Human-inspired time-series health evaluation with an adaptive multimodal electronic skin
This work presents a multimodal electronic skin that captures diverse physiological signs with an adaptive learning framework that rapidly generalizes to unseen tasks with minimal labeled data and demonstrates robust adaptation with 94.7% accuracy in activity recognition and 90.2% precision in fatigue assessment.