All-optical sensing of dermal activity enables an AI-powered wearable ring for continuous stress monitoring

AI Summary

AI-powered ring enables all-optical dermal activity sensing using dual wavelengths (960 and 1250 nm) to track sweat gland activation and photoplethysmography, electrode free.
Integrates temperature and motion sensors with on-device one-dimensional CNN inference for autonomous, multimodal monitoring and robust long-term stress tracking.
ODA shows 86% agreement with EDA and superior classification: 92.3% binary versus 77.95%; multiclass short term 100% versus 74.58%, long term 89.72% versus 43.20%.

Sci Adv. 2026 Nov 13;12(46):eaeh0459. doi: 10.1126/sciadv.aeh0459. Epub 2026 Nov 13.

ABSTRACT

Electrodermal activity (EDA), a primary stress-sensing signal in today’s wearables, requires skin-electrode contact that drifts and degrades over time, limiting long-term use. Here, we present an artificial intelligence (AI)-powered ring for all-optical sensing of dermal activity (ODA), using light rather than electrodes to track sweat gland activation, thereby enhancing durability and comfort. An optimized dual-wavelength design (960 and 1250 nanometers) captures perspiration dynamics while concurrently recording photoplethysmography. The ODA ring integrates temperature and motion sensors for multimodal monitoring with on-device inference. In correlation studies, ODA and EDA signals showed 86% agreement. In classification tasks, the ODA ring achieved 92.3% binary accuracy versus 77.95% for EDA. In multiclass tasks, ODA outperforms EDA by 100 to 74.58% (short term) and 89.72 to 43.20% (long term). On-device one-dimensional convolutional neural network inference enabled 87.53% autonomous accuracy. This electrode-free modality establishes a scalable, all-optical platform for rings, watches, and patches for accurate, robust, and long-term stress monitoring.

PMID:43024732 | DOI:10.1126/sciadv.aeh0459

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