Cognitive Stress Clustering Using Self-Reports and Physiological Signals
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Solution Overview
Problem
Existing systems for detecting and classifying cognitive stress levels using physiological signals and user reports are limited in clustering users based on their stress patterns during task performance, leading to inefficiencies in training classifiers.
Innovation Solution
A method and system that utilize user evaluated cognitive stress reports and physiological signals to cluster users into primary and secondary clusters using unsupervised learning, followed by feature extraction and training a classifier model to classify stress levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users are clustered based on physiological signals alone, then the clustering may capture objective stress responses, but it fails to account for subjective stress perception and individual differences in stress reporting
Solution Approach 1:
The patent combines objective physiological signals (heart rate, skin conductance, temperature) with subjective cognitive stress reports from users to create a comprehensive clustering approach. This merging of data sources allows the system to capture both the physiological response to stress and the individual's subjective perception, thereby improving measurement precision while accounting for individual variability in stress reporting.
Solution Approach 2:
The cognitive stress report acts as an intermediary that bridges the gap between objective physiological measurements and subjective stress experience. By incorporating user self-assessments as a mediating factor, the system can better interpret physiological signals in the context of individual stress perception, resolving the contradiction between objective measurement and subjective adaptability.
2Device complexity
If a single classifier model is trained on all user data, then the system is simpler to implement, but it fails to capture individual stress patterns and reduces classification accuracy
Solution Approach 1:
The patent segments the user population into distinct clusters based on their stress patterns derived from both physiological signals and cognitive reports. Instead of using a single classifier for all users, the system creates multiple cluster-specific classifiers that are trained on segmented data. This segmentation approach reduces system complexity by organizing users into manageable groups while significantly improving classification accuracy by accounting for individual stress pattern variations.
3Adaptability or versatility
If cognitive stress reports are used to cluster users, then individual stress perception is captured, but the system requires additional data collection and processing
Solution Approach 1:
The cognitive stress report serves multiple functions within the system: it acts as a direct measure of subjective stress, serves as a clustering feature to group similar users, and provides contextual information for interpreting physiological signals. This multi-functionality allows the system to capture individual stress patterns effectively while minimizing additional complexity, as the same data collection serves multiple purposes.
Data Source
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AI summary
A method and system for clustering users using cognitive stress report for classifying stress levels is provided. Detection and monitoring of cognitive stress experienced by users while performing a task is very crucial. The method includes receiving, user evaluated cognitive stress reports and the physiological signals of the user during the performance of the task. A normalized cognitive report is generated from the user evaluated cognitive stress report by computing mode and range value. The normalized cognitive stress reports of the users are used to cluster the users into a primary cluster and a secondary cluster. Feature sets are extracted from the physiological signals of the said users associated with the primary cluster. Using the said feature sets a classifier model is trained to classify the cognitive stress levels of the users as stressful class and /or stressless class.