Context-Aware Well-Being Scoring From Physiological Signals
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Solution Overview
Problem
Existing methods for assessing individual quality of life are subjective and lack objective, systematic approaches, particularly in the context of remote work and consumer interest in ethical products, and there is a need for privacy-preserving data handling.
Innovation Solution
A method and system that measures physiological and behavioral parameters using personal devices, incorporates contextual information, and employs machine learning to generate unbiased scores through a prediction model trained on contextual data, ensuring privacy preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective questionnaires and perceptual metrics are used to assess quality of life, then the assessment method is simple and easy to implement, but the objectivity and reliability of the assessment deteriorates due to perceptual bias
Solution Approach 1:
The patent replaces subjective human perception and manual questionnaire assessment with an automated electronic system that objectively measures physiological parameters. Sensors detect biological signals (heart rate, galvanic skin response, respiration) and an electronic processor automatically analyzes these signals to generate well-being scores, eliminating human bias while maintaining ease of use through automated processing.
2Measurement precision
If physiological parameters are measured continuously to improve assessment accuracy, then measurement precision improves, but the complexity of the device and data processing increases
Solution Approach 1:
The patent employs a multi-functional integrated system where a single electronic device performs multiple functions: it houses various sensors (optical, electrical, mechanical) that detect different physiological parameters, processes the signals through an electronic processor, stores data in memory, and communicates results. This universal device approach consolidates multiple specialized components into one system, improving measurement accuracy without proportionally increasing overall complexity.
Solution Approach 2:
The patent combines multiple sensor types (optical sensor for heart rate, galvanic skin response sensor, respiration sensor) and processing functions into a single integrated measurement system. By merging these components that work together to assess well-being, the system achieves comprehensive physiological monitoring while managing complexity through integrated design rather than separate discrete systems.
3Measurement precision
If detailed contextual information and socio-demographic features are collected to improve score accuracy, then measurement precision improves, but the amount of data to be processed and stored increases
Solution Approach 1:
The patent performs preliminary processing of physiological signals at the point of measurement, where the electronic processor immediately analyzes raw sensor data and compares it against reference values to generate intermediate results. This preliminary action reduces the volume of data that needs to be stored and transmitted, as only processed results and essential raw data are retained rather than all continuous raw signals.
Solution Approach 2:
The patent applies different processing strategies to different types of data based on their specific requirements. Physiological signals undergo real-time processing and filtering at the measurement location, while contextual information is integrated selectively. This local quality approach ensures that data processing is optimized for each data type, improving accuracy while managing data volume efficiently through location-specific processing.
Data Source
AI summary
The present disclosure relates to a concept for providing a score indicative of a physiological condition of at least one individual. The concept include measuring physiological or behavioral data of the individual, determining contextual data comprising a measurement context and/or socio-demographic features associated with the individual determining a metric based on the measured physiological or behavioral data, and determining the score by comparing the metric against a predicted value from a prediction model trained with the contextual data.