Literature DB >> 32589065

Objective stress monitoring based on wearable sensors in everyday settings.

Hee Jeong Han1, Sina Labbaf1, Jessica L Borelli2, Nikil Dutt1, Amir M Rahmani1,3.   

Abstract

Monitoring people's stress levels has become an essential part of behavioural studies for physical and mental illnesses conducted within the biopsychosocial framework. There have been several stress assessment studies in laboratory-based controlled settings. However, the results of these studies do not always translate effectively to an everyday context. The current state of wearable sensor technology allows us to develop systems measuring the physiological signals reflecting stress 24/7 while capturing the context. In this paper, we present a stress monitoring system that provides objective daily stress measurements in everyday settings based on three physiological signals: electrocardiogram (ECG), photoplethysmogram (PPG), and galvanic skin response (GSR) using Shimmer3 ECG, Shimmer3 GSR+, and Empatica E4 wearable sensors. We perform controlled stress assessment experiments on 17 participants in which we successfully detect stress with a 94.55% accuracy for 10-fold cross-validation and an 85.71% accuracy for subject-wise cross-validation. In everyday settings, the system assesses stress with an 81.82% accuracy. We also examine whether motion artefacts affect stress assessment and filter the low-confidence readings to minimise false alarms.

Entities:  

Keywords:  Stress monitoring; bio-signal processing; internet-of-things; physiological signals; wearable sensors

Mesh:

Year:  2020        PMID: 32589065     DOI: 10.1080/03091902.2020.1759707

Source DB:  PubMed          Journal:  J Med Eng Technol        ISSN: 0309-1902


  5 in total

Review 1.  Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview.

Authors:  Ahmed A Al-Saedi; Veselka Boeva; Emiliano Casalicchio; Peter Exner
Journal:  Sensors (Basel)       Date:  2022-07-25       Impact factor: 3.847

2.  Prenatal stress assessment using heart rate variability and salivary cortisol: A machine learning-based approach.

Authors:  Rui Cao; Amir M Rahmani; Karen L Lindsay
Journal:  PLoS One       Date:  2022-09-09       Impact factor: 3.752

Review 3.  Occupational Stress Monitoring Using Biomarkers and Smartwatches: A Systematic Review.

Authors:  Analúcia Morales; Maria Barbosa; Laura Morás; Silvio César Cazella; Lívia F Sgobbi; Iwens Sene; Gonçalo Marques
Journal:  Sensors (Basel)       Date:  2022-09-02       Impact factor: 3.847

4.  Personal mental health navigator: Harnessing the power of data, personal models, and health cybernetics to promote psychological well-being.

Authors:  Amir M Rahmani; Jocelyn Lai; Salar Jafarlou; Iman Azimi; Asal Yunusova; Alex P Rivera; Sina Labbaf; Arman Anzanpour; Nikil Dutt; Ramesh Jain; Jessica L Borelli
Journal:  Front Digit Health       Date:  2022-09-22

5.  Accuracy Assessment of Oura Ring Nocturnal Heart Rate and Heart Rate Variability in Comparison With Electrocardiography in Time and Frequency Domains: Comprehensive Analysis.

Authors:  Rui Cao; Iman Azimi; Fatemeh Sarhaddi; Hannakaisa Niela-Vilen; Anna Axelin; Pasi Liljeberg; Amir M Rahmani
Journal:  J Med Internet Res       Date:  2022-01-18       Impact factor: 5.428

  5 in total

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