Falling is one of the main causes of trauma, disability, and death among older people. Inertial sensors-based devices are able to detect falls in controlled environments. Often this kind of solution presents poor performances in real conditions. The aim of this work is the development of a computationally low-cost algorithm for feature extraction and the implementation of a machine-learning scheme for people fall detection, by using a triaxial MEMS wearable wireless accelerometer. The proposed approach allows to generalize the detection of fall events in several practical conditions. It appears invariant to the age, weight, height of people, and to the relative positioning area (even in the upper part of the waist), overcoming the drawbacks of well-known threshold-based approaches in which several parameters need to be manually estimated according to the specific features of the end user. In order to limit the workload, the specific study on posture analysis has been avoided, and a polynomial kernel function is used while maintaining high performances in terms of specificity and sensitivity. The supervised clustering step is achieved by implementing an one-class support vector machine classifier in a stand-alone PC. 1. Introduction The problem of falls in the elderly has become a health care priority due to the related high social and economic costs [1]. In fact the European population aged 65 years or more, which may be in need of assistance is increasing. This trend asks care-holders institutions to employ more efficient and optimized methods in order to be able to grant the required service at lower costs. The consequences of falls in the elderly may lead to psychological trauma, physical injuries, hospitalization, and even death in the worst scenario [2–5]. The main reason that pushed for the development of the presented system is to allow noncompletely self-sufficient people (e.g., older people) to live safely in their own houses as long as possible. This is important not only for aspects of health regarding assisted people, but also for the consequent social advantages. The European community issued and funded various projects and consortia. The mission focuses on several purposes, all addressed to older people, varying from the assistance in case of need, to the prevention of dangerous or unhealthy situations. The purpose of the work described in this paper is to focus on people fall detection. Many solutions have been proposed in the detection and prevention of falls, and some excellent review studies were presented [1, 6]. Basically,
References
[1]
N. Noury, P. Rumeau, A. K. Bourke, G. óLaighin, and J. E. Lundy, “A proposal for the classification and evaluation of fall detectors,” IRBM, vol. 29, no. 6, pp. 340–349, 2008.
[2]
M. C. Chung, K. J. McKee, C. Austin et al., “Posttraumatic stress disorder in older people after a fall,” International Journal of Geriatric Psychiatry, vol. 24, no. 9, pp. 955–964, 2009.
[3]
S. Sadigh, A. Reimers, R. Andersson, and L. Laflamme, “Falls and fall-related injuries among the elderly: a survey of residential-care facilities in a Swedish municipality,” Journal of Community Health, vol. 29, no. 2, pp. 129–140, 2004.
[4]
A. Shumway-Cook, A. M. Ciol, J. Hoffman, J. B. Dudgeon, K. Yorkston, and L. Chan, “Falls in the medicare population: incidence, associated factors, and on health Care,” Physical Therapy, vol. 89, no. 4, pp. 324–332, 2009.
[5]
S. Elliott, J. Painter, and S. Hudson, “Living alone and fall risk factors in community-dwelling middle age and older adults,” Journal of Community Health, vol. 34, no. 4, pp. 301–310, 2009.
[6]
X. Yu, “Approaches and principles of fall detection for elderly and patient,” in Proceedings of the 10th IEEE International Conference on E-Health Networking, Applications and Service (HEALTHCOM '08), pp. 42–47, Singapore, July 2008.
[7]
D. M. Karantonis, M. R. Narayanan, M. Mathie, N. H. Lovell, and B. G. Celler, “Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring,” IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 1, pp. 156–167, 2006.
[8]
J. Y. Hwang, J. M. Kang, Y. W. Jang, and H. C. Kim, “Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly,” in Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '04), pp. 2204–2207, San Francisco, Calif, USA, September 2004.
[9]
H. J. Luinge and P. H. Veltink, “Inclination measurement of human movement using a 3-D accelerometer with autocalibration,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 12, no. 1, pp. 112–121, 2004.
[10]
G. Anania, A. Tognetti, N. Carbonaro et al., “Development of a novel algorithm for human fall detection using wearable sensors,” in Proceedings of the IEEE Sensors (SENSORS '08), pp. 1336–1339, Lecce, Italy, October 2008.
