LiteHAR: Lightweight Human Activity Recognition from WiFi Signals with Random Convolution Kernels

TL;DR

LiteHAR is a lightweight human activity recognition method using WiFi CSI signals that employs random convolution kernels and simple classification, achieving high accuracy with low computational cost suitable for resource-constrained devices.

Contribution

This paper introduces a novel HAR approach that uses randomly initialized convolution kernels and a linear classifier, reducing complexity compared to deep learning models.

Findings

High classification accuracy on benchmark dataset

Significantly lower computational complexity

Effective for resource-limited devices

Abstract

Anatomical movements of the human body can change the channel state information (CSI) of wireless signals in an indoor environment. These changes in the CSI signals can be used for human activity recognition (HAR), which is a predominant and unique approach due to preserving privacy and flexibility of capturing motions in non-line-of-sight environments. Existing models for HAR generally have a high computational complexity, contain very large number of trainable parameters, and require extensive computational resources. This issue is particularly important for implementation of these solutions on devices with limited resources, such as edge devices. In this paper, we propose a lightweight human activity recognition (LiteHAR) approach which, unlike the state-of-the-art deep learning models, does not require extensive training of large number of parameters. This approach uses randomly initialized convolution kernels for feature extraction from CSI signals without training the kernels. The extracted features are then classified using Ridge regression classifier, which has a linear computational complexity and is very fast. LiteHAR is evaluated on a public benchmark dataset and the results show its high classification performance in comparison with the complex deep learning models with a much lower computational complexity.