Key result
Machine learning classifiers utilizing physiological responses effectively determined higher or lower levels of rehabilitation participation in elderly in-patients with mild cognitive impairment.
Why the study?
This study investigated the possibility of utilizing physiological responses and machine learning techniques to determine the degree of rehabilitation participation in elderly in-patients with mild cognitive impairment.
Can physiological responses and machine learning techniques determine the degree of rehabilitation participation in elderly in-patients with mild cognitive impairment?
Observational
Can physiological responses and machine learning techniques determine the degree of rehabilitation participation in elderly in-patients with mild cognitive impairment?
Machine learning classifiers using physiological signals can effectively assess rehabilitation participation levels in elderly patients with mild cognitive impairment.
May aid rehab participation assessment in elderly MCI inpatients; hypothesis-generating and requires prospective validation before practice change.
This study investigated the possibility of utilising physiological responses and machine learning techniques to determine the degree of participation of in-patients with mild cognitive impairment at rehabilitation institutions. Physiological signals related to autonomic functions, cardio-activity, sweat gland activation, and skin surface temperature were obtained, and machine learning classifiers were used to classify rehabilitation participation levels as higher or lower participation when participants were required to perform a VR-based rehabilitative task. Classifiers such as a decision tree or support vector machine can effectively determine two different levels of participation suggesting the proposed approach can help therapists assess an important aspect of client satisfaction.
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Kim et al. (2019) conducted an observational in Mild cognitive impairment. Physiological responses and machine learning classifiers was evaluated on Classification of rehabilitation participation levels (higher or lower). Machine learning classifiers utilizing physiological responses effectively determined higher or lower levels of rehabilitation participation in elderly in-patients with mild cognitive impairment.
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