[11]
A. Leone, G. Diraco, and P. Siciliano, “Detecting falls with 3D range camera in ambient assisted living applications: a preliminary study,” Medical Engineering and Physics, vol. 33, no. 6, pp. 770–781, 2011.
[12]
J. Chen, K. Kwong, D. Chang, J. Luk, and R. Bajcsy, “Wearable sensors for reliable fall detection,” in Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE-EMBS '05), pp. 3551–3554, Shanghai, China, September 2005.
[13]
M. Kangas, A. Konttila, P. Lindgren, I. Winblad, and T. J？ms？, “Comparison of low-complexity fall detection algorithms for body attached accelerometers,” Gait & Posture, vol. 28, no. 2, pp. 285–291, 2008.
[14]
A. K. Bourke, J. V. O'Brien, and G. M. Lyons, “Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm,” Gait & Posture, vol. 26, no. 2, pp. 194–199, 2007.
[15]
A. K. Bourke, P. Van de Ven, A. E. Chaya, G. M. Olaighin, and J. Nelson, “Testing of a long-term fall detection system incorporated into a custom vest for the elderly,” in Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '08), pp. 2844–2847, Vancouver, Canada, August 2008.
[16]
A. K. Bourke, P. van de Ven, M. Gamble et al., “Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities,” Journal of Biomechanics, vol. 43, no. 15, pp. 3051–3057, 2010.
[17]
F. Bagalà, C. Becker, A. Cappello et al., “Evaluation of accelerometer-based fall detection algorithms on real-world falls,” PLoS One, vol. 7, Article ID e37062, 2012.
[18]
S. -H. Liu and W. -C. Cheng, “Fall detection with the support vector machine during scripted and continuous unscripted activities,” Sensor, vol. 12, no. 9, pp. 12301–12316, 2012.
[19]
A. Leone, G. Diraco, C. Distante et al., “A multi-sensor approach for people fall detection in home environment,” in Proceedings of the Workshop on Multi-Camera and Multi-Modal Sensor Fusion Algorithms and Applications (M2SFA2 '08), pp. 1–12, 2008.
[20]
N. Noury, A. Fleury, P. Rumeau et al., “Fall detection—principles and methods,” in Proceedings of the 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society (EMBC '07), pp. 1663–1666, Lyon, France, August 2007.
[21]
L. M. Manevitz and M. Yousef, “One-class SVMs for document classification,” Journal of Machine Learning Research, vol. 2, no. 1, pp. 139–154, 2001.
[22]
V. Franc and V. Hlavac, “Statistical Pattern Recognition Toolbox (STPRtool)”.
[23]
T. Zhang, J. Wang, L. Xu, and P. Liu, “Fall detection by wearable sensor and one-class SVM algorithm,” Lecture Notes in Control and Information Sciences, vol. 345, pp. 858–863, 2006.
[24]
J. Yin, Q. Yang, and J. J. Pan, “Sensor-based abnormal human-activity detection,” IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 8, pp. 1082–1090, 2008.
[25]
H. Cheng, H. Luo, and F. Zhao, “A fall detection algorithm based on pattern recognition and human posture analysis,” in Proceedings of the IET International Conference on Communication Technology and Application (ICCTA '11), vol. 2011, pp. 853–857, Beijing, China.
[26]
R. Sangeetha and B. Kalpana, “A comparative study and choice of an appropriate kernel for support vector machines,” in Information and Communication Technologies, V. V. Das and R. Vijaykumar, Eds., vol. 101 of Communications in Computer and Information Science, pp. 549–553, Springer, Heidelberg, Germany, 2010.
[27]
R. Sangeetha and B. Kalpana, “Performance evaluation of kernels in multiclass support vector machines,” International Journal of Soft Computing and Engineering, vol. 1, no. 5, pp. 138–145, 2011.
[28]
M. Grassi, A. Lombardi, G. Rescio et al., “A hardware-software framework for high-reliability people fall detection,” in Proceedings of the IEEE Sensors (SENSORS '08), pp. 1328–1331, Lecce, Italy, October 2009.
