This paper presents a comprehensive review of methods covering significant subjective and objective human stress detection techniques available in the literature. The methods for measuring human stress responses could include subjective questionnaires (developed by psychologists) and objective markers observed using data from wearable and non-wearable sensors. In particular, wearable sensor-based methods commonly use data from electroencephalography, electrocardiogram, galvanic skin response, electromyography, electrodermal activity, heart rate, heart rate variability, and photoplethysmography both individually and in multimodal fusion strategies. Whereas, methods based on non-wearable sensors include strategies such as analyzing pupil dilation and speech, smartphone data, eye movement, body posture, and thermal imaging. Whenever a stressful situation is encountered by an individual, physiological, physical, or behavioral change is induced which help in coping with the challenge at hand. A wide range of studies has attempted to establish a relationship between these stressful situations and the response of human beings by using different kinds of psychological, physiological, physical, and behavioral measures. Inspired by the lack of availability of a definitive verdict about the relationship of human stress with these different kinds of markers, a detailed survey about human stress detection methods is conducted in this paper. In particular, we explore how stress detection methods can benefit from artificial intelligence utilizing relevant data from various sources. This review will prove to be a reference document that would provide guidelines for future research enabling effective detection of human stress conditions.

翻译：本文对现有文献中涵盖主客观的人类压力检测技术进行了全面综述。测量人类压力反应的方法包括主观问卷（由心理学家开发）以及利用可穿戴与非可穿戴传感器数据观测到的客观标记。具体而言，基于可穿戴传感器的方法通常使用脑电图、心电图、皮电反应、肌电图、皮肤电活动、心率、心率变异性及光电容积描记术的数据，既可单独使用，也可采用多模态融合策略。而基于非可穿戴传感器的方法则包括分析瞳孔扩张与语音、智能手机数据、眼动、身体姿态及热成像等策略。当个体遭遇压力情境时，会诱发生理、物理或行为上的变化，从而帮助应对当前挑战。大量研究试图通过使用不同类型的心理、生理、物理及行为测量指标，建立这些压力情境与人类反应之间的关系。鉴于现有文献中关于人类压力与这些不同标记之间关系尚未得出明确结论，本文对此进行了详细调研。具体而言，我们探讨了如何通过利用来自多种来源的相关数据，使人工智能助力压力检测方法。本综述将作为一份参考文档，为未来研究提供指导方针，以实现对人类压力状况的有效检测。