论文标题
基于可穿戴的人类活动识别具有时空峰值神经网络
Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks
论文作者
论文摘要
我们研究人类活动识别（HAR）任务，该任务可以根据可穿戴传感器的时间序列数据来预测用户日常活动。最近，研究人员使用端到端人工神经网络（ANN）提取功能并在HAR中进行分类。但是，ANN对可穿戴设备造成了巨大的计算负担，并且缺乏时间特征提取。在这项工作中，我们利用尖峰神经网络（SNNS）（一种灵感来自生物神经元的启发的架构）。 SNN允许时空提取特征，并使用二进制尖峰享受低功率计算。我们在三个带有SNN的HAR数据集上进行了广泛的实验，这表明SNN与ANN相同，同时降低了94％的能耗。该代码可在https://github.com/intelligent-computing-lab-yale/snn_har中公开获得
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
