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2015
In the sports fields and technology, the accuracy of an athlete can never be monitored with 100 % accuracy with naked eyes. Moves that are done by an athlete or a sports person in the game such as his stride length and the stride frequency cannot be easily seen by a person and hence, detection and correction of the minute mistakes that are being made are overlooked. For instance, while doing push-ups a person‟s head, neck, waist and ankle should be in a single straight line. In this paper, we discuss a sports training system which uses Microsoft Kinect for motion sensing. The infrared depth stream translates the presence of a user in real world, into digital space by tracking his/her joint coordinates. As per our empirical studies, Kinect maps the x,y and z coordinates of 20 primary joints of the body. This data forms the basis of our calculations as elaborated in the literature. This work gives a basis of how movements and postures may be corrected by the use of sensors like Kinect...
International Journal of Computer Applications, 2014
In the sports fields and technology, the accuracy of an athlete can never be monitored with 100% accuracy with naked eyes. Moves that are done by an athlete or a sports person in the game such as his stride length and the stride frequency cannot be easily seen by a person and hence, detection and correction of the minute mistakes that are being made are overlooked. For instance, while doing push-ups a person"s head, neck, waist and ankle should be in a single straight line. In this paper, we discuss a sports training system which uses Microsoft Kinect for motion sensing. The infrared depth stream translates the presence of a user in real world, into digital space by tracking his/her joint coordinates. As per our empirical studies, Kinect maps the x,y and z coordinates of 20 primary joints of the body. This data forms the basis of our calculations as elaborated in the literature. This work gives a basis of how movements and postures may be corrected by the use of sensors like Kinect in an effective and efficient manner.
2014 IEEE 5th International Conference on Software Engineering and Service Science, 2014
In this paper, we describe the design and implementation of a Kinect-based system for rehabilitation exercises monitoring and guidance. We choose to use the Unity framework to implement our system because it enables us to use virtual reality techniques to demonstrate detailed movements to the patient, and to facilitate examination of the quality and quantity of the patient sessions by the clinician. The avatar-based rendering of motion also preserves the privacy of the patients, which is essential for healthcare systems. The key contribution of our research is a rule-based approach to realtime exercise quality assessment and feedback. We developed a set of basic rule elements that can be used to express the correctness rules for common rehabilitation exercises.
ArXiv, 2020
Sports franchises invest a lot in training their athletes. use of latest technology for this purpose is also very common. We propose a system of capturing motion of athletes during weight training and analyzing that data to find out any shortcomings and imperfections. Our system uses Kinect depth image to compute different parameters of athlete's selected joints. These parameters are passed through certain algorithms to process them and formulate results on their basis. Some parameters like range of motion, speed and balance can be analyzed in real time. But for comparison to be performed between motions, data is first recorded and stored and then processed for accurate results. Our results depict that this system can be easily deployed and implemented to provide a very valuable insight to dynamics of a work out and help an athlete in improving his form.
AIP Conf. Proc. 2845, 030008, 2023
Motion detection and tracking systems used to quantify the mechanics of motion in many fields of research. Despite their high accuracy, industrial systems are expensive and sophisticated to use. However, it has shown imprecision in activity-delicate motions, to deal with the limitations. The Microsoft Kinect Sensor used as a practical and cheap device to access skeletal data, so it can be used to detect and track the body in different subjects such as, medical, sports, and analysis fields, because it has very good degrees of accuracy and its ability to track six people in real time. Sometimes research uses single or multiple Kinect devices based on different classification methods and approaches such as machine learning algorithms, neural networks, and others. Researches used global database like, CAD-60, MSRAction3D, 3D Action Pairs and others, while the others used their on database by collected them from different ages and genders. Some research connected a Kinect device to a robot to simulate movements, or the process done in virtual reality by using an avatar, where an unreal engine used to make it. In this research, we presents the related works in this subject, the used methods, database and applications.
Journal of physics, 2016
Subjects who practice sports either as professionals or amateurs, have a high incidence of knee injuries. There are a few publications that show studies from a kinematic point of view of lateral-structure-knee injuries, including meniscal (meniscal tears or chondral injury), without anterior cruciate ligament rupture. The use of standard motion capture systems for measuring outdoors sport is hard to implement due to many operative reasons. Recently released, the Microsoft Kinect™ is a sensor that was developed to track movements for gaming purposes and has seen an increased use in clinical applications. The fact that this device is a simple and portable tool allows the acquisition of data of sport common movements in the field. The development and testing of a set of protocols for 3D kinematic measurement using the Microsoft Kinect™ system is presented in this paper. The 3D kinematic evaluation algorithms were developed from information available and with the use of Microsoft's Software Development Kit 1.8 (SDK). Along with this, an algorithm for calculating the lower limb joints angles was implemented. Thirty healthy adult volunteers were measured, using five different recording protocols for sport characteristic gestures which involve high knee injury risk in athletes.
