RF-Based Human Activity Recognition Using Signal Adapted Convolutional Neural Network

TL;DR

This paper introduces HAR-SAnet, a lightweight RF-based human activity recognition framework that fuses time and frequency domain features using an efficient neural network architecture, achieving superior accuracy with reduced computation.

Contribution

HAR-SAnet is a novel RF-based HAR framework that adaptively fuses features from multiple domains and employs efficient convolutions for improved performance and practicality.

Findings

Outperforms state-of-the-art algorithms in recognition accuracy.

Uses point-wise grouped and depth-wise separable convolutions for efficiency.

Achieves high accuracy with limited computational resources.

Abstract

Human Activity Recognition (HAR) plays a critical role in a wide range of real-world applications, and it is traditionally achieved via wearable sensing. Recently, to avoid the burden and discomfort caused by wearable devices, device-free approaches exploiting RF signals arise as a promising alternative for HAR. Most of the latest device-free approaches require training a large deep neural network model in either time or frequency domain, entailing extensive storage to contain the model and intensive computations to infer activities. Consequently, even with some major advances on device-free HAR, current device-free approaches are still far from practical in real-world scenarios where the computation and storage resources possessed by, for example, edge devices, are limited. Therefore, we introduce HAR-SAnet which is a novel RF-based HAR framework. It adopts an original signal adapted convolutional neural network architecture: instead of feeding the handcraft features of RF signals into a classifier, HAR-SAnet fuses them adaptively from both time and frequency domains to design an end-to-end neural network model. We apply point-wise grouped convolution and depth-wise separable convolutions to confine the model scale and to speed up the inference execution time. The experiment results show that the recognition accuracy of HAR-SAnet outperforms state-of-the-art algorithms and systems.