Comparison of Joint Angle Measurements from Three Types of Motion Capture Systems for Ergonomic Postural Assessment

2020 ◽  
pp. 3-11
Author(s):  
Woojoo Kim ◽  
Chunxi Huang ◽  
Donghyeok Yun ◽  
Daniel Saakes ◽  
Shuping Xiong
Keyword(s):  
Motion Capture ◽  
Joint Angle ◽  
Angle Measurements ◽  
Postural Assessment ◽  
Motion Capture Systems

scholarly journals Validation of a Commercially Available Markerless Motion-Capture System for Trunk and Lower Extremity Kinematics During a Jump-Landing Assessment

2021 ◽  
Vol 56 (2) ◽  
pp. 177-190
Author(s):  
Timothy C. Mauntel ◽  
Kenneth L. Cameron ◽  
Brian Pietrosimone ◽  
Stephen W. Marshall ◽  
Anthony C. Hackney ◽  
...  
Keyword(s):  
Lower Extremity ◽  
Motion Capture ◽  
Joint Angle ◽  
Sagittal Plane ◽  
Frontal Plane ◽  
Lower Extremity Injury ◽  
Joint Angles ◽  
Jump Landing ◽  
Markerless Motion Capture ◽  
Motion Capture Systems

Context Field-based, portable motion-capture systems can be used to help identify individuals at greater risk of lower extremity injury. Microsoft Kinect-based markerless motion-capture systems meet these requirements; however, until recently, these systems were generally not automated, required substantial data postprocessing, and were not commercially available. Objective To validate the kinematic measures of a commercially available markerless motion-capture system. Design Descriptive laboratory study. Setting Laboratory. Patients or Other Participants A total of 20 healthy, physically active university students (10 males, 10 females; age = 20.50 ± 2.78 years, height = 170.36 ± 9.82 cm, mass = 68.38 ± 10.07 kg, body mass index = 23.50 ± 2.40 kg/m2). Intervention(s) Participants completed 5 jump-landing trials. Kinematic data were simultaneously recorded using Kinect-based markerless and stereophotogrammetric motion-capture systems. Main Outcome Measure(s) Sagittal- and frontal-plane trunk, hip-joint, and knee-joint angles were identified at initial ground contact of the jump landing (IC), for the maximum joint angle during the landing phase of the initial landing (MAX), and for the joint-angle displacement from IC to MAX (DSP). Outliers were removed, and data were averaged across trials. We used intraclass correlation coefficients (ICCs [2,1]) to assess intersystem reliability and the paired-samples t test to examine mean differences (α ≤ .05). Results Agreement existed between the systems (ICC range = −1.52 to 0.96; ICC average = 0.58), with 75.00% (n = 24/32) of the measures being validated (P ≤ .05). Agreement was better for sagittal- (ICC average = 0.84) than frontal- (ICC average = 0.35) plane measures. Agreement was best for MAX (ICC average = 0.77) compared with IC (ICC average = 0.56) and DSP (ICC average = 0.41) measures. Pairwise comparisons identified differences for 18.75% (6/32) of the measures. Fewer differences were observed for sagittal- (0.00%; 0/15) than for frontal- (35.29%; 6/17) plane measures. Between-systems differences were equivalent for MAX (18.18%; 2/11), DSP (18.18%; 2/11), and IC (20.00%; 2/10) measures. The markerless system underestimated sagittal-plane measures (86.67%; 13/15) and overestimated frontal-plane measures (76.47%; 13/17). No trends were observed for overestimating or underestimating IC, MAX, or DSP measures. Conclusions Moderate agreement existed between markerless and stereophotogrammetric motion-capture systems. Better agreement existed for larger (eg, sagittal-plane, MAX) than for smaller (eg, frontal-plane, IC) joint angles. The DSP angles had the worst agreement. Markerless motion-capture systems may help clinicians identify individuals at greater risk of lower extremity injury.

Extrinsic Calibration of Camera and Motion Capture Systems

2021 ◽  
Author(s):  
Prashant Ganesh ◽  
Kyle Volle ◽  
Paul Buzaud ◽  
Kevin Brink ◽  
Andrew Willis
Keyword(s):  
Motion Capture ◽  
Extrinsic Calibration ◽  
Motion Capture Systems

Validation of wearable inertial sensor-based gait analysis system for measurement of spatiotemporal parameters and lower extremity joint kinematics in sagittal plane

