Prediction of functional independence measure (FIM) using machine learning and wearable data of stroke inpatients in a convalescent rehabilitation ward

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  • ウェアラブルデータと機械学習を用いた回復期リハビリ病棟脳卒中患者の機能指標(FIM)予測

Abstract

<p>This study aimed to estimate the score of motor FIM (Functional independence measure) of stroke inpatients, which is used as a clinical indicator in rehabilitation medicine, using wearable data recorded in a convalescent rehabilitation ward. We recorded the electrocardiogram and acceleration data of 192 stroke inpatients over a day. To estimate the score of motor FIM, we trained neural network using the wearable data and basic information of inpatients such as weight, height, sexuality and age, and then, performed five-fold cross validation. In the result, the coefficient of determination between estimated FIM by neural network and ground truth scored by therapists showed 0.73 and it was significant (p < 0.001), suggesting the possibility of estimation of motor function of stroke inpatients using activity record obtained with wearable devices.</p>

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