Electromechanical Rehabilitation Machine With Real-Time Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing telemedicine systems face limitations in providing effective rehabilitation protocols due to the inability of healthcare professionals to conduct physical examinations remotely, leading to inefficiencies and inaccuracies in treatment plan selection and monitoring.
Innovation Solution
A system utilizing an electromechanical machine and artificial intelligence to determine a maximum target heart rate, adjust resistance based on perceived exertion, and provide real-time feedback for personalized treatment plans, enabling remote rehabilitation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If telemedicine systems are used for remote rehabilitation, then accessibility and convenience are improved, but measurement precision and treatment accuracy deteriorate due to inability to conduct physical examinations
Solution Approach 1:
The patent introduces an electromechanical machine as an intermediary device between the healthcare professional and the patient. This machine includes sensors that objectively measure physiological parameters (heart rate, blood pressure, respiratory rate) and treatment response, replacing the need for direct physical examination while maintaining measurement accuracy. The machine acts as a mediator that captures data the healthcare professional would otherwise obtain through hands-on assessment.
Solution Approach 2:
The patent replaces the mechanical system of physical examination (hands-on assessment by healthcare professional) with an automated sensing and measurement system. The electromechanical machine uses sensors, processors, and algorithms to objectively measure treatment effects, substituting the human physical examination process with instrumental measurement while improving consistency and accuracy.
2Ease of manufacture
If standardized treatment protocols are used, then ease of implementation is improved, but adaptability to individual patient needs deteriorates
Solution Approach 1:
The patent implements dynamic treatment protocols that automatically adjust treatment parameters based on real-time physiological feedback from sensors. The system monitors heart rate, blood pressure, and other vital signs during treatment, and automatically modifies treatment intensity and duration to match the patient's actual response, enabling personalization without requiring complex manual adjustments.
Solution Approach 2:
The patent incorporates continuous feedback loops where sensor data from the patient during treatment is processed by algorithms that determine optimal treatment adjustments. The system uses real-time physiological measurements to feedback into the treatment protocol, automatically personalizing the treatment based on the patient's actual response rather than relying on pre-set standardized protocols.
3Device complexity
If manual monitoring of treatment progress is used, then device complexity is reduced, but productivity and treatment efficiency deteriorate
Solution Approach 1:
The patent implements a self-monitoring system where the electromechanical machine automatically collects physiological data, processes it through embedded algorithms, and adjusts treatment parameters without requiring continuous manual intervention. The system serves itself by autonomously monitoring treatment progress and making real-time adjustments, eliminating the need for constant healthcare professional involvement while maintaining high treatment efficiency.
Solution Approach 2:
The patent uses accelerated data processing and analysis algorithms that rapidly process sensor inputs and generate treatment adjustments in real-time. The system employs sophisticated computational algorithms that quickly analyze multiple physiological parameters simultaneously and determine optimal treatment modifications, dramatically increasing treatment efficiency compared to manual monitoring methods.
Data Source
AI summary
A computer-implemented method is disclosed. The method includes receiving, from one or more sensors, one or more measurements associated with a user, where the one or more measurements are received while the user performs a treatment plan, and an electromechanical machine is configured to be manipulated by the user while the user is performing the treatment plan. The method also includes determining, via one or more machine learning models, one or more content items to present to the user, where the determining is based on the one or more measurements and one or more characteristics of the user. A presentation of the one or more content items is provided on an interface while the user performs the treatment plan using the electromechanical machine.


