Advancing Sport Biomechanics with Depth Cameras: Systematic Review of Current Applications and Future Directions

Background: Research on the application of technology in sports in Romania is completely lacking, and the existing studies at the international level have mainly been carried out in recent years. We considered it appropriate to highlight the best practice models of technology application in sports that can be multiplied, adapted, improved, and widely used. The paper aims to identify the use of technology and devices in sports, with an emphasis on their role in training and competitions with the aim of improving sports performance, to provide sports specialists, organizations, and authorities with a wide range of information regarding the connection between sport and technology. The results obtained regarding the application of technology in sports refer mainly to the following: techniques and technologies used in training and competition (portable localization technology and global positioning systems (GPS); Virtual Reality (VR) technology; video analysis; digital technologies integrated into sports training); aspects of sports training targeted through the use of technology (use of technology for athlete health, recovery, and injury management; use of technology for monitoring sports performance and various body indicators); training optimization and ecological dynamics and the sustainable development of sports. Conclusions: Unitary research, at a European or even global level, in a uniform theoretical and practical framework, could lead to much more efficient training with large increases in sports performance. The coaches and specialists working with the athlete determine the specificity of some elements of the training, depending on the characteristics of each athlete. Large clubs could become a factor in generating and disseminating knowledge related to training and competition monitoring, sports performance enhancement, and health, recovery, and injury management. Research directions for the use of technology in sport and the formation of connections with other fields can be extended. For example, combined technologies assisted by specialized software can be used. Creativity must be the starting point for the use and combination of existing technologies in sports and for the creation of new ones. Their creation and use involve the teamwork of athletes, coaches, and specialists from different fields, such as sports, physiology, psychology, biomechanics, informatics, etc.

This study tested the hypothesis of the strong scholar perception of the journal Sports Biomechanics with a bibliometric analysis of top-cited articles. Three major databases, Google Scholar, Scopus and Web of Science, were searched for the most cited articles published in Sports Biomechanics for the first 15 years (2002–2016) of publication. The top 20 (4%) cited articles from each database were qualitatively analysed for research themes and descriptive statistics calculated for citations and citation rates. The top-cited articles published in Sports Biomechanics had high citation rates and included several citation classics, indicating strong contributions to the advancement of knowledge in applied biomechanics and beyond. The results support previous high ratings of the journal by sport and exercise biomechanics scholars and refute the biased and lower ranking based on the Web of Science Impact Factor. There was moderate (40–70%) agreement on the top 20 cited articles between the three databases due to differences in indexing and temporal coverage.

Introduction: Recognizing human activity in a daily environment has attracted much research in computer vision and recognition in recent years. It is a difficult and challenging topic not only inasmuch as the variations of background clutter, occlusion or intra-class variation in image sequences but also inasmuch as complex patterns of activity are created by interactions among people-people or people-objects. In addition, it also is very valuable for many practical applications, such as smart home, gaming, health care, human-computer interaction and robotics. Now, we are living in the beginning age of the industrial revolution 4.0 where intelligent systems have become the most important subject, as reflected in the research and industrial communities. There has been emerging advances in 3D cameras, such as Microsoft's Kinect and Intel's RealSense, which can capture RGB, depth and skeleton in real time. This creates a new opportunity to increase the capabilities of recognizing the human activity in the daily environment. In this research, we propose a novel approach of daily activity recognition and hypothesize that the performance of the system can be promoted by combining multimodal features.
 Methods: We extract spatial-temporal feature for the human body with representation of parts based on skeleton data from RGB-D data. Then, we combine multiple features from the two sources to yield the robust features for activity representation. Finally, we use the Multiple Kernel Learning algorithm to fuse multiple features to identify the activity label for each video. To show generalizability, the proposed framework has been tested on two challenging datasets by cross-validation scheme.
 Results: The experimental results show a good outcome on both CAD120 and MSR-Daily Activity 3D datasets with 94.16% and 95.31% in accuracy, respectively.
 Conclusion: These results prove our proposed methods are effective and feasible for activity recognition system in the daily environment.

