EVALUATION OF SILHOUETTE-BASED MARKERLESS TRACKING FOR KINEMATICS IN SPORT

The purpose of this study was to evaluate markerless, silhouette-based tracking for different applications in sports science. Data of segment center of gravity locations, joint center locations as well as joint angles were taken into account. To quantify the accuracy of silhouette-based in comparison to marker-based tracking, all mentioned parameters were compared with the correlation coefficient and standard deviation of the differences for three classes of movements: specific joint movements, complex movements, and highly dynamic movements (with racquet). Very strong correlations result for the segment center of gravity locations, the joint center locations as well as for joint angles in the sagittal plane except the elbow joints. Joint angle accuracy impairs in the transversal and the frontal plane with an increasing complexity and speed in the movement patterns. To obtain accurate joint angles separated into the three body planes, however, we recommend to enhance the tracking of segment rotations should be stabilized by additional information (e.g. marker or IMU)

CONCURRENT VALIDITY OF LOWER LIMB KINEMATICS BETWEEN MARKERLESS AND MARKER-BASED MOTION CAPTURE SYSTEMS IN GAIT AND RUNNING.

The goal of this study was to evaluate the accuracy of a markerless silhouette-based tracking and hybrid tracking against traditional marker tracking. Different speeds in gait and running conditions were analysed. In the literature, studies most often make use of low cost rather than high performance systems. Markerless systems allow us to evaluate in the most natural conditions. Very high correlations were obtained depending on the joint. The use of markerless tracking is still new regarding motion analysis in sports or for clinical purposes. This technology could be a very good solution for clinical rehabilitation and real sports situations

Markerless measurement techniques for motion analysis in sports science

Markerless motion capture system and X-ray fluoroscopy as two markerless measurement systems were introduced to the application method in sports biomechanical areas. An overview of the technological process, data accuracy, suggested movements, and recommended body parts were explained. The markerless motion capture system consists of four parts: camera, body model, image feature, and algorithms. Even though the markerless motion capture system seems promising, it is not yet known whether these systems can be used to achieve the required accuracy and whether they can be appropriately used in sports biomechanics and clinical research. The biplane fluoroscopy technique analyzes motion data by collecting, image calibrating, and processing, which is effective for determining small joint kinematic changes and calculating joint angles. The method was used to measure walking and jumping movements primarily because of the experimental conditions and mainly to detect the data of lower limb joints

Accuracy of Monocular Two-Dimensional Pose Estimation Compared With a Reference Standard for Kinematic Multiview Analysis: Validation Study

Background: Expensive optoelectronic systems, considered the gold standard, require a laboratory environment and the attachment of markers, and they are therefore rarely used in everyday clinical practice. Two-dimensional (2D) human pose estimations for clinical purposes allow kinematic analyses to be carried out via a camera-based smartphone app. Since clinical specialists highly depend on the validity of information, there is a need to evaluate the accuracy of 2D pose estimation apps. Objective: The aim of the study was to investigate the accuracy of the 2D pose estimation of a mobility analysis app (Lindera-v2), using the PanopticStudio Toolbox data set as a reference standard. The study aimed to assess the differences in joint angles obtained by 2D video information generated with the Lindera-v2 algorithm and the reference standard. The results can provide an important assessment of the adequacy of the app for clinical use. Methods: To evaluate the accuracy of the Lindera-v2 algorithm, 10 video sequences were analyzed. Accuracy was evaluated by assessing a total of 30,000 data pairs for each joint (10 joints in total), comparing the angle data obtained from the Lindera-v2 algorithm with those of the reference standard. The mean differences of the angles were calculated for each joint, and a comparison was made between the estimated values and the reference standard values. Furthermore, the mean absolute error (MAE), root mean square error, and symmetric mean absolute percentage error of the 2D angles were calculated. Agreement between the 2 measurement methods was calculated using the intraclass correlation coefficient (ICC[A,2]). A cross-correlation was calculated for the time series to verify whether there was a temporal shift in the data. Results: The mean difference of the Lindera-v2 data in the right hip was the closest to the reference standard, with a mean value difference of –0.05° (SD 6.06°). The greatest difference in comparison with the baseline was found in the neck, with a measurement of –3.07° (SD 6.43°). The MAE of the angle measurement closest to the baseline was observed in the pelvis (1.40°, SD 1.48°). In contrast, the largest MAE was observed in the right shoulder (6.48°, SD 8.43°). The medians of all acquired joints ranged in difference from 0.19° to 3.17° compared with the reference standard. The ICC values ranged from 0.951 (95% CI 0.914-0.969) in the neck to 0.997 (95% CI 0.997-0.997) in the left elbow joint. The cross-correlation showed that the Lindera-v2 algorithm had no temporal lag. Conclusions: The results of the study indicate that a 2D pose estimation by means of a smartphone app can have excellent agreement compared with a validated reference standard. An assessment of kinematic variables can be performed with the analyzed algorithm, showing only minimal deviations compared with data from a massive multiview system

