[1M5-GS-10] AI application

[1M5-GS-10-04] Future prediction of functional independence measure (FIM) of stroke inpatients in a rehabilitation ward using a wearable device.

〇Takayuki Ogasawara¹, Yoshitaka Wada², Masahiko Mukaino², Eiich Saitoh², Shingo Tsukada¹, Yohei Otaka², Masumi Yamaguchi¹ (1. NTT Corporation, 2. Fujita Health University)

Keywords:rehabilitation, stroke, wearable

This study aimed to clarify that the body function of stroke inpatients in the future could be predicted from activity data using a consumer-grade wearable device. Experiments began within one week of admission, and heart rate and acceleration were continuously obtained on the chest for 48 hours. We predicted the Functional Independence Measure (FIM) as rehabilitation outcome. Random forest was used as the predictor. Results with 5-fold cross-validation showed that the coefficient of determinations between the predicted and actual values of FIM based on activity data were 0.74 (n=1196) in the week that the measurement was conducted, 0.81 (n=850) in two weeks, and 0.79 (n=394) in nine weeks, which corresponds to the typical period of discharge. All results were statistically significant (p < 0.001). These results suggest the predictability of clinical indicators from activity data from the early stages of hospitalization to discharge.