Sensorized Rowing Machine Feedback for Remote Rehabilitation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing telemedicine systems lack the ability to conduct physical examinations of patients, relying solely on verbal communication and limited remote observation, which is inadequate for comprehensive rehabilitation and cardiovascular health management.
Innovation Solution
A computer-implemented system using a rowing machine equipped with sensors and machine learning models to monitor user interactions, determine exercise regimens, and provide personalized treatment plans, enabling remote rehabilitation and cardiovascular health improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If telemedicine systems are used for cardiovascular rehabilitation, then remote monitoring capability is provided, but treatment plans are not personalized to individual needs
Solution Approach 1:
The system dynamically adjusts treatment plans in real-time based on patient progress data, transitioning from static to adaptive rehabilitation protocols. The processing device continuously monitors patient performance and modifies exercise parameters accordingly.
Solution Approach 2:
The system incorporates feedback mechanisms where patient performance data is collected, analyzed by machine learning models, and used to automatically adjust treatment plans. This closed-loop feedback enables continuous personalization without requiring manual intervention.
2Productivity
If machine learning models are used to analyze user data, then treatment plan adjustment is improved, but data processing requirements increase
Solution Approach 1:
The system performs preliminary data processing and feature extraction locally at the rowing machine before transmitting to the processing device. This pre-processing reduces the amount of data requiring complex analysis, thereby lowering overall energy consumption while maintaining rehabilitation effectiveness.
3Measurement precision
If real-time monitoring is implemented, then patient progress tracking is improved, but system response time requirements increase
Solution Approach 1:
The monitoring system is segmented into multiple components: sensors on the rowing machine, local processing units, and remote analysis servers. This segmentation allows for distributed real-time monitoring while reducing the computational burden on any single system component, maintaining both accuracy and response speed.
Data Source
AI summary
A computer-implemented system may include a rowing machine, an interface comprising a display configured to present information pertaining to the treatment plan, and at least one processing device configured to receive, from sensors, measurements associated with the user, wherein the measurements are received while the user performs the treatment plan; determine, via machine learning models, content items to present to the user, wherein the determining is based on the measurements and one or more characteristics of the user; and while the user performs the treatment plan using the rowing machine, cause presentation of the content items on the interface, wherein the content items comprise at least information related to a state of the user, and the state of the user is associated with the measurements, the characteristics, or some combination thereof.