Key result
Random forest model using wearable sensor data classifies stress levels with 99% accuracy.
Why the study?
Stress detection employing physiological data aims to capture and interpret physical and physiological signals.
A random forest machine learning model using physiological data from wearable sensors achieved 99% accuracy in classifying stress levels.
Answers are grounded in this paper and cite their sources.
Wearable stress classification at 99% accuracy requires validation before clinical use; leaves open prospective cardiovascular applications.
Stress Detection employing physiological data desires to grasp and apprehend physical and physiological signals. Skin conductance, three axes acceleration and temperature data are gathered by the system using various sensors. The data from the deployed wearable sensors is the basis of data collection and is stored in the AWS cloud ecosystem. The stress levels are classified as stress, non-stress and amusement by random forest and logistic regression machine learning models. Random forest model, based on the data collected from the wearable sensors, classifies stress levels, achieving an astonishing accuracy of 99%.
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Karpagam et al. (2024) studied Stress. Wearable sensors and random forest machine learning model was evaluated on Stress level classification accuracy. A random forest machine learning model using physiological data from wearable sensors classified stress levels with 99% accuracy.
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