The lack of low cost devices apt to collaborate both researches and clinical intervention s quality for health promotion is quite significant, peculiarly in developing countries. The objective of this study consisted in calculating the accuracy of the hardware Kinect™ by Microsoft™. Methods: anthropometric data were collected from a subject in orthostatic position, at four different distances from the optical axes of the hardware, on X, Y and Z. The normality and the variances homogeinity of the data were stated through Kolmogorov-Smirnov and Barlett’s tests, in this order. It has been adopted a significance P < 0.05 for all the statistical tests, and the size effect for all of the spatial coordinates (in the four different placements) exceeded 0.80. Results: the relative error presented no significant differences in all of those distances in the three spatial axels and the accuracy averaged 0.047m; such result allows to conclude that the hardware presents satisfactory both scientific and clinical applicability, embracing potentially human movement investigations and interventions, as well as orthopedics, physiotherapy, physical education, and sports among others.
International Journal of Sports Science, 2015
The purpose of this study was to provide evidence of reliability and validity for the use of a Microsoft Kinect system to measure displacement in human movement analysis. Three dimensional (3D) video motion systems are commonly used to analyze human movement kinematics of body joints and segments for many diverse applications related to gait analysis, rehabilitation, sports performance, medical robotics, and biofeedback. These systems, however, have certain drawbacks pertaining to the use of markers, calibration time, number of cameras, and high cost. Microsoft Kinect systems create 3D images and are low cost, portable, not markers required, and easy to set up. They lack, however, evidence of reliability and validity for human movement kinematics analysis. Twenty-six participants were recruited for this study. Peak Motus version 9 and Microsoft Kinect system with customized skeleton software were used to collect data from each subject sitting on a platform moving horizontally at the...
MATEC Web of Conferences, 2017
Motion capture system has recently being brought to light and drawn much attention in many fields of research, especially in biomechanics. Marker-based motion capture systems have been used as the main tool in capturing motion for years. Marker-based motion capture systems are very pricey, lab-based and beyond reach of many researchers, hence it cannot be applied to ubiquitous applications. The game however has changed with the introduction of depth camera technology, a markerless yet affordable motion capture system. By means of this system, motion capture has been promoted as more portable application and does not require substantial time in setting up the system. Limitation in terms of nodal coverage of single depth camera has widely accepted but the performance of dual depth camera system is still doubtful since it is expected to improve the coverage issue but at the same time has bigger issues on data merging and accuracy. This work appraises the accuracy performance of dual depth camera motion capture system specifically for athletes' running biomechanics analysis. Kinect sensors were selected to capture motions of an athlete simultaneously in three-dimension, and fused the recorded data into an analysable data. Running was chosen as the biomechanics motion and interpreted in the form of angle-time, angleangle and continuous relative phase plot. The linear and angular kinematics were analysed and represented graphically. Quantitative interpretations of the result allowed the deep insight of the movement and joint coordination of the athlete. The result showed that the root-mean-square error of the Kinect sensor measurement to exact measurement data and rigid transformation were 0.0045 and 0.0077291 respectively. The velocity and acceleration of the subject were determined to be 3.3479 ms-1 and −4.1444 ms-2. The result showed that the dual Kinect camera motion capture system was feasible to perform athletes' biomechanics analysis.
European Research in Telemedicine / La Recherche Européenne en Télémédecine, 2016
Étude de faisabilité d'un système de jeux sérieux basé sur le système Kinect pour la rééducation fonctionnelle des membres inférieurs
Jurnal Teknologi, 2015
Microsoft Kinect has been identified as a potential alternative tool in the field of motion capture due to its simplicity and low cost. To date, the application and potential of Microsoft Kinect has been vigorously explored especially for entertainment and gaming purposes. However, its motion capture capability in terms of repeatability and reproducibility is still not well addressed. Therefore, this study aims to explore and develop a motion capture system using Microsoft Kinect; focusing on developing the interface, motion capture protocol as well as measurement analysis. The work is divided into several stages which include installation (Microsoft Kinect and MATLAB); parameters and experimental setup, interface development; protocols development; motion capture; data tracking and measurement analysis. The results are promising, where the variances are found to be less than 1% for both repeatability and reproducibility analysis. This proves that the current study is significant an...
2019
A Kinect sensor based basketball game is developed for delivering post-stroke exercises in association with a newly developed elbow exoskeleton. Few interesting features such as audiovisual feedback and scoring have been added to the game platform to enhance patient's engagement during exercises. After playing the game, the performance score has been calculated based on their reachable points and reaching time to measure their current health conditions. During exercises, joint parameters are measured using the motion capture technique of Kinect sensor. The measurement accuracy of Kinect sensor is validated by two comparative studies where two healthy subjects were asked to move elbow joint in front of Kinect sensor wearing the developed elbow exoskeleton. In the first study, the joint information collected from Kinect sensor was compared with the exoskeleton based sensor. In the next study, the length of upperarm and forearm measured by Kinect were compared with the standard anthropometric data. The measurement errors between Kinect and exoskeleton are turned out to be in the acceptable range; 1% for subject 1 and 0.44% for subject 2 in case of joint angle; 5.55% and 3.58% for subject 1 and subject 2 respectively in case of joint torque. The average errors of Kinect measurement as compared to the anthropometric data of the two subjects are 16.52% for upperarm length and 9.87% for forearm length. It shows that Kinect sensor can measure the activity of joint movement with a minimum margin of error.
