###### Abstract  
The resilience of machine learning models for anxiety detection through wearable technology was explored. The effectiveness of feature-based and end-to-end machine learning models for anxiety detection was evaluated under varying conditions of Gaussian noise. By adding synthetic Gaussian noise to a well-known open access affective states dataset collected with commercially available wearable devices (WESAD), a performance baseline was established using the original dataset. This was followed by an examination of the impact of noise on model accuracy to better understand model performance (F\({}_{1}\)-score and Accuracy) changes as a function of noise. The results of the analysis revealed that with the increase in noise, the performance of feature-based models dropped from a high of 90% F1-score and 92% accuracy to 65% and 70%, respectively; while end-to-end models showed a decrease from an 85% F1-score and 87% accuracy to below 60% and 65%, respectively. This indicated a proportional decline in performance across both feature-based and end-to-end models as noise levels increased, challenging initial assumptions about model resilience. This analysis highlights the need for more robust algorithms capable of maintaining accuracy in noisy, real-world environments and emphasizes the importance of considering environmental factors in the development of wearable anxiety detection systems. article  
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wearable sensor data for use in mental health detection and monitoring [14; 15; 16]. However, most of these studies have not explicitly examined the effect of confounding factors, such as noise, on the detection of anxiety using ML. Studying noise is essential for several reasons. First, it allows for a more accurate simulation of real-world conditions, as noise is an inherent part of data collected through wearable technology. Understanding the effect of noise on data helps in creating models that are resilient and reflective of real-life scenarios. Second, studying noise aids in enhancing the robustness of ML algorithms. By analyzing how noise influences data and model performance, researchers can develop algorithms that maintain high accuracy levels, even in suboptimal conditions. Last, a thorough analysis of noise contributes to the reduction of potential errors, leading to more reliable and trustworthy applications of ML. Stress and anxiety are closely intertwined. Stress is typically characterized as an adverse stimulus [17]. According to [PERSON]'s Trait-State anxiety theory, anxiety is bifurcated into state and trait anxiety [17; 18; 19]. State anxiety represents a temporary, transient emotional response to stress, marked by increased sympathetic nervous system activity. In contrast, trait anxiety denotes an individual's inherent tendency to experience state anxiety under stress. This study primarily focused on state anxiety, hereafter referred to as 'anxiety', especially in the context of detection methodologies. The distinction between anxiety and stress in this study hinges on the use of standardized methods for evaluating or inducing it; the former is considered anxiety, while the latter is categorized as stress. The pervasive impact of anxiety on long-term health and job performance underscores the urgency for developing effective anxiety detection tools [20; 21; 22; 23]. These tools could significantly enhance accessibility to diagnosis and treatment and serve as valuable resources for mental health professionals. Anxiety's detrimental effects extend to increased cardiovascular disease risks, weakened immune responses, and reduced performance efficiency [24; 25; 26; 27]. The prevalence of anxiety is alarmingly high, with significant portions of the world populations affected, contributing to substantial economic losses [1; 2; 3; 4]. This prevalence is juxtaposed against the stark reality of inadequate mental health services, highlighting a critical area of need.