Machine-Learning Mental-Fatigue-Measuring μm-Thick Elastic Epidermal Electronics (MMMEEE).

Abstract

Electrophysiological (EP) signals are key biomarkers for monitoring mental fatigue (MF) and general health, but state-of-the-art wearable EP-based MF monitoring systems are bulky and require user-specific, labeled data. Ultrathin epidermal electrodes with high performance are ideal for constructing imperceptive EP sensing systems; however, the lack of a simple and scalable fabrication delays their application in MF recognition. Here, we report a facile, scalable printing-welding-transferring strategy (PWT) for printing μm-thickness micropatterned silver nanowires (AgNWs)/sticky polydimethylsiloxane, welding the AgNWs via plasmonic effect, and transferring the electrode to skin as tattoos. The PWT provides electrodes with conformability, comfort, and stability for EP sensing. Leveraging the facile and scalable PWT, we develop plug-and-play wireless multimodal epidermal electronics integrated with an unsupervised transfer learning (UTL) scheme for MF recognition across various users. The UTL adaptively minimizes the intersubject difference and achieves high accuracy, without demand of expensive computation and labels from target users.