In healthcare, detecting stress and enabling individuals to monitor their mental health and wellbeing is challenging. Advancements in wearable technology now enable continuous physiological data collection. This data can provide insights into mental health and behavioural states through psychophysiological analysis. However, automated analysis is required to provide timely results due to the quantity of data collected. Machine learning has shown efficacy in providing an automated classification of physiological data for health applications in controlled laboratory environments. Ambulatory uncontrolled environments, however, provide additional challenges requiring further modelling to overcome. This work empirically assesses several approaches utilising machine learning classifiers to detect stress using physiological data recorded in an ambulatory setting with self-reported stress annotations. A subset of the training portion SMILE dataset enables the evaluation of approaches before submission. The optimal stress detection approach achieves 90.77% classification accuracy, 91.24 F1-Score, 90.42 Sensitivity and 91.08 Specificity, utilising an ExtraTrees classifier and feature imputation methods. Meanwhile, accuracy on the challenge data is much lower at 59.23% (submission #54 from BEaTS-MTU, username ZacDair). The cause of the performance disparity is explored in this work.

翻译：在医疗健康领域，检测压力并使个体能够监测自身心理健康与福祉仍面临挑战。可穿戴技术的进步现已实现连续生理数据采集，这些数据可通过心理生理学分析揭示心理健康和行为状态。然而，由于采集数据量庞大，需采用自动化分析手段以及时获取结果。在受控实验室环境中，机器学习已在生理数据的自动化分类方面展现出应用于健康领域的效能。但非受控动态环境会带来额外挑战，需通过进一步建模加以克服。本研究实证评估了多种基于机器学习分类器的方法，利用动态环境下记录的生理数据（辅以自我报告的压力标注）进行压力检测。通过运用SMILE数据集中训练子集的部分数据，可在提交前对各方法进行预评估。采用ExtraTrees分类器与特征插补方法的最优压力检测方案实现了90.77%的分类准确率，F1分数达91.24，灵敏度为90.42%，特异度为91.08%。然而，在挑战数据集上的准确率显著降低至59.23%（BEaTS-MTU团队提交编号#54，用户名ZacDair）。本研究深入探究了此性能差异的成因。