Efficient human activity recognition with spatio-temporal spiking neural networks

Frontiers in Neuroscience

Neuroscience

Ruokai Yin¹  Youngeun Kim¹  Priyadarshini Panda¹  Yuhang Li² 

[1] Department of Electrical Engineering, Yale University, New Haven, CT, United States;null;

关键词: brain-inspired computing;    neuromorphic computing;    human activity recognition;    spiking neural networks;    hardware efficiency;   

DOI  :  10.3389/fnins.2023.1233037

 received in 2023-06-01, accepted in 2023-07-25,  发布年份 2023

来源: Frontiers

【 摘 要 】

In this study, we explore Human Activity Recognition (HAR), a task that aims to predict individuals' daily activities utilizing time series data obtained from wearable sensors for health-related applications. Although recent research has predominantly employed end-to-end Artificial Neural Networks (ANNs) for feature extraction and classification in HAR, these approaches impose a substantial computational load on wearable devices and exhibit limitations in temporal feature extraction due to their activation functions. To address these challenges, we propose the application of Spiking Neural Networks (SNNs), an architecture inspired by the characteristics of biological neurons, to HAR tasks. SNNs accumulate input activation as presynaptic potential charges and generate a binary spike upon surpassing a predetermined threshold. This unique property facilitates spatio-temporal feature extraction and confers the advantage of low-power computation attributable to binary spikes. We conduct rigorous experiments on three distinct HAR datasets using SNNs, demonstrating that our approach attains competitive or superior performance relative to ANNs, while concurrently reducing energy consumption by up to 94%.

【 授权许可】

Unknown   
Copyright © 2023 Li, Yin, Kim and Panda.