Version 1
: Received: 30 March 2020 / Approved: 8 May 2020 / Online: 3 June 2020 (00:00:00 CEST)
Version 2
: Received: 30 March 2020 / Approved: 8 May 2020 / Online: 5 July 2020 (00:00:00 CEST)
Version 3
: Received: 30 March 2020 / Approved: 8 May 2020 / Online: 18 July 2020 (00:00:00 CEST)
Zhang, J.; Soangra, R.; E. Lockhart, T. Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines. Sci2020, 2, 62.
Zhang, J.; Soangra, R.; E. Lockhart, T. Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines. Sci 2020, 2, 62.
Zhang, J.; Soangra, R.; E. Lockhart, T. Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines. Sci2020, 2, 62.
Zhang, J.; Soangra, R.; E. Lockhart, T. Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines. Sci 2020, 2, 62.
Abstract
Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the elderly is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of an SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the elderly. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter —are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying static and dynamic activities of daily life in the elderly.
Keywords
locomotion; machine learning; support vector machines; activity classification; activity of daily life (ADL)
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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