Virtual Apparatus Control for Personalized Rehab Motion Profiles
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Solution Overview
Problem
Current rehabilitation systems face challenges in determining personalized treatment plans and remotely monitoring patient progress during telemedicine sessions, especially in orthopedic joint rehabilitation, due to the complexity of processing multiple patient characteristics and the difficulty in adapting exercise apparatuses to individual needs.
Innovation Solution
A computer-implemented system that uses machine learning models to generate motion profiles for electromechanical machines, integrating data from treatment plans, user characteristics, and performance metrics to control motors and adjust exercise protocols dynamically, enabling remote monitoring and adaptation of rehabilitation exercises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a standardized rehabilitation exercise apparatus is used, then the device complexity is reduced and ease of manufacture is improved, but the adaptability to individual patient needs deteriorates
Solution Approach 1:
The system dynamically adjusts exercise parameters including resistance levels, range of motion, speed, and duration based on real-time patient performance data and therapeutic goals. The electromechanical apparatus transitions from static pre-programmed exercises to dynamic adaptive exercises that automatically modify parameters during execution, resolving the contradiction between standardized manufacturing and individualized adaptation.
Solution Approach 2:
The system changes multiple operational parameters simultaneously including force resistance, angular velocity, positional limits, and temporal duration of exercises. These parameter modifications are driven by machine learning models that analyze patient characteristics and progress, enabling a standardized apparatus to deliver personalized rehabilitation protocols without requiring custom hardware for each patient.
2Adaptability or versatility
If personalized treatment plans are created for each patient, then the adaptability to individual needs is improved, but the device complexity and difficulty of operation increase
Solution Approach 1:
The system performs self-adjustment of exercise parameters by automatically analyzing patient performance data and modifying subsequent exercise protocols without requiring manual intervention from therapists or patients. The machine learning models embedded in the control system enable the apparatus to self-optimize treatment parameters, reducing operational complexity while maintaining high adaptability to individual patient needs.
Solution Approach 2:
The system continuously monitors patient performance through sensors that capture kinematic data, force application, and range of motion. This feedback loop feeds real-time data to machine learning models that adjust exercise parameters dynamically, creating a closed-loop control system that personalizes treatment automatically without increasing apparent device complexity to the user.
3Measurement precision
If multiple patient characteristics are processed to determine personalized treatment plans, then the measurement precision and treatment accuracy are improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system employs a unified machine learning framework that processes multiple patient characteristics including demographic data, medical history, real-time biometric sensors, and performance metrics through a single integrated analysis platform. This multi-functional approach consolidates what would otherwise require multiple separate measurement and analysis systems, reducing the practical difficulty of detecting and measuring while maintaining high precision through comprehensive data integration.
Data Source
AI summary
A computer-implemented system includes an electromechanical machine and a processing device communicatively coupled to motors. The processing device executes instructions to receive data comprising a treatment plan including one or more prescribed exercises for a user to perform using the electromechanical machine; generate, based on the data, a motion profile for an assembly of the electromechanical machine, wherein the assembly is coupled to a carriage to enable movement along a length of an arm, wherein the carriage is coupled to each cable of the first set of cables; execute a transformation function to implement a desired virtual apparatus model using the electromechanical machine, wherein, to implement the desired virtual apparatus model, the transformation function maps the motion profile to one or more coordinates in a domain; and control, using the desired virtual apparatus model, the one or more motors of the electromechanical machine.