Elliptical Machine Rehabilitation Control via Real-Time Sensor Feedback
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Solution Overview
Problem
Current cardiovascular rehabilitation methods face challenges in providing personalized and efficient treatment plans, especially for remote patients, due to limitations in monitoring progress and adapting treatment protocols in real-time.
Innovation Solution
A system incorporating an elliptical machine, sensors, a display, and a processing device that uses machine learning models to receive user data, determine appropriate actions, and adjust treatment plans dynamically, enabling personalized cardiovascular rehabilitation both in-center and remotely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional cardiovascular rehabilitation programs are used, then treatment protocols can be established, but they cannot be dynamically adapted to individual patient progress in real-time
Solution Approach 1:
The system continuously monitors patient vital signs and exercise performance data during elliptical machine usage, feeding this information back to the processing device which automatically adjusts treatment parameters in real-time. This closed-loop feedback mechanism enables dynamic adaptation of exercise intensity and duration based on individual patient response without manual intervention delays.
Solution Approach 2:
The treatment protocol transitions from static to dynamic through automated real-time adjustments. The processing device modifies exercise parameters such as resistance, speed, and duration based on live patient data, allowing the rehabilitation program to adapt its intensity and duration dynamically throughout each session according to individual progress and physiological response.
2Ease of operation
If remote patient monitoring is implemented, then accessibility is improved, but monitoring precision and real-time detection capability are limited
Solution Approach 1:
The system employs sensors and communication devices as intermediaries between the patient and the processing device. These intermediaries continuously transmit vital sign data and exercise metrics from the remote patient location to the processing system, enabling accurate monitoring and real-time detection of physiological changes without requiring the patient to be physically present at a clinic.
Solution Approach 2:
Manual monitoring methods are replaced with automated electronic sensing and data transmission systems. The system uses electronic sensors to continuously measure vital signs and exercise parameters, substituting mechanical or manual assessment methods with automated electronic detection that provides higher precision and continuous monitoring capability from remote locations.
3Reliability
If personalized treatment plans are created, then rehabilitation effectiveness is improved, but system complexity increases
Solution Approach 1:
The system performs self-service through automated processing of patient data and autonomous generation of personalized treatment plans. The processing device automatically analyzes individual patient characteristics, exercise performance, and physiological responses to create customized rehabilitation protocols without requiring manual intervention from healthcare professionals, thereby reducing operational complexity while maintaining personalization.
Solution Approach 2:
The system manages complexity by systematically varying key parameters such as exercise intensity, duration, frequency, and type based on individual patient data. Rather than creating entirely different programs, the system adjusts parameters within established frameworks, making personalization achievable through parameter optimization rather than system redesign.
4Productivity
If real-time monitoring of patient progress is implemented, then treatment adaptation is improved, but energy consumption increases
Solution Approach 1:
The system maintains continuous monitoring and real-time adjustment throughout the entire exercise session without interruption. Sensors continuously collect data, the processing device continuously analyzes progress against treatment goals, and adjustments are made continuously as needed, ensuring that useful action is performed throughout the entire rehabilitation session rather than in intermittent intervals.
Data Source
AI summary
Systems including an elliptical machine and a processing device. The processing device may be configured to receive, before or while a user operates the elliptical machine, one or more messages pertaining to the user or a use of the elliptical machine by the user. The processing device may be also configured to determine whether the one or more messages were received by the processing device. In response to determining that the one or more messages were not received by the processing device, the processing device may be configured to determine, via one or more machine learning models, one or more actions to perform. The one or more actions may include at least one of initiating a telecommunications transmission, stopping operation of the elliptical machine, and modifying one or more parameters associated with the operation of the elliptical machine.