PAS-Net is a fully multiplier-free spiking neural network that enforces human joint constraints spatially and uses causal neuromodulation temporally to achieve state-of-the-art accuracy on IMU HAR with up to 98% lower dynamic energy via early-exit.
arXiv preprint arXiv:2402.01533 , year=
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Frontal and fronto-central EEG regions show the most consistent predictive utility for cognitive workload in subject-independent settings, outperforming full-scalp baselines by 15-20% in relative rank across datasets.
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Towards Green Wearable Computing: A Physics-Aware Spiking Neural Network for Energy-Efficient IMU-based Human Activity Recognition
PAS-Net is a fully multiplier-free spiking neural network that enforces human joint constraints spatially and uses causal neuromodulation temporally to achieve state-of-the-art accuracy on IMU HAR with up to 98% lower dynamic energy via early-exit.
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Assessing Region-Level EEG Contributions to Cognitive Workload Prediction
Frontal and fronto-central EEG regions show the most consistent predictive utility for cognitive workload in subject-independent settings, outperforming full-scalp baselines by 15-20% in relative rank across datasets.