Wearable Temperature Proxy Detection for Respiratory Symptom Assessment
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Solution Overview
Problem
Conventional wearable electronic devices are limited in accurately detecting user temperature and other physical parameters, making it difficult to assess physiological symptoms associated with respiratory diseases and other medical conditions.
Innovation Solution
A method using a wearable computing device with first and second sensors to generate temperature data from different locations, determining a proxy temperature, and comparing it to a threshold to assess the presence or likelihood of a medical condition, with machine learning algorithms for preliminary assessments and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional wearable electronic devices use single-location temperature sensors, then device complexity is reduced, but measurement precision and reliability of temperature detection deteriorate
Solution Approach 1:
The patent divides temperature measurement into multiple segments by using multiple sensors at different locations (e.g., wrist, chest, forehead) rather than relying on a single sensor. This segmentation allows the system to capture temperature variations across different body parts, improving overall measurement precision and reliability while maintaining manageable device complexity through modular sensor integration.
Solution Approach 2:
The patent transitions from single-point temperature measurement to multi-dimensional temperature mapping by measuring temperature at multiple spatial locations simultaneously. This dimensional expansion provides a more comprehensive picture of the user's thermal state, enabling better detection of fever patterns and temperature trends without requiring overly complex device architecture.
2Loss of information
If conventional devices collect limited biometric data, then ease of operation is maintained, but loss of information increases regarding user health status
Solution Approach 1:
The patent implements multi-functional sensing capabilities where the wearable device collects a comprehensive set of biometric data including temperature, heart rate, respiratory rate, oxygen saturation, and activity levels. This universal data collection approach ensures no critical health information is lost while managing complexity through integrated sensor design and unified data processing architecture.
Solution Approach 2:
The patent performs preliminary data processing and analysis continuously in the background, pre-processing sensor data to identify patterns, anomalies, and health trends before they become critical. This preliminary action ensures complete health information capture and preparation for timely alerts or recommendations without adding significant operational complexity for the user.
3Reliability
If devices lack comprehensive sensor data for temperature and physiological parameters, then device complexity is reduced, but reliability of medical condition assessment deteriorates
Solution Approach 1:
The patent merges multiple sensor types (temperature sensors, heart rate monitors, respiratory rate sensors, oxygen saturation sensors) into a unified wearable system that processes all data types together. This merging approach enhances the reliability of medical condition assessment by considering multiple physiological parameters simultaneously, while managing complexity through integrated data fusion algorithms and coordinated sensor operation.
Solution Approach 2:
The patent implements continuous feedback loops where sensor data is constantly monitored, analyzed, and used to adjust assessments and provide real-time recommendations. This feedback mechanism improves reliability by allowing the system to adapt to changing physiological conditions and refine its assessments over time, while managing complexity through automated feedback processing and decision algorithms.
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 early detection of illnesses, such as fever or respiratory issues, providing users with timely recommendations and potentially reducing the spread of infectious diseases.
Implementation Method 1
receiving, from a first sensor on the wearable computing device, first temperature data relating to a first temperature measurement of the user. The method also includes receiving, from a second sensor on the wearable computing device, second temperature data relating to a second temperature measurement of the user
Data Source
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AI summary
Temperature data acquired from a wearable device, for example at a user's wrist or within the device itself, can be used as a proxy to evaluate core body temperature changes. Sensor data may be provided to determine a skin temperature of a user and also an internal device temperature. A correlation between these two temperatures may be used to monitor subsequent temperature changes, which may be indicative of changes in the user's core body temperature. Temperature changes to the proxy temperature may be evaluated against a threshold to determine whether the user's core body temperature has also increased, which may be indicative of one or more physiological symptoms or events. Furthermore, additional physiological variables such as respiration rate, nocturnal heart rate, and heart rate variability may be analyzed for early signs of impending illness. A trained machine learning classifier can output the predicted illness status of an individual based on these parameters.