2022 ◽  
pp. 095441192110729
Author(s):  
Gunjan Patel ◽  
Rajani Mullerpatan ◽  
Bela Agarwal ◽  
Triveni Shetty ◽  
Rajdeep Ojha ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Motion Analysis ◽  
Motion Capture ◽  
Inertial Sensor ◽  
Joint Kinematics ◽  
Percentage Error ◽  
Motion Capture System ◽  
Spatiotemporal Parameters ◽  
Analysis System ◽  
Motion Capture Systems

Wearable inertial sensor-based motion analysis systems are promising alternatives to standard camera-based motion capture systems for the measurement of gait parameters and joint kinematics. These wearable sensors, unlike camera-based gold standard systems, find usefulness in outdoor natural environment along with confined indoor laboratory-based environment due to miniature size and wireless data transmission. This study reports validation of our developed (i-Sens) wearable motion analysis system against standard motion capture system. Gait analysis was performed at self-selected speed on non-disabled volunteers in indoor ( n = 15) and outdoor ( n = 8) environments. Two i-Sens units were placed at the level of knee and hip along with passive markers (for indoor study only) for simultaneous 3D motion capture using a motion capture system. Mean absolute percentage error (MAPE) was computed for spatiotemporal parameters from the i-Sens system versus the motion capture system as a true reference. Mean and standard deviation of kinematic data for a gait cycle were plotted for both systems against normative data. Joint kinematics data were analyzed to compute the root mean squared error (RMSE) and Pearson’s correlation coefficient. Kinematic plots indicate a high degree of accuracy of the i-Sens system with the reference system. Excellent positive correlation was observed between the two systems in terms of hip and knee joint angles (Indoor: hip 3.98° ± 1.03°, knee 6.48° ± 1.91°, Outdoor: hip 3.94° ± 0.78°, knee 5.82° ± 0.99°) with low RMSE. Reliability characteristics (defined using standard statistical thresholds of MAPE) of stride length, cadence, walking speed in both outdoor and indoor environment were well within the “Good” category. The i-Sens system has emerged as a potentially cost-effective, valid, accurate, and reliable alternative to expensive, standard motion capture systems for gait analysis. Further clinical trials using the i-Sens system are warranted on participants across different age groups.

scholarly journals Use of computer innovative technologies in improving the dartsmen training

2020 ◽  
Vol 26 ◽  
pp. 00061
Author(s):  
Elina Makarova ◽  
Vladislav Dubatovkin ◽  
Nataliya Berezinskaya ◽  
Lyudmila Barkhatova ◽  
Elena Oleynik
Keyword(s):  
Motion Capture ◽  
Body Parts ◽  
Visual Material ◽  
Video Cameras ◽  
Dart Throwing ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Effective Use ◽  
Near Future ◽  
Motion Capture Systems

The research is focused on studying the possibility of effective use of the dart grip system, the work of the athlete’s hand, to prepare the dartsman for competitions using the MOSAR complex. The experiment uses optical motion capture systems, a set of video cameras, led parameter sensors, and devices that allow to record the movement of body parts and a dart. This method of training and controlling dart throwing can serve as educational and visual material for training future athletes. The use of such motion capture systems in the near future may become one of the main aspects of training, both beginners and professionals, in many sports.

A Mobile Motion Capture System Based on Inertial Sensors and Smart Shoes

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Pyeong-Gook Jung ◽  
Sehoon Oh ◽  
Gukchan Lim ◽  
Kyoungchul Kong
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Kinematic Model ◽  
Three Dimensional ◽  
Health Condition ◽  
Body Motion ◽  
Whole Body ◽  
Motion Capture System ◽  
Outdoor Sports ◽  
Motion Capture Systems

Motion capture systems play an important role in health-care and sport-training systems. In particular, there exists a great demand on a mobile motion capture system that enables people to monitor their health condition and to practice sport postures anywhere at any time. The motion capture systems with infrared or vision cameras, however, require a special setting, which hinders their application to a mobile system. In this paper, a mobile three-dimensional motion capture system is developed based on inertial sensors and smart shoes. Sensor signals are measured and processed by a mobile computer; thus, the proposed system enables the analysis and diagnosis of postures during outdoor sports, as well as indoor activities. The measured signals are transformed into quaternion to avoid the Gimbal lock effect. In order to improve the precision of the proposed motion capture system in an open and outdoor space, a frequency-adaptive sensor fusion method and a kinematic model are utilized to construct the whole body motion in real-time. The reference point is continuously updated by smart shoes that measure the ground reaction forces.

Motion Capture Systems for Jump Analysis

2015 ◽  
pp. 111-124 ◽  
Author(s):  
Sendoa Rojas-Lertxundi ◽  
J. Ramón Fernández-López ◽  
Sergio Huerta ◽  
Pablo Garía Bringas
Keyword(s):  
Motion Capture ◽  
Motion Capture Systems
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