The conventional ‘keyboard and workstation’ approach allows complex medical image presentation and manipulation during mammographic interpretation. Nevertheless, providing rich interaction and feedback in real time for navigational training or computer assisted detection of disease remains a challenge. Through computer vision and state of the art AR (Augmented Reality) technique, this study proposes an ‘AR mammographic workstation’ approach which could support workstation-independent rich interaction and real-time feedback. This flexible AR approach explores the feasibility of facilitating various mammographic training scenes via AR as well as its limitations.

3D difference detection is the task to verify whether the 3D geometry of a real object exactly corresponds to a 3D model of this object. This thesis introduces real-time 3D difference detection with a hand-held depth camera. In contrast to previous works, with the proposed approach, geometric differences can be detected in real time and from arbitrary viewpoints. Therefore, the scan position of the 3D difference detection be changed on the fly, during the 3D scan. Thus, the user can move the scan position closer to the object to inspect details or to bypass occlusions. The main research questions addressed by this thesis are: Q1: How can 3D differences be detected in real time and from arbitrary viewpoints using a single depth camera? Q2: Extending the first question, how can 3D differences be detected with a high precision? Q3: Which accuracy can be achieved with concrete setups of the proposed concept for real time, depth image based 3D difference detection? This thesis answers Q1 by introducing a real-time approach for depth image based 3D difference detection. The real-time difference detection is based on an algorithm which maps the 3D measurements of a depth camera onto an arbitrary 3D model in real time by fusing computer vision (depth imaging and pose estimation) with a computer graphics based analysis-by-synthesis approach. Then, this thesis answers Q2 by providing solutions for enhancing the 3D difference detection accuracy, both by precise pose estimation and by reducing depth measurement noise. A precise variant of the 3D difference detection concept is proposed, which combines two main aspects. First, the precision of the depth camera’s pose estimation is improved by coupling the depth camera with a very precise coordinate measuring machine. Second, measurement noise of the captured depth images is reduced and missing depth information is filled in by extending the 3D difference detection with 3D reconstruction. The accuracy of the proposed 3D difference detection is quantified by a quantitative evaluation. This provides an anwer to Q3. The accuracy is evaluated both for the basic setup and for the variants that focus on a high precision. The quantitative evaluation using real-world data covers both the accuracy which can be achieved with a time-of-flight camera (SwissRanger 4000) and with a structured light depth camera (Kinect). With the basic setup and the structured light depth camera, differences of 8 to 24 millimeters can be detected from one meter measurement distance. With the enhancements proposed for precise 3D difference detection, differences of 4 to 12 millimeters can be detected from one meter measurement distance using the same depth camera. By solving the challenges described by the three research question, this thesis provides a solution for precise real-time 3D difference detection based on depth images. With the approach proposed in this thesis, dense 3D differences can be detected in real time and from arbitrary viewpoints using a single depth camera. Furthermore, by coupling the depth camera with a coordinate measuring machine and by integrating 3D reconstruction in the 3D difference detection, 3D differences can be detected in real time and with a high precision.

Sports technologies have been in constant 'change and development' since the day they first emerged until today. With each passing year, sports technologies develop by taking reference from each other through new technologies. With the development of sports technologies, generations are encountering more 'new and advanced' technologies every day. Sports have an important place in protecting the physical and psychological health of individuals. Mental health has an important place in sports as well as physical health. In this respect, the aim of the current research is; It is aimed to examine different technology areas in sports in terms of mental health. As a data collection tool for this purpose; For Turkish sources, DergiPark, Higher Education Institution National Thesis Center (YÖKTEZ) and Google Scholar, and for English sources, in Web of Science and PubMed databases, with the keywords "Sports Technology", "Sports Mental Health", "Sports Entertainment" and "Technology Addiction". scanning has been done. As a result of the research; It is seen that technology in sports is used for different purposes such as 'entertainment, exercise and performance sports'. In some cases, the use of technology for more equipment or time than necessary reveals that it can create technology addiction in individuals and negatively affect their mental health. On the other hand, it is thought that conscious technology users can turn their current sports/exercises into productive and entertaining ones with sports technologies developed for different purposes. In this respect, as with all newly developing technologies, it has been concluded that determining the needs well and being a conscious sports technology user while using these technologies is important for mental health.

