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.

1†GHz electro-optical silicon-germanium modulator in the 5-9 µm wavelength range.

Spectroscopy in the mid-infrared (mid-IR) wavelength range is a key technique to detect and identify chemical and biological substances. In this context, the development of integrated optics systems paves the way for the realization of compact and cost-effective sensing systems. Among the required devices, an integrated electro-optical modulator (EOM) is a key element for advanced sensing circuits exploiting dual comb spectroscopy. In this paper, we have experimentally demonstrated an integrated EOM operating in a wide wavelength range, i.e. from 5 to 9 µm at radio frequency (RF) as high as 1 GHz. The modulator exploits the variation of free carrier absorption in a Schottky diode embedded in a graded silicon germanium (SiGe) photonic waveguide.

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.

Bioglue endovascular treatment of fusiform aneurysm rupture of the distal anterior temporal artery of the middle cerebral artery: a case report.

The treatment of a ruptured fusiform distal anterior temporal artery aneurysm is a challenge for the stroke physician, however surgical closure and coil endovascular intervention are options. A total blockage can result in memory problems as well as object-related questions. We'd like to provide the clinical example of a 56-year-old woman with many underlying medical illnesses who was admitted to the hospital with a grade 7/10 headache and a Glasgow score of 15, but no focal neurological deficits, and was diagnosed with a ruptured distal temporal artery aneurysm. The aneurysm is positioned in the distal region, making endovascular intervention difficult to perform. As a result, we used an endovascular approach to repair with bioglue. When a patient develops fusiform aneurysms of the distal temporal artery, our findings provide an additional therapy option.