Abstract
Falls in elderlies are a major health and economic problem. Research on falls in elderly people has the great social significance under the population aging. Previous smartphone-based fall detection systems have not both fall detection and fall prevention, and the feasibility has not been fully examined. In this paper, we propose a smartphone-based fall detection system using a threshold-based algorithm to distinguish between Activities of Daily Living (ADL) and falls in real time. The smartphone with built-in tri-accelerometer is used for detecting early-warning of fall based on pre-impact phase and post-fall based on impact phase. Eight healthy Asian adult subjects who wear phone at waist were arranged to perform three kinds of daily living activities and three kinds of fall activities. By comparative analysis of threshold levels for acceleration, in order to get the best sensitivity and specificity, acceleration thresholds were determined for early pre-impact alarm (4.5-5m/s2) and post-fall detection (21-28 m/s2) under experimental conditions.
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References
Bourke, A.K., O’Brien, J.V., Lyons, G.M.: Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 26(2), 194–199 (2007)
Thierauf, A., Preu, J., Lignitz, E., Madea, B.: Retrospective analysis of fatal falls. J. Forensic Sci. Int. 198(1-3), 92–96 (2010)
Alemdar, H., Yavuz, G.R., Özen, M.O., Kara, Y.E., İncel, Ö.D., Akarun, L., Ersoy, C.: A Robust Multimodal Fall Detection Method for Ambient Assisted Living Applications. In: Proc. of IEEE Signal Processing and Communications Applications Conference, SIU 2010, Turkey (2010)
Zigel, Y., Litvak, D., Gannot, I.: A Method for Automatic Fall Detection of Elderly People Using Floor Vibrations and Sound Proof of Concept on Human Mimicking Doll Falls. Proc. of Transactions on Biomedical Eng. 56(12), 2858–2867 (2009)
Luo, S., Hu, Q.: A Dynamic Motion Pattern Analysis Approach to Fall Detection. In: Proc. of IEEE International Workshop on Biomedical Circuits and Systems, pp. 1–5 (2004)
Bouten, C.V.C., Koekkoek, K.T.M., Verduin, M., Kodde, R., Janssen, J.D.: A Triaxial Accelerometer and Portable Data Processing Unit for the Assessment of Daily Physical Activity. Proc. of Transactions on Biomedical Engineering 44(3) (1997)
Biddargaddi, N., Sarela, A., Klingbeil, L., Karunanithi, M.: Detecting Walking Activity in Cardiac Rehabilitation by Using Accelerometer. In: Proc. of International Conference on Intelligent Sensors, Sensor Networks and Information, pp. 555–560 (2007)
Lee, R.Y.W., Carlisle, A.J.: Detection of falls using accelerometers and mobile phone technology. Age and Ageing, 1–7 (2011)
Yavuz, G., Kocak, M., Ergun, G., Alemdar, H.O., Yalcin, H., Incel, O.D., Ersoy, C.: A Smartphone Based Fall Detector with Online Location Support. In: Proceedings of PhoneSense, pp. 31–35 (2010)
Dai, J., Bai, X., Yang, Z., Shen, Z., Xuan, D.: PerFallD: A pervasive fall detection system using mobile phones. In: Proceedings of the 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 292–297 (2010)
Sposaro, F., Tyson, G.: iFall: an Android application for fall monitoring and response. Journal of Eng. Med. Biol. Soc., 6119–6122 (2009)
Jiangpeng, D., Xiaole, B., Zhimin, Y., Zhaohui, S., Dong, X.: Mobile phone-based pervasive fall detection. Journal of Personal Ubiquitous Computing 14(7), 633–643 (2010)
Majumder, A.J.A., Rahman, F., Zerin, I., et al.: iPrevention: towards a novel real-time smartphone-based fall prevention system. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 513–518. ACM (2013)
Kangas, M., Vikman, I., Wiklander, J., Lindgren, P., Nyberg, L., Jämsä, T.: Sensitivity and specificity of fall detection in people aged 40 years and over. Gait & Posture 29(4), 571–574 (2009)
Kangas, M., Konttila, A., Lindgren, P., Winblad, I., Jämsä, T.: Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait & Posture 28(2), 285–291 (2008)
Bourke, A.K., van de Ven, P., Gamble, M., O’Connor, R., Murphy, K., Bogan, E., McQuade, E., Finucane, P., ÓLaighin, G., Nelson, J.: Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities. Journal of Biomechanics 43(15), 3051–3057 (2010)
Liang, D., Zhao, G., Guo, Y., Wang, L.: Pre-impact & impact detection of falls using wireless body sensor network. In: 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 763–766 (2012)
He, Y., Li, Y., Bao, S.D.: Fall Detection by built-in tri-accelerometer of smartphone. In: 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 184–187. IEEE (2012)
Kangas, M., Konttila, A., Winblad, I., Jamsa, T.: Determination of simple thresholds for accelerometry-based parameters for fall detection. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2007, pp. 1367–1370 (2007)
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Mao, L., Liang, D., Ning, Y., Ma, Y., Gao, X., Zhao, G. (2014). Pre-impact and Impact Detection of Falls Using Built-In Tri-accelerometer of Smartphone. In: Zhang, Y., Yao, G., He, J., Wang, L., Smalheiser, N.R., Yin, X. (eds) Health Information Science. HIS 2014. Lecture Notes in Computer Science, vol 8423. Springer, Cham. https://doi.org/10.1007/978-3-319-06269-3_18
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DOI: https://doi.org/10.1007/978-3-319-06269-3_18
Publisher Name: Springer, Cham
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