Key result
A kernel-based extreme-learning machine using integrated physiological features classified VR-induced stress levels with an average accuracy of over 95%.
Population
12 healthy subjects
Design
Other
Authors
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VR-based multimodal biosignal monitoring may aid stress research; leaves open clinical translation pending prospective validation.
Observational (n=12)
A kernel-based extreme learning machine can accurately classify stress levels using physiological signals measured in a VR environment, suggesting potential for compact wearable devices.
Cho et al. (2017) conducted an observational in Healthy (n=12). Kernel-based extreme-learning machine (K-ELM) using integrated physiological features was evaluated on Classification accuracy of stress levels. A kernel-based extreme-learning machine using integrated physiological features classified VR-induced stress levels with an average accuracy of over 95%.
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