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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2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
TCNet modulates handcrafted feature anchors with neural context from raw signals to achieve higher mF1 scores on five HAR benchmarks than prior methods like rTsfNet.
citing papers explorer
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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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Feature Anchors for Time-Series Sensor-Based Human Activity Recognition
TCNet modulates handcrafted feature anchors with neural context from raw signals to achieve higher mF1 scores on five HAR benchmarks than prior methods like rTsfNet.