Stress Detection System Using Machine Learning Profiles
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Solution Overview
Problem
Conventional diagnostic systems and methods often overlook and discard valuable stress parameters, failing to recognize stress as an indication of worsening patient health or unaddressed medical conditions.
Innovation Solution
A stress detection system that records and compares physiological parameters with stress profiles generated by a machine-learning algorithm to detect and classify stress, alerting healthcare specialists to treatable conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional diagnostic systems are used to monitor patients, then the systems are simple and easy to operate, but they overlook and discard valuable stress parameters, failing to recognize stress as an indication of worsening patient health
Solution Approach 1:
The patent segments the monitoring system into distinct functional modules: physiological parameter acquisition module, stress calculation module (using PSE formula), and alert generation module. This segmentation allows the system to process and retain stress parameters without overwhelming complexity, as each module handles a specific aspect of the monitoring process independently.
Solution Approach 2:
The system performs preliminary calculation of the stress parameter PSE using the formula PSE = Σ(wi × |Pi - Pi,ref|) before clinical decision-making. By pre-calculating and retaining these stress parameters, the system ensures that valuable information is not lost during subsequent analysis, and healthcare providers can immediately utilize the computed stress levels without additional processing complexity.
2Measurement precision
If stress parameters are monitored and analyzed in detail, then stress detection accuracy improves, but the system complexity and computational requirements increase
Solution Approach 1:
The patent transforms multiple physiological parameters (heart rate, respiratory rate, blood pressure, oxygen saturation) into a single composite stress parameter PSE through weighted summation. This parameter transformation maintains high measurement precision by considering multiple factors, while simplifying the overall system architecture by reducing multiple complex measurements to one interpretable stress index.
Solution Approach 2:
The stress parameter PSE acts as an intermediary between raw physiological measurements and clinical decision-making. Instead of directly analyzing complex multi-parameter data, the system uses PSE as a mediator that consolidates physiological information into a single stress indicator, thereby improving detection accuracy while managing system complexity through this intermediate computational layer.
3Reliability
If multiple physiological sensors are used to capture comprehensive patient data, then the quality of stress detection improves, but the device complexity and setup requirements increase
Solution Approach 1:
The patent employs a universal patient monitoring system architecture that can accommodate multiple physiological sensors (ECG, respiratory rate, blood pressure, oxygen saturation) through a common data acquisition interface. This multi-functional design allows the system to reliably capture comprehensive physiological data for accurate stress detection, while maintaining manageable complexity through standardized connection protocols and unified processing pathways for all sensor inputs.
Data Source
AI summary
The present disclosure relates to systems and methods of detecting stress. As described herein, a system for measuring a specific detection of stress is provided that includes a plurality of physiological sensors coupled to a smart cable assembly that can provide a plurality of configurations of the physiological sensors. Physiological measurements are recorded and compared with stress profiles generated by a machine-learning algorithm to determine whether the patient is experiencing stress that should or can be treated by a healthcare specialist.