Three custom TENG sensors integrated into eyeglasses capture arterial pulse, jaw kinematics, and facial activity at 4.1 µW total front-end power, achieving 93.8% activity accuracy and 1.82 BPM heart rate error in a 20-participant study.
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An end-to-end hardware-aware optimization pipeline produces DNNs for PPG-based blood pressure estimation with up to 7.99% lower error and 83x fewer parameters that fit on ultra-low-power SoCs like GAP8.
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GlassTENG: Self-Powered Triboelectric Nanogenerator based Sensing of Pulse, Jaw, and Upper Facial Activity from Everyday Glasses
Three custom TENG sensors integrated into eyeglasses capture arterial pulse, jaw kinematics, and facial activity at 4.1 µW total front-end power, achieving 93.8% activity accuracy and 1.82 BPM heart rate error in a 20-participant study.
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End-to-end Automated Deep Neural Network Optimization for PPG-based Blood Pressure Estimation on Wearables
An end-to-end hardware-aware optimization pipeline produces DNNs for PPG-based blood pressure estimation with up to 7.99% lower error and 83x fewer parameters that fit on ultra-low-power SoCs like GAP8.