Sports technologies have been in continuous and uninterrupted 'change and development' from the day they first started to emerge until today. With each passing year, sports technologies are developing by taking reference from each other through new technologies. With the development of sports technologies, generations, encounter more 'new and advanced' technologies every day. Sports have an important place in protecting the physical and psychological health of individuals. In addition to physical health, mental health also has an important place in sports. In this respect, the aim of the current research is to examine different technology areas in sports in terms of mental health. For this purpose, as a data collection tool; DergiPark, Higher Education Institution National Thesis Center (YÖKTEZ) and Google Scholar were searched for Turkish sources and Web of Science and PubMed databases for English sources with the keywords "Sport Technology", "Sport Mental Health", "Sport Entertainment" and "Technology Addiction". As a result of the research; it is seen that technology in sports is used for different purposes such as 'entertainment, exercise and performance sports'. In some cases, the use of technology in more equipment or time than needed can create technology addiction in individuals and negatively affect their mental health. On the other hand, it is thought that conscious technology users can transform their existing sports/exercises into an efficient and fun way with sports technologies developed for different purposes. In this respect, it was concluded that, as with all new developing technologies, it is important to determine the needs well when using these technologies and to be a conscious user of sports technologies in terms of mental health.

Artificial neural network helps humans in a wide range of activities, such as sports. This paper aims to investigate the effect of artificial intelligence on decision-making related to human gait and sports biomechanics, using computer-based software, and to investigate the impact of artificial intelligence on individuals' biomechanics during gait and sports performance. This review was conducted in compliance with the PRISMA guidelines. Abstracts and citations were identified through a search based on Science Direct, Google Scholar, PubMed, Elsevier, Springer Link, Web of Science, and Scopus search engines from 1995 up to 2023 to obtain relevant literature about the impact of artificial intelligence on biomechanics. A total of 1000 articles were found related to biomechanical characteristics of gait and sport and 26 articles were directly pertinent to the subject. The extent of the application of artificial intelligence in sports biomechanics in various fields. In addition, various variables in the fields of kinematics, kinetics, and the field of time can be investigated based on artificial intelligence. Conventional computational techniques are limited by the inability to process data in its raw form. Artificial Intelligence (AI) and Machine Learning (ML) techniques can handle complex and high-dimensional data. The utilization of specialized systems and neural networks in gait analysis has shown great potential in sports performance analysis. Integrating AI into this field would be a significant advancement in sport biomechanics. Coaches and athletes can develop more precise training regimens with specialized performance prediction models.

The system, which takes hi-tech Olympics and improving national sports science and technology as well as training levels of athletes as the background, and considers current three-dimensional video analysis system for diagnosis of sports technology as the platform, constructs data and knowledge base based on three-dimensional information and parameters by mainly utilizing modern computer technology. It analyzes human movement behaviors with systematic conceptions, and studies technical movements of athletes through input-output relationship of human movement behaviors. Through fuzzy mathematics, mathematical statistics and artificial intelligence, integrating and drawing reference from sport biomechanics, graphics, human anatomy and expert system etc., the system solves a series of key problems both in theory and application from information extraction, inference to multi-objective parameter fusion and decision-making, and then designs and develops biomechanical evaluation system for shot sport technology. Considering the multiple events that the sport contains, the research primarily proceeds from shot, and establishes “coach-athlete” expert evaluation system.

