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Refinement of Bio-signal Data from Wearables for Stress Recognition Using Machine Learning and Transparent Artificial Intelligence

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This analysis demonstrates improved stress detection accuracy with wearables, suggesting enhanced methods for real-time monitoring and intervention.

Achieving peak classification accuracy of 98.28% using EDA, BVP, and HRV shows significant advancements in stress detection.
Experimental results indicate that integrating machine learning and wearable data can effectively monitor stress levels in real-time.
Feature selection employed Bayesian optimization and genetic algorithms to enhance the accuracy of stress classification models.
The calming effects of meditation audio on stress highlight potential interventions for improving mental well-being among students.

M et al. (2025) studied this question.

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This analysis demonstrates improved stress detection accuracy with wearables, suggesting enhanced methods for real-time monitoring and intervention.