Discrimination of simultaneous psychological and physical stressors using wristband biosignals. (February 2021)

• Record Type: Journal Article
• Authors: Sevil, Mert; Rashid, Mudassir; Hajizadeh, Iman; Askari, Mohammad Reza; Hobbs, Nicole; Brandt, Rachel; Park, Minsun; Quinn, Laurie; Cinar, Ali
• Is Part Of: Computer methods and programs in biomedicine. Volume 199(2021)
• Publication Date: 2021-02
• Subjects: Acute psychological stress -- Physical activity -- Discrimination of physical and psychological stressors -- Wearable devices -- Machine learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105898
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Algorithms are developed for detecting and discriminating acute psychological stress in the presence of concurrent physical activities. Wristband biosignals are used for conducting the detection and discrimination under daily free-living. Various classification algorithms are compared to simultaneously detect the physical activities (sedentary state, treadmill running, and stationary bike) and the type of psychological stress (non-stress state, mental stress, and emotional anxiety). Accurate classification of concurrent physical activities (PA) and acute psychological stress (APS) is achieved with an overall classification accuracy of 96% for PA and 92% for APS. Abstract: Background and objective: In this work, we address the problem of detecting and discriminating acute psychological stress (APS) in the presence of concurrent physical activity (PA) using wristband biosignals. We focused on signals available from wearable devices that can be worn in daily life because the ultimate objective of this work is to provide APS and PA information in real-time management of chronic conditions such as diabetes by automated personalized insulin delivery. Monitoring APS noninvasively throughout free-living conditions remains challenging because the responses to APS and PA of many physiological variables measured by wearable devices are similar. Methods: Various classification algorithms are compared to simultaneously detect and discriminate the PA (sedentary state, treadmill running, and stationary bike) and the type of APS (non-stress state, mental stress, and emotional anxiety). The impact of APS inducements is verified with commonly used self-reported questionnaires (The State-Trait Anxiety Inventory (STAI)). To aid the classification algorithms, novel features are generated from the physiological variables reported by a wristband device during 117 hours of experiments involving simultaneous APS inducement and PA. We also translate the APS assessment into a quantitative metric for use in predicting the adverse outcomes. Results: An accurate classification of the concurrent PA and APS states is achieved with an overall classification accuracy of 99% for PA and 92% for APS. The average accuracy of APS detection during sedentary state, treadmill running, and stationary bike is 97.3, 94.1, and 84.5%, respectively. Conclusions: The simultaneous assessment of APS and PA throughout free-living conditions from a convenient wristband device is useful for monitoring the factors contributing to an elevated risk of acute events in people with chronic diseases like cardiovascular complications and diabetes.