Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks
This work addresses energy efficiency for wearable device users, but it is incremental as it applies an existing SNN method to a new domain.
The paper tackles the problem of high computational burden and poor temporal feature extraction in Human Activity Recognition (HAR) using wearable sensors by applying Spiking Neural Networks (SNNs), achieving comparable accuracy to Artificial Neural Networks while reducing energy consumption by up to 94%.
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