AI Cardio Protocol Adaptation for Remote Rehab Monitoring
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Solution Overview
Problem
Existing telemedicine systems face challenges in effectively monitoring and adapting cardiovascular health treatment plans for patients due to the inability to conduct physical examinations, leading to inefficiencies and inaccuracies in treatment plan selection and implementation, especially in rehabilitative settings.
Innovation Solution
A computer-implemented system using machine learning models to generate and adapt cardiovascular health treatment plans based on real-time cardiovascular data, perceived exertion, and patient-specific health issues, enabling remote control of treatment apparatuses to optimize exercise levels and personalize treatment protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If telemedicine systems are used to provide remote cardiovascular treatment plans, then patient accessibility to care is improved, but the ability to conduct physical examinations and accurately monitor patient status deteriorates
Solution Approach 1:
The patent introduces wearable sensors and telemedicine platforms as intermediaries between patients and healthcare providers. These intermediaries continuously collect physiological data (heart rate, blood pressure, activity levels) and transmit it remotely, enabling accurate monitoring without direct physical contact. The system acts as a mediator that bridges the distance between provider and patient while maintaining measurement accuracy through sophisticated sensor technology and data transmission protocols.
2Reliability
If standardized treatment protocols are used for cardiovascular patients, then treatment consistency and safety are improved, but individualization to patient-specific characteristics deteriorates
Solution Approach 1:
The patent implements dynamic treatment protocols that automatically adjust based on real-time patient data. The system continuously monitors physiological parameters and modifies exercise intensity, duration, and type according to individual patient responses and characteristics. This dynamic adaptation maintains treatment consistency through structured protocols while achieving individualization through real-time adjustments tailored to each patient's unique profile and progress.
Solution Approach 2:
The system changes treatment parameters (exercise intensity, duration, frequency) based on monitored physiological parameters (heart rate, blood pressure, activity levels). The algorithm adjusts these parameters dynamically to match individual patient characteristics and responses, transforming static standardized protocols into flexible, personalized treatment plans that adapt to each patient's specific needs while maintaining overall treatment consistency.
3Productivity
If remote monitoring without physical examination is used, then healthcare cost and time efficiency are improved, but treatment plan selection accuracy deteriorates
Solution Approach 1:
The patent implements continuous feedback loops where patient physiological data is collected, analyzed, and used to automatically adjust treatment plans. The system receives real-time feedback on patient responses to exercise and modifies the treatment protocol accordingly. This feedback mechanism enables accurate treatment plan selection by continuously learning from individual patient responses, maintaining high accuracy while improving efficiency through automation.
Data Source
AI summary
A computer-implemented method including receiving, at a computing device, a first treatment plan designed to treat a cardiovascular health issue of a user. The first treatment plan comprises at least two exercise sessions that, based on the cardiovascular health issue, enable the user to perform an exercise at different exertion levels. While the user uses a treatment apparatus to perform the first treatment plan for the user, the computing device receives cardiovascular data from one or more sensors configured to measure the cardiovascular data associated with the user, and transmits the cardiovascular data. Wherein a machine learning model is used to generate a second treatment plan. The second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the cardiovascular data, and the cardiovascular health issue of the user. The method includes receiving the second treatment plan.