Abstract
Elderly fall detection is an important health care application as falls represent the major reason of injuries. An efficient design of a machine learning-based wearable fall detection system is proposed in this paper. The proposed system depends only on a 3-axial accelerometer to capture the elderly motion. As the power consumption is proportional to the sampling frequency, the performance of the proposed fall detector is analyzed as a function of this frequency in order to determine the best trade-off between performance and power consumption. Thanks to efficient extracted features, the proposed system achieves a sensitivity of 99.73% and a specificity of 97.7% using a 40 Hz sampling frequency notably outperforming reference algorithms when tested on a large dataset.
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References
Bloom, D.E., Boersch-Supan, A., McGee, P., Seike, A.: Population aging: facts, challenges, and responses. Program on the Global Demography of Aging, Massachusetts (2011)
Yu, X.: Approaches and principles of fall detection for elderly and patient. In: 10th International Conference on e-Health Networking, Applications and Services (2008)
Chaccour, K., Darazi, R., Hassani, A.H., Andrès, E.: From fall detection to fall prevention: a generic classification of fall-related systems. IEEE Sens. J. 17, 812–822 (2017)
Attal, F., Mohammed, S., Dedabrishvili, M., Chamroukhi, F., Oukhellou, L., Amirat, Y.: Physical human activity recognition using wearable sensors. Sensors 15, 31314–31338 (2015)
Pierleoni, P., Belli, A., Maurizi, L., Palma, L., Pernini, L., Paniccia, M., Valenti, S.: A wearable fall detector for elderly people based on AHRS and barometric sensor. IEEE Sens. J. 16, 6733–6744 (2016)
Madgwick, S.O.H., Harrison, A.J.L., Vaidyanathan, R.: Estimation of IMU and MARG orientation using a gradient descent algorithm. In: IEEE International Conference on Rehabilitation Robotics, Zurich, Switzerland (2011)
Sabatini, A.M.: Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing. IEEE Trans. Biomed. Eng. 53(7), 1346–1356 (2006)
Abdelhedi, S., Bourguiba, R., Mouine, J., Baklouti, M.: Development of a two-threshold-based fall detection algorithm for elderly health monitoring. In: IEEE 10th International Conference on Research Challenges in Information Science (RCIS), Grenoble, France (2016)
Mezghani, N., Ouakrim, Y., Islam, M.R., Yared, R., Abdulrazak, B.: Context aware adaptable approach for fall detection bases on smart textile. In: IEEE International Conference on Biomedical & Health Informatics (BHI), Orlando, USA (2017)
Cleland, I., Kikhia, B., Nugent, C., Boytsov, A., Hallberg, J., Synnes, K., McClean, S., Finlay, D.: Optimal placement of accelerometers for the detection of everyday activities. Sensors 13, 9183–9200 (2013)
Savitzky, A., Golay, M.J.E.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627–1639 (1964)
Casilari, E., Luque, R., Morón, M.J.: Analysis of android device-based solutions for fall detection. Sensors 15, 17827–17894 (2015)
Sucerquia, A., López, J.D., Vargas-Bonilla, J.F.: SisFall: a fall and movement dataset. Sensors 17, 198 (2017)
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This publication is supported by the European Union through the European Regional Development Fund (ERDF), the Ministry of Higher Education and Research, the French region of Brittany and Rennes Métropole.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Nguyen, L.P., Saleh, M., Le Bouquin Jeannès, R. (2018). An Efficient Design of a Machine Learning-Based Elderly Fall Detector. In: Ahmed, M., Begum, S., Fasquel, JB. (eds) Internet of Things (IoT) Technologies for HealthCare. HealthyIoT 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 225. Springer, Cham. https://doi.org/10.1007/978-3-319-76213-5_5
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DOI: https://doi.org/10.1007/978-3-319-76213-5_5
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