Wearable Sensor System for COPD Symptom Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods lack effective monitoring and prediction systems for Chronic Obstructive Pulmonary Disease (COPD) symptoms, leading to potential hospitalizations and increased healthcare costs.
Innovation Solution
A wearable sensor system that uses a combination of sensors (digital microphone array, digital stethoscope, thermometer, goniometer, and electrocardiogram) to collect data, which is then processed by a machine learning model to predict the onset of COPD symptoms, allowing for early detection and prevention of exacerbations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If no monitoring system is implemented, then healthcare costs and hospitalizations remain high, but implementing a monitoring system increases device complexity and cost
Solution Approach 1:
The patent combines multiple sensors (microphone array, stethoscope, thermometer, goniometer, ECG) into a single wearable device that collects various physiological parameters simultaneously. This merging approach improves monitoring reliability by capturing comprehensive patient data while consolidating what would otherwise be multiple separate devices into one integrated system.
Solution Approach 2:
The wearable device performs multiple functions including audio recording, stethoscope monitoring, temperature measurement, motion tracking, and ECG recording all through a single device. This multi-functionality improves patient monitoring reliability without requiring multiple separate devices, thereby managing system complexity through consolidation rather than proliferation of separate components.
2Measurement precision
If continuous monitoring is implemented, then early detection of COPD symptoms is improved, but energy consumption and battery life become problematic
Solution Approach 1:
The system employs periodic action by using the machine learning model to analyze sensor data at strategically determined intervals rather than continuously processing all data streams. The ML model identifies when analysis is necessary based on detected patterns or threshold crossings, enabling early symptom detection while consuming energy only when needed rather than maintaining constant processing power.
Solution Approach 2:
The machine learning model operates autonomously to determine when symptom analysis is required, self-managing the energy consumption by activating processing only when clinically relevant patterns are detected in the sensor data. This self-service approach allows continuous monitoring capability while the intelligent algorithm controls energy usage to preserve battery life.
3Loss of information
If multiple sensors are integrated, then data comprehensiveness is improved, but device size and comfort become compromised
Solution Approach 1:
The patent integrates five different sensor types (microphone array, stethoscope, thermometer, goniometer, ECG) into a single consolidated wearable device. This merging captures comprehensive physiological data for complete COPD monitoring while consolidating the weight of multiple separate devices into one unit worn on the patient's body.
Solution Approach 2:
The single wearable device provides universal monitoring capabilities across multiple physiological parameters through integrated sensors. This multi-functionality ensures complete data collection for COPD assessment without requiring the patient to wear or carry multiple separate devices, thereby managing weight and comfort while maintaining data completeness.
Data Source
AI summary
A system of networked sensors designed to predict the onset of chronic obstructive pulmonary disease (COPD) symptoms is disclosed. The system is worn by an individual and the sensors collect data correlated with COPD symptoms. The collected sensor data is transmitted from the device to the user's mobile device for analysis. The results of the analysis may be forwarded to a health care provider.