Wearable HRV Illness Detection with Personalized Baseline Learning
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Solution Overview
Problem
Existing wearable devices lack the capability to provide early detection of transitions from a healthy state to an unhealthy state before symptom onset, limiting users' insight into their physical health and potential illness progression.
Innovation Solution
Utilizing wearable devices to collect physiological data, including heart rate variability (HRV) and temperature data, and employing machine learning classifiers to identify deviations from baseline parameters, enabling early detection of illness transitions through deviation criteria satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wearable devices collect and analyze physiological data using machine learning classifiers, then illness detection capability is improved, but device complexity increases
Solution Approach 1:
The patent introduces machine learning classifiers as intermediary components that process physiological data and generate illness risk scores. These classifiers act as mediators between raw sensor data and health insights, enabling sophisticated illness detection without requiring complex hardware modifications. The classifiers process heart rate variability, temperature, and activity data to produce interpretable health metrics.
Solution Approach 2:
The system segments the illness detection process into distinct functional modules: data collection from multiple sensors, baseline parameter calculation, deviation detection using classifiers, and risk score generation. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by breaking down the complex illness detection task into manageable stages.
2Measurement precision
If personalized baseline parameters are used for illness detection, then measurement precision is improved, but loss of time for baseline establishment increases
Solution Approach 1:
The system performs preliminary action by establishing personalized baseline parameters during an initial period when the user is healthy. These baselines are calculated in advance and stored for future comparison. The machine learning classifiers are also trained preliminarily on user-specific data to adapt to individual physiological patterns, enabling rapid and accurate illness detection once the baseline phase is complete.
Solution Approach 2:
The system implements feedback mechanisms where baseline parameters are continuously refined based on new physiological data. As users provide more data over time, the baselines are updated to improve accuracy. This feedback loop allows the system to maintain high measurement precision while reducing the initial time required for baseline establishment, as the system adapts and learns from ongoing data collection.
Data Source
AI summary
Methods, systems, and devices for illness detection are described. A method may include receiving heart rate variability (HRV) data associated with a user from a wearable device, the HRV data collected via the wearable device throughout a first time interval and a second time interval subsequent to the first time interval. The method may include inputting the HRV data into a classifier, and identifying a satisfaction of deviation criteria between a first subset of the HRV data collected throughout the first time interval and a second subset of the HRV data collected throughout the second time interval. The method may include causing a graphical user interface (GUI) of a user device to display an illness risk metric for the user based on the satisfaction of the deviation criteria, the illness risk metric associated with a relative probability that the user will transition from a healthy state to an unhealthy state.