Mobile Stress Estimation Using Physiological and Self-Reported Data
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Solution Overview
Problem
Existing stress estimation systems are complex, costly, and unreliable due to individual variability in physiological responses to stress, and current methods like questionnaires are subjective and time-consuming.
Innovation Solution
A system comprising a mobile device and a network unit that measures physiological parameters and combines them with self-reported stress levels to calculate a stress discrepancy, using low-complexity functions to determine a stress condition, reducing latency and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple physiological parameters are measured using multiple sensors to improve stress estimation accuracy, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The system divides the stress estimation function into two independent modules: (1) physiological parameter measurement by sensors, and (2) self-reported stress level input via user interface. This segmentation allows each module to be simple while the combination provides comprehensive stress assessment, resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The mobile device serves multiple functions: it acts as a physiological sensor hub, a user interface for stress level input, and a data transmission device. This multi-functionality consolidates what would otherwise require multiple separate devices into a single universal platform, improving stress estimation accuracy without proportionally increasing device complexity.
2Ease of operation
If traditional questionnaires are used to assess stress condition, then ease of operation is improved, but time consumption and subjectivity increase
Solution Approach 1:
The system enables continuous stress monitoring by combining ongoing physiological parameter measurement with periodic self-reported stress level inputs. This continuous data stream replaces discrete questionnaire administrations, eliminating time loss while maintaining ease of operation through automated background sensing.
Solution Approach 2:
The system provides immediate feedback by calculating stress discrepancy between physiological measurements and self-reported levels in real-time. This continuous feedback loop replaces delayed questionnaire results, reducing time consumption while keeping the user interface simple and easy to operate.
3Measurement precision
If physiological parameters alone are used to estimate stress, then objective measurement is improved, but reliability decreases due to individual variability and confounding factors
Solution Approach 1:
The system merges objective physiological parameter measurements with subjective self-reported stress levels into a unified stress assessment. This combination compensates for the limitations of each individual approach: physiological data provides objective baseline measurement while self-reported data accounts for individual stress interpretation variability, thereby improving overall reliability.
Solution Approach 2:
The system transforms multiple different physiological parameters (heart rate, skin conductance, etc.) and subjective stress ratings into a unified stress discrepancy metric. This parameter transformation normalizes diverse measurements into a common scale, enabling reliable comparison and assessment despite individual variability in physiological responses.
4Measurement precision
If complex algorithms are used to process physiological data for stress estimation, then measurement precision is improved, but computational complexity and latency increase
Solution Approach 1:
The system extracts only the essential features from physiological parameters needed for stress assessment, rather than processing all available sensor data through complex algorithms. By focusing on key stress-related physiological signals and their discrepancy with self-reported levels, the system achieves accurate stress estimation with minimal computational complexity and latency.
Data Source
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AI summary
The present invention relates to a system for estimating a stress condition of an individual, the system comprising a mobile device and a network unit, the mobile device being connected to the network unit and to one or more sensors, the mobile device comprising circuity configured to: for each occasion of a plurality of occasions: measure a set of physiological parameters using the one or more sensors, and transmit first data relating to the set of physiological parameters to the network unit; and prompt the individual to input a perceived stress-level for the occasion via a user interface of the mobile device, and transmit second data relating to the perceived stress-level to the network unit.