AI Rehabilitation System for Predicting Optimal Session Counts
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Solution Overview
Problem
Existing telemedicine systems face challenges in effectively monitoring patient progress and adapting treatment plans remotely due to the inability of healthcare professionals to conduct physical examinations, leading to inefficiencies and inaccuracies in treatment planning for rehabilitation.
Innovation Solution
A computer-implemented system using artificial intelligence and machine learning to analyze treatment data from a patient interface, determine the probability of compliance with exercise regimens, and generate personalized treatment plans that can be dynamically adjusted based on real-time patient feedback and characteristics, enabling remote control of rehabilitation apparatuses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If telemedicine systems are used to provide remote medical assistance, then patient accessibility to healthcare professionals is improved, but the ability to conduct physical examinations and accurately monitor patient progress deteriorates
Solution Approach 1:
The patent introduces an AI-based intermediary system that bridges the gap between remote telemedicine communication and accurate patient progress monitoring. The AI system processes multiple data streams (sensor data, patient responses, treatment outcomes) to generate precise progress assessments, effectively mediating between the limitations of remote communication and the need for accurate monitoring.
Solution Approach 2:
The patent replaces the mechanical/physical examination process with an AI-driven digital assessment system. Instead of relying on healthcare professionals to physically examine patients, the system uses machine learning algorithms to analyze treatment data, sensor information, and patient feedback, substituting the physical examination mechanism with an automated intelligent system.
2Adaptability or versatility
If treatment plans are manually created by healthcare professionals, then customization to individual patient needs is improved, but time consumption and productivity efficiency deteriorate
Solution Approach 1:
The patent implements preliminary action by having the AI system pre-process and analyze patient data, treatment protocols, and outcome predictions before healthcare professionals need to create treatment plans. The system prepares customized recommendations and progress monitoring frameworks in advance, reducing the manual work required during treatment planning.
Solution Approach 2:
The AI system performs self-service by automatically generating treatment plan recommendations, predicting patient progress, and monitoring outcomes without requiring constant manual intervention from healthcare professionals. The system serves itself by processing data, making predictions, and providing actionable insights, freeing professionals to focus on higher-value activities.
3Reliability
If center-based rehabilitation is used, then comprehensive monitoring and treatment is improved, but cost and accessibility for patients deteriorates
Solution Approach 1:
The patent applies universality by creating a multi-functional AI system that can operate in various settings (home, clinic, hospital) and serve multiple purposes (treatment planning, progress monitoring, outcome prediction, compliance tracking). This universal system replaces the need for specialized center-based facilities while maintaining comprehensive monitoring capabilities.
Solution Approach 2:
The system enables self-service rehabilitation by providing patients with AI-driven treatment plans and progress monitoring that can be executed independently at home. The AI system continuously adapts to patient needs and provides automated feedback, eliminating the requirement for constant center-based supervision while maintaining treatment quality.
Data Source
AI summary
A system includes a treatment apparatus configured to implement a treatment plan for rehabilitation to be performed by a user and a processing device configured to receive attribute data associated with the user; generate, based on the rehabilitation, selected attribute data; determine, based on the selected attribute data, the rehabilitation, and a rehabilitation goal associated with the rehabilitation, one or more probabilities of attaining the rehabilitation goal within respective one or more numbers of rehabilitation sessions to be performed by the user using the treatment apparatus; provide, based on the one or more probabilities, an indication of the one or more numbers of rehabilitation sessions; and generate, based on a selected number of rehabilitation sessions from among the one or more numbers of rehabilitation sessions, the treatment plan. The treatment plan includes one or more exercises directed to attaining the rehabilitation goal within the selected number of rehabilitation sessions.