Monitoring elderly people who are living alone is a crucial task as they are at great risk of fall occurrence. In this paper, we present a robust framework for fall detection that makes use of two different signals namely tri-axial data from an accelerometer and depth maps from a Kinect sensor. Our approach functions at two stages. At the first stage, the accelerometer data is continuously being monitored and is used to indicate fall whenever the sum vector magnitude of the tri-axial data crosses a specific threshold. This fall indication denotes a high probability of fall occurrence. To confirm this and to avoid false alarms, the depth maps of a predefined window length captured prior to the instant of fall indication are obtained and processed. We propose a new descriptor, Entropy of Depth Difference Gradient Map that acts as a discriminative descriptor in differentiating fall from other daily activities. Finally, fall confirmation is done by employing a sparse representation-based classifier using the extracted descriptors. To ascertain the proposed model, we have performed experimental analysis using a publicly available UR Fall Detection dataset and also using a Synthetic dataset. The experimental results clearly depict the superior performance of our model.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Abbate, S., Avvenuti, M., Bonatesta, F., Cola, G., Corsini, P., & Vecchio, A. (2012). A smartphone-based fall detection system. Pervasive and Mobile Computing, 8(6), 883–899. https://doi.org/10.1016/j.pmcj.2012.08.003.
Alhimale, L., Zedan, H., & Al-Bayatti, A. (2014). The implementation of an intelligent and video-based fall detection system using a neural network. Applied Soft Computing Journal, 18, 56–69. https://doi.org/10.1080/14747731.2015.1085211.
Aslan, M., Sengur, A., Xiao, Y., Wang, H., Ince, M. C., & Ma, X. (2015). Shape feature encoding via Fisher Vector for efficient fall detection in depth-videos. Applied Soft Computing Journal, 37, 1023–1028. https://doi.org/10.1016/j.asoc.2014.12.035.
Auvinet, E., Multon, F., Aubin, C. E., Meunier, J., & Raison, M. (2015). Detection of gait cycles in treadmill walking using a Kinect. Gait and Posture, 41(2), 722–725. https://doi.org/10.1016/j.gaitpost.2014.08.006.
Bourke, A. K., & Lyons, G. M. (2008). A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Medical Engineering & Physics, 30(1), 84–90. https://doi.org/10.1016/j.medengphy.2006.12.001.
Bourke, A. K., O’Brien, J. V., & Lyons, G. M. (2007). Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait and Posture, 26(2), 194–199. https://doi.org/10.1016/j.gaitpost.2006.09.012.
Burns, A., Greene, B. R., McGrath, M. J., O’Shea, T. J., Kuris, B., Ayer, S. M., et al. (2010). SHIMMERTM—A wireless sensor platform for noninvasive biomedical research. IEEE Sensors Journal, 10(9), 1527–1534. https://doi.org/10.1109/JSEN.2010.2045498.
Chen, Y. C., & Lin, Y. W. (2010). Indoor RFID gait monitoring system for fall detection. In 2010 2nd international symposium on aware computing, ISAC 2010—symposium guide (pp. 207–212). https://doi.org/10.1109/isac.2010.5670478.
De Miguel, K., Brunete, A., Hernando, M., & Gambao, E. (2017). Home camera-based fall detection system for the elderly. Sensors (Switzerland). https://doi.org/10.3390/s17122864.
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.
Jansi, R., & Amutha, R. (2018). A novel chaotic map based compressive classification scheme for human activity recognition using a tri-axial accelerometer. Multimedia Tools and Applications, 77(23), 31261–31280. https://doi.org/10.1007/s11042-018-6117-z.
Jansi, R., & Amutha, R. (2019). Sparse representation based classification scheme for human activity recognition using smartphones. Multimedia Tools and Applications, 78(8), 11027–11045. https://doi.org/10.1007/s11042-018-6662-5.
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.
Kwolek, B., & Kepski, M. (2015). Improving fall detection by the use of depth sensor and accelerometer. Neurocomputing, 168, 637–645. https://doi.org/10.1016/j.neucom.2015.05.061.
Kwolek, B., & Kepski, M. (2016). Fuzzy inference-based fall detection using Kinect and body-worn accelerometer. Applied Soft Computing Journal, 40, 305–318. https://doi.org/10.1016/j.asoc.2015.11.031.
Lee, H., Battle, A., Raina, R., & Ng, A. (2006). Efficient Sparse coding algorithms. NIPS. https://doi.org/10.7551/mitpress/7503.003.0105.
Li, B. Y. L., Xue, M., Mian, A., Liu, W., & Krishna, A. (2016). Robust RGB-D face recognition using Kinect sensor. Neurocomputing, 214, 93–108. https://doi.org/10.1016/j.neucom.2016.06.012.
