A long short-term memory recurrent neural network model using wristband signals achieved balanced accuracy scores between 96.82% and 99.99% for classifying physical activity and psychological stress.
Can a long short-term memory recurrent neural network model accurately detect and classify physical activity and acute psychological stress using wristband device signals?
A machine learning model using wristband physiological signals can highly accurately classify physical activity and acute psychological stress, potentially aiding precision medicine in diabetes.
Detection and classification of acute psychological stress (APS) and physical activity (PA) in daily lives of people with chronic diseases can provide precision medicine for the treatment of chronic conditions such as diabetes. This study investigates the classification of different types of APS and PA, along with their concurrent occurrences, using the same subset of feature maps via physiological variables measured by a wristband device. Random convolutional kernel transformation is used to extract a large number of feature maps from the biosignals measured by a wristband device (blood volume pulse, galvanic skin response, skin temperature, and 3D accelerometer signals). Three different feature selection techniques (principal component analysis, partial least squares–discriminant analysis (PLS-DA), and sequential forward selection) as well as four approaches for addressing imbalanced sizes of classes (upsampling, downsampling, adaptive synthetic sampling (ADASYN), and weighted training) are evaluated for maximizing detection and classification accuracy. A long short-term memory recurrent neural network model is trained to estimate PA (sedentary state, treadmill run, stationary bike) and APS (non-stress, emotional anxiety stress, mental stress) from wristband signals. The balanced accuracy scores for various combinations of data balancing and feature selection techniques range between 96.82% and 99.99%. The combination of PLS–DA for feature selection and ADASYN for data balancing provide the best overall performance. The detection and classification of APS and PA types along with their concurrent occurrences can provide precision medicine approaches for the treatment of diabetes.
Askari et al. (Tue,) conducted a other in Diabetes. Long short-term memory recurrent neural network model using wristband signals vs. Various combinations of data balancing and feature selection techniques was evaluated on Detection and classification accuracy of physical activity and acute psychological stress. A long short-term memory recurrent neural network model using wristband signals achieved balanced accuracy scores between 96.82% and 99.99% for classifying physical activity and psychological stress.
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