부산대학교 도서관

Continuous monitoring of hand function in individuals with upper limb paresis, such as stroke survivors, could provide a quantitative assessment of their real-world functional performance, which has great potential to enhance the clinical guidance of rehabilitation interventions. In this paper, we explore a novel wearable approach to quantify the amount of hand use by leveraging Radio Frequency Identification (RFID) technologies. We introduce a prototype implementation of our wearable RFID system composed of a wrist-worn reader (antenna) and a small passive tag placed on a fingernail. Then, we discuss a machine learning-based data analytic pipeline that analyzes the backscattered RF signal to estimate the amount of hand use. The accuracy of the system is validated against an optoelectronic motion capture system - the gold standard for human movement analyses - using a dataset collected from five neurologically intact individuals. The proposed wearable RFID system could accurately estimate the amount of hand use with $R^{2}$ of 0.67 and Normalized Root Mean Square Error of 7.3%, and shows great potential for clinical applications.