Depth sensing devices enabled with an RGB camera, can be used to augment conventional images with depth information on a per-pixel basis. Currently available RGB-D sensors include the Asus Xtion Pro, Microsoft Kinect and Intel RealSense <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">™</sup> . However, these sensors have certain limitations. Objects that are shiny, transparent or have an absorbing matte surface, create problems due to reflection. Also, there can be an interference in the IR pattern due to the use of multiple RGB-D cameras and the depth information is correctly interpreted only for short distances between the camera and the object. The proposed system, block matching stereo vision (BMSV) uses an RGB-D camera with rectified/non-rectified block matching and image pyramiding along with dynamic programming for human tracking and capture of accurate depth information from shiny/transparent objects. Here, the IR emitter generates a known IR pattern and the depth information is recovered by comparing the multiple views of the focused object. The depth map of the BMSV RGB-D camera and the resultant disparity map are fused. This fills any void regions that may have emerged due to interference or because of the reflective transparent surfaces and an enhanced dense stereo image is obtained. The proposed method is applied to a 3D realistic head model, a functional magnetic resonance image (fMRI) and the results are presented. Results showed an improvement in speed and accuracy of RGB-D sensors which in turn provided accurate depth information density irrespective of the object surface.

Introduction: Motion capture technology is vital in industries like sports biomechanics, cinema, Robotics traditional system rely on fixed cameras limited flexibility in dynamic outdoor environments autonomous rule equipped with AI and computer vision of announced mobility, real time tracking of moving subject across diversity. Drones enable resize performing analysis in sports and dynamic film without need for complex setup all the challenges like environmental conditions and hardware limitation remains research in sensor fusion and optimization is improving reliability Drone represent a major advancement in motion capture, unlocking new possibilities across multiple field as technology evolves motion capture will become more efficient and accessible.Objectives: Recent advancement in motion capture technology have addressed limitation of traditional system, which relay on stationery cameras and makers in control environment. These traditional system are expensive in mobile and labour intensive Drone based motion capture offers greater mobility, flexibility and real time tracking in divers environment making it suitable for application of film, sports and biomechanics. Researchers are exploring rules stabilization techniques real time tracking algorithm and multi-drone coordination for large scale projects. Drones represent the next evolution unable in dynamic tracking in complex &amp; natural settings.Methods: The study proposes fully automated Drone- based motion capture system leveraging advanced computer vision real time processing and stabilization for accurate tracking in dynamic environments. The system architecture integrates higher revolution cameras, depth sensor, IMUs and on board processing unit unable real time motion tracking. Computer vision technique including deep learning models like YOLO and SSD, facilitate high speed detection and classification, while optical flow methods and features tracking enhance accuracy. Sensor fusion integrates data from various sources to improve tracking precision and Kalman filtering reduces noise. AI-driven flight control and autonomous navigation algorithm ensure stable positioning and obstacle avoidance. Multi-drone coordination allows for large scale motion capture while performance strategies like energy-efficient and adaptive frame rates improve system efficiency.Results: The innovative fusion of autonomous drones and motion capture technology provide flexible, precise and low-cost alternative to traditional motion capture system. Bye integrating computer vision, real-time sensor fusion and AI driven tracking algorithm, it overcome the limitation of fixed camera setup, improving tracking in dynamic and unstructured environments. The system ability to track multiple subjects without makers reduce operational cost and complexity. AI driven object recognisation models achieve high accuracy, even in challenging lightning and occulasion condition. Future improvements will focus on multi-drone cordination, avoidance and incorporating thermal or infrared imaging to address poor lighting condition. Collaborative Drone efforts could good expand the systems’s application in technical field.Conclusions: The integration of autonomous Drone with motion capture technology offers promising advancement in multiple domains, with potential for enhanced efficiency and accuracy. Future research will focus on improving real time motion tracking processing through energy efficient AI chips unable in faster data fusion and on board processing. Edge AI models will allow drones to make autonomous decisions in dynamic environment, without the need for external servers. Multi-drone coordination will unable large scale motion capture applicable to sports, film production and biomechanics. Enhancing object detection in challenging environment like low light and weather, conditions along with thermal imaging and LIDAR will improve robustness. The systems integration with AR and VR technology could transform gaming, training and immersive stimulations. Future developments will focus on energy-efficient flight algorithms, longer battery life and application in industries like healthcare, Robotics, defence and disaster management. Additionally improved safety mechanisms including collision avoidance, will ensure safe operation in complex environments.

