Wearable Acoustic Sensor System for Multi-Organ Health Monitoring
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Solution Overview
Problem
Current devices for detecting sounds from the human body are limited in their ability to continuously and simultaneously analyze sounds from multiple organs, leading to incomplete health monitoring and lack of correlation between organ functions.
Innovation Solution
A wearable device equipped with multiple acoustic sensors and waveguides that detect a wide range of frequencies, allowing for continuous monitoring of various body sounds, preprocessing of data to identify abnormal patterns, and analysis using machine learning models to detect biomarkers, with optional notification systems for immediate attention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple acoustic sensors and waveguides are used to detect sounds from different organs simultaneously, then the comprehensiveness of health monitoring is improved, but the device complexity increases
Solution Approach 1:
The device is divided into multiple independent sensor units, each equipped with acoustic sensors and waveguides that can detect sounds from specific organs. This segmentation allows each module to function independently while contributing to comprehensive health monitoring, resolving the contradiction by making the complex system modular and manageable.
Solution Approach 2:
The device incorporates multiple acoustic sensors capable of detecting sounds across different frequency ranges, allowing a single device to monitor multiple organs (heart, lungs, digestive system, etc.) simultaneously. This multi-functionality approach enables comprehensive health monitoring without requiring separate devices for each organ.
2Measurement precision
If a wide frequency range is detected to capture sounds from different organs, then the measurement coverage is improved, but the difficulty of detecting and measuring abnormal patterns increases
Solution Approach 1:
The frequency detection range is segmented into multiple bands, with each acoustic sensor configured to detect specific frequency ranges associated with different organs. This segmentation simplifies the detection process by focusing on organ-specific frequency characteristics rather than analyzing the entire frequency spectrum simultaneously.
Solution Approach 2:
Different acoustic sensors are configured with different frequency detection characteristics optimized for specific organs. For example, certain sensors are tuned to detect low-frequency heart sounds while others detect high-frequency respiratory sounds. This local optimization of detection capabilities improves measurement coverage while simplifying abnormal pattern detection for each organ type.
3Reliability
If continuous long-term detection is performed to provide comprehensive health information, then the reliability of health assessment is improved, but the energy consumption increases
Solution Approach 1:
The device performs continuous detection but processes and transmits data periodically rather than continuously. The processor analyzes detected sounds locally and only transmits abnormal patterns or summary statistics, reducing energy consumption while maintaining reliable health assessment through continuous monitoring capability.
Solution Approach 2:
The device includes onboard processing capabilities that automatically detect and flag abnormal sound patterns without requiring constant external analysis. The processor performs preliminary analysis locally, saving energy by only transmitting data when abnormalities are detected, thus maintaining reliability while reducing overall energy consumption.
4Loss of information
If multiple acoustic sensors detect sounds from different directions and frequency ranges, then the information completeness is improved, but the data processing complexity increases
Solution Approach 1:
The data processing is segmented by organ type and frequency range, with each sensor's data processed independently according to organ-specific analysis protocols. This segmentation reduces overall processing complexity by breaking down the complex task of analyzing multi-organ sounds into manageable, organ-specific processing streams while maintaining information completeness.
Solution Approach 2:
The processor dynamically adjusts detection parameters and analysis methods based on the specific organ being monitored and the detected sound characteristics. By changing processing parameters according to the data type rather than using a fixed complex algorithm for all data, the system maintains information completeness while reducing processing complexity through adaptive parameter optimization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables comprehensive, long-term health monitoring by correlating organ functions, enhancing the accuracy of health assessments and providing timely notifications for abnormal conditions.
Implementation Method 1
an acoustic waveguide connected to the support and configured to guide the sounds from within the subject's body to the acoustic sensor
Implementation Method 2
an acoustic sensor connected to the support and configured to detect sounds from within the subject's body and generate an output signal
Data Source
AI summary
Device and system for detecting sounds from the subject's body are disclosed. The device may include: a support configured to be removably attached to a subject's body or a subject's clothing; an acoustic sensor connected to the support and configured to detect sounds from within the subject's body and generate an output signal; an acoustic waveguide connected to the support and configured to guide the sounds from within the subject's body to the acoustic sensor; a digital storage unit connected to the support; and a processor connected to the support and configured to: at least one of: save at least a portion of the output signal in the digital storage unit; preprocess the output signal, and analyze the output signal.