AI Rehab Machine Motion Profiles for Personalized Exercise Setup
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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 for 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 assign patients to cohorts based on their characteristics, generate treatment plans, and control electromechanical rehabilitation machines, including motors and cables, to provide personalized exercise protocols and adapt to individual progress in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If personalized treatment plans are generated based on multiple patient characteristics, then patient-specific rehabilitation effectiveness is improved, but system complexity increases
Solution Approach 1:
The system segments patients into cohorts based on shared characteristics, dividing the complex problem of personalized treatment for each individual into manageable groups. This allows the system to handle multiple patient characteristics without overwhelming complexity by processing patients in segmented cohorts rather than as individual complex cases.
Solution Approach 2:
The system transforms multiple patient characteristics into cohort assignments, changing the parameters from individualized multi-dimensional data to grouped categorical data. This parameter transformation simplifies the treatment plan generation process while maintaining personalized effectiveness within each cohort.
2Reliability
If exercise apparatuses are adapted to individual needs in real-time, then rehabilitation quality is improved, but control system complexity increases
Solution Approach 1:
The system applies local quality by assigning specific treatment plans tailored to each cohort's characteristics rather than using a uniform approach for all patients. This allows rehabilitation quality to be optimized for each group's specific needs while maintaining manageable control system complexity through cohort-based segmentation.
Solution Approach 2:
The system enables dynamic adaptation of exercise apparatuses to individual needs through real-time control based on cohort assignments. The control system dynamically adjusts treatment parameters while maintaining manageable complexity through the cohort framework that structures the adaptation logic.
3Measurement precision
If machine learning models process multiple patient characteristics, then treatment plan accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-processing patient characteristics and assigning patients to cohorts before generating treatment plans. This preliminary cohort assignment reduces the processing burden during treatment plan generation, maintaining accuracy while reducing overall processing time through advance preparation.
Solution Approach 2:
The machine learning process is segmented into two stages: cohort assignment based on patient characteristics, and treatment plan generation within cohorts. This segmentation allows the system to process multiple patient characteristics efficiently by first grouping similar cases, then applying treatment logic to each group, reducing overall processing time while maintaining accuracy.
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; generate, based on the data, a motion profile for an assembly of the electromechanical machine; determine, based on a prescribed exercise of the one or more prescribed exercises, components to include to enable the user to perform, using the electromechanical machine, the prescribed exercise; validate, based on the components and configuration information pertaining to the electromechanical machine, whether the motion profile associated with the prescribed exercise is achievable with respect to a threshold achievement level; and determining, based on the one or more components and the configuration information, at least one missing component needed to achieve, with respect to the threshold achievement level, the motion profile for the prescribed exercise.