Cable-Driven Pedal Rehabilitation Machine for Adaptive Tele-Rehab
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Solution Overview
Problem
Current rehabilitation systems face challenges in determining personalized treatment plans for patients, especially when healthcare professionals are remotely located from the patients, as they struggle to monitor progress and adapt exercises based on individual characteristics during telemedicine sessions.
Innovation Solution
A system utilizing artificial intelligence and machine learning to assign patients to cohorts based on their characteristics, dynamically control exercise apparatuses, and generate personalized treatment plans, including prescribed pedaling patterns and resistance levels, through a network of motors, cables, and control systems integrated with telemedicine interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rehabilitation systems use remote telemedicine assistance, then patient monitoring capability is improved, but treatment plan personalization and adaptability deteriorate
Solution Approach 1:
The system uses AI and machine learning algorithms to automatically analyze patient data, assign cohorts, and generate personalized treatment plans without requiring continuous remote healthcare professional intervention. The exercise apparatus autonomously adjusts resistance levels and pedaling patterns based on real-time sensor feedback, enabling self-service personalization while maintaining remote monitoring capabilities.
Solution Approach 2:
The system incorporates real-time feedback loops where sensors monitor patient performance and physiological data, which is then processed by AI algorithms to dynamically adjust treatment parameters. This closed-loop feedback mechanism enables continuous personalization of treatment plans based on actual patient response, resolving the contradiction between remote monitoring and personalized adaptation.
2Manufacturing precision
If exercise apparatuses are highly customizable for individual patients, then treatment accuracy is improved, but device complexity increases
Solution Approach 1:
The system employs dynamic adjustment mechanisms where resistance levels, pedaling speeds, and exercise intensities are automatically modified in real-time based on patient performance. The AI-driven control system continuously optimizes treatment parameters without requiring manual reconfiguration, achieving high treatment accuracy while managing complexity through automation rather than static mechanical complexity.
Solution Approach 2:
The patent replaces complex mechanical customization systems with electronic and software-based control mechanisms. Motors, sensors, and AI algorithms substitute for traditional mechanical adjustment systems, enabling precise treatment personalization through digital control rather than mechanical complexity. The exercise apparatus uses electronic resistance control and programmable motion patterns instead of multiple mechanical configurations.
Data Source
AI summary
A machine includes a body with an arm having a proximal end and a distal end. The proximal end is pivotally mounted to the body, and the distal end is free to move relative to the body. A carriage is slidably mounted to the arm for movement along a length of the arm. A pedal assembly is rotatably coupled to the carriage for movement with the carriage along the length of the arm. The pedal assembly supports a foot of a user of the machine. Motors are mounted to the body and spaced apart from each other and spaced apart from the distal end of the arm. Cables are coupled to respective ones of the motors and to the carriage. A control system selectively extends and retracts the cables with the motors to cause the pedal assembly to move in prescribed pedaling patterns relative to the body.