Enhanced Classification of Human Fall and Sit Motions Using Ultra-Wideband Radar and Hidden Markov Models

Thottempudi Pardhu, Vijay Kumar, Andreas Kanavos (), Vassilis C. Gerogiannis and Biswaranjan Acharya
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Thottempudi Pardhu: Department of Electronics and Communications Engineering, BVRIT HYDERABAD College of Engineering for Women, Hyderabad 500090, Telangana, India
Vijay Kumar: School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
Andreas Kanavos: Department of Informatics, Ionian University, 49100 Corfu, Greece
Vassilis C. Gerogiannis: Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece
Biswaranjan Acharya: Department of Computer Engineering-AI and BDA, Marwadi University, Rajkot 360003, Gujarat, India

Mathematics, 2024, vol. 12, issue 15, 1-23

Abstract: In this study, we address the challenge of accurately classifying human movements in complex environments using sensor data. We analyze both video and radar data to tackle this problem. From video sequences, we extract temporal characteristics using techniques such as motion history images (MHI) and Hu moments, which capture the dynamic aspects of movement. Radar data are processed through principal component analysis (PCA) to identify unique detection signatures. We refine these features using k-means clustering and employ them to train hidden Markov models (HMMs). These models are tailored to distinguish between distinct movements, specifically focusing on differentiating sitting from falling motions. Our experimental findings reveal that integrating video-derived and radar-derived features significantly improves the accuracy of motion classification. Specifically, the combined approach enhanced the precision of detecting sitting motions by over 10% compared to using single-modality data. This integrated method not only boosts classification accuracy but also extends the practical applicability of motion detection systems in diverse real-world scenarios, such as healthcare monitoring and emergency response systems.

Keywords: motion history image; principal component analysis; ultra-wideband (UWB) radar; human motion classification; fall detection; sitting motion; hidden Markov models (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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