Fuzzy-Logic based Care Training Quantitative Assessment using Care Training Assistant Robot (CaTARo)

Abstract

With the recent expansion of the geriatric population, caregivers and therapists have become increasingly important for improving the quality of life of elderly persons; therefore, having properly trained professionals in these fields is critical. To ensure accurate and efficient elderly care training, this study focuses on the development of an automated care training assessment method based on fuzzy-logic for calculating care training quantitative score (CTQS) for improving care skill of caregivers or therapists. Five elderly care experts and three trainees performed the exercise in flexion and extension using the elbow part of care training assistant robot (CaTARo). We obtained quantitative data from the CaTARo, such as elbow external angle, angular velocity, torque, pressure, for all participants and evaluated performance by combining four key parameters using fuzzy-logic method. As a result, we found that the result of trainees had showed a significant difference between pre-test and post-test, and the CTQS of trainees in post-test could be improved by training with CaTARo than that of pre-test.