Ethogram-based Personalization of Human Activity and Agility from Radar Micro-Doppler Signatures

Emre Kurtoglu, Sultanus Salehin, Moeness Amin, Irene Kan, Michelle McKay, Sevgi Zubeyde Gurbuz

Keywords: Radar, human activity recognition, mmWave, remote health monitoring, micro-Doppler

TL;DR: Radar-based human activity and agility recognition.

Abstract: Radar has garnered great interest for remote health monitoring due to its ambient operation, effectiveness in the dark, and inability to make visual recordings of private scenes/faces. However, the current state-of-the-art in human activity recognition (HAR) focuses on the classification of persistent gaits, such as walking, and ignores the transitions between activities. The characterization of a person’s ability to transition between postural states is highly individual and influenced by the person’s physical and mental health. This paper presents a personalized, ethogram-based approach to HAR, which jointly characterizes the agility of transitions in addition to activity classification. We develop a multi-input multi-task learning (MIMTL) approach to simultaneously classify both human activity and agility. Our proposed approach yields accuracies of over 98% and 90% for the joint characterization tasks. Various interventions affecting gait are applied to show how the proposed approach can lead to agility-based detection of changes in gait.

Track: 11. General Track

Registration Id: XVNJ4TQV5G5

Submission Number: 384