AI Heart Rhythm Detection in Electromechanical Rehabilitation
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Solution Overview
Problem
Current telemedicine systems face challenges in effectively monitoring patients' progress during remote rehabilitation, especially for cardiovascular health, as they rely on verbal communication and limited remote observations, lacking the ability to conduct physical examinations and provide tailored treatment plans based on real-time data.
Innovation Solution
A computer-implemented system incorporating an electromechanical machine, sensors, and processing devices that use machine learning models to analyze user measurements during treatment plans, detecting abnormal heart rhythms and performing preventative actions, such as telecommunications transmissions or modifying the machine's operation, to ensure user safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If telemedicine systems rely on verbal communication and limited remote observation, then device complexity is reduced, but measurement precision and reliability of patient monitoring deteriorate
Solution Approach 1:
The patent introduces sensors as intermediary devices that objectively measure patient physiological parameters (heart rate, blood pressure, respiratory rate) and transmit this data to the electromechanical machine. This mediator approach resolves the contradiction by enabling precise monitoring without requiring complex in-person physical examinations, thus improving measurement precision while maintaining relative system simplicity.
Solution Approach 2:
The patent replaces manual physical examination methods with automated sensor-based measurement systems. Instead of relying on healthcare professionals to conduct physical exams during telemedicine consultations, the system uses electronic sensors to automatically capture physiological data, thereby improving monitoring accuracy without significantly increasing device complexity.
2Reliability
If real-time sensor monitoring and machine learning analysis are implemented, then reliability of abnormal heart rhythm detection is improved, but device complexity and processing requirements worsen
Solution Approach 1:
The patent implements preliminary action by continuously monitoring patient physiological parameters in real-time during exercise and immediately analyzing the data through machine learning models to detect abnormal heart rhythms. This proactive detection approach improves reliability by identifying cardiac issues before they become critical, while the automated nature of the process manages system complexity through predefined algorithms and thresholds.
Solution Approach 2:
The patent employs feedback mechanisms where sensor data is continuously fed into machine learning models that analyze heart rhythm patterns and provide real-time feedback on abnormal conditions. This closed-loop system improves detection reliability by continuously adapting to patient responses during exercise, while the automated feedback process manages complexity through algorithmic decision-making rather than requiring complex manual intervention.
3Productivity
If adaptive treatment plans based on real-time data are implemented, then effectiveness of rehabilitation is improved, but ease of operation and system complexity worsen
Solution Approach 1:
The patent implements self-service by enabling the electromechanical machine to automatically adjust exercise parameters based on real-time sensor data and machine learning analysis. The system autonomously modifies treatment plans without requiring manual intervention from healthcare professionals or patients, thereby improving rehabilitation effectiveness while maintaining ease of operation through automated decision-making processes.
Solution Approach 2:
The patent applies dynamics by making the treatment plan adaptive and responsive to real-time patient conditions. The system dynamically adjusts exercise intensity, duration, and type based on continuous monitoring of physiological parameters, improving rehabilitation effectiveness. This dynamic adaptation is achieved through programmed algorithms that automatically respond to sensor data, maintaining operational simplicity while enhancing productivity.
4Reliability
If continuous monitoring during exercise is implemented, then safety and reliability are improved, but use of energy and device complexity worsen
Solution Approach 1:
The patent applies periodic action by implementing continuous monitoring during exercise intervals followed by rest periods. The sensors continuously collect data during exercise phases, and the machine learning models analyze this periodic data to detect abnormal heart rhythms. This approach improves patient safety through ongoing monitoring while managing energy consumption by aligning monitoring intensity with exercise intensity, avoiding unnecessary energy use during rest periods.
Data Source
AI summary
Computer-implemented systems, methods, and tangible, non-transitory computer-readable media for detecting abnormal heart rhythms of a user performing treatment plan with an electromechanical machine. The system includes, in one embodiment, an electromechanical machine, and one or more processing devices. The electromechanical machine is configured to be manipulated by a user while the user is performing a treatment plan. The processing devices are configured to receive, while the user performs the treatment plan, measurements. The processing devices also configured to determine, using machine learning models, a probability that the measurements satisfy a threshold for a condition associated with an abnormal heart rhythm. The processing devices are further configured to perform preventative actions.