVPR.2011.5995316 [Link]

Soomro, K., Roshan, A., & Shah, M. (2012). UCF101: A Dataset of 101 human actions classes from videos in the wild. arXiv preprint. https://doi.org/10.48550/arXiv.1212.0402 [Link]

Sreenidhi, I. (2020). Real-time human fall detection and emotion recognition using embedded device and deep learning. International Journal of Emerging Trends in Engineering Research, 8(3), 780-786. https://doi.org/10.30534/ijeter/2020/28832020 [Link]

Suto, J., & Oniga, S. (2019). Efficiency investigation from shallow to deep neural network techniques in human activity recognition. Cognitive Systems Research, 54, 37-49. https://doi.org/10.1016/j.cogsys.2018.11.009 [Link]

Uzunovic, T., Golubovic, E., Tucakovic, Z., Acikmese, Y., & Sabanovic, A. (2018, October 21-23). Task-based control and human activity recognition for human-robot collaboration [Conference presentation]. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington DC, USA. https://doi.org/10.1109/IECON.2018.8591206 [Link]

Venkatesha, S., & Turk, M. (2010, August 23-26). Human activity recognition using local shape descriptors [Conference presentation]. 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey. https://doi.org/10.1109/ICPR.2010.902 [Link]

Vrigkas, M., Nikou, C., & Kakadiaris, I. A. (2015). A review of human activity recognition methods. Frontiers in Robotics and AI, 2, 28. https://doi.org/10.3389/frobt.2015.00028 [Link]

Wang, L., Qiao, Y., & Tang, X. (2015, June 7-12). Action recognition with trajectory-pooled deep-convolutional descriptors [Conference presentation]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.

Xu, Q., Huang, G., Yu, M., & Guo, Y. (2020). Fall prediction based on key points of human bones. Physica A: Statistical Mechanics and Its Applications, 540, 123205. https://doi.org/10.1016/j.physa.2019.123205 [Link]

Yan, S., Xiong, Y., & Lin, D. (2018). Spatial temporal graph convolutional networks for skeleton-based action recognition. Computer Vision and Pattern Recognition, 32(1). 12328. https://doi.org/10.1609/aaai.v32i1.12328 [Link]

Yang, L., Ren, Y., & Zhang, W. (2016). 3D depth image analysis for indoor fall detection of elderly people. Digital Communications and Networks, 2(1), 24-34. https://doi.org/10.1016/j.dcan.2015.12.001 [Link]

Yang, X., & Tian, Y. (2014). Effective 3D action recognition using EigenJoints. Journal of Visual Communication and Image Representation, 25(1), 2-11. https://doi.org/10.1016/j.jvcir.2013.03.001 [Link]

Yang, Y., Hou, C., Lang, Y., Guan, D., Huang, D., & Xu, J. (2019). Open-set human activity recognition based on micro-Doppler signatures. Pattern Recognition, 85, 60-69. https://doi.org/10.1016/j.patcog.2018.07.030 [Link]

Yao, L., Min, W., & Lu, K. (2017). A new approach to fall detection based on the human torso motion model. Applied Sciences, 7(10), 993. https://doi.org/10.3390/app7100993 [Link]

Yong Du, Wang, W., & Wang, L. (2015, June 7-12). Hierarchical recurrent neural network for skeleton based action recognition [Conference presentation]. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA. https://doi.org/10.1109/CVPR.2015.7298714 [Link]

Yu, M., Naqvi, S. M., Rhuma, A., & Chambers, J. (2012). One class boundary method classifiers for application in a video-based fall detection system. IET Computer Vision, 6(2), 90-100. https://doi.org/10.1049/iet-cvi.2011.0046 [Link]

Yu, M., Yu, Y., Rhuma, A., Naqvi, S. M. R., Wang, L., & Chambers, J. A. (2013). An online one class support vector machine-based person-specific fall detection system for monitoring an elderly individual in a room environment. IEEE Journal of Biomedical and Health Informatics, 17(6), 1002-1014. https://doi.org/10.1109/JBHI.2013.2274479 [Link]

Zhang, H.-B., Zhang, Y.-X., Zhong, B., Lei, Q., Yang, L., Du, J.-X., & Chen, D.-S. (2019). A comprehensive survey of vision-based human action recognition methods. Sensors, 19(5), 1005. https://doi.org/10.3390/s19051005 [Link]

Zhang, S., Wei, Z., Nie, J., Huang, L., Wang, S., & Li, Z. (2017). A review on human activity recognition using vision-based method. Journal of Healthcare Engineering, 2017, 3090343. https://doi.org/10.1155/2017/3090343 [Link]

Zhu, Y., Zhao, X., Fu, Y., & Liu, Y. (2011). Sparse coding on local spatial-temporal volumes for human action recognition. In R. Kimmel, R. Klette, & A. Sugimoto (Eds.), Computer Vision - ACCV 2010 (pp. 660-671). Springer. https://doi.org/10.1007/978-3-642-19309-5_51 [Link]

Eraso-Guerrero., J.C. Muñoz-España., E. y Muñoz-Añasco., M. (2022). Reconocimiento de actividades humanas por medio de extracción de características y técnicas de inteligencia artificial: una revisión. Tecnura, 26(74), 213-236.. https://doi.org/10.14483/22487638.17413
Loading...