Abstract
The monitoring of human physical activities using wearable sensors, such as inertial-based sensors, plays a significant role in various current and potential applications. These applications include physical health tracking, surveillance systems, and robotic assistive technologies. Despite the wide range of applications, classification and recognition of human activities remains imprecise and this may contribute to unfavorable reactions and responses. To improve the recognition of human activities, we designed a dataset in which ten participants (five male and five female) performed 11 different activities wearing three body-worn inertial sensors in different locations on the body. Our model extracts data via a hierarchical feature-based technique. These features include time, wavelet, and time-frequency domains, respectively. Stochastic gradient descent (SGD) is then introduced to optimize selective features. The selected features with optimized patterns are further processed by multi-layered kernel sliding perceptron to develop adaptive learning for the classification of physical human activities. Our proposed model was experimentally evaluated and applied on three benchmark datasets: IM-WSHA, a self-annotated dataset, PAMAP2 dataset which is comprised of daily living activities, and an HuGaDB, a dataset which contains physical activities for aging people. The experimental results show that the proposed method achieves better results and outperforms others in terms of recognition accuracy, achieving an accuracy rate of 83.18%, 94.16%, and 92.50% respectively, when IM-WSHA, PAMAP2, and HuGaDB datasets are applied.
Highlights
In recent times, there has been an increasing demand to improve community health and safety by the use of body-worn sensor technologies
PARAMETER EVALUATION VIA RECOGNITION ACCURACIES In this experiment, we evaluate the performance of the novel kernel sliding perceptron by providing optimized feature descriptors of time, wavelet, and time-frequency using the IM-WSHA, PAMAP2 and Human Gait Database (HuGaDB) datasets
Features are optimized by Stochastic Gradient Descent (SGD) and classified via Kernel Sliding Perceptron to enhance the recognition rate of human locomotion activities via three inertial based body-worn sensors
Summary
There has been an increasing demand to improve community health and safety by the use of body-worn sensor technologies. Body-worn inertial sensors such as accelerometers, gyroscopes, and magnetometers have gained wide research attention and encouraged the development of novel HAR applications [2]–[5] These applications include e-health, rehabilitation, security surveillance, emergency services, wellbeing assistance, smart homes and biofeedback systems. Body-worn inertial sensors track, record and transmit a substantial amount of information on general health, dietary habits and physical activities which help monitor and secure the wearer’s wellbeing [13]. In order to optimize the functionalities of such sensors they are fused together into single units called Inertial Measurement Units (IMUs) These inertial based body-worn sensors provide real-time monitoring of human activities. We applied the proposed model to our self-annotated dataset named IM-WSHA which is based on different patterns of human locomotion activities.
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