2015
The high criminal rates in Indonesia have caused in increased interest in martial arts among the local populace, but the means to access training are largely unavailable. New technology may make it possible for civilians to study with a virtual coach without leaving their home. This allows them to learn proficiency with the detail oriented software that tracks proper form and range of motion. Using the motion capture features from a Microsoft Kinect, the user can develop basic taekwondo training and practice by using the virtual coach. Hopefully this progress in motion capture will afford a multitude of people the means to practice taekwondo for self defense or sport needs. © 2015 Published by ISICO
IEEE transactions on cybernetics, 2013
The recent advancement of motion recognition using Microsoft Kinect stimulates many new ideas in motion capture and virtual reality applications. Utilizing a pattern recognition algorithm, Kinect can determine the positions of different body parts from the user. However, due to the use of a single-depth camera, recognition accuracy drops significantly when the parts are occluded. This hugely limits the usability of applications that involve interaction with external objects, such as sport training or exercising systems. The problem becomes more critical when Kinect incorrectly perceives body parts. This is because applications have limited information about the recognition correctness, and using those parts to synthesize body postures would result in serious visual artifacts. In this paper, we propose a new method to reconstruct valid movement from incomplete and noisy postures captured by Kinect. We first design a set of measurements that objectively evaluates the degree of reliabi...
Jurnal Sistem Informasi Dan Komputerisasi Akuntansi, 2014
Kihon is the fundamental knowledge which is important to learn Karate. Kihon consists of stances, punches, and kicks. In traditional learning environment, Karate teachers have difficulty in monitoring student position in learning Karate. This paper present an approach to overcome this problem by using Kinect sensor data to capture student position then compare the elevation degree of selected joint. As a result, we developed an application that could measuring body position compared to stored model position. By using skeleton stream which has vector data, we can estimate the angle between two vector and save it as reference for further assessment with user model. This application has accuracy rate of 83% for performing above mentioned task.
2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE), 2014
In this paper, we present a feasibility study for using a single Microsoft Kinect sensor to assess the quality of rehabilitation exercises. Unlike competing studies that have focused on the validation of the accuracy of Kinect motion sensing data at the level of joint positions, joint angles, and displacement of joints, we take a rule based approach. The advantage of our approach is that it provides a concrete context for judging the feasibility of using a single Kinect sensor for rehabilitation exercise monitoring. Our study aims to answer the following question: if it is found that Kinect's measurement on a metric deviates from the ground truth by some amount, is this an acceptable error? By defining a set of correctness rules for each exercise, the question will be answered definitively with no ambiguity. Defining appropriate context in a validation study is especially important because (1) the deviation of Kinect measurement from the ground truth varies significantly for different exercises, even for the same joint, and (2) different exercises have different tolerance levels for the movement restrictions of body segments. In this study, we also show that large but systematic deviations of the Kinect measurement from the ground truth are not as harmful as it seems because the problem can be overcome by adjusting parameters in the correctness rules.
2020
Due to the high growth usage of technology in modern sports, it became more complex. Both neural networks and data mining are frequently used as effective approaches for analyzing the huge amounts of data collected in sports. Fencing is one of most popular sports. Due to its dynamics, a large number of events occur during a single match. Fencing statisticians have the task of noting as many of these events as possible. In this paper, the main target is to use neural networks to collect statistics through Kinect - a line of motion sensing input device – to create a dateset to train a software system; in order to enhance the fencer movement through a training course. A software system was<br>designed and applied regarding this issue. <br>
Exercise plays an important role in our day to day life as it helps people remain in shape, fit and to prevent from many disease. Regular physical activities such as weight training and cardio exercises are part of everyday modern life. If performed correctly, it contributes to the health of a person. Exercises helps to prevent obesity and stimulate the immune system. Many people practice physical exercises without an assistance of an expert in home. This paper aims to present a software that offers virtual trainer with real-time feedback and the assessment score to different exercise postures presented by an animated 3D character using Kinect sensor. This tool allows people to observe the correct execution of each exercise. Recognition of the exercise has been performed using Random Forest (RF) classifier. The computer must first understand what a user is doing before it can respond. This has always been an active research field in computer vision, but it has proven formidably difficult with video cameras. With help of Kinect sensor, the computer directly sense the third dimension, making the task much easier
Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare, 2012
The use of Virtual Reality technology for developing tools for rehabilitation has attracted significant interest in the physical therapy arena. This paper presents a comparison of motion tracking performance between the low-cost Microsoft Kinect and the high fidelity OptiTrack optical system. Data is collected on six upper limb motor tasks that have been incorporated into a game-based rehabilitation application. The experiment results show that Kinect can achieve competitive motion tracking performance as OptiTrack and provide "pervasive" accessibility that enables patients to take rehabilitation treatment in clinic and home environment.
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