Manh-Tuyen Vi1, Duc-Nghia Tran2, Vu Thi Thuong3,4, Nguyen Ngoc Linh5,*, Duc-Tan Tran1,*
CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1729-1756, 2024, DOI:10.32604/cmc.2024.055511
- 15 October 2024
Abstract Analyzing physical activities through wearable devices is a promising research area for improving health assessment. This research focuses on the development of an affordable and real-time Human Activity Recognition (HAR) system designed to operate on low-performance microcontrollers. The system utilizes data from a body-worn accelerometer to recognize and classify human activities, providing a cost-effective, easy-to-use, and highly accurate solution. A key challenge addressed in this study is the execution of efficient motion recognition within a resource-constrained environment. The system employs a Random Forest (RF) classifier, which outperforms Gradient Boosting Decision Trees (GBDT), Support Vector Machines… More >
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