Continuous stress detection using the sensors of commercial smartwatch

Pekka Siirtola

107 citations0/0 files storedACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)

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Emotion and Mood Recognition Heart Rate Variability and Autonomic Control Context-Aware Activity Recognition Systems

Abstract

Stress detection is becoming a popular field in machine learning and this study focuses on recognizing stress using the sensors of commercially available smartwatches. In most of the previous studies, stress detection is based on partly or fully on electrodermal activity sensor (EDA). However, if the final aim of the study is to build a smartwatch application, using EDA signal is problematic as the smartwatches currently in the market do not include sensor to measure EDA signal. Therefore, this study surveys what sensors the smartwatches currently in the market include, and which of them 3rd party developers have access to. Moreover, it is studied how accurately stress can be detected user-independently using different sensor combinations. In addition, it is studied how detection rates vary between study subjects and what kind of effect window size has to the recognition rates. All of the experiments are based on publicly available WESAD dataset. The results show that, indeed, EDA signal is not necessary when detecting stress user-independently, and therefore, commercial smartwatches can be used for recognizing stress when the used window length is big enough. However, it is also noted that recognition rate varies a lot between the study subjects.

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References

2018Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection 2008Tightening the link between employee wellbeing at work and performance 2016Continuous stress detection using a wrist device

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2023Stress Detection Through Wrist-Based Electrodermal Activity Monitoring and Machine Learning 2020Comparison of Regression and Classification Models for User-Independent and Personal Stress Detection 2024A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study 2024Optimization of Wearable Biosensor Data for Stress Classification Using Machine Learning and Explainable AI 2021Genetically optimized Fuzzy C-means data clustering of IoMT-based biomarkers for fast affective state recognition in intelligent edge analytics 2023Wrist-Based Electrodermal Activity Monitoring for Stress Detection Using Federated Learning 2021Ultra-Short Window Length and Feature Importance Analysis for Cognitive Load Detection from Wearable Sensors 2020Multi-level Stress Assessment Using Multi-domain Fusion of ECG Signal

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