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학술대회 Fall Detection based on Interpretation of Important Features with Wrist-Wearable Sensors
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저자
김정균, 오다솜, 이강복, 홍상기
발행일
202210
출처
International Conference on Mobile Computing and Networking (MobiCom) 2022, pp.823-825
DOI
https://dx.doi.org/10.1145/3495243.3558250
협약과제
22HR5700, 0.5mm 급 이하 초정밀 가시·비가시 정보 표출을 위한 다차원 시각화 디지털 트윈 프레임워크 기술 개발, 홍상기
초록
Smartwatches are attracting great industrial attention, and the demand for daily monitoring is increasing. Thus, a method for high fall detection with the smallest data is required to minimize the load on telecommunication. It is difficult to run machine learning on a smartwatch for fall detection, and the data must be transmitted to a mobile phone. Therefore, we propose a method for detecting a fall with few features by detecting important features in the inertial signal acquired from the wristband sensor. We used the FallAllD dataset for evaluation and detected 60 descriptive statistical features from each of the three-Axis acceleration and angular velocity. Additionally, important features were detected through SHapley additive exPlanations. The sampling frequency of the inertial signal obtained the highest accuracy of 94% at 120 Hz, with at least 14 important features.
키워드
fall, fall detection, SHAP, wearable, XAI
KSP 제안 키워드
20 Hz, Angular Velocity, Daily monitoring, Fall Detection, Sampling frequency, Statistical Features, Three-axis acceleration, Wearable sensors, machine Learning, mobile phone