AHAR: Adaptive CNN for Energy-efficient Human Activity Recognition in Low-power Edge Devices. (arXiv:2102.01875v3 [cs.LG] UPDATED)

Human Activity Recognition (HAR) is one of the key applications of health
monitoring that requires continuous use of wearable devices to track daily
activities. This paper proposes an Adaptive CNN for energy-efficient HAR (AHAR)
suitable for low-power edge devices. Unlike traditional early exit architecture
that makes the exit decision based on classification confidence, AHAR proposes
a novel adaptive architecture that uses an output block predictor to select a
portion of the baseline architecture to use during the inference phase.
Experimental …

arxiv devices edge edge devices energy power