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(HA...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 bodyworn 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(SVM),and K-Nearest Neighbors(KNN)in terms of accuracy and computational efficiency.The proposed features Average absolute deviation(AAD),Standard deviation(STD),Interquartile range(IQR),Range,and Root mean square(RMS).The research has conducted numerous experiments and comparisons to establish optimal parameters for ensuring system effectiveness,including setting a sampling frequency of 50 Hz and selecting an 8-s window size with a 40%overlap between windows.Validation was conducted on both the WISDM public dataset and a self-collected dataset,focusing on five fundamental daily activities:Standing,Sitting,Jogging,Walking,and Walking the stairs.The results demonstrated high recognition accuracy,with the system achieving 96.7%on the WISDM dataset and 97.13%on the collected dataset.This research confirms the feasibility of deploying HAR systems on low-performance microcontrollers and highlights the system’s potential applications in patient support,rehabilitation,and elderly care.展开更多
基金Human activity data for the experiments were sourced from the Ethics Council for Grassroots Biomedical Research at Phenikaa University.The data collection adhered to Decision No.476/QD-DHP-HÐÐÐthe Ethics Council for Grassroots Biomedical Research at Phenikaa University(No.023.07.01/DHP-HÐÐÐ,2023 Dec).
文摘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 bodyworn 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(SVM),and K-Nearest Neighbors(KNN)in terms of accuracy and computational efficiency.The proposed features Average absolute deviation(AAD),Standard deviation(STD),Interquartile range(IQR),Range,and Root mean square(RMS).The research has conducted numerous experiments and comparisons to establish optimal parameters for ensuring system effectiveness,including setting a sampling frequency of 50 Hz and selecting an 8-s window size with a 40%overlap between windows.Validation was conducted on both the WISDM public dataset and a self-collected dataset,focusing on five fundamental daily activities:Standing,Sitting,Jogging,Walking,and Walking the stairs.The results demonstrated high recognition accuracy,with the system achieving 96.7%on the WISDM dataset and 97.13%on the collected dataset.This research confirms the feasibility of deploying HAR systems on low-performance microcontrollers and highlights the system’s potential applications in patient support,rehabilitation,and elderly care.
