scholarly journals Construction and Simulation of a Multiattribute Training Data Mining Model for Basketball Players Based on Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yunbin Li ◽  
Jinyan Ge ◽  
Wei Hao
Keyword(s):  
Big Data ◽  
Three Dimensional ◽  
Injury Rate ◽  
Training System ◽  
Training Data ◽  
Human Model ◽  
Basketball Players ◽  
Physiological Indicators ◽  
Depth Analysis ◽  
Mining Model

This paper provides an in-depth analysis and research on the construction and simulation of a big data model for multiattribute training of basketball players. To get a more accurate and three-dimensional information, the training can use a multitraining target robot, i.e., to detect feedback on multiple indicators at the same time and correct the player’s errors in time; the other is an auxiliary robot, which can actively correct technical movements and train the player to form muscle memory, compared with general training. The analysis results show that by either constructing a human model or designing an active assistive robot, the player’s technical movements can be regulated accordingly, protecting the player’s body laterally and improving the player’s ability. An assisted training system with an accurate model of physiological indicators is constructed based on the data of the player throughout the season. The Warriors, who have applied this system, not only have the best record in recent years but also have the lowest injury rate in the league, indicating that this method has indeed reduced the injury rate of players.

scholarly journals XRSISE: An XR Training System for Interactive Simulation and Ergonomics Assessment

2021 ◽  
Vol 2 ◽  
Author(s):  
Michail Pavlou ◽  
Dimitrios Laskos ◽  
Evangelia I. Zacharaki ◽  
Konstantinos Risvas ◽  
Konstantinos Moustakas
Keyword(s):  
Virtual World ◽  
Three Dimensional ◽  
Cost Effective ◽  
Knowledge Retention ◽  
Training System ◽  
Human Model ◽  
Interactive Simulation ◽  
Industrial Training ◽  
The Real ◽  
Physical Constraints

The use of virtual reality (VR) techniques for industrial training provides a safe and cost effective solution that contributes to increased engagement and knowledge retention levels. However, the process of experiential learning in a virtual world without biophysical constraints might contribute to muscle strain and discomfort, if ergonomic risk factors are not considered in advance. Under this scope, we have developed a digital platform which employs extended reality (XR) technologies for the creation and delivery of industrial training programs, by taking into account the users and workplace specificities through the adaptation of the 3D virtual world to the real environment. Our conceptual framework is composed of several inter-related modules: 1) the XR tutorial creation module, for automatic recognition of the sequence of actions composing a complex scenario while this is demonstrated by the educator in VR, 2) the XR tutorial execution module, for the delivery of visually guided and personalized XR training experiences, 3) the digital human model (DHM) based simulation module for creation and demonstration of job task simulations avoiding the need of an actual user and 4) the biophysics assessment module for ergonomics analysis given the input received from the other modules. Three-dimensional reconstruction and aligned projection of the objects situated in the real scene facilitated the imposition of inherent physical constraints, thereby allowed to seamlessly blend the virtual with the real world without losing the sense of presence.

scholarly journals Trends of developing training in advanced countries and direction of the Army

2020 ◽  
Vol 3 (2) ◽  
pp. 1-15
Author(s):  
Heenam Lee
Keyword(s):  
Industrial Revolution ◽  
Low Cost ◽  
Effective Means ◽  
Three Dimensional ◽  
Developed Countries ◽  
Education And Training ◽  
Training System ◽  
Training Data ◽  
Training Environment ◽  
And Training

This study examines the history of LVCG training, which is attracting attention as the army’s education and training method in the era of the 4th industrial revolution, and the recent trends in advanced countries, and the direction of the army’s LVCG training development. LVCG training is an effective means for soldiers to improve their survival rate and combat skills by experiencing realistic virtual battles iteratively prior to actual deployment, thereby ultimately improving their readiness. The LVCG training system is classified into four categories: live, virtual training, war game training, and game, collectively referred to as LVCG. In the 1980s, advanced countries began to use the LVCG training system in various areas of education and training, and after the 1990s, they promoted a synthetic environment incorporating the LVCG training system. Recently, the synthetic training environment (STE) that integrates LVCG into a three-dimensional virtual environment. This is attracting attention and developed countries are rapidly transitioning to STE. The Army is also promoting education and training innovation based on big data and artificial intelligence by establishing a low-cost, highly efficient LVCG training environment with LVCG integrated around the synthetic training environment platform and securing education and training data using STE.

