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 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.
Highlights
Fall accidents are a significant problem for the older adults [1,2,3], and several studies have shown that fall risks in this population can be identified with motion patterns associated with the activities of daily life [4,5]
We investigate variations of the optimal parameters involved in the Support Vector Machines (SVM) algorithm by taking a closer look into the performance of SVM classifier for detecting dynamic and static motions of the older adults
The SVM algorithm was investigated for classification accuracy
Summary
Fall accidents are a significant problem for the older adults [1,2,3], and several studies have shown that fall risks in this population can be identified with motion patterns associated with the activities of daily life [4,5]. While the classification of dynamic and static activities of daily life is a key point for fall prevention research efforts, the greater variability of motion patterns associated with aging [6,7,8,9,10] and its effect on classification performance are not well understood. We investigate the use of support vector machine (SVM) classifier to identify dynamic as well as static activities of daily life in the older adults. Numerous classification algorithms exist to provide human motion classification patterns, such as the wavelet method, the linear discriminant analysis (LDA) method, the ambient assisted living (AAL). A Multi-Classifier Adaptive-Training algorithm (MCAT) was proposed to improve the classifier for activity recognition by Cvetković et al [14]
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