Li, Q., Stankovic, J. A., Hanson, M. A., Barth, A. T., Lach, J., & Zhou, G. (2009). Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. In Proceedings—2009 6th international workshop on wearable and implantable body sensor networks, BSN 2009 (pp. 138–143). https://doi.org/10.1109/bsn.2009.46.
Lim, D., Park, C., Kim, N. H., Kim, S. H., & Yu, Y. S. (2014). Fall-detection algorithm using 3-axis acceleration: Combination with simple threshold and hidden markov model. Journal of Applied Mathematics. https://doi.org/10.1155/2014/896030.
Liu, T., Guo, X., & Wang, G. (2012). Elderly-falling detection using distributed direction-sensitive pyroelectric infrared sensor arrays. Multidimensional Systems and Signal Processing, 23(4), 451–467. https://doi.org/10.1007/s11045-011-0161-4.
Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2009). Online dictionary learning for sparse coding. In Proceedings of the 26th international conference on machine learning. https://doi.org/10.1145/1553374.1553463.
Mazurek, P., Wagner, J., & Morawski, R. Z. (2018). Use of kinematic and mel-cepstrum-related features for fall detection based on data from infrared depth sensors. Biomedical Signal Processing and Control. https://doi.org/10.1016/j.bspc.2017.09.006.
Nissimov, S., Goldberger, J., & Alchanatis, V. (2015). Obstacle detection in a greenhouse environment using the Kinect sensor. Computers and Electronics in Agriculture, 113, 104–115. https://doi.org/10.1016/j.compag.2015.02.001.
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.
Pierleoni, P., Belli, A., Maurizi, L., Palma, L., Pernini, L., Paniccia, M., et al. (2016). A wearable fall detector for elderly people based on AHRS and barometric sensor. IEEE Sensors Journal, 16(17), 6733–6744. https://doi.org/10.1109/JSEN.2016.2585667.
Rimminen, H., Lindström, J., Linnavuo, M., & Sepponen, R. (2010). Detection of falls among the elderly by a floor sensor using the electric near field. IEEE Transactions on Information Technology in Biomedicine, 14(6), 1475–1476. https://doi.org/10.1109/TITB.2010.2051956.
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.
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(4), 623–656. https://doi.org/10.1002/j.1538-7305.1948.tb00917.x.
Shen, V. R. L., Lai, H. Y., & Lai, A. F. (2015). The implementation of a smartphone-based fall detection system using a high-level fuzzy Petri net. Applied Soft Computing Journal, 26, 390–400. https://doi.org/10.1016/j.asoc.2014.10.028.
Sokolova, M. V., Serrano-Cuerda, J., Castillo, J. C., & Fernández-Caballero, A. (2013). A fuzzy model for human fall detection in infrared video. Journal of Intelligent and Fuzzy Systems, 24(2), 215–228. https://doi.org/10.3233/IFS-2012-0548.
Tran, T. H., Le, T. L., Hoang, V. N., & Vu, H. (2017). Continuous detection of human fall using multimodal features from Kinect sensors in scalable environment. Computer Methods and Programs in Biomedicine. https://doi.org/10.1016/j.cmpb.2017.05.007.
Tran, T. T. H., Le, T. L., & Morel, J. (2014). An analysis on human fall detection using skeleton from Microsoft kinect. In 2014 IEEE 5th international conference on communications and electronics, IEEE ICCE 2014 (pp. 484–489). https://doi.org/10.1109/cce.2014.6916752.
Wu, F., Zhao, H., Zhao, Y., & Zhong, H. (2015). Development of a wearable-sensor-based fall detection system. International Journal of Telemedicine and Applications. https://doi.org/10.1155/2015/576364.
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.
Yang, S. W., & Lin, S. K. (2014). Fall detection for multiple pedestrians using depth image processing technique. Computer Methods and Programs in Biomedicine, 114(2), 172–182. https://doi.org/10.1016/j.cmpb.2014.02.001.
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.
Zigel, Y., Litvak, D., & Gannot, I. (2009). A method for automatic fall detection of elderly people using floor vibrations and soundProof of concept on human mimicking doll falls. IEEE Transactions on Biomedical Engineering, 56(12), 2858–2867. https://doi.org/10.1109/TBME.2009.2030171.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Jansi, R., Amutha, R. Detection of fall for the elderly in an indoor environment using a tri-axial accelerometer and Kinect depth data. Multidim Syst Sign Process 31, 1207–1225 (2020). https://doi.org/10.1007/s11045-020-00705-4
- Fall detection
- Sparse representation