A wearable device dataset for mental health assessment using laser Doppler flowmetry and fluorescence spectroscopy sensors

AI Summary

Wearable optical sensing combining laser Doppler flowmetry and fluorescence spectroscopy captures fingertip microvascular perfusion and tissue metabolism for objective assessment of stress-related mental health.
Ensemble machine learning under subject-wise validation achieved ROC AUC 0.72 and PR AUC 0.89, with microcirculatory variability and fluorescence features driving predictions.
Demographics including sex, age, BMI, and heart rate modulate stress-related risk; interpretable models support scalable, data-driven tools for objective mental health monitoring.

Commun Med (Lond). 2026 Jul 4. doi: 10.1038/s43856-026-01766-5. Online ahead of print.

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.

PMID:42401748 | DOI:10.1038/s43856-026-01766-5

Document this CPD