scholarly journals A three-dimensional virtual mouse generates synthetic training data for behavioral analysis

2021 ◽  
Vol 18 (4) ◽  
pp. 378-381 ◽  
Author(s):  
Luis A. Bolaños ◽  
Dongsheng Xiao ◽  
Nancy L. Ford ◽  
Jeff M. LeDue ◽  
Pankaj K. Gupta ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Training Data ◽  
Behavioral Analysis ◽  
Virtual Mouse ◽  
Synthetic Training Data

scholarly journals Coronary Centerline Extraction from CCTA Using 3D-UNet

2021 ◽  
Vol 13 (4) ◽  
pp. 101
Author(s):  
Alexandru Dorobanțiu ◽  
Valentin Ogrean ◽  
Remus Brad
Keyword(s):  
Computed Tomography ◽  
Cardiac Computed Tomography ◽  
Three Dimensional ◽  
Class Imbalance ◽  
Training Data ◽  
Cardiac Computed Tomography Angiography ◽  
Centerline Extraction ◽  
Network Training ◽  
Dimensional Reconstruction ◽  
Coronary Centerline

The mesh-type coronary model, obtained from three-dimensional reconstruction using the sequence of images produced by computed tomography (CT), can be used to obtain useful diagnostic information, such as extracting the projection of the lumen (planar development along an artery). In this paper, we have focused on automated coronary centerline extraction from cardiac computed tomography angiography (CCTA) proposing a 3D version of U-Net architecture, trained with a novel loss function and with augmented patches. We have obtained promising results for accuracy (between 90–95%) and overlap (between 90–94%) with various network training configurations on the data from the Rotterdam Coronary Artery Centerline Extraction benchmark. We have also demonstrated the ability of the proposed network to learn despite the huge class imbalance and sparse annotation present in the training data.

scholarly journals Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 884
Author(s):  
Chia-Ming Tsai ◽  
Yi-Horng Lai ◽  
Yung-Da Sun ◽  
Yu-Jen Chung ◽  
Jau-Woei Perng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Training Data ◽  
Network Architectures ◽  
Point Cloud Data ◽  
Data Types ◽  
Raw Data ◽  
Cloud Data

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

Research on the university intelligent learning analysis system based on AI

2021 ◽  
pp. 1-10
Author(s):  
Meng Huang ◽  
Shuai Liu ◽  
Yahao Zhang ◽  
Kewei Cui ◽  
Yana Wen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Academic Performance ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Three Dimensional ◽  
Training Model ◽  
Future Trend ◽  
Artificial Intelligence Technology ◽  
Visualization Technology

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

scholarly journals Spatiotemporal Analysis of COVID-19 Spread with Emerging Hotspot Analysis and Space–Time Cube Models in East Java, Indonesia

2021 ◽  
Vol 10 (3) ◽  
pp. 133
Author(s):  
Purwanto Purwanto ◽  
Sugeng Utaya ◽  
Budi Handoyo ◽  
Syamsul Bachri ◽  
Ike Sari Astuti ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Distribution Patterns ◽  
Spatiotemporal Analysis ◽  
Space Time ◽  
Road Density ◽  
Hotspot Analysis ◽  
Spatiotemporal Information ◽  
Depth Analysis ◽  
Analysis Models ◽  
Oscillating Patterns

