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 the classification in HAR. However, ANNs incur huge computation burdens to wearables devices and lacks 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 by 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.