Wearable Health Profile Deviation Detection
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Solution Overview
Problem
Current health monitoring technologies struggle to detect subtle changes in biomechanical patterns over time, making it difficult to identify health issues such as injury or disease progression before they become clinically observable.
Innovation Solution
A system utilizing wearable devices equipped with inertial sensors, heart rate sensors, and in-ear devices to collect sensor data, which is then used to generate baseline and current health profiles through machine learning models. These profiles can be compared to trigger notifications for any deviations in health metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wearable devices collect and analyze sensor data continuously over time, then detection precision of biomechanical changes is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments the health monitoring task into multiple components: inertial sensors capture motion data, heart rate sensors capture physiological data, machine learning models process the data, and health profiles store the results. This segmentation allows each component to specialize, improving detection precision while distributing complexity across multiple manageable elements rather than requiring a single complex device
Solution Approach 2:
The patent introduces machine learning models as intermediaries between raw sensor data and health conclusions. These models act as mediators that automatically process complex sensor patterns and translate them into interpretable health profiles, reducing the complexity burden on the wearable device itself while maintaining high detection precision through sophisticated analysis
2Measurement precision
If baseline health profiles are stored and compared over long periods, then detection of gradual health changes is improved, but loss of time for data collection and analysis increases
Solution Approach 1:
The system performs preliminary action by establishing baseline health profiles during initial periods when the user is healthy or at earlier stages. These baselines are stored and used for future comparisons, allowing the system to detect gradual changes without requiring continuous long-term data collection. The baseline serves as a reference point that enables future detection without repeating the entire data collection process
Solution Approach 2:
The system implements feedback by continuously comparing current sensor data against stored baseline health profiles. This feedback mechanism allows the system to detect deviations from the baseline and identify gradual health changes over time. The comparison process provides ongoing feedback about health status without requiring extensive new data collection, as the baseline serves as a stable reference
Data Source
AI summary
Embodiments are disclosed for detecting biomechanical impairment using wearable devices. An example method comprises: obtaining a first set of sensor data, a location of the wearable device and a timestamp; determining a first set of fitness metrics based on the sensor data; predicting a baseline health profile based on the first set of fitness metrics; storing the baseline health profile, location and timestamp; at a second time after the timestamp: detecting that the wearable device is at the location; obtaining a second set of sensor data from the sensors of the wearable device; determining a second set of fitness metrics based on the second set of sensor data; predicting a current health profile for the user based on the second set of fitness metrics; comparing the current health profile with the baseline health profile; and responsive to a result of the comparing, performing an action.