In this research, we analyzed COVID-19 distribution patterns based on hotspots and space–time cubes (STC) in East Java, Indonesia. The data were collected based on the East Java COVID-19 Radar report results from a four-month period, namely March, April, May, and June 2020. Hour, day, and date information were used as the basis of the analysis. We used two spatial analysis models: the emerging hotspot analysis and STC. Both techniques allow us to identify the hotspot cluster temporally. Three-dimensional visualizations can be used to determine the direction of spread of COVID-19 hotspots. The results showed that the spread of COVID-19 throughout East Java was centered in Surabaya, then mostly spread towards suburban areas and other cities. An emerging hotspot analysis was carried out to identify the patterns of COVID-19 hotspots in each bin. Both cities featured oscillating patterns and sporadic hotspots that accumulated over four months. This pattern indicates that newly infected patients always follow the recovery of previous COVID-19 patients and that the increase in the number of positive patients is higher when compared to patients who recover. The monthly hotspot analysis results yielded detailed COVID-19 spatiotemporal information and facilitated more in-depth analysis of events and policies in each location/time bin. The COVID-19 hotspot pattern in East Java, visually speaking, has an amoeba-like pattern. Many positive cases tend to be close to the city, in places with high road density, near trade and business facilities, financial storage, transportation, entertainment, and food venues. Determining the spatial and temporal resolution for the STC model is crucial because it affects the level of detail for the information of endemic disease distribution and is important for the emerging hotspot analysis results. We believe that similar research is still rare in Indonesia, although it has been done elsewhere, in different contexts and focuses.

scholarly journals A Novel Suture Training System for Open Surgery Replicating Procedures Performed by Experts Using Augmented Reality

2021 ◽  
Vol 45 (5) ◽  
Author(s):  
Yuri Nagayo ◽  
Toki Saito ◽  
Hiroshi Oyama
Keyword(s):  
Augmented Reality ◽  
Motion Capture ◽  
Open Surgery ◽  
Three Dimensional ◽  
Training System ◽  
Surgical Skills ◽  
Surgical Instruments ◽  
Interrupted Suture ◽  
Education Environment ◽  
Surgical Field

AbstractThe surgical education environment has been changing significantly due to restricted work hours, limited resources, and increasing public concern for safety and quality, leading to the evolution of simulation-based training in surgery. Of the various simulators, low-fidelity simulators are widely used to practice surgical skills such as sutures because they are portable, inexpensive, and easy to use without requiring complicated settings. However, since low-fidelity simulators do not offer any teaching information, trainees do self-practice with them, referring to textbooks or videos, which are insufficient to learn open surgical procedures. This study aimed to develop a new suture training system for open surgery that provides trainees with the three-dimensional information of exemplary procedures performed by experts and allows them to observe and imitate the procedures during self-practice. The proposed system consists of a motion capture system of surgical instruments and a three-dimensional replication system of captured procedures on the surgical field. Motion capture of surgical instruments was achieved inexpensively by using cylindrical augmented reality (AR) markers, and replication of captured procedures was realized by visualizing them three-dimensionally at the same position and orientation as captured, using an AR device. For subcuticular interrupted suture, it was confirmed that the proposed system enabled users to observe experts’ procedures from any angle and imitate them by manipulating the actual surgical instruments during self-practice. We expect that this training system will contribute to developing a novel surgical training method that enables trainees to learn surgical skills by themselves in the absence of experts.

Design of a 6-DOF Robotic Gait Training System With Closed-Chain Foot Initiated Kinematics Control

2015 ◽  
Author(s):  
Wei Liu ◽  
John Kovaleski ◽  
Marcus Hollis
Keyword(s):  
Three Dimensional ◽  
Computer Software ◽  
Gait Training ◽  
Gait Pattern ◽  
Training System ◽  
Normal Walking ◽  
Robot Assisted ◽  
Walking Gait ◽  
The Right ◽  
Joint Motions

Robotic assisted rehabilitation, taking advantage of neuroplasticity, has been shown to be helpful in regaining some degree of gait performance. Robot-applied movement along with voluntary efferent motor commands coordinated with the robot allows optimization of motion training. We present the design and characteristics of a novel foot-based 6-degree-of-freedom (DOF) robot-assisted gait training system where the limb trajectory mirrored the normal walking gait. The goal of this study was to compare robot-assisted gait to normal walking gait, where the limb moved independently without robotics. Motion analysis was used to record the three-dimensional kinematics of the right lower extremity. Walking motion data were determined and transferred to the robotic motion application software for inclusion in the robotic trials where the robot computer software was programmed to produce a gait pattern in the foot equivalent to the gait pattern recorded from the normal walking gait trial. Results demonstrated that ankle; knee and hip joint motions produced by the robot are consistent with the joint motions in walking gait. We believe that this control algorithm provides a rationale for use in future rehabilitation, targeting robot-assisted training in people with neuromuscular disabilities such as stroke.

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