Deep Learning for Human Activity Recognition on 3D Human Skeleton: Survey and Comparative Study.

Human activity recognition (HAR) is an important research problem in computer vision. This problem is widely applied to building applications in human-machine interactions, monitoring, etc. Especially, HAR based on the human skeleton creates intuitive applications. Therefore, determining the current results of these studies is very important in selecting solutions and developing commercial products. In this paper, we perform a full survey on using deep learning to recognize human activity based on three-dimensional (3D) human skeleton data as input. Our research is based on four types of deep learning networks for activity recognition based on extracted feature vectors: Recurrent Neural Network (RNN) using extracted activity sequence features; Convolutional Neural Network (CNN) uses feature vectors extracted based on the projection of the skeleton into the image space; Graph Convolution Network (GCN) uses features extracted from the skeleton graph and the temporal-spatial function of the skeleton; Hybrid Deep Neural Network (Hybrid-DNN) uses many other types of features in combination. Our survey research is fully implemented from models, databases, metrics, and results from 2019 to March 2023, and they are presented in ascending order of time. In particular, we also carried out a comparative study on HAR based on a 3D human skeleton on the KLHA3D 102 and KLYOGA3D datasets. At the same time, we performed analysis and discussed the obtained results when applying CNN-based, GCN-based, and Hybrid-DNN-based deep learning networks.

YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos.

Hand detection and classification is a very important pre-processing step in building applications based on three-dimensional (3D) hand pose estimation and hand activity recognition. To automatically limit the hand data area on egocentric vision (EV) datasets, especially to see the development and performance of the "You Only Live Once" (YOLO) network over the past seven years, we propose a study comparing the efficiency of hand detection and classification based on the YOLO-family networks. This study is based on the following problems: (1) systematizing all architectures, advantages, and disadvantages of YOLO-family networks from version (v)1 to v7; (2) preparing ground-truth data for pre-trained models and evaluation models of hand detection and classification on EV datasets (FPHAB, HOI4D, RehabHand); (3) fine-tuning the hand detection and classification model based on the YOLO-family networks, hand detection, and classification evaluation on the EV datasets. Hand detection and classification results on the YOLOv7 network and its variations were the best across all three datasets. The results of the YOLOv7-w6 network are as follows: FPHAB is P = 97% with TheshIOU = 0.5; HOI4D is P = 95% with TheshIOU = 0.5; RehabHand is larger than 95% with TheshIOU = 0.5; the processing speed of YOLOv7-w6 is 60 fps with a resolution of 1280 × 1280 pixels and that of YOLOv7 is 133 fps with a resolution of 640 × 640 pixels.

Unified End-to-End YOLOv5-HR-TCM Framework for Automatic 2D/3D Human Pose Estimation for Real-Time Applications.

Three-dimensional human pose estimation is widely applied in sports, robotics, and healthcare. In the past five years, the number of CNN-based studies for 3D human pose estimation has been numerous and has yielded impressive results. However, studies often focus only on improving the accuracy of the estimation results. In this paper, we propose a fast, unified end-to-end model for estimating 3D human pose, called YOLOv5-HR-TCM (YOLOv5-HRet-Temporal Convolution Model). Our proposed model is based on the 2D to 3D lifting approach for 3D human pose estimation while taking care of each step in the estimation process, such as person detection, 2D human pose estimation, and 3D human pose estimation. The proposed model is a combination of best practices at each stage. Our proposed model is evaluated on the Human 3.6M dataset and compared with other methods at each step. The method achieves high accuracy, not sacrificing processing speed. The estimated time of the whole process is 3.146 FPS on a low-end computer. In particular, we propose a sports scoring application based on the deviation angle between the estimated 3D human posture and the standard (reference) origin. The average deviation angle evaluated on the Human 3.6M dataset (Protocol #1-Pro #1) is 8.2 degrees.

Multiple routes of hepatitis C virus transmission among injection drug users in Hai Phong, Northern Vietnam.

To identify hepatitis C virus (HCV) transmission routes among injection drug users in Northern Vietnam, plasma samples were collected from 486 drug users in Hai Phong. Plasma viral RNA was extracted from 323 (66.5%) samples that were positive for anti-HCV antibodies. Portions of the HCV 5'-untranslated (5'UTR)-Core and NS5B genes were amplified by reverse-transcriptase polymerase chain reaction, sequenced directly, and genotyped in 194 and 195 specimens, respectively. Both regions were genotyped in 137 specimens. In the 5'UTR-Core region, genotype 6a was predominant (32.5%), followed by genotype 1a (23.7%), genotype 1b (20.6%), and genotype 6e (14.4%). In the NS5B region, genotype 1a was predominant (42.6%), followed by genotype 1b (24.1%), genotype 6a (14.4%), genotype 3b (7.2%), and genotype 6e (5.1%). Of the 137 specimens with both regions genotyped, 23 (16.8%) showed discordant genotyping results between the two regions, suggesting possible recombination and/or dual infection. Phylogenetic analysis revealed close associations between Hai Phong strains and strains from Southern China: the Yunnan province for genotype 3b; the Guangxi province for genotype 6e; the USA for genotype 1a; and Southern Vietnam for genotypes 1a and 6e. The human immunodeficiency virus (HIV) infection rate among HCV-infected injection drug users was 52.6-55.4% and did not differ significantly by HCV genotype. Most drug users infected with HIV-1 [98.8% (171/173)] were co-infected with HCV. These results suggest multiple routes of HCV transmission among injection drug users in Northern Vietnam that may also be HIV transmission routes.