Motion Sensor System for Early Shallow Breathing Detection
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Solution Overview
Problem
Current methods for monitoring chronic diseases lack accurate and early detection of abnormal physiological conditions, such as shallow breathing and sepsis, which can lead to delayed preventive treatment and increased morbidity and mortality.
Innovation Solution
A system comprising a motion sensor and a control unit that analyzes breathing patterns to identify shallow breathing, poor pulmonary function, and sepsis, generating alerts and calculating a sepsis score based on detected parameters, enabling early intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current monitoring methods are used for chronic diseases, then device complexity is reduced, but measurement precision and detection accuracy of abnormal physiological conditions deteriorate
Solution Approach 1:
The monitoring system is segmented into multiple independent sensor modules (motion sensor, respiratory sensor, temperature sensor, etc.) that each detect specific physiological parameters. This segmentation allows the system to achieve high measurement precision for multiple conditions simultaneously while keeping each individual sensor relatively simple and manageable.
Solution Approach 2:
The monitoring system is designed with multi-functional sensors and processing units that can detect multiple types of abnormal physiological conditions (shallow breathing, sepsis, respiratory distress) using a single integrated platform. This universality improves measurement precision across different conditions without proportionally increasing device complexity.
2Productivity
If early detection of abnormal physiological conditions is implemented, then productivity and treatment effectiveness are improved, but loss of time for analysis and response increases
Solution Approach 1:
The system performs preliminary analysis of physiological parameters continuously in the background, establishing baseline patterns and detecting early deviations before clinical episodes fully develop. This preliminary action enables early detection and intervention while minimizing the time required for formal analysis when treatment is needed.
Solution Approach 2:
The monitoring system implements real-time feedback mechanisms where detected abnormal patterns immediately trigger alerts to healthcare providers. This feedback loop reduces response time by providing continuous information about patient status, enabling timely intervention while maintaining high productivity through automated monitoring.
3Reliability
If comprehensive monitoring of multiple physiological parameters is performed, then reliability of disease prediction is improved, but device complexity and measurement difficulty increase
Solution Approach 1:
Multiple physiological parameters (respiratory rate, motion patterns, temperature, oxygen saturation) are merged into a single integrated monitoring system that processes all data through unified algorithms. This combining approach improves the reliability of disease prediction by considering multiple indicators simultaneously while reducing the difficulty of detection through integrated sensing and centralized analysis.
Data Source
AI summary
Apparatus is described, including a motion sensor configured to sense motion of a patient and to generate a motion signal in response thereto. The apparatus includes an output unit and a control unit. The control unit is configured to analyze the motion signal, to identify breathing of the patient as being shallow breathing in response to the analyzing, and to drive the output unit to generate an alert in response to identifying the breathing of the patient as being shallow breathing. Other applications are also described.