A wearable device dataset for mental health assessment using laser Doppler flowmetry and fluorescence spectroscopy sensors
9 affiliations
- 1Aston University, Birmingham, UK
- 2Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam
- 3University of Toronto, Toronto, ON Canada
- 4University Health Network, Toronto, ON Canada
- 5Florida Institute of Technology, Melbourne, FL USA
- 6Pukyong National University, Busan, Republic of Korea
- 7Stanford University, Stanford, CA USA
- 8Hai Duong Central College of Pharmacy, Hai Duong, Vietnam
- 9The University of Alabama at Birmingham, Birmingham, AL USA
Abstract
Background: Mental health conditions such as depression, anxiety, and stress are commonly assessed using self-reported questionnaires and limited wearable physiological measures. However, reliance on subjective reporting, restricted sensor modalities such as heart rate variability and electrodermal activity, and small or homogeneous datasets may limit generalizability. We aimed to evaluate whether wearable optical sensing of microcirculation and tissue metabolism enables objective assessment of stress-related mental health states.
Methods: We conducted a prospective observational study including 132 adults aged 18 to 94 years (58% female) from 19 countries. Participants underwent repeated fingertip measurements using a non-invasive wearable device combining laser Doppler flowmetry and fluorescence spectroscopy to capture microvascular perfusion and metabolic signals. Frequency-domain features were extracted using wavelet analysis. Depression, anxiety, and stress levels were assessed using a standardized 21-item questionnaire. Multiple machine learning models were evaluated under subject-wise validation, and model interpretability was assessed using Shapley-based feature attribution.
Results: Here we show that ensemble-based models distinguish individuals with stress-related symptoms from those without with a receiver operating characteristic area under the curve of 0.72 and a precision-recall area under the curve of 0.89 under subject-wise validation. Microcirculatory variability and metabolic fluorescence features contribute substantially to prediction performance. Demographic variables, including sex, age, body mass index, and heart rate, are associated with increased stress-related risk.
Conclusions: Wearable optical sensing combined with interpretable machine learning provides physiological signatures associated with stress-related mental health conditions. This framework supports development of scalable and data-driven tools for objective mental health monitoring.
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BibTeX
@article{nguyen2026wearable,
title = {{A wearable device dataset for mental health assessment using laser Doppler flowmetry and fluorescence spectroscopy sensors}},
author = {Minh Ngoc Nguyen and Khai Le-Duc and Tan-Hanh Pham and Trong Nhan Nguyen and Trang Nguyen and Ba Kien Tran and Viktor Dremin and Sergei Sokolovsky and Edik Rafailov and Truong-Son Hy},
journal = {Communications Medicine},
year = {2026},
month = jul,
doi = {10.1038/s43856-026-01766-5},
url = {https://doi.org/10.1038/s43856-026-01766-5},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - A wearable device dataset for mental health assessment using laser Doppler flowmetry and fluorescence spectroscopy sensors
AU - Minh Ngoc Nguyen
AU - Khai Le-Duc
AU - Tan-Hanh Pham
AU - Trong Nhan Nguyen
AU - Trang Nguyen
AU - Ba Kien Tran
AU - Viktor Dremin
AU - Sergei Sokolovsky
AU - Edik Rafailov
AU - Truong-Son Hy
PY - 2026
DA - 2026/07/04
JO - Communications Medicine
DO - 10.1038/s43856-026-01766-5
UR - https://doi.org/10.1038/s43856-026-01766-5
AB - Background: Mental health conditions such as depression, anxiety, and stress are commonly assessed using self-reported questionnaires and limited wearable physiological measures. However, reliance on subjective reporting, restricted sensor modalities such as heart rate variability and electrodermal activity, and small or homogeneous datasets may limit generalizability. We aimed to evaluate whether wearable optical sensing of microcirculation and tissue metabolism enables objective assessment of stress-related mental health states. Methods: We conducted a prospective observational study including 132 adults aged 18 to 94 years (58% female) from 19 countries. Participants underwent repeated fingertip measurements using a non-invasive wearable device combining laser Doppler flowmetry and fluorescence spectroscopy to capture microvascular perfusion and metabolic signals. Frequency-domain features were extracted using wavelet analysis. Depression, anxiety, and stress levels were assessed using a standardized 21-item questionnaire. Multiple machine learning models were evaluated under subject-wise validation, and model interpretability was assessed using Shapley-based feature attribution. Results: Here we show that ensemble-based models distinguish individuals with stress-related symptoms from those without with a receiver operating characteristic area under the curve of 0.72 and a precision-recall area under the curve of 0.89 under subject-wise validation. Microcirculatory variability and metabolic fluorescence features contribute substantially to prediction performance. Demographic variables, including sex, age, body mass index, and heart rate, are associated with increased stress-related risk. Conclusions: Wearable optical sensing combined with interpretable machine learning provides physiological signatures associated with stress-related mental health conditions. This framework supports development of scalable and data-driven tools for objective mental health monitoring.
SN - 2730-664X
ER -