## 1 Introduction  
Meals, physical activities (PA), and acute psychological stress (APS) strongly affect the blood glucose concentrations (BGC) of people with diabetes. Diabetes is a worldwide public health problem, with an estimated 537 million people living with diabetes in 2021 [1]. While people with diabetes can take appropriate actions to prevent large variations in BGCs that can lead to hypoglycemia and hyperglycemia (glucose levels lower or higher than the safe zone, respectively), they may not be able to make adjustments when PA and APS are unexpected or unplanned. People with Type 1 diabetes (T1D) regulate their BGC by manipulating insulin injections or infusions (by an insulin pump) to subcutaneous tissue. There is a significant time delay before insulin in the subcutaneous tissue diffuses and reaches the bloodstream to affect BGC (approximately 45 min or more). Consequently, a significant amount of time is lost in making adjustments if the decision is solely based on glucose concentration readings, for example, from a continuous glucose monitoring (CGM) system. The delay in recognizing the presence of these factors (PA and APS) that disrupt glucose homeostasis can be reduced by interpreting physiological signals measured by wearable devices commonly used in free living, such as wristbands. The information captured from wearable devices can be used for better manual adjustment of insulin dosing or incorporated into the control algorithms of artificial pancreas systems in order to improve the regulation and time-in-range of BGC [2; 3]. Decisions about the presence of PA or APS are difficult to make based solely on direct signals from wearable devices. For example, both blood volume pulse (BVP) and heart rate (HR) increase with PA and APS. Erroneous selection of the cause of BGC variations can have severe consequences, as most PA reduces BGC while APS increases it, with the former leading to hypoglycemia and the latter to hyperglycemia. In the former case, intervention is intended to reduce insulin flow, while in the later it is intended to increase infusion. Erroneous determination of the source cause (PA or APS) and the wrong intervention can accentuate undesirable changes in BGC variation. Several devices and algorithms have been reported for characterizing APS and PA in clinical environments with high accuracy [4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. In free-living environments, sensors are limited to those that can be worn comfortably and accepted as part of daily wear. Recent advances in wearable devices have provided wristbands with three-axis accelerometers (ACC) as well as sensors for measuring galvanic skin response (electrodermal activity) (GSR), skin temperature (ST), and blood volume pulse (to infer HR and HR variability). The data reported by these sensors must be preprocessed in order to reduce sensor noise, handle missing values, and accommodate imbalances in the number of data points for various types of events. For example, the amount of data during sedentary states can be much greater than during PA or during non-stress states compared to APS episodes, which can bias the models that interpret the data. As such, features must be extracted and refined in order to accurately interpret, classify, and incorporate the effects of APS and PA into treatment decisions. The detection and discrimination of APS and PA can be complemented by determining the characteristics of these psychological and physiological stressors in order to propose individualized intervention strategies and mitigate their effects by manual adjustment or automated insulin delivery by adaptive multivariable artificial pancreas systems [14; 15; 16]. We have previously reported data preprocessing, feature definition, selection of most informative features, and use of computationally efficient algorithms in multivariable statistical and machine learning (ML) techniques [17; 18; 19; 20].