## 1 Introduction  
Identifying the aerobic threshold (AerT) is vital for crafting individualized exercise programs and optimizing both health and athletic performance [1; 2; 3]. The AerT is defined as the exercise intensity at which there is a shift from predominantly aerobic to anaerobic energy production [2]. Traditionally, the AerT has been determined through visually detected and manually uncomfortable methods during physical activities, including physiological variables like gas exchange, heart rate, blood lactate concentration, and rate of perceived exertion, respectively [4; 5]. However, these approaches are often time-consuming, and their result is subject to operator-dependent variability during interpretation. To address these limitations, we recently introduced an objective, efficient, nonlinear method based on Recurrence Quantification Analysis (RQA) that employs heart rate variability (HRV) for automatic AerT detection [6; 7; 8]. The autonomic nervous system, which controls the body's physiological reactions to exercise, is reflected in HRV. Consequently, a more thorough and dynamic examination of the physiological reactions to different exercise intensities is provided by utilizing HRV to measure AerT. This connection is particularly significant in clinical settings, where monitoring and management of training, particularly in patients with heart problems or on medications that affect HRV, depend heavily on a thorough understanding of physiological responses to workload. Although HRV-based techniques might not always be more effective, they do have some clear benefits in terms of simplicity of usage and reduced bulky. We hope to simplify the evaluation procedure while preserving the association with recognized physiological measures by employing HRV. This approach allows for a more practical and user-friendly method of AerT evaluation, which could potentially enhance its applicability in various settings. Our previous study tested the HRV-based algorithm on a homogenous sample in terms of age (15.26 \(\pm\) 1.87 years) and health status (healthy athletes) undergoing the cardiovascular test while performing a single type of activity (cycling) [6]. Indeed, the method's initial focus was primarily on optimizing athletic performance, which underscores the significance of accurately identifying the AerT not only in sports but also in managing health conditions. Our first study demonstrated a strong correlation (r > 0.77) between the RQA-based method and traditional techniques when applied to a homogenous group of athletes, with a technical error of less than 5% for HR detection at AerT [6]. The precise detection of AerT is critical for assessing the risk of cardiovascular and metabolic disorders in obese individuals [9; 10]. For cardiac patients, particularly those on beta-blockers, accurately determining AerT is vital because it is significantly influenced by medications that affect HRV, a crucial indicator of autonomic nervous system function [11; 12; 13]. Given the foundational importance in both athletic and health contexts, this study aims to expand the utility of our automatic AerT detection method. We are broadening its application to include various physical activities and a more diverse demographic encompassing different ages, genders, fitness levels, and, most importantly, specific health conditions such as obesity and cardiovascular issues treated with beta-blockers. Specifically, we focus on four case studies: athletes, young athletes ages 10-17, obese individuals, and cardiac patients. These groups were selected because they are paradigmatic for evaluating metabolic thresholds. One of the main advantages of this algorithm is its ability to be integrated into wearable systems for real-time detection, which is becoming increasingly feasible [14]. Flexible bioelectronics, like wearable and epidermal electronics that can be attached to the skin, clothing, or even the body itself, would be ideal for an objective evaluation of physical activity [15].