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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).
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