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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.

Monitoring Real-Time Personal Locomotion Behaviors Over Smart Indoor-Outdoor Environments Via Body-Worn Sensors