Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser.
2013, 2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom)
Non-verbal communications such as kinesthetics, or body language and posture are important codes used to establish and maintain interpersonal relationships. They can also be utilized for safe and efficient human robot interactions. A correct interpretation of the human activity through the analysis of certain spatio-temporal and dynamic parameters represent an outstanding benefit for the quality of human machine communication in general. This paper presents an effective markerless motion capture system provided by a mobile robot for sensing human activity, in non-invasive fashion. We present a physical model based method exploiting the embedded Kinect. Its performances are evaluated first comparing the results to those obtained with a precise 3D motion capture marker based system and to data obtained from a dynamic posturography platform. Then an experiment in real life conditions is performed to assess the system sensitivity to some gait disturbances.
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.
International Journal of Pattern Recognition and Artificial Intelligence
Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this article, we present a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection, and human pose estimation.
In this paper, we propose and experiment a system for human motion capture which can be incorporated into a mobility aid device such as a walker. This measurement system uses two Kinect 3D vision sensors whose data are mapped after treatment on a physical model of virtual human. This filtering method ensures data consistency as well as the observability of the whole body movement. It is beyond a basis for the analysis of human motor activity and more specifically here of locomotion (gait parameters, posture, joint efforts, energy). We detail the entire process for rebuilding the movement and discuss its performance from the experimental results.
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...
—Tracking Human activity at home plays a growing factor in fields of security, and of bio-medicine. Microsoft Kinect is a non-wearable sensor that aggregate depth images with traditional optical video frames to estimate individuals' joints' location for kinematic analysis. When the subject of interest is out of Kinect coverage, or not in line of sight, the joints' estimations are distorted, which reduce the estimation accuracy, and can lead, in a scenario of multiple subjects, to erroneous estimations' assignment. In this work we derive features from Kinect joints and form a Kinect Signature (KS). This signature is used to identify different patients, differentiate them from others, exclude artifacts and derive the tracking quality. The suggested technology has the potential to assess human kinematics at home, reduce the cost of the patient traveling to the hospital, and improve the medical treatment follow-up.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2012
Human gait is an important indicator of health, with applications ranging from diagnosis, monitoring, and rehabilitation. In practice, the use of gait analysis has been limited. Existing gait analysis systems are either expensive, intrusive, or require well-controlled environments such as a clinic or a laboratory. We present an accurate gait analysis system that is economical and non-intrusive. Our system is based on the Kinect sensor and thus can extract comprehensive gait information from all parts of the body. Beyond standard stride information, we also measure arm kinematics, demonstrating the wide range of parameters that can be extracted. We further improve over existing work by using information from the entire body to more accurately measure stride intervals. Our system requires no markers or battery-powered sensors, and instead relies on a single, inexpensive commodity 3D sensor with a large preexisting install base. We suggest that the proposed technique can be used for co...
Electronic Workshops in Computing, 2017
This study uses machine learning methods to analyse Kinect body gestures involved in the user interaction with exergaming systems designed for physical rehabilitation. We propose a method to improve gesture recognition accuracy and motion analysis, by extracting from the full body motion data recorded by the Kinect sensor three important features which are relevant to physical therapy exercises: body posture, movement trajectory and range of motion. By applying the Hidden Markov Model (HMM) and Dynamic Time Warping (DTW) algorithms, we obtained an improved accuracy by selecting specific features from the public UTD-MHAD full body gestures database (with up to 56% for HMM and 32% for DTW). Preliminary results show a positive correlation between the movement amplitude and the envelope feature (r = 0.92). Thus, this approach has the potential to improve gesture recognition accuracy and provide user feedback on how to improve the movement performed, in particular the movement amplitude. We propose further improvements and method validations to be the basis of creating an intelligent virtual rehabilitation assistant.
2012
We propose a human motion capture system embedded on a walker. It is constituted by a SwissRanger 3D camera and two infrared distance sensors. This paper deals especially with an original prediction module which is added to the observation system in order to improve its accuracy. A Kalman filter is used to estimate some parameters of the gait, the step length and step period. They are used to predict the next positions of the feet and a reference trajectory for the Zero Moment Point (ZMP). We deduce a future generalized position of the model of walking by taking into account these data. The trajectories of joints are then reconstructed by using an anatomical model from the Humanoid Motion Analysis and Simulation (HuMAnS) toolbox. In order to validate our approach, the result obtained with our embedded system is compared with ones obtained using Codamotion system. The experimental results shows that, as expected, the prediction module using a walking model in the monitoring scheme reduces the error values.
2014
Gait analysis is a complex process since it involves tracking motion with high degrees of freedom. It has seen a lot of development in recent years with approaches changing from Markerbased to Markerless systems. This paper presents a new approach for gait analysis that is based on Markerless human motion capture using Microsoft’s popular gaming console Kinect XBOX. For this study, the RGB camera mode output of the Kinect system was used as Markerbased system. The skeleton mode output of the Kinect system was used as Markerless system. The system introduced in this paper tracked the human motion in a real time environment using foreground segmentation and computer vision algorithms developed for this purpose. The study shows that Kinect can be used both as Markerbased and Markerless systems for tracking human motion. The degree angles formed from the motion of 5 joints namely shoulder, elbow, hip, knee and ankle were calculated. The RGB camera of Kinect was used to track marks place...
