Open AccessAutomatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines
Author(s) -
Jian Zhang,
Rahul Soangra,
Thurmon E. Lockhart
Publication year - 2020
Publication title -
sci
Language(s) - English
Resource type - Journals
ISSN - 2413-4155
DOI - 10.3390/sci2030060
Subject(s) - support vector machine , inertial measurement unit , wearable computer , artificial intelligence , computer science , accelerometer , noise (video) , dynamic balance , activity recognition , pattern recognition (psychology) , kernel (algebra) , radial basis function kernel , engineering , kernel method , mathematics , embedded system , mechanical engineering , combinatorics , image (mathematics) , operating system
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 healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. 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 γ—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 dynamic and static activities of daily life in the older adults.