# Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data  
[PERSON]  
1 Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA; [EMAIL_ADDRESS] (M.S. ); [EMAIL_ADDRESS] (Z.M.) [PERSON]  
2 Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA; [EMAIL_ADDRESS] (M.R. ); [EMAIL_ADDRESS] (M.R.A. ); [EMAIL_ADDRESS] (I.H.) [PERSON]  
2 Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA; [EMAIL_ADDRESS] (M.R. ); [EMAIL_ADDRESS] (M.R.A. ); [EMAIL_ADDRESS] (I.H.) [PERSON]  
1 Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA; [EMAIL_ADDRESS] (M.R.A. ); [EMAIL_ADDRESS] (M.R.A. ); [EMAIL_ADDRESS] (I.H.) 1 Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA; [EMAIL_ADDRESS] (M.R.A. ); [EMAIL_ADDRESS] (M.  
lactate, and epinephrine, which cannot be measured in real-time, noninvasively, and in daily living, to indicate PA and APS [7, 8, 9, 10, 11, 12]. Physiological measurements are also useful in automated medical intervention decisions in chronic diseases. For example, in diabetes, PA and APS may affect blood glucose concentrations in opposite directions. Signals from a wearable device would complement the information received from a continuous glucose monitoring device and provide advance information of the presence of PA and/or APS, which will ultimately affect the glucose level, enabling better insulin dosing decisions [6, 9, 13, 14, 15, 16]. The convenience of noninvasive wearable sensors means that the devices can be worn continuously in daily living to monitor the PA of users without hindering or limiting the motions of the users. However, the signals from wearable devices such as wristbands are corrupted by noise and artifacts. They require powerful signal processing algorithms to extract reliable information from noisy data and eliminate the effects of artifacts. The physiological variables collected from wearable devices have been useful in noninvasive detection of PA and APS [7, 8, 9, 10, 11]. Recent developments in signal processing of wearable device biosignals and machine learning (ML) algorithms enabled the integrated analysis of PA and APS by enabling the detection and discrimination of concurrent incidences of PA and APS [17, 18]. This was made possible by clinical experiments designed to enrich the training data with various PA and APS inducements, multiple physiological biosignals measured using a single convenient wearable device, and recent advances in signal processing, feature extraction, and ML [17, 18, 19, 20, 21]. The recent developments in signal processing are particularly important since noise and artifacts are routinely encountered in real-world data, which can easily mask the differentiating features among the possibly simultaneous incidences of PA and APS [19, 22, 23, 24, 25, 26]. Despite the significant impact of signal processing on the accuracy of the algorithms, the effects of signal processing on the performance of ML algorithms is often not reported in studies on the detection and discrimination of PA and APS [17, 18]. The role of signal processing is even more critical in discriminating between concurrent PA and APS because they may result in similar responses in measurable physiological variables [17, 18]. The psychological stressors prompt the activation of various physiological systems that result in the overall stress response with the aim of restoring homeostasis [7, 27, 28]. The response to APS is typically coordinated by the hypothalamus through the activation of the autonomic nervous system and the pituitary and adrenal glands in the hypothalamus-pituitary-adrenal axis, resulting in the release of stress hormones such as catecholamines and cortisol [27, 28]. The stress hormones are difficult to measure continuously in free-living ambulatory conditions [7].