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Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

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Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

Shaibal Barua¹, Shahina Begum¹, Mobyen Uddin Ahmed¹

¹School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden.

Studies in Health Technology and Informatics

|May 19, 2015

Summary

Case-based reasoning effectively diagnoses driver stress using physiological signals, outperforming neural networks and support vector machines. This machine learning approach offers higher accuracy for stress detection in clinical applications.

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Area of Science:

Computer Science
Clinical Sciences
Biomedical Engineering

Background:

Advancements in sensor technology generate complex, heterogeneous clinical data, challenging manual analysis for patient diagnosis and prognosis.
Knowledge-based systems are crucial for supporting clinical decision-making, but require comparative analysis of machine learning methods.
Selecting appropriate machine learning algorithms is vital for accurate interpretation of physiological sensor data.

Purpose of the Study:

To compare the classification performance of three machine learning methods: case-based reasoning, neural networks, and support vector machines.
To evaluate the effectiveness of these methods in diagnosing driver stress using finger temperature and heart rate variability.
To identify the most accurate machine learning approach for stress detection based on physiological signals.

Main Methods:

Comparative analysis of case-based reasoning, neural networks, and support vector machines.
Utilized finger temperature and heart rate variability as physiological indicators of driver stress.
Experimental evaluation of classification accuracy for each machine learning method.

Main Results:

Case-based reasoning demonstrated superior classification accuracy compared to neural networks and support vector machines.
Case-based reasoning achieved 80% and 86% accuracy for stress classification using finger temperature and heart rate variability, respectively.
Neural networks and support vector machines achieved less than 80% accuracy when using both physiological signals.

Conclusions:

Case-based reasoning is a highly effective method for diagnosing driver stress using physiological data.
The findings suggest case-based reasoning as a preferred machine learning approach for stress detection in similar clinical applications.
Further research can explore integrating case-based reasoning into real-time driver monitoring systems.