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Abstract BackgroundA 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. MethodsAs 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. ResultsAnalyses 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. ConclusionsThough 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.
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A Generalized Framework for Concentric Tube Robot Design Using Gradient-Based Optimization
Concentric tube robots (CTRs) show particular promise for minimally invasive surgery due to their inherent compliance and ability to navigate in constrained environments. Due to variations in anatomy among patients and variations in task requirements among procedures, it is necessary to customize the design of these robots on a patient- or population-specific basis. However, the complex kinematics and large design space make the design problem challenging. Here we propose a computational framework that can efficiently optimize a robot design and a motion plan to enable safe navigation through the patient’s anatomy. The current framework is the first fully gradient-based method for CTR design optimization and motion planning, enabling an efficient and scalable solution for simultaneously optimizing continuous variables, even across multiple anatomies. The framework is demonstrated using two clinical examples, laryngoscopy and heart biopsy, where the optimization problems are solved for a single patient and across multiple patients, respectively.
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- Award ID(s):
- 1850400
- PAR ID:
- 10331248
- Date Published:
- Journal Name:
- IEEE transactions on robotics
- ISSN:
- 1941-0468
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TRO.2022.3180627
