Artificial intelligence tools for engagement prediction in neuromotor disorder patients during rehabilitation.

ABSTRACT:

Background

Robot-Assisted Gait Rehabilitation (RAGR) is an established clinical practice to encourage neuroplasticity in patients with neuromotor disorders. Nevertheless, tasks repetition imposed by robots may induce boredom, affecting clinical outcomes. Thus, quantitative assessment of engagement towards rehabilitation using physiological data and subjective evaluations is increasingly becoming vital. This study aimed at methodologically exploring the performance of artificial intelligence (AI) algorithms applied to structured datasets made of heart rate variability (HRV) and electrodermal activity (EDA) features to predict the level of patient engagement during RAGR.

Methods

The study recruited 46 subjects (38 underage, 10.3 ± 4.0 years old; 8 adults, 43.0 ± 19.0 years old

SUBMITTER: Costantini S 

PROVIDER: S-EPMC11657850 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature