A Systematic Literature Review on Affective Computing Techniques for Workplace Stress Detection Challenges, Future Directions, from Data Collection to Stress Detection

被引:0
作者
Mezieres, Iris [1 ]
Gorrab, Abir [2 ]
Deneckere, Rebecca [1 ]
Ben Rabah, Nourhene [1 ]
Le Grand, Benedicte [1 ]
机构
[1] Univ Paris 1 Pantheon Sorbonne, Ctr Rech Informat, Paris, France
[2] Univ Manouba, Natl Sch Comp Sci, RIADI Lab, Manouba, Tunisia
来源
ADVANCES IN COMPUTATIONAL COLLECTIVE INTELLIGENCE, ICCCI 2024, PART I | 2024年 / 2165卷
关键词
Affective Computing; Stress; Workplace; SLR;
D O I
10.1007/978-3-031-70248-8_4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In a world where work significantly impacts daily life, its influence on well-being is undeniable. The prevalence of stress at work has gain an increased attention due to its profound effects on both individual health and corporate performance. Addressing and detecting stress has become an essential challenge in fostering a healthy work environment. Technological innovations, especially in the field of affective computing, which involves various IT resources for analyzing human behavior and emotions, offer promising solutions for measuring employee stress. This paper provides a Systematic Literature Review (SLR) focusing on the existing scientific research in stress at work assessment using affective computing technologies. What distinguishes our work from others is that we deeply focus on each phase of the stress quantification process. We start by reviewing application contexts, before detailing the data collection process, including data sources and collection devices. We then highlight data analysis techniques used in the literature. Finally, we emphasize the challenges discussed by researchers during their work and give insight into future work.
引用
收藏
页码:44 / 56
页数:13
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