AI Rehabilitation Plan Control for Pain-Driven Regression
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Solution Overview
Problem
Conventional rehabilitation methods lack objective, standardized data collection and analysis, leading to subjective and inconsistent treatment plans, which can result in increased pain levels and opioid dependency, hindering patient recovery and compliance.
Innovation Solution
A computer-implemented system using artificial intelligence and machine learning to generate and modify treatment plans based on patient input and data, including pain levels, exercise routines, and treatment data, to optimize patient outcomes and reduce opioid dependency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional rehabilitation methods are used with provider judgment, then treatment plans can be customized, but objectivity and consistency are lost
Solution Approach 1:
The patent replaces the mechanical system of provider-based subjective assessment with an automated machine learning system that objectively analyzes patient data. The ML model processes structured data from multiple sources (sensor data, treatment logs, patient reports) to generate quantitative progress metrics, eliminating the mechanical dependency on provider judgment while maintaining personalized treatment adaptation through data-driven insights.
Solution Approach 2:
The system enables self-service through automated progress tracking and treatment plan adjustments. The machine learning model continuously monitors patient progress using available data and automatically generates treatment recommendations without requiring constant provider intervention. This allows the system to serve itself by autonomously analyzing data patterns and adjusting treatment parameters based on objective criteria.
2Adaptability or versatility
If subjective provider assessments are used, then treatment flexibility is maintained, but comparability across patients is impossible
Solution Approach 1:
The patent transforms subjective provider assessments into objective quantitative parameters through machine learning. The system converts diverse data types (sensor readings, patient reports, treatment logs) into standardized numerical progress metrics that can be consistently compared across patients. This parameter transformation maintains treatment flexibility by allowing continuous adaptation based on individual response patterns while enabling rigorous comparability through standardized metrics.
Solution Approach 2:
The machine learning model serves as an intermediary between raw patient data and treatment decisions. It processes and translates diverse, unstructured data into standardized progress metrics that facilitate comparison while preserving individual patient characteristics. This intermediary layer enables both comparability across patients and maintenance of personalized treatment approaches.
3Ease of operation
If pain medication is increased to manage pain levels, then patient comfort is improved, but opioid dependency increases
Solution Approach 1:
The system implements continuous feedback loops where machine learning models analyze patient progress data in real-time and automatically adjust treatment recommendations. The model monitors pain levels, treatment compliance, and functional outcomes to dynamically optimize treatment plans, providing timely feedback that reduces the need for escalating pain medication while maintaining patient comfort through data-driven interventions.
Solution Approach 2:
The system performs preliminary actions by continuously monitoring and analyzing patient data to predict future progress trajectories and identify optimal intervention timing. The machine learning model proactively adjusts treatment parameters before pain levels escalate to require medication increases, preventing the harmful cycle of opioid dependency while maintaining comfort through anticipatory treatment optimization.
4Reliability
If treatment plans are extended to address chronic pain, then patient recovery is achieved, but time consumption increases
Solution Approach 1:
The patent implements dynamic treatment plans that automatically adapt to changing patient conditions through machine learning. The system continuously monitors progress metrics and adjusts treatment intensity, duration, and parameters in real-time based on individual response patterns. This dynamic adaptation allows the system to optimize rehabilitation duration by intensifying interventions when progress is slow and reducing intensity when goals are approached, thereby achieving reliable recovery outcomes while minimizing time consumption.
Solution Approach 2:
The system changes treatment parameters dynamically based on real-time patient data analysis. The machine learning model adjusts key parameters such as exercise intensity, frequency, and duration to optimize the rehabilitation trajectory. By continuously optimizing these parameters, the system achieves effective recovery outcomes in the minimum necessary time, preventing unnecessary extension of treatment duration while maintaining reliability of recovery achievement.
Data Source
AI summary
Systems, methods, and computer-readable media for and improvement rehabilitation infrastructure. The method includes receiving data associated with a user that uses a electromechanical machine to perform a treatment plan. The method also includes generating, using an artificial intelligence engine, a unique data signature associated with a regression of the user's condition. The unique data signal is generated when an indicator in the data satisfies a threshold indicator level. The method further includes generating, using the artificial engine and based on the unique data signature associated with the regression of the user's condition, a modified treatment plan that modifies a parameters or activity associated with at least one of the pain measurement, the measurement of revolutions per minute, and the session pedaling time. The method also includes controlling, while the user uses the electromechanical machine and using the modified treatment plan, the electromechanical machine.