Heart Rate Variability-Based Mental Stress Detection: An Explainable Machine Learning Approach

• Published in: SN computer science Vol. 4; no. 2; p. 176
• Main Authors: Banerjee, Jyoti Sekhar, Mahmud, Mufti, Brown, David
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.03.2023; Springer Nature B.V
• Subjects: Advances in Computational Intelligence for Artificial Intelligence; Algorithms; Classification; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Datasets; Decision making; Decision trees; End users; Heart rate; Information Systems and Communication Service; Internet of Things and Data Analytics; Machine Learning; Original Research; Pattern Recognition and Graphics; Physiology; Psychological stress; Research methodology; Software Engineering/Programming and Operating Systems; Stress; Variability; Vision; Wearable technology; Multimodal dataset; Stress detection; Wearable sensor; Heart rate variability; Explainable machine learning; Human–computer interaction
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-022-01605-z

Stress may be identified by examining changes in everyone’s physiological reactions. Due to its usefulness and non-intrusive appearance, wearable devices have gained popularity in recent years. Sensors provide the possibility of continuous and real-time data gathering, which is useful for tracking one’s own stress levels. Numerous studies have shown that emotional stress has an impact on heart rate variability (HRV). Through the collection of multimodal information from the wearable sensor, our framework is able to accurately classify HRV based users’ stress levels using explainable machine learning (XML). Sometimes, ML algorithms are referred to as black boxes. XML is a model of ML that is designed to explain its objectives, decision-making, and reasoning to end users. End users may include users, data scientists, regulatory bodies, domain experts, executive board members, and managers who utilize machine learning with or without understanding or anybody whose choices are impacted by an ML model. The purpose of this work is to construct an XML-enabled, uniquely adaptable system for detecting stress in individuals. The results show promising qualitative and quantifiable visual representations that may provide the physician with more detailed knowledge from the outcomes offered by the learnt XAI models, hence improving their comprehension and decision making.