Wearable sensors are widely used to collect physiological data and develop stress detection models. However, most studies focus on a single dataset, rarely evaluating model reproducibility across devices, populations, or study conditions. We previously assessed the reproducibility of stress detection models across multiple studies, testing models trained on one dataset against others using heart rate (with R-R interval) and electrodermal activity (EDA). In this study, we extended our stress detection reproducibility to consumer wearable sensors. We compared validated research-grade devices, to consumer wearables - Biopac MP160, Polar H10, Empatica E4, to the Garmin Forerunner 55s, assessing device-specific stress detection performance by conducting a new stress study on undergraduate students. Thirty-five students completed three standardized stress-induction tasks in a lab setting. Biopac MP160 performed the best, being consistent with our expectations of it as the gold standard, though performance varied across devices and models. Combining heart rate variability (HRV) and EDA enhanced stress prediction across most scenarios. However, Empatica E4 showed variability; while HRV and EDA improved stress detection in leave-one-subject-out (LOSO) evaluations (AUROC up to 0.953), device-specific limitations led to underperformance when tested with our pre-trained stress detection tool (AUROC 0.723), highlighting generalizability challenges related to hardware-model compatibility. Garmin Forerunner 55s demonstrated strong potential for real-world stress monitoring, achieving the best mental arithmetic stress detection performance in LOSO (AUROC up to 0.961) comparable to research-grade devices like Polar H10 (AUROC 0.954), and Empatica E4 (AUROC 0.905 with HRV-only model and AUROC 0.953 with HRV+EDA model), with the added advantage of consumer-friendly wearability for free-living contexts.

Automatic stress detection using heart rate variability (HRV) features has gained significant traction as it utilizes unobtrusive wearable sensors measuring signals like electrocardiogram (ECG) or blood volume pulse (BVP). However, detecting stress through such physiological signals presents a considerable challenge owing to the variations in recorded signals influenced by factors, such as perceived stress intensity and measurement devices. Consequently, stress detection models developed on one dataset may perform poorly on unseen data collected under different conditions. To address this challenge, this study explores the generalizability of machine learning models trained on HRV features for binary stress detection. Our goal extends beyond evaluating generalization performance; we aim to identify the characteristics of datasets that have the most significant influence on generalizability. We leverage four publicly available stress datasets (WESAD, SWELL-KW, ForDigitStress, VerBIO) that vary in at least one of the characteristics such as stress elicitation techniques, stress intensity, and sensor devices. Employing a cross-dataset evaluation approach, we explore which of these characteristics strongly influence model generalizability. Our findings reveal a crucial factor affecting model generalizability: stressor type. Models achieved good performance across datasets when the type of stressor (e.g., social stress in our case) remains consistent. Factors like stress intensity or brand of the measurement device had minimal impact on cross-dataset performance. Based on our findings, we recommend matching the stressor type when deploying HRV-based stress models in new environments. To the best of our knowledge, this is the first study to systematically investigate factors influencing the cross-dataset applicability of HRV-based stress models.

The language diversity in India's education sector poses a significant challenge, hindering inclusivity. Despite the democratization of knowledge through online educational content, the dominance of English, as the internet's lingua franca, limits accessibility, emphasizing the crucial need for translation into Indian languages. Despite existing Speech-to-Speech Machine Translation (SSMT) technologies, the lack of intonation in these systems gives monotonous translations, leading to a loss of audience interest and disengagement from the content. To address this, our paper introduces a dataset with stress annotations in Indian English and also a Text-to-Speech (TTS) architecture capable of incorporating stress into synthesized speech. This dataset is used for training a stress detection model, which is then used in the SSMT system for detecting stress in the source speech and transferring it into the target language speech. The TTS architecture is based on FastPitch and can modify the variances based on stressed words given. We present an Indian English-to-Hindi SSMT system that can transfer stress and aim to enhance the overall quality and engagement of educational content.