This paper presents a framework for the creation of virtual human behavioural profile which will account for physiological parameters of the body during motion activities. Initially, the subject's movements are captured along with the corresponding joint trajectories using a single Kinect sensor. To improve the fidelity of the motion trajectories a series of filters were implemented so as to diminish the effect of noise. Next, a musculoskeletal model of the human lower extremity with twenty degrees of freedom and 86 muscles is introduced and registered to the captured motion data through the inverse kinematics method. For the computational analysis and the estimation of joint torques during movements inverse and forward dynamics are utilized respectively. Finally, static optimization and computed muscle control are employed to estimate the muscle forces and muscle excitations necessary to perform the captured movements. Experimental results demonstrate the potential of the proposed approach, leading to numerous applications in the field of physics-based computer animation and virtual physiological human modeling.
The Kinect sensor offers new perspectives for the development and application of affordable, portable and easy-to-use markerless motion capture (MMC) technology. However, at the moment, accuracy of this device is still not known. In this study we compare results from Kinect (MMC) with those of a stereophotogrammetric system (marker based system). 27 subjects performed a deep squatting motion. Parameters studied were segments lengths and joint angles. Results varied significantly depending on the joint or segment analyzed. For segment length MMC shows poor results when subjects were performing movement. Differences were also found concerning some joint angles but for major joints involved during squatting (shoulder, hip and knee) no difference was found.
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...
Lecture Notes in Computer Science, 2014
In this paper authors have presented a method to recognize basic human activities such as sitting, walking, laying, and standing in real time using simple features to accomplish a bigger goal of developing an elderly people health monitoring system using Kinect. We have used the skeleton joint positions obtained from the software development kit (SDK) of Microsoft as the input for the system. We have evaluated our proposed system against our own data set as well as on a subset of the MSR 3Ddaily activity data set and observed that our proposed method out performs state-of-the-art methods.
2014 International Conference on Cyberworlds, 2014
This paper presents a framework for the creation of virtual human behavioural profile which will account for physiological parameters of the body during motion activities. Initially, the subject's movements are captured along with the corresponding joint trajectories using a single Kinect sensor. To improve the fidelity of the motion trajectories a series of filters were implemented so as to diminish the effect of noise. Next, a musculoskeletal model of the human lower extremity with twenty degrees of freedom and 86 muscles is introduced and registered to the captured motion data through the inverse kinematics method. For the computational analysis and the estimation of joint torques during movements inverse and forward dynamics are utilized respectively. Finally, static optimization and computed muscle control are employed to estimate the muscle forces and muscle excitations necessary to perform the captured movements. Experimental results demonstrate the potential of the proposed approach, leading to numerous applications in the field of physics-based computer animation and virtual physiological human modeling.
Lecture Notes in Computer Science, 2014
Besides the emergence of many input devices and sensors, they are still unable to provide good and simple recognition of human postures and gestures. The recognition using simple algorithms implemented on top of these devices (like the Kinect) enlarges use cases for these gestures and postures to newer domains and systems. Our methods cuts the needed computation and allow the integration of other algorithms to run in parallel. We present a system able to track the hand in 3D, log its position and surface information during the time, and recognize hand postures and gestures. We present our solution based on simple geometric algorithms, other tried algorithms, and we discuss some concepts raised from our tests.
2012
A method is proposed to validate Microsoft’s Kinect as a device and, hence, to enable low fidelity, unobtrusive, robust sensing of behavior. The Xsens MVN suit is proposed as the measurements’ ground truth. An overarching framework is introduced that facilitates a mapping of both devices upon each other. This framework includes a complete processing pipeline for both the Xsens and the Kinect data, recorded in parallel, which, at the end of both pipelines, are mapped upon reach other. Next, two strategies are presented that aim to interpret the data gathered. We close with a brief discussion on the pros and cons of the protocol proposed.
2009 IEEE International Conference on Control and Automation, 2009
Abstract This survey reviews motion capture technologies and the current challenges associated with their application in robotic systems. Various sensor systems used in current literature are introduced and evaluated based on the relative strengths and weaknesses. Some research problems pursued with these sensors in robotics are reviewed and application areas are discussed. Significant methodologies in analysing the sensor data are discussed and evaluated based on the perceived benefits and limitations. Finally, results ...
Iraqi Journal for Electrical and Electronic Engineering, 2021
In this paper, a new method is proposed for people tracking using the human skeleton provided by the Kinect sensor, Our method is based on skeleton data, which includes the coordinate value of each joint in the human body. For data classification, the Support Vector Machine (SVM) and Random Forest techniques are used. To achieve this goal, 14 classes of movements are defined, using the Kinect Sensor to extract data containing 46 features and then using them to train the classification models. The system was tested on 12 subjects, each of whom performed 14 movements in each experiment. Experiment results show that the best average accuracy is 90.2 % for the SVM model and 99 % for the Random forest model. From the experiments, we concluded that the best distance between the Kinect sensor and the human body is one meter.
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...
Motion capture systems are gaining more and more importance in different fields of research. In the field of biomechanics, marker-based systems have always been used as an accurate and precise method to capture motion. However, attaching markers on the subject is a time-consuming and laborious method. As a consequence, this problem has given rise to a new concept of motion capture based on marker-less systems. By means of these systems, motion can be recorded without attaching any markers to the skin of the subject and capturing colour-depth data of the subject in movement. The current thesis has researched on marker-less motion capture using the Kinect sensor, and has compared the two motion capture systems, marker-based and marker-less, by analysing the results of several captured motions. In this thesis, two takes have been recorded and only motion of the pelvis and lower limb segments have been analysed. The methodology has consisted of capturing the motions using the marker-based and marker-less systems simultaneously and then processing the data by using specific software. At the end, the angles of hip flexion, hip adduction, knee and ankle obtained through the two systems have been compared. In order to obtain the three-dimensional joint angles using the marker-less system, a new software named iPi Soft has been introduced to process the data from the Kinect sensor. Finally, the results of two systems have been compared and thoroughly discussed, so as to assess the accuracy of the Kinect system.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.