Automated patient-robot assignment for a robotic rehabilitation gym: a simplified simulation model.

Autor: Miller BA; Department of Electrical and Computer Engineering, University of Wyoming, 1000 E University Ave., Laramie, WY, 82071, USA.; Department of Electrical Engineering and Computer Science, University of Cincinnati, 2600 Clifton Ave., Cincinnati, OH, 45221, USA., Adhikari B; Department of Electrical and Computer Engineering, University of Wyoming, 1000 E University Ave., Laramie, WY, 82071, USA., Jiang C; Department of Electrical and Computer Engineering, University of Wyoming, 1000 E University Ave., Laramie, WY, 82071, USA., Novak VD; Department of Electrical and Computer Engineering, University of Wyoming, 1000 E University Ave., Laramie, WY, 82071, USA. novakdn@ucmail.uc.edu.; Department of Electrical Engineering and Computer Science, University of Cincinnati, 2600 Clifton Ave., Cincinnati, OH, 45221, USA. novakdn@ucmail.uc.edu.
Jazyk: angličtina
Zdroj: Journal of neuroengineering and rehabilitation [J Neuroeng Rehabil] 2022 Nov 16; Vol. 19 (1), pp. 126. Date of Electronic Publication: 2022 Nov 16.
DOI: 10.1186/s12984-022-01105-4
Abstrakt: Background: A robotic rehabilitation gym can be defined as multiple patients training with multiple robots or passive sensorized devices in a group setting. Recent work with such gyms has shown positive rehabilitation outcomes; furthermore, such gyms allow a single therapist to supervise more than one patient, increasing cost-effectiveness. To allow more effective multipatient supervision in future robotic rehabilitation gyms, we propose an automated system that could dynamically assign patients to different robots within a session in order to optimize rehabilitation outcome.
Methods: As a first step toward implementing a practical patient-robot assignment system, we present a simplified mathematical model of a robotic rehabilitation gym. Mixed-integer nonlinear programming algorithms are used to find effective assignment and training solutions for multiple evaluation scenarios involving different numbers of patients and robots (5 patients and 5 robots, 6 patients and 5 robots, 5 patients and 7 robots), different training durations (7 or 12 time steps) and different complexity levels (whether different patients have different skill acquisition curves, whether robots have exit times associated with them). In all cases, the goal is to maximize total skill gain across all patients and skills within a session.
Results: Analyses of variance across different scenarios show that disjunctive and time-indexed optimization models significantly outperform two baseline schedules: staying on one robot throughout a session and switching robots halfway through a session. The disjunctive model results in higher skill gain than the time-indexed model in the given scenarios, and the optimization duration increases as the number of patients, robots and time steps increases. Additionally, we discuss how different model simplifications (e.g., perfectly known and predictable patient skill level) could be addressed in the future and how such software may eventually be used in practice.
Conclusions: Though it involves unrealistically simple scenarios, our study shows that intelligently moving patients between different rehabilitation robots can improve overall skill acquisition in a multi-patient multi-robot environment. While robotic rehabilitation gyms are not yet commonplace in clinical practice, prototypes of them already exist, and our study presents a way to use intelligent decision support to potentially enable more efficient delivery of technologically aided rehabilitation.
(© 2022. The Author(s).)
Databáze: MEDLINE
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