Systematic data management for effective AI-driven decision support systems in robotic rehabilitation.

ABSTRACT: Robotic rehabilitation is becoming a standard in post-stroke physical rehabilitation, and these setups, often coupled with virtual exercises, collect a large and finely grained amount of data about patients' motor performance, in terms of kinematics and force interactions. Given the high resolution of data throughout the rehabilitation treatment, invaluable information is concealed, especially if oriented towards predictive systems and decision support systems. Nevertheless, a comprehensive understanding of how manipulating these datasets with machine-learning to produce such outputs is still missing. This study leverages comprehensive robotic-assisted rehabilitation data to systematically investigate clinical outcome predictions (FMA, ARAT and MI) and robot parameters suggestions based so

SUBMITTER: Tzepkenlis A 

PROVIDER: S-EPMC12310958 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature