Wearable Physiological Event Detection Using Sensor Fusion
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Solution Overview
Problem
Current methods for monitoring adverse physiological events, such as seizures and cardiac arrests, are limited by the need for bulky equipment and specialized analysis, making it difficult to detect these events outside clinical settings and in a timely manner.
Innovation Solution
A wearable device that combines ECG, motion sensor signals, and potentially reconstructed EEG data to detect imminent adverse physiological events using machine learning algorithms, allowing for continuous monitoring and alerting the user or medical services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring equipment is used, then measurement precision is improved, but device complexity and portability are worsened
Solution Approach 1:
The patent combines multiple monitoring functions (ECG, EEG, motion sensing) into a single wearable device that integrates various sensors and processing capabilities, eliminating the need for separate bulky equipment while maintaining comprehensive monitoring precision
Solution Approach 2:
The wearable device performs multiple monitoring functions simultaneously including cardiac monitoring via ECG, neurological monitoring via EEG reconstruction from ECG, and motion tracking, making a single device replace multiple specialized equipment
2Measurement precision
If specialized technician operation is required, then measurement precision is improved, but ease of operation is worsened
Solution Approach 1:
The device performs automated analysis of physiological data using embedded algorithms that process ECG and motion sensor data to detect adverse events, eliminating the need for specialized technicians to interpret the data while maintaining high detection accuracy
Solution Approach 2:
The patent replaces manual technical analysis with automated computational algorithms that analyze physiological signals, substituting human expert intervention with machine-based detection systems
3Reliability
If clinical setting monitoring is used, then reliability is improved, but adaptability is worsened
Solution Approach 1:
The system dynamically adapts to different environments and usage scenarios, providing reliable monitoring whether the user is stationary or mobile, indoors or outdoors, by maintaining continuous real-time analysis of physiological parameters
4Measurement precision
If comprehensive physiological monitoring is implemented, then measurement precision is improved, but use of energy is worsened
Solution Approach 1:
The system uses motion sensor data selectively to reconstruct EEG signals only when motion patterns indicate potential neurological events, rather than continuously processing all data, thereby reducing energy consumption while maintaining detection precision
Data Source
AI summary
Systems, methods, and an event detection apparatus, for the detection of an imminent adverse physiological event, such as an epileptic seizure or cardiac event. Physiological state data can be generated based on at least one of pre-processed physiological state data, electrocardiogram (ECG) signals, and motion sensor signals. A plurality of features from the physiological state data can be extracted. The plurality of features can be classified based on a predetermined classifier. Responsive to the plurality of features corresponding to at least one of a plurality of adverse physiological event profiles, an adverse physiological event can be determined to be imminent.