Effects of IMU Sensor Location and Number on the Validity of Vertical Acceleration Time-Series Data in Countermovement Jumping

Many devices are available for measuring the height of a CMJ. An inertial measurement unit (IMU) measures linear acceleration, orientation, and angular velocity. As an alternative to using IMU estimates of flight time, CMJ height could be estimated by integrating the IMU time-series signal for vertical acceleration to derive CMJ take-off velocity in order to track whole-body center of mass (WBCoM) movement, yet this approach would require valid IMU acceleration data. Thus, the purpose of this study was to quantify the effects of IMU sensor location and number on the validity of vertical acceleration estimation in CMJ. Thirty young adults from a university setting completed this study. Seven IMUs were placed at the approximate center of mass of the trunk, thighs, shanks, and feet. A total of 15 WBCoM models were created from the 7 IMUs. Using the four segments of the lower body, 1-,2-,3-, and 4-segment IMU models were constructed. Root mean square error (RMSE) was estimated between the acceleration derived from each IMU model against acceleration derived from a force platform. RMSE values from the best performing 1-,2-,3-, and 4-segment IMU models were analyzed for main effects using a 1-way analysis of variance. Notably, all of the best performing models contained IMU acceleration data from the trunk. The best performing 2- and 3-segment IMU models returned significantly lower RMSE values, on average, than the 4- segment model (p = 0.041, p = 0.021, p = 0.061). The average RMSE of the best performing 2- and 3-segment models produced an error of 20% relative to gravitational acceleration, with this error likely to be lower when viewed within the context of specific CMJ events and peak forces. Further investigation into improving IMU technology, procedures, and data processing are needed to reduce RMSE errors to a more acceptable level of validity relative to force platform dynamometry

Single view silhouette fitting techniques for estimating tennis racket position

Stereo camera systems have been used to track markers attached to a racket, allowing its position to be obtained in three-dimensional (3D) space. Typically, markers are manually selected on the image plane, but this can be time-consuming. A markerless system based on one stationary camera estimating 3D racket position data is desirable for research and play. The markerless method presented in this paper relies on a set of racket silhouette views in a common reference frame captured with a calibrated camera and a silhouette of a racket captured with a camera whose relative pose is outside the common reference frame. The aim of this paper is to provide validation of these single view fitting techniques to estimate the pose of a tennis racket. This includes the development of a calibration method to provide the relative pose of a stationary camera with respect to a racket. Mean static racket position was reconstructed to within ±2 mm. Computer generated camera poses and silhouette views of a full size racket model were used to demonstrate the potential of the method to estimate 3D racket position during a simplified serve scenario. From a camera distance of 14 m, 3D racket position was estimated providing a spatial accuracy of 1.9 ± 0.14 mm, similar to recent 3D video marker tracking studies of tennis

Probabilistic Deformable Surface Tracking From Multiple Videos

International audienceIn this paper, we address the problem of tracking the temporal evolution of arbitrary shapes observed in multi-camera setups. This is motivated by the ever growing number of applications that require consistent shape information along temporal sequences. The approach we propose considers a temporal sequence of independently reconstructed surfaces and iteratively deforms a reference mesh to fit these observations. To effectively cope with outlying and missing geometry, we introduce a novel probabilistic mesh deformation framework. Using generic local rigidity priors and accounting for the uncertainty in the data acquisition process, this framework effectively handles missing data, relatively large reconstruction artefacts and multiple objects. Extensive experiments demonstrate the effectiveness and robustness of the method on various 4D datasets