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Fall Detection with the Support Vector Machine during Scripted and Continuous Unscripted Activities
Department of Computer Science and Information Engineering, Chaoyang University of Technology, 168, Jifong E. Rd., Wufong District, Taichung, 41349, Taiwan
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Received: 30 July 2012; in revised form: 1 September 2012 / Accepted: 5 September 2012 / Published: 7 September 2012
Abstract: In recent years, the number of proposed fall-detection systems that have been developed has increased dramatically. A threshold-based algorithm utilizing an accelerometer has been used to detect low-complexity falling activities. In this study, we defined activities in which the body’s center of gravity quickly declines as falling activities of daily life (ADLs). In the non-falling ADLs, we also focused on the body’s center of gravity. A hyperplane of the support vector machine (SVM) was used as the separating plane to replace the traditional threshold method for the detection of falling ADLs. The scripted and continuous unscripted activities were performed by two groups of young volunteers (20 subjects) and one group of elderly volunteers (five subjects). The results showed that the four parameters of the input vector had the best accuracy with 99.1% and 98.4% in the training and testing, respectively. For the continuous unscripted test of one hour, there were two and one false positive events among young volunteers and elderly volunteers, respectively.
Keywords: accelerometer; threshold-based classifier; falling detection; activities of daily life; support vector machine
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MDPI and ACS Style
Liu, S.-H.; Cheng, W.-C. Fall Detection with the Support Vector Machine during Scripted and Continuous Unscripted Activities. Sensors 2012, 12, 12301-12316.
Liu S-H, Cheng W-C. Fall Detection with the Support Vector Machine during Scripted and Continuous Unscripted Activities. Sensors. 2012; 12(9):12301-12316.
Liu, Shing-Hong; Cheng, Wen-Chang. 2012. "Fall Detection with the Support Vector Machine during Scripted and Continuous Unscripted Activities." Sensors 12, no. 9: 12301-12316.