Recent advances in Computer Vision are changing our way of living and enabling new applications for both leisure and professional use. Regrettably, in many industrial domains the spread of state-of-the-art technologies is made challenging by the abundance of nuisances that corrupt existing techniques beyond the required dependability. This is especially true for object localization and tracking, that is, the problem of detecting the presence of objects on images and videos and estimating their pose. This is a critical task for applications such as Augmented Reality (AR), robotic autonomous navigation, robotic object grasping, or production quality control; unfortunately, the reliability of existing techniques is harmed by visual features such as the abundance of specular and poorly textured objects, cluttered scenes, or artificial and in-homogeneous lighting. In this thesis, we propose two methods for robustly estimating the pose of a rigid object under the challenging conditions typical of industrial environments. Both methods rely on monocular images to handle metallic environments, on which depth cameras would fail; both are conceived with a limited computational and memory footprint, so that they are suitable for real-time applications such as AR. We test our methods on datasets issued from real user case scenarios, exhibiting challenging conditions. The first method is based on a global image alignment framework and a robust dense descriptor. Its global approach makes it robust in presence of local artifacts such as specularities appearing on metallic objects, ambiguous patterns like screws or wires, and poorly textured objects. Employing a global approach avoids the need of reliably detecting and matching local features across images, that become ill-conditioned tasks in the considered environments; on the other hand, current methods based on dense image alignment usually rely on luminous intensities for comparing the pixels, which is not robust in presence of challenging illumination artifacts. We show how the use of a dense descriptor computed as a non-linear function of luminous intensities, that we refer to as ``Descriptor Fields'', greatly enhances performances at a minimal computational overhead. Their low computational complexity and their ease of implementation make Descriptor Fields suitable for replacing intensities in a wide number of state-of-the-art techniques based on dense image alignment. Relying on a global approach is appropriate for overcoming local artifacts, but it can be un-effective when the target object undergoes extreme occlusions in cluttered environments. For this reason, we propose a second approach based on the detection of discriminative object parts. At the core of our approach is a novel representation for the 3D pose of the parts, that allows us to predict the 3D pose of the object even when only a single part is visible; when several parts are visible, we can easily combine them to compute a better pose of the object. The 3D pose we obtain is usually very accurate, even when only few parts are visible. We show how to use this representation in a robust 3D tracking framework. In addition to extensive comparisons with the state-of-the-art, we demonstrate our method on a practical Augmented Reality application for maintenance assistance in the ATLAS particle detector at CERN.

One-dimension statistical parametric mapping in lower limb biomechanical analysis: A systematic scoping review

Evaluation of low-cost depth cameras for agricultural applications

This paper presents many common areas of interest of different specialists. There are problems described from sport, biomechanics, sport biomechanics, sport engineering, robotics, biomechanics and robotics, sport biomechanics and robotics. There are many approaches to sport from different sciences and engineering. Robotics is a relatively new area and has had moderate attention from sport specialists. The aim of this paper is to present several areas necessary to develop sport robots based on biomechanics and also to present different types of sport robots: serving balls, helping to provide sports training, substituting humans during training, physically participating in competitions, physically participating in competitions against humans, serving as models of real sport performance, helping organizers of sport events and robot toys. Examples of the application of robots in sports communities are also given.