Leveraging ECG signals and social media for stress detection

Feng, Zhuonan; Li, Ningyun; Feng, Ling; Chen, Diyi; Zhu, Congshan

doi:10.1080/0144929x.2019.1673820

Cited by 9 publications

(16 citation statements)

References 47 publications

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“…In the context of digital stress pattern recognition, it is particularly 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].…”

Section: Introductionmentioning

confidence: 99%

“…However, previous research has not clearly established the accuracy of measurements based on indirect measurements through unsupervised algorithms [3][4][5][6] and has not formalized the process of longitudinal evaluation of stress-related states.…”

Section: Introductionmentioning

confidence: 99%

“…Algorithms can be trained by combining physiological and non-physiological (contextual) activations in the time and frequency domains to infer stress-related states. 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.…”

Section: Introductionmentioning

confidence: 99%

“…The measurement of stress-related signals can be direct [18,19] or indirect [3][4][5][6]. Direct measurement is usually conducted through isolated signals, while the stress level can be determined based on the level of variable(s) associated with stress.…”

Section: Introductionmentioning

confidence: 99%

“…Some indirect algorithm-based measures follow a fully unsupervised approach [4,5] that does not include validation. Two recent studies include implied IW validation [3,6]. Yet, the role of validation in the indirect measurement remains underexplored.…”

Section: Introductionmentioning

confidence: 99%

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

Mozgovoy

2021

ACI

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PurposeThe 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/approachThe 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.FindingsThe 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/implicationsProposed 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 implicationsLongitudinal 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 implicationsTimely identification of excessive stress may improve individual well-being and prevent development stress-related diseases.Originality/valueThe 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.

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Section: Introductionmentioning

confidence: 99%