Literature DB >> 29295060

cHRV Uncovering Daily Stress Dynamics Using Bio-Signal from Consumer Wearables.

Tian Hao1, Henry Chang1, Marion Ball1, Kun Lin1, Xinxin Zhu1.   

Abstract

Knowing the dynamics of one's daily stress is essential to effective stress management in the context of smart and connected health. However, there lacks a practical and unobtrusive means to obtain real-time and longitudinal stress information. In this paper, we attempt to derive a convenient HRV-based (heart rate variability) biomarker named cHRV, which can be used to reliably reflect stress dynamics. cHRV's key advantage lies in its low maintenance and high practicality. It can be efficiently calculated only using data from photoplethysmography (PPG) sensors, the mainstream heart rate sensor embedded in most of the consumer wearables like Apple Watch. Benefiting from the proliferation of wearables, cHRV is ideal for day-to-day stress monitoring. To evaluate its feasibility and performance, we have conducted 14 in-lab controlled experiments. The result shows that the proposed cHRV has strong correlation with the stress dynamics (r > 0.95), therefore exhibits great potential for continuous stress assessment.

Entities:  

Keywords:  Biomarkers; Heart Rate; Psychological; Stress

Mesh:

Substances:

Year:  2017        PMID: 29295060

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  5 in total

1.  Outcome measures based on digital health technology sensor data: data- and patient-centric approaches.

Authors:  Kirsten I Taylor; Hannah Staunton; Florian Lipsmeier; David Nobbs; Michael Lindemann
Journal:  NPJ Digit Med       Date:  2020-07-23

2.  Individualized Modeling to Distinguish Between High and Low Arousal States Using Physiological Data.

Authors:  Ame Osotsi; Zita Oravecz; Qunhua Li; Joshua Smyth; Timothy R Brick
Journal:  J Healthc Inform Res       Date:  2020-01-22

3.  Evaluating the Reproducibility of Physiological Stress Detection Models.

Authors:  Varun Mishra; Sougata Sen; Grace Chen; Tian Hao; Jeffrey Rogers; Ching-Hua Chen; David Kotz
Journal:  Proc ACM Interact Mob Wearable Ubiquitous Technol       Date:  2020-12-18

4.  Outcome measures based on digital health technology sensor data: data- and patient-centric approaches.

Authors:  Kirsten I Taylor; Hannah Staunton; Florian Lipsmeier; David Nobbs; Michael Lindemann
Journal:  NPJ Digit Med       Date:  2020-07-23

Review 5.  The Extent and Coverage of Current Knowledge of Connected Health: Systematic Mapping Study.

Authors:  Maria Karampela; Minna Isomursu; Talya Porat; Christos Maramis; Nicola Mountford; Guido Giunti; Ioanna Chouvarda; Fedor Lehocki
Journal:  J Med Internet Res       Date:  2019-09-25       Impact factor: 5.428

  5 in total

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