Discrimination of Fall and Fall-like ADL Using Tri-axial Accelerometer and Bi-axial Gyroscope
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초록
A threshold-based fall recognition algorithm using a tri-axial accelerometer and a bi-axial gyroscope mounted on the skin above the upper sternum was proposed to recognize fall-like activities of daily living (ADL) events. The output signals from the tri-axial accel-erometer and bi-axial gyroscope were obtained during eight falls and eleven ADL action sequences. The thresholds of signal vectormagnitude (SVM_Acc), angular velocity (ωres), and angular variation (θres) were calculated using MATLAB. When the measured valuesof SVM_Acc, ωres, and θres were compared to the threshold values (TH1, TH2, and TH3), fall-like ADL events could be distinguished from a fall. When SVM_Acc was larger than 2.5 g (TH1), ωres was larger than 1.75 rad/s (TH2), and θres was larger than 0.385 rad (TH3), eight falls and eleven ADL action sequences were recognized as falls. When at least one of these three conditions was not satisfied,the action sequences were recognized as ADL. Fall-like ADL events such as jogging and jumping up (or down) have posed a problemin distinguishing ADL events from an actual fall. When the measured values of SVM_Acc, ωres, and θres were applied to the sequentialprocessing algorithm proposed in this study, the sensitivity was determined to be 100% for the eight fall action sequences and the spec- ificity was determined to be 100% for the eleven ADL action sequences.
Abstract
A threshold-based fall recognition algorithm using a tri-axial accelerometer and a bi-axial gyroscope mounted on the skin above the upper sternum was proposed to recognize fall-like activities of daily living (ADL) events. The output signals from the tri-axial accel-erometer and bi-axial gyroscope were obtained during eight falls and eleven ADL action sequences. The thresholds of signal vectormagnitude (SVM_Acc), angular velocity (ωres), and angular variation (θres) were calculated using MATLAB. When the measured valuesof SVM_Acc, ωres, and θres were compared to the threshold values (TH1, TH2, and TH3), fall-like ADL events could be distinguished from a fall. When SVM_Acc was larger than 2.5 g (TH1), ωres was larger than 1.75 rad/s (TH2), and θres was larger than 0.385 rad (TH3), eight falls and eleven ADL action sequences were recognized as falls. When at least one of these three conditions was not satisfied,the action sequences were recognized as ADL. Fall-like ADL events such as jogging and jumping up (or down) have posed a problemin distinguishing ADL events from an actual fall. When the measured values of SVM_Acc, ωres, and θres were applied to the sequentialprocessing algorithm proposed in this study, the sensitivity was determined to be 100% for the eight fall action sequences and the spec- ificity was determined to be 100% for the eleven ADL action sequences.
참고문헌 (19)
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