The intelligent and autonomous learning of patients' activities will lead to an incredible progression toward future smart e-health systems. With the recent advances in artificial intelligence, signal processing, and computational capabilities; light detection and ranging (LiDAR) technology can play a significant role in enhancing the current patients' activity recognition (PAR) systems. In this paper, we propose confidential and accurate patient arms behavior monitoring using a standalone three-dimensional (3D) LiDAR sensor. Due to the unavailability of LiDAR data, we use a computer-programmed 3D simulator to generate virtual-LiDAR (V-LiDAR) 3D point cloud data that simulates real patient movements. These virtual data are used to train a multi-layer-perception (MLP) model to segment the data points of the patient's body into arms versus not arms. We further propose a sub-segmentation technique to segment patient's arms point cloud data into upper or lower arms. Finally, we demonstrate uses of arms gesture identification using the proposed scheme. The numerical results show that the proposed MLP model achieves a test accuracy of $90.8\%$ and a cross-validation accuracy of $87.4\%$.


Electrical and Computer Engineering

Keywords and Phrases

Arms; Laser radar; light detection and ranging (LiDAR); Medical services; multilayer perceptron (MLP), arms segmentation; neural networks; Point cloud compression; Program processors; Sensors; Three-dimensional displays

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Document Type

Article - Journal

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Publication Date

01 Jan 2023