An appropriate physical functionality status assessment is necessary after rehabilitation to determine the level of assistance required by the patient and the efficacy of rehabilitation. The effectiveness of rehabilitation can be determined by computing a functional independence measure (FIM) score. The FIM score measurement process evaluates the amount of assistance associated with activities of daily living; however, it is influenced by evaluator subjectivity and can vary for the same patient assessed by different evaluators. Furthermore, it is time-consuming and laborious because of the large number of component items. Therefore, a new evaluation system that is easily implementable and based on objective criteria is needed. Several machine learning techniques have been suggested for evaluating the progress of rehabilitation in an objective manner, and their efficacy has been proven. However, the FIM score includes complex movement items, necessitating the evaluation of factors from multiple angles. In this study, a method for estimating FIM values using machine learning was investigated to evaluate the effectiveness of rehabilitation objectively. A simple exercise measurement experiment was conducted, and a musculoskeletal model was used to analyze the data to obtain movement and other mechanical indices, and these were subsequently used as features of machine learning. Based on the FIM values, an estimation experiment was conducted in three groups: independent, modified independent, and assisted groups. The statistical approaches of random forest and logistic regression were used in conjunction with a support vector machine for FIM estimation. The highest accuracy was estimated to be approximately 0.9. However, the accuracy varied with each method and item; the lowest accuracy was approximately 0.3. Statistical analysis showed clear differences in the indicators, with significant differences between the groups. These differences were considered to increase the accuracy of FIM estimation. Additionally, the accuracy of some items was improved by changing the feature values used. The best results were obtained when only the joint angle was used for two items, joint torque and muscle strength were used for seven items, and all indicators were used for two items. This suggests that a comprehensive evaluation, including that of joint torque and muscle strength, is effective for estimating FIM score.

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