Longitudinal estimation of stress-related states through bio-sensor data

Vadym Mozgovoy

doi:10.1108/aci-03-2021-0070

Abstract

Purpose The authors aim to develop a conceptual framework for longitudinal estimation of stress-related states in the wild (IW), based on the machine learning (ML) algorithms that use physiological and non-physiological bio-sensor data. Design/methodology/approach The authors propose a conceptual framework for longitudinal estimation of stress-related states consisting of four blocks: (1) identification; (2) validation; (3) measurement and (4) visualization. The authors implement each step of the proposed conceptual framework, using the example of Gaussian mixture model (GMM) and K-means algorithm. These ML algorithms are trained on the data of 18 workers from the public administration sector who wore biometric devices for about two months. Findings The authors confirm the convergent validity of a proposed conceptual framework IW. Empirical data analysis suggests that two-cluster models achieve five-fold cross-validation accuracy exceeding 70% in identifying stress. Coefficient of accuracy decreases for three-cluster models achieving around 45%. The authors conclude that identification models may serve to derive longitudinal stress-related measures. Research limitations/implications Proposed conceptual framework may guide researchers in creating validated stress-related indicators. At the same time, physiological sensing of stress through identification models is limited because of subject-specific reactions to stressors. Practical implications Longitudinal indicators on stress allow estimation of long-term impact coming from external environment on stress-related states. Such stress-related indicators can become an integral part of mobile/web/computer applications supporting stress management programs. Social implications Timely identification of excessive stress may improve individual well-being and prevent development stress-related diseases. Originality/value The study develops a novel conceptual framework for longitudinal estimation of stress-related states using physiological and non-physiological bio-sensor data, given that scientific knowledge on validated longitudinal indicators of stress is in emergent state.

Highlights

Digital technology, including bio-sensor devices and well-being applications, may facilitate coping with stress and improve individual well-being [1]
4.1 Identification To describe the data characteristics, means and standard deviations were computed for heart rate (HR) (M 5 76.17; SD 5 11.57), galvanic skin response (GSR) (M 5 3.75; SD 5 2.96) and motion (M 5 1.75; SD 5 1.17) and average cluster characteristics for two-cluster K-means and Gaussian mixture model (GMM) models in standardized form and variance–covariance matrices for GMM (Table 2)
5.1 Findings This study proposed a conceptual framework for the longitudinal estimation of stress-related states through bio-sensor data consisting of four stages: (1) identification, (2) validation, (3) measurement and (4) visualization

Summary

Introduction

Digital technology, including bio-sensor devices and well-being applications, may facilitate coping with stress and improve individual well-being [1]. Ubiquitous computing literature examines remote stress pattern recognition, data processing and feedback to users. In the context of digital stress pattern recognition, it is important to understand how algorithms for remote stress identification can be used effectively. Automatic remote identification of stress episodes requires training ML algorithms using related data [2, 3]. Academic literature on pattern recognition, data mining and ML [7,8,9] provides a theoretical explanation of how stress identification algorithms operate. Algorithms for stress identification may follow supervised [10, 11] or unsupervised [2, 3] learning approaches They involve individual [12, 13] and ensemble learning [14, 15] models. Different stress identification models are designed for different uses, which correspond to specific types of environments: restricted, semi-restricted and nonrestricted daily life environments [1]

Results

Discussion

Conclusion