Radar Human Motion Recognition Using Motion States and Two-Way Classifications

TL;DR

This paper introduces a radar-based method for classifying daily activities by analyzing motion states and transitions, improving recognition accuracy for contiguous and inseparable motions using a two-way classification framework.

Contribution

It proposes a novel framework that models activities as states and transitions, utilizing micro-Doppler and range-map data for improved activity classification in radar signals.

Findings

Considering only physically possible motion classes enhances classification accuracy.

The framework effectively distinguishes between translation and in-place motions.

Two-way classifiers improve recognition of contiguous activities.

Abstract

We perform classification of activities of daily living (ADL) using a Frequency-Modulated Continuous Waveform (FMCW) radar. In particular, we consider contiguous motions that are inseparable in time. Both the micro-Doppler signature and range-map are used to determine transitions from translation (walking) to in-place motions and vice versa, as well as to provide motion onset and the offset times. The possible classes of activities post and prior to the translation motion can be separately handled by forward and background classifiers. The paper describes ADL in terms of states and transitioning actions, and sets a framework to deal with separable and inseparable contiguous motions. It is shown that considering only the physically possible classes of motions stemming from the current motion state improves classification rates compared to incorporating all ADL for any given time.