Multi-Task Classification of Physical Activity and Acute Psychological Stress for Advanced Diabetes Treatment

• Published in: Signals (Basel) Vol. 4; no. 1; pp. 167 - 192
• Main Authors: Abdel-Latif, Mahmoud, Askari, Mohammad Reza, Rashid, Mudassir M., Park, Minsun, Sharp, Lisa, Quinn, Laurie, Cinar, Ali
• Format: Journal Article
• Language: English
• Published: Tokyo MDPI AG 01-02-2023
• Subjects: acute psychological stress classification; Algorithms; Anxiety; Classification; Diabetes; Discriminant analysis; energy expenditure estimation; Exercise; Fitness equipment; Glucose; Insulin; long short-term memory; Machine learning; Metabolism; multi-task learning; Neural networks; physical activity classification; Physiology; recurrent neural network; Sensors; Wearable computers
• ISSN: 2624-6120; 2624-6120
• DOI: 10.3390/signals4010009

Wearable sensor data can be integrated and interpreted to improve the treatment of chronic conditions, such as diabetes, by enabling adjustments in treatment decisions based on physical activity and psychological stress assessments. The challenges in using biological analytes to frequently detect physical activity (PA) and acute psychological stress (APS) in daily life necessitate the use of data from noninvasive sensors in wearable devices, such as wristbands. We developed a recurrent multi-task deep neural network (NN) with long-short-term-memory architecture to integrate data from multiple sensors (blood volume pulse, skin temperature, galvanic skin response, three-axis accelerometers) and simultaneously detect and classify the type of PA, namely, sedentary state, treadmill run, stationary bike, and APS, such as non-stress, emotional anxiety stress, mental stress, and estimate the energy expenditure (EE). The objective was to assess the feasibility of using the multi-task recurrent NN (RNN) rather than independent RNNs for detection and classification of AP and APS. The multi-task RNN achieves comparable performance to independent RNNs, with the multi-task RNN having F1 scores of 98.00% for PA and 98.97% for APS, and a root mean square error (RMSE) of 0.728 calhr.kg for EE estimation for testing data. The independent RNNs have F1 scores of 99.64% for PA and 98.83% for APS, and an RMSE of 0.666 calhr.kg for EE estimation. The results indicate that a multi-task RNN can effectively interpret the signals from wearable sensors. Additionally, we developed individual and multi-task extreme gradient boosting (XGBoost) for separate and simultaneous classification of PA types and APS types. Multi-task XGBoost achieved F1 scores of 99.89% and 98.31% for the classification of PA types and APS types, respectively, while the independent XGBoost achieved F1 scores of 99.68% and 96.77%, respectively. The results indicate that both multi-task RNN and XGBoost can be used for the detection and classification of PA and APS without loss of performance with respect to individual separate classification systems. People with diabetes can achieve better outcomes and quality of life by including physical activity and psychological stress assessments in treatment decision-making.