Convolutional neural network for wearable fall detection systems
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Abstract
Fall accidents are a common cause of critical injuries among older adults. Therefore, fall detection systems have garnered considerable attention in research and industry. Feature extraction is the key for detecting falls, but it is time-consuming and tedious process. Deep learning can autonomously extract features from raw sensor data. In this study, we proposed a fall detection algorithm for wearable devices using a convolutional neural network (CNN) to differentiate falls from activities of daily living. The proposed model achieved over 99% metrics (sensitivity, specificity, precision, accuracy, and F1 score) by evaluating two different public datasets and provided better classification performance compared to other fall detection models in the same dataset. The higher CNN performance was recognized without requiring complex data preparation and manual feature extraction. Results from this study could induce CNN through the application of classification problems in technological environments.
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References
Abouzari, M., Goshtasbi, K., Sarna, B., Khosravi, P., Reutershan, T., Mostaghni, N., Lin, H. W., and Djalilian, H. R. (2020). Prediction of vestibular schwannoma recurrence using artificial neural network. Laryngoscope Investigative Otolaryngology, 5(2), 278–285.
Al Rakhami, M. S., Gumaei, A., Altaf, M., Hassan, M. M., Alkhamees, B. F., Muhammad, K., and Fortino, G. (2021). FallDeF5: A fall detection framework using 5G-based deep gated recurrent unit networks. IEEE Access, 9, 94299–94308.
Al Nahian, M. J., Ghosh, T., Uddin, M. N., Islam, M. M., Mahmud, M., and Kaiser, M. S. (2020). Towards artificial intelligence driven emotion aware fall monitoring framework suitable for elderly people with neurological disorder. In Brain Informatics (Mahmud, M., Vassanelli, S., Kaiser, and M.S., Zhong, N., Eds.), pp. 275–286. Cham: Springer.
Badgujar, S., and Pillai, A. S. (2020). Fall detection for elderly people using machine learning. In Proceedings of the 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–4. Kharagpur, India.
Carletti, V., Greco, A., Saggese, A., and Vento, M. (2017). A Smartphone-based system for detecting falls using anomaly detection. In Image Analysis and Processing - ICIAP 2017 (Battiato, S., Gallo, G., Schettini, R., and Stanco, F., Eds.), pp. 490–499. Cham: Springer.
Chen, L., Li, R., Zhang, H., Tian, L., and Chen, N. (2019). Intelligent fall detection method based on accelerometer data from a wrist-worn smart watch. Measurement, 140, 215–226.
Chen, Z., and Ho, P. H. (2019). Global-connected network with generalized ReLU activation. Pattern Recognition, 96, 106961.
Gadaleta, M., and Rossi, M. (2018). IDNet: Smartphone-based gait recognition with convolutional neural networks. Pattern Recognition, 74, 25–37.
Gao, W., Zhang, L., Huang, W., Min, F., He, J., and Song, A. (2021). Deep neural networks for sensor-based human activity recognition using selective kernel convolution. IEEE Transactions on Instrumentation and Measurement, 70, 2512313.
Hadjadji, B., Saumard, M., and Aron, M. (2022). Multi-oriented run length based static and dynamic features fused with Choquet fuzzy integral for human fall detection in videos. Journal of Visual Communication and Image Representation, 82, 103375.
He, J., Zhang, Z., Wang, X., and Yang, S. (2019). A low power fall sensing technology based on fd-cnn. IEEE Sensors Journal, 19(13), 5110–5118.
Hossain, H. M. S., Khan, M. A. A. H., and Roy, N. (2018). DeActive: Scaling activity recognition with active deep learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(2), 66.
Hosseinian, S. M., Zhu, Y., Mehta, R. K., Erraguntla, M., and Lawley, M. A. (2019). Static and dynamic work activity classification from a single accelerometer: Implications for ergonomic assessment of manual handling tasks. IISE Transactions on Occupational Ergonomics and Human Factors, 7(1), 59–68.
Ide, H., and Kurita, T. (2017). Improvement of learning for CNN with ReLU activation by sparse regularization. In Proceedings of the International Joint Conference on Neural Networks, pp. 2684–2691. Alaska, USA.
Jia, L., Gu, Y., Cheng, K., Yan, H., and Ren, F. (2020). BeAware: Convolutional neural network (CNN) based user behavior understanding through WiFi channel state information. Neurocomputing, 397, 457–463.
Kau, L. J., and Chen, C. S. (2015). A smart phone-based pocket fall accident detection, positioning, and rescue system. IEEE Journal of Biomedical and Health Informatics, 19(1), 44–56.
Kerdjidj, O., Ramzan, N., Ghanem, K., Amira, A., and Chouireb, F. (2020). Fall detection and human activity classification using wearable sensors and compressed sensing. Journal of Ambient Intelligence and Humanized Computing, 11(1), 349–361.
Khojasteh, S. B., Villar, J. R., Chira, C., González, V. M., and De la Cal, E. (2018). Improving fall detection using an on-wrist wearable accelerometer. Sensors, 18(5), 1350.
Kwolek, B., and 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.
Liu, K. C., Hsieh, C. Y., Hsu, S. J. P., and Chan, C. T. (2018). Impact of sampling rate on wearable-based fall detection systems based on machine learning models. IEEE Sensors Journal, 18(23), 9882–9890.
