# Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data
This necessitated research into surrogate biomarkers of APS, including readily measurable physiological biosignals such as heart rate (HR), respiration rate, pupil diameter, skin temperature (ST), electrodermal activity (EDA, also known as galvanic skin response (GSR)), blood volume pulse (BVP), electrocardiogram, and blood pressure [7, 11, 13, 29, 30]. The validity of these conveniently measured physiological biosignals for detecting APS has been demonstrated in several studies [7, 11, 13, 19, 29, 30, 31]. Previous works illustrated that reliably discriminating between different types of APS is possible, including emotional anxiety stress and mental stress [7, 11, 32, 33]. Figure 1 illustrates the structure of use of biosignals for APS detection, PA classification, and for various healthcare and public health research applications. The proposed work consists of signals processing, feature extraction, data preparation, machine learning algorithm development, and evaluation of results (Figure 2). To achieve this aim, we designed experimental protocols to collect data, and we developed ML algorithms to detect APS in the presence of PA, which is a challenging problem as the readily-measurable physiological biosignals used to detect APS, such as HR and GSR, are also affected by PA [13]. The challenges in discriminating among different types of APS during periods of possibly simultaneous occurrences of PA are not only due to the overlapping responses in the physiological measurements, but also due to the noise and artifacts in the biosignals measured from wearable devices, which necessitates effective data filtering and adaptive noise cancellation (ANC) algorithms to extract and enhance the informative signal from the measurements for use in ML algorithms [7, 17, 18, 19, 13]. The practicality of simultaneous PA and APS detection extends beyond routine monitoring of health. The detection and discrimination of simultaneous PA and APS can generate reliable digital biomarkers of the physiological and psychological states of people for use in the treatment of chronic conditions like diabetes and cardiovascular disease [34, 35]. Diabetes treatment, in particular, can significantly benefit from more accurate assessments of the PA and APS states of people with Type 1 diabetes (T1D) [9, 13, 14, 15, 16]. People with T1D must administer exogenous insulin to compensate for the loss of pancreatic insulin production and maintain their blood glucose concentration within the desired safe range [36]. However, insulin requirements vary due to the type and intensity of PA and the possibly concurrent incidence of APS [9, 13, 14, 15, 16, 37]. This creates difficulties for people with T1D in effectively regulating their glucose levels because PA and APS typically have divergent effects on glucose levels [9, 13, 14, 37]. Prolonged low- and moderate-intensity aerobic exercise causes a reduction in glucose concentrations because of the increase in glucose use by the working muscles and  
Figure 1: Flowchart of physiological and psychological state estimation using wristband biosignals. Figure 2: Flowchart of proposed approach. a heightened sensitivity to insulin as muscle cells are more effective using any available insulin to take up glucose during and after PA [9; 14; 37]. In contrast to PA, APS causes the release of neuroendocrine hormones that stimulate the release of free energy and restricts the uptake of glucose into various tissues, causing the blood glucose concentration to temporarily rise [38]. This can be problematic for regulating glucose levels in people with T1D. Tasks considered routine by healthy individuals, like training or recreational activity versus competitive sporting events, can cause glucose values to drift out of the desired range and increase the risk of developing diabetes-related complications.