Human Behavior Analysis Using Radar Data: A Survey

• Published in: IEEE access 2024, Vol.12, p.153188-153202
• Main Authors: Miazek, Patrycja, Zmudzinska, Alicja, karczmarek, Pawel, Kiersztyn, Adam
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Behavioral sciences; convolutional neural network (CNN); Convolutional neural networks; Data analysis; Data integration; Data processing; Deep learning; deep learning (DL); Fall detection; Feature extraction; Fuzzy sets; Human activity recognition; Human activity recognition (HAR); Human behavior; Human factors; Human motion; Laser radar; Lidar; Machine learning; Monitoring; Motion perception; Neural networks; Radar; Radar data; Radar detection; Radar imaging; Radar tracking; Recurrent neural networks; Surveys
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3474100

Innovative monitoring solutions utilizing radar have been developed to meet various safety needs. Radar technologies, equipped with unique features, have proven to be effective tools for human activity recognition in diverse environments. Radar complements other sensors by ensuring privacy while recording movement, particularly in situations where video cameras may be intrusive. Deep learning techniques, including convolutional neural networks (CNN) and recurrent neural networks (RNN), enhance the recognition of human behaviors based on radar, providing solutions for fall detection and motion tracking. Transfer learning addresses issues related to data scarcity, while data fusion models integrate information from multiple sources. Advances in machine learning, data processing, and GPU speed contribute to the increased efficiency of radar technology for indoor monitoring, opening possibilities for more advanced applications. The aim of this article is to discuss the evolving field of human behavior analysis using radar and LiDAR. To this end, the literature in this field has been reviewed. In this study, we focus not only on the analysis of human behavior using radar but also on video observations. We review methods of human activity recognition using LiDAR. We discuss potential directions for development, draw conclusions, and outline future research in the area of Human Activity Recognition (HAR).