Fiber Memristor-Based Physical Reservoir Computing for Multimodal Sleep Monitoring.

ABSTRACT: Real-time wearable sleep monitors process diverse biological signals while operating under tight energy and computation budgets. The existing algorithms are facing problems of high energy consumption due to separate hardware storage and computation units. In this work, textile-integrated in-memory neuromorphic computing electronics based on MoS₂ quantum dot fiber memristors was proposed for physical reservoir computing for the first time. Textile electronics convert raw electroencephalogram (EEG)and snoring audio directly into rich, high-dimensional state vectors based on intrinsic nonlinear dynamics. Leveraging 16 pulse-programmable conductance levels, the reservoir realizes an accuracy of 94.8%, 95.4%, and 93.5% in snoring events, sleep stages, and multimodal fusion, respectiv

SUBMITTER: Zhang J 

PROVIDER: S-EPMC12417633 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature