Acquiring Kinematics of Lower extremity with Kinect (original) (raw)
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Biomechanical application: exploitation of kinect sensor for gait analysis
2017
Human gait recognition is an important indicator and are extensively studied research area especially with the aging population and rehabilitation applications. Application of gait analysis ranges from diagnosis, monitoring and early detection of potential hazards such as human fall. There are various types of approaches used in gait analysis including wearable, ambient and vision based devices. Microsoft Kinect sensor is well-known among researchers since it can give depth and normal colour images as well. This paper presents a preliminary study on gait analysis of lower body parts. The measurement taken includes step width, step lengths, stride lengths and angles of knee respective hip and ankle while walking. The results showed that the algorithms implemented were able to accurately measure the lengths with low error rate.
The MS Kinect Use for 3D Modelling and Gait Analysis in the MATLAB Environment
2013
The three-dimensional modelling presents a very important tool for analysis of the movement both in medicine and engineering. While video sequences acquired by a complex camera system enable a very precise data analysis it is possible to use much more simple technical devices to analyse video frames with the sufficient accuracy. The use of MS Kinect allows a simple 3-D modelling using image and depth sensors for data acquisition with the precision of 4-40 mm depending upon the distance from the sensor. The matrix of 640 x 480 pixels can be then used for the spatial modelling of a moving body. The goal of the paper is to present (i) the use of MS Kinect for data acquisition, (ii) data processing in the MATLAB environment, and (iii) application of the 3D skeleton model in biomedicine.
Use of the image and depth sensors of the Microsoft Kinect for the detection of gait disorders
Neural Computing and Applications, 2015
This paper presents a novel method of gait recognition that uses the image and depth sensors of the Microsoft (MS) Kinect to track the skeleton of a moving body and allows for simple human-machine interaction. While video sequences acquired by complex camera systems enable very precise data analyses and motion detection, much simpler technical devices can be used to analyze video frames with sufficient accuracy in many cases. The experimental part of this paper is devoted to gait data acquisition from 18 individuals with Parkinson's disease and 18 healthy age-matched controls via the proposed MS Kinect graphical user interface. The methods designed for video frame data processing include the selection of gait segments and data filtering for the estimation of chosen gait characteristics. The proposed computational algorithms for the processing of the matrices acquired by the image and depth sensors were then used for spatial modeling of the moving bodies and the estimation of selected gait features. Normalized mean stride lengths were evaluated for the individuals with Parkinson's disease and those in the control group and were determined to be 0.38 and 0.53 m, respectively. These mean stride lengths were then used as features for classification. The achieved accuracy was[90 %, which suggests the potential of the use of the image and depth sensors of the MS Kinect for these applications. Further potential increases in classification accuracy via additional biosensors and body features are also discussed.
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...
Gait Change Detection Using Parameters Generated from Microsoft Kinect Coordinates
arXiv: Computation, 2019
This paper describes a method to convert Microsoft Kinect coordinates into gait parameters in order to detect a person's gait change. The proposed method can help quantify the progress of physical therapy. Microsoft Kinect, a popular platform for video games, was used to generate 25 joints to form a human skeleton, and then the proposed method converted the coordinates of selected Kinect joints into gait parameters such as spine tilt, hip tilt, and shoulder tilt, which were tracked over time. Sample entropy measure was then applied to quantify the variability of each gait parameter. Male and female subjects walked a three-meter path multiple times in initial experiments, and their walking patterns were recorded via the proposed Kinect device through the frontal plane. Time series of the gait parameters were generated for subjects with and without knee braces. Sample entropy was used to transform these time series into numerical values for comparison of these two conditions.
Feasibility Study of Markerless Gait Tracking Using Kinect
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...
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.
Comparative abilities of Microsoft Kinect and Vicon 3D motion capture for gait analysis '14
Biomechanical analysis is a powerful tool in the evaluation of movement dysfunction in orthopaedic and neurologic populations. Three-dimensional (3D) motion capture systems are widely used, accurate systems, but are costly and not available in many clinical settings. The Microsoft Kinect! has the potential to be used as an alternative low-cost motion analysis tool. The purpose of this study was to assess concurrent validity of the Kinect! with Brekel Kinect software in comparison to Vicon Nexus during sagittal plane gait kinematics. Twenty healthy adults (nine male, 11 female) were tracked while walking and jogging at three velocities on a treadmill. Concurrent hip and knee peak flexion and extension and stride timing measurements were compared between Vicon and Kinect!. Although Kinect measurements were representative of normal gait, the Kinect! generally under-estimated joint flexion and over-estimated extension. Kinect! and Vicon hip angular displacement correlation was very low and error was large. Kinect! knee measurements were somewhat better than hip, but were not consistent enough for clinical assessment. Correlation between Kinect! and Vicon stride timing was high and error was fairly small. Variability in Kinect! measurements was smallest at the slowest velocity. The Kinect! has basic motion capture capabilities and with some minor adjustments will be an acceptable tool to measure stride timing, but sophisticated advances in software and hardware are necessary to improve Kinect! sensitivity before it can be implemented for clinical use.
Sensors
Several studies have examined the accuracy of the Kinect V2 sensor during gait analysis. Usually the data retrieved by the Kinect V2 sensor are compared with the ground truth of certified systems using a Euclidean comparison. Due to the Kinect V2 sensor latency, the application of a uniform temporal alignment is not adequate to compare the signals. On that basis, the purpose of this study was to explore the abilities of the dynamic time warping (DTW) algorithm to compensate for sensor latency (3 samples or 90 ms) and develop a proper accuracy estimation. During the experimental stage, six iterations were performed using the a dual Kinect V2 system. The walking tests were developed at a self-selected speed. The sensor accuracy for Euclidean matching was consistent with that reported in previous studies. After latency compensation, the sensor accuracy demonstrated considerably lower error rates for all joints. This demonstrated that the accuracy was underestimated due to the use of in...