Exploration of Low-Cost but Accurate Radar-Based Human Motion Direction Determination

TL;DR

This paper presents a low-cost, accurate radar-based method for determining human motion direction using gait Doppler-Time maps and a hybrid neural network, verified on open-source data.

Contribution

Introduces a novel low-cost radar approach with a hybrid neural network for accurate human motion direction determination.

Findings

Effective feature augmentation via feature linking model

High accuracy demonstrated on open-source dataset

Fast and lightweight model suitable for real-time applications

Abstract

This work is completed on a whim after discussions with my junior colleague. The motion direction angle affects the micro-Doppler spectrum width, thus determining the human motion direction can provide important prior information for downstream tasks such as gait recognition. However, Doppler-Time map (DTM)-based methods still have room for improvement in achieving feature augmentation and motion determination simultaneously. In response, a low-cost but accurate radar-based human motion direction determination (HMDD) method is explored in this paper. In detail, the radar-based human gait DTMs are first generated, and then the feature augmentation is achieved using feature linking model. Subsequently, the HMDD is implemented through a lightweight and fast Vision Transformer-Convolutional Neural Network hybrid model structure. The effectiveness of the proposed method is verified through open-source dataset. The open-source code of this work is released at: https://github.com/JoeyBGOfficial/Low-Cost-Accurate-Radar-Based-Human-Motion-Direction-Determination.