  We study the Human Activity Recognition (HAR) task, which predicts user daily
activity based on time series data from wearable sensors. Recently, researchers
use end-to-end Artificial Neural Networks (ANNs) to extract the features and
perform classification in HAR. However, ANNs pose a huge computation burden on
wearable devices and lack temporal feature extraction. In this work, we
leverage Spiking Neural Networks (SNNs)--an architecture inspired by biological
neurons--to HAR tasks. SNNs allow spatio-temporal extraction of features and
enjoy low-power computation with binary spikes. We conduct extensive
experiments on three HAR datasets with SNNs, demonstrating that SNNs are on par
with ANNs in terms of accuracy while reducing up to 94% energy consumption. The
code is publicly available in
https://github.com/Intelligent-Computing-Lab-Yale/SNN_HAR
