Abstract
Monitoring elderly and weak diseased people is one of the biggest issues in this modern world. Framing a technology for them is one of the wise contributions that can be done to society. More than 30% elderly people of age above 70 are falling every year due to bad health conditions. Fall identification is a significant issue in the medical care office. Elderly people are more inclined to fall than the others; accidental falls cause injuries, severe injuries and lead to death too. In our country, more than 30% are elderly people aged above 70, and they fall every year due to bad health conditions; nearly 40%–50% of elderly people fall every year most of them experiencing recurrent falls which may cause injuries, and it may lead to death too. Most of the elderly people experiencing recurrent falls which may cause injuries, to reduce the incident a system of monitoring and control is developed to detect the elderly person falls and can take immediate action, so here we considered two ways for preventing: one is smartphone-based and the other is a wearable device based recently in the fall detection wearable devices is the best choice because they are very much less in cost than the ambient-based overall features is to increase the acceptance and continue to monitor with its deep neural networks, deep learning has quickly altered the language processing domain. The LSTM is a typical recurring cell unit for deep learning models based on recurrent neural networks; here, in my paper I have proposed a new advanced version of Long Short-Term Memory (LSTM) which is Cerebral LSTM which shows better accuracy while training and testing the data and better ability to know about the time series prediction; using the RNN, the elderly person who falls is detected and with the help of the sensor the data gets collected and is allowed to training and testing validation with the MobiFall dataset; I have achieved a fall detection accuracy of about 98%.
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References
Musci M, De Martini D, Blago N, Facchinetti T, Piastra M (2018) Online fall detection using recurrent neural networks. arXiv preprint arXiv:1804.04976
Nyan MN, Tay FE, Murugasu E (2008) A wearable system for pre-impact fall detection. J Biomech 41(16):3475–3481
Jung S, Hong S, Kim J, Lee S, Hyeon T, Lee M, Kim DH (2015) Wearable fall detector using integrated sensors and energy devices. Sci Rep 5(1):1–9
Cola G, Avvenuti M, Piazza P, Vecchio A (2016) Fall detection using a head-worn barometer. In: International conference on wireless mobile communication and healthcare 2016 Nov 14. Springer, Cham, pp 217–224
Hossain F, Ali ML, Islam MZ, Mustafa H (2016) A direction-sensitive fall detection system using single 3D accelerometer and learning classifier. In: 2016 international conference on medical engineering, health informatics and technology (MediTec). IEEE, pp 1–6
Talathi SS, Vartak A (2015) Improving performance of recurrent neural network with relu nonlinearity. arXiv preprint arXiv:1511.03771
Fafoutis X, Marchegiani L, Elsts A, Pope J, Piechocki R, Craddock I (2018) Extending the battery lifetime of wearable sensors with embedded machine learning. In: 2018 IEEE 4th world forum on internet of things (WF-IoT). IEEE, pp 269–274
Pannurat N, Thiemjarus S, Nantajeewarawat E (2014) Automatic fall monitoring: a review. Sensors 14(7):12900–12936
Canale P, Fontanella A, Torti E, Danese G, Leporati F (2017) Development of a real-time heart rate estimation algorithm on a low-power device. In: 2017 6th mediterranean conference on embedded computing (MECO) 2017 Jun 11. IEEE, pp 1–4
Li L, Rehr R, Bruns P, Gerkmann T, Röder B (2020) A survey on probabilistic models in human perception and machines. Front Robot AI 7(7):85
Li X, Pang T, Liu W, Wang T (2017) Fall detection for elderly person care using convolutional neural networks. In: 2017 10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI) 2017 Oct 14. IEEE, pp 1–6
Wang J, Zhang Z, Li B, Lee S, Sherratt RS (2014) An enhanced fall detection system for elderly person monitoring using consumer home networks. IEEE Trans Consum Electron 60(1):23–29
Yacchirema D, de Puga JS, Palau C, Esteve M (2019) Fall detection system for elderly people using IoT and ensemble machine learning algorithm. Pers Ubiquit Comput 23(5):801–817
Ajerla D, Mahfuz S, Zulkernine F (2019) A real-time patient monitoring framework for fall detection. Wirel Commun Mob Comput 22:2019
Mrozek D, Koczur A, Małysiak-Mrozek B (2020) Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge. Inf Sci 1(537):132–147
Wu F, Zhao H, Zhao Y, Zhong H (2015) Development of a wearable-sensor-based fall detection system. Int J Telemed Appl 1:2015
Abbate S, Avvenuti M, Bonatesta F, Cola G, Corsini P, Vecchio A (2012) A smartphone-based fall detection system. Perv Mob Comput J 8:883–899
Theodoridis T, Solachidis V, Vretos N, Daras P (2017) Human fall detection from acceleration measurements using a recurrent neural network. In: International conference on biomedical and health informatics 2017 Nov 18. Springer, Singapore, pp 145–149
Zigel Y, Litvak D, Gannot I (2009) A method for automatic fall detection of elderly people using floor vibrations and sound—Proof of concept on human mimicking doll falls. IEEE Trans Biomed Eng 56(12):2858–2867
Aphairaj D, Kitsonti M, Thanapornsawan T (2019) Fall detection system with 3-axis accelerometer. In: Journal of physics: conference series 2019 Nov 1, vol 1380, No. 1, p 012060. IOP Publishing
Turkane SM, Vikhe SJ, Kadu CB, Vikhe PS, Automatic body fall detection system for elderly people using accelerometer and vision based
Solbach MD, Tsotsos JK (2017) Vision-based fallen person detection for the elderly. In: Proceedings of the IEEE international conference on computer vision workshops 2017, pp 1433–1442
Lezzar F, Benmerzoug D, Kitouni I (2020) Camera-based fall detection system for the elderly with occlusion recognition. Appl Med Inf 42(3):169–179
Nahian M, Raju MH, Tasnim Z, Mahmud M, Ahad MAR, Kaiser MS (2021) Contactless fall detection for the elderly. In: Contactless human activity analysis. Springer, Cham, pp 203–235
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Jagedish, S.A., Ramachandran, M., Kumar, A., Sheikh, T.H. (2023). Wearable Devices with Recurrent Neural Networks for Real-Time Fall Detection. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 471. Springer, Singapore. https://doi.org/10.1007/978-981-19-2535-1_28
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DOI: https://doi.org/10.1007/978-981-19-2535-1_28
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