Multiple Kinect based system to monitor and analyze key performance indicators of physical training (original) (raw)
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Multiple Kinect Sensor Fusion for Human Skeleton Tracking Using Kalman Filtering
International Journal of Advanced Robotic Systems
Kinect sensors are able to achieve considerable skeleton tracking performance in a convenient and low-cost manner. However, Kinect sensors often generate poor skeleton poses due to self-occlusion, which is a common problem among most vision-based sensing systems. A simple way to solve this problem is to use multiple Kinect sensors in a workspace and combine the measurements from the different sensors. However, this method creates a new issue known as the data fusion problem. In this research, we developed a human skeleton tracking system using the Kalman filter framework, in which multiple Kinect sensors are used to correct inaccurate tracking data from a single Kinect sensor. Our contribution is to propose a method to determine the reliability of each tracked 3D position of a joint and then combine multiple observations based on measurement confidence. We evaluate the proposed approach by comparison with the ground truth obtained using a commercial marker-based motion-capture system.
Body motion detection and tracking using a Kinect sensor: Review
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.
The Use of Microsoft Kinect for Human Movement Analysis
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...
LOW-COST ACCURATE SKELETON TRACKING BASED ON FUSION OF KINECT AND WEARABLE INERTIAL SENSORS
In this paper, we present a novel multi-sensor fusion method to build a human skeleton. We propose to fuse the joint position information obtained from the popular Kinect sensor with more precise estimation of body segment orientations provided by a small number of wearable inertial sensors. The use of inertial sensors can help to address many of the well known limitations of the Kinect sensor. The precise calculation of joint angles potentially allows the quantification of movement errors in technique training, thus facilitating the use of the low-cost Kinect sensor for accurate biomechanical purposes e.g. the improved human skeleton could be used in visual feedback-guided motor learning, for example. We compare our system to the gold standard Vicon optical motion capture system, proving that the fused skeleton achieves a very high level of accuracy.
A comparative study of the clinical use of motion analysis from Kinect skeleton data
2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
The analysis of human motion as a clinical tool can bring many benefits such as the early detection of disease and the monitoring of recovery, so in turn helping people to lead independent lives. However, it is currently under used. Developments in depth cameras, such as Kinect, have opened up the use of motion analysis in settings such as GP surgeries, care homes and private homes. To provide an insight into the use of Kinect in the healthcare domain, we present a review of the current state of the art. We then propose a method that can represent human motions from time-series data of arbitrary length, as a single vector. Finally, we demonstrate the utility of this method by extracting a set of clinically significant features and using them to detect the age related changes in the motions of a set of 54 individuals, with a high degree of certainty (F1score between 0.9-1.0). Indicating its potential application in the detection of a range of age-related motion impairments.
Smart Innovation, Systems and Technologies, 2020
Human-robot interaction requires a robust estimate of human motion in real-time. This work presents a fusion algorithm for joint center positions tracking from multiple depth cameras to improve human motion analysis accuracy. The main contribution is the proposed algorithm based on body tracking measurements fusion with an extended Kalman filter and anthropomorphic constraints, independent of sensors. As an illustration of the use of this algorithm, this paper presents the direct comparison of joint center positions estimated with a reference stereophotogrammetric system and the ones estimated with the new Kinect 3 (Azure Kinect) sensor and its older version the Kinect 2 (Kinect for Windows). The experiment was made in two parts, one for each model of Kinect, by comparing raw and merging body tracking data of two sided Kinect with the proposed algorithm. The proposed approach improves body tracker data for Kinect 3 which has not the same characteristics as Kinect 2. This study shows also the importance of defining good heuristics to merge data depending on how the body tracking works. Thus, with proper heuristics, the joint center position estimates are improved by at least 14.6 %. Finally, we propose an additional comparison between Kinect 2 and Kinect 3 exhibiting the pros and cons of the two sensors.
Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect
2015 International Conference on Healthcare Informatics, 2015
Microsoft Kinect camera and its skeletal tracking capabilities have been embraced by many researchers and commercial developers in various applications of real-time human movement analysis. In this paper, we evaluate the accuracy of the human kinematic motion data in the first and second generation of the Kinect system, and compare the results with an optical motion capture system. We collected motion data in 12 exercises for 10 different subjects and from three different viewpoints. We report on the accuracy of the joint localization and bone length estimation of Kinect skeletons in comparison to the motion capture. We also analyze the distribution of the joint localization offsets by fitting a mixture of Gaussian and uniform distribution models to determine the outliers in the Kinect motion data. Our analysis shows that overall Kinect 2 has more robust and more accurate tracking of human pose as compared to Kinect 1.
Validation study of a Kinect based body imaging system
Work: A Journal of Prevention, Assessment & Rehabilitation, 2017
BACKGROUND Understanding the reliability and precision of the data obtained using three-dimensional body scanners is very important if it is intended to replace the traditional data collection methods. If the collection of anthropometric data with three-dimensional body scanners is a fast and reliable process that produces precise data at a low price, it could be used for numerous applications worldwide. Many studies have addressed data collected by white light and laser based scanners. OBJECTIVE This study provides a comparative analysis between the anthropometric data collected using a Kinect body imaging system with the data collected using traditional manual methods. Moreover, a comparison is also made between the results obtained in this study and the results of previous studies of different types of body scanners. METHODS The Mean Absolute Difference was calculated and all the values were compared to the maximum allowable error defined in ISO 20685. Additionally, an analysis of the significant differences between the two acquisition methods was also applied to a physical mannequin, to understand how the body movement and body stance variation in human participants impacts the results obtained. RESULTS There are few body measurements that are close to this restricted allowable error. The results were better when the mannequin was measured. Although they were still above the ISO 20685 limit, they were much closer than the results obtained for human participants. CONCLUSION The main cause of the differences between the two methods is the time required for the 3D system to acquire the data. The involuntary body sway of human participants is more difficult to control when the time span is too long.
A Real-Time Kinect Signature-Based Patient Home Monitoring System
Sensors, 2016
Assessment of body kinematics during performance of daily life activities at home plays a significant role in medical condition monitoring of elderly people and patients with neurological disorders. The affordable and non-wearable Microsoft Kinect ("Kinect") system has been recently used to estimate human subject kinematic features. However, the Kinect suffers from a limited range and angular coverage, distortion in skeleton joints' estimations, and erroneous multiplexing of different subjects' estimations to one. This study addresses these limitations by incorporating a set of features that create a unique "Kinect Signature". The Kinect Signature enables identification of different subjects in the scene, automatically assign the kinematics feature estimations only to the subject of interest, and provide information about the quality of the Kinect-based estimations. The methods were verified by a set of experiments, which utilize real-time scenarios commonly used to assess motor functions in elderly subjects and in subjects with neurological disorders. The experiment results indicate that the skeleton based Kinect Signature features can be used to identify different subjects in high accuracy. We demonstrate how these capabilities can be used to assign the Kinect estimations to the Subject of Interest, and exclude low quality tracking features. The results of this work can help in establishing reliable kinematic features, which can assist in future to obtain objective scores for medical analysis of patient condition at home while not restricted to perform daily life activities.
A Kinect-based rehabilitation exercise monitoring and guidance system
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.