Medrano, C., Igual, R., Plaza, I., and Castro, M. (2014). Detecting falls as novelties in acceleration patterns acquired with smartphones. PLoS ONE, 9(4), e94811.
Mubashir, M., Shao, L., and Seed, L. (2013). A survey on fall detection: Principles and approaches. Neurocomputing, 100, 144–152.
Nguyen, L. P., Saleh, M., and Le Bouquin Jeannès, R. (2018). An efficient design of a machine learning-based elderly fall detector. In Internet of Things (IoT) Technologies for HealthCare (Ahmed, M., Begum, S., and Fasquel, J. B., Eds.), pp. 34–41. Cham: Springer.
Özdemir, A. T., and Barshan, B. (2014). Detecting falls with wearable sensors using machine learning techniques. Sensors, 14(6), 10691–10708.
Panahi, L., and Ghods, V. (2018). Human fall detection using machine vision techniques on RGB–D images. Biomedical Signal Processing and Control, 44, 146–153.
Patil, A., and Rane, M. (2021). Convolutional neural networks: An overview and its applications in pattern recognition. In Information and Communication Technology for Intelligent Systems ICTIS 2020. (Senjyu, T., Mahalle, P. N., Perumal, T., and Joshi, A., Eds.), pp. 21–30. Singapore: Springer.
Pires, I. M., Marques, G., Garcia, N. M., Flórez-Revuelta, F., Teixeira, M. C., Zdravevski, E., Spinsante, S., and Coimbra, M. (2020). Pattern recognition techniques for the identification of activities of daily living using a mobile device accelerometer. Electronics, 9(3), 509.
Razum, D., Seketa, G., Vugrin, J., and Lackovic, I. (2018). Optimal threshold selection for threshold-based fall detection algorithms with multiple features. In Proceedings of the 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1513-1516. Opatija, Croatia.
Ren, L., and Peng, Y. (2019). Research of fall detection and fall prevention technologies: A systematic review. IEEE Access, 7, 77702–77722.
Rodríguez, P., Bautista, M. A., Gonzàlez, J., and Escalera, S. (2018). Beyond one-hot encoding: Lower dimensional target embedding. Image and Vision Computing, 75, 21–31.
Ronao, C. A., and Cho, S. B. (2016). Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications, 59, 235–244.
Souza, R. M., Nascimento, E. G. S., Miranda, U. A., Silva, W. J. D., and Lepikson, H. A. (2021). Deep learning for diagnosis and classification of faults in industrial rotating machinery. Computers and Industrial Engineering, 153, 107060.
Sucerquia, A., López, J. D., and Vargas-Bonilla, J. F. (2017). SisFall: A fall and movement dataset. Sensors, 17(1), 198.
Torti, E., Fontanella, A., Musci, M., Blago, N., Pau, D., Leporati, F., and Piastra, M. (2019). Embedding recurrent neural networks in wearable systems for real-time fall detection. Microprocessors and Microsystems, 71, 102895.
Tucker, M. R., Olivier, J., Pagel, A., Bleuler, H., Bouri, M., Lambercy, O., Millán, J. R., Riener, R., Vallery, H., and Gassert, R. (2015). Control strategies for active lower extremity prosthetics and orthotics: A review. Journal of NeuroEngineering and Rehabilitation, 12, 1.
Vernikos, I., Mathe, E., Spyrou, E., Mitsou, A., Giannakopoulos, T., and Mylonas, P. (2019). Fusing handcrafted and contextual features for human activity recognition. In Proceedings of the 14th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), pp. 1–6. Larnaca, Cyprus.
Waheed, M., Afzal, H., and Mehmood, K. (2021). Nt-fds-a noise tolerant fall detection system using deep learning on wearable devices. Sensors, 21(6), 1–26.
Wang, G., Li, Q., Wang, L., Zhang, Y., and Liu, Z. (2019). Elderly fall detection with an accelerometer using lightweight neural networks. Electronics, 8(11), 1354.
Wang, P., Li, Q., Yin, P., Wang, Z., Ling, Y., Gravina, R., and Li, Y. (2022). A convolution neural network approach for fall detection based on adaptive channel selection of UWB radar signals. Neural Computing and Applications, 35, 15967–15980.
Xu, T., Se, H., and Liu, J. (2021). A fusion fall detection algorithm combining threshold-based method and convolutional neural network. Microprocessors and Microsystems, 82, 103828.
Xu, T., Zhou, Y., and Zhu, J. (2018). New advances and challenges of fall detection systems: A survey. Applied Sciences, 8(3), 418.
Yadav, S. K., Luthra, A., Tiwari, K., Pandey, H. M., and Akbar, S. A. (2022). ARFDNet: An efficient activity recognition and fall detection system using latent feature pooling. Knowledge-Based Systems, 239, 107948.
Zhang, Z., Zhou, Z., Yang, X., Meng, H., and Wu, G. (2022). Convolutional neural network based on multi-directional local coding for finger vein recognition. Information Sciences, 623, 633–647.
Zurbuchen, N., Bruegger, P., and Wilde, A. (2020). A comparison of machine learning algorithms for fall detection using wearable sensors. In Proceedings of the International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 427–431. Fukuoka, Japan.