scholarly journals Comparison Between Inertial Sensors and Motion Capture System to Quantify Flexion-Extension Motion in the Back of a Horse

2014 ◽  
Vol 46 ◽  
pp. 43-43 ◽  
Author(s):  
P Martin ◽  
H Chateau ◽  
P Pourcelot ◽  
L Duray ◽  
L Cheze
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Motion Capture System ◽  
Flexion Extension

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.

scholarly journals Dual Kinect v2 system can capture lower limb kinematics reasonably well in a clinical setting: concurrent validity of a dual camera markerless motion capture system in professional football players

2018 ◽  
Vol 4 (1) ◽  
pp. e000441 ◽  
Author(s):  
Argyro Kotsifaki ◽  
Rodney Whiteley ◽  
Clint Hansen
Keyword(s):  
Motion Capture ◽  
Excellent Agreement ◽  
Lower Limb ◽  
Motion Capture System ◽  
Kinect V2 ◽  
Markerless Motion Capture ◽  
Flexion Extension ◽  
Limb Kinematics ◽  
Kinematic Evaluation ◽  
Lower Limb Kinematics

ObjectivesTo determine whether a dual-camera markerless motion capture system can be used for lower limb kinematic evaluation in athletes in a preseason screening setting.DesignDescriptive laboratory study.SettingLaboratory setting.ParticipantsThirty-four (n=34) healthy athletes.Main outcome measuresThree dimensional lower limb kinematics during three functional tests: Single Leg Squat (SLS), Single Leg Jump, Modified Counter-movement Jump. The tests were simultaneously recorded using both a marker-based motion capture system and two Kinect v2 cameras using iPi Mocap Studio software.ResultsExcellent agreement between systems for the flexion/extension range of motion of the shin during all tests and for the thigh abduction/adduction during SLS were seen. For peak angles, results showed excellent agreement for knee flexion. Poor correlation was seen for the rotation movements.ConclusionsThis study supports the use of dual Kinect v2 configuration with the iPi software as a valid tool for assessment of sagittal and frontal plane hip and knee kinematic parameters but not axial rotation in athletes.

scholarly journals Comparison of the Performance of the Leap Motion ControllerTM with a Standard Marker-Based Motion Capture System

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1750
Author(s):  
Amartya Ganguly ◽  
Gabriel Rashidi ◽  
Katja Mombaur
Keyword(s):  
Motion Capture ◽  
Gold Standard ◽  
Low Cost ◽  
Ring Finger ◽  
Healthcare Services ◽  
Leap Motion ◽  
Motion Capture System ◽  
Flexion Extension ◽  
Standard Marker ◽  
Motion Capture Systems

Over the last few years, the Leap Motion Controller™ (LMC) has been increasingly used in clinical environments to track hand, wrist and forearm positions as an alternative to the gold-standard motion capture systems. Since the LMC is marker-less, portable, easy-to-use and low-cost, it is rapidly being adopted in healthcare services. This paper demonstrates the comparison of finger kinematic data between the LMC and a gold-standard marker-based motion capture system, Qualisys Track Manager (QTM). Both systems were time synchronised, and the participants performed abduction/adduction of the thumb and flexion/extension movements of all fingers. The LMC and QTM were compared in both static measuring finger segment lengths and dynamic flexion movements of all fingers. A Bland–Altman plot was used to demonstrate the performance of the LMC versus QTM with Pearson’s correlation (r) to demonstrate trends in the data. Only the proximal interphalangeal joint (PIP) joint of the middle and ring finger during flexion/extension demonstrated acceptable agreement (r = 0.9062; r = 0.8978), but with a high mean bias. In conclusion, the study shows that currently, the LMC is not suitable to replace gold-standard motion capture systems in clinical settings. Further studies should be conducted to validate the performance of the LMC as it is updated and upgraded.

scholarly journals Placement Recommendations for Single Kinect-Based Motion Capture System in Unilateral Dynamic Motion Analysis

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1076
Author(s):  
Laisi Cai ◽  
Dongwei Liu ◽  
Ye Ma
Keyword(s):  
Motion Analysis ◽  
Motion Capture ◽  
Upper Limb ◽  
Kinematic Model ◽  
Sensor Placement ◽  
Motion Capture System ◽  
Kinect Sensor ◽  
3D Motion ◽  
Flexion Extension ◽  
3D Motion Capture

Low-cost, portable, and easy-to-use Kinect-based systems achieved great popularity in out-of-the-lab motion analysis. The placement of a Kinect sensor significantly influences the accuracy in measuring kinematic parameters for dynamics tasks. We conducted an experiment to investigate the impact of sensor placement on the accuracy of upper limb kinematics during a typical upper limb functional task, the drinking task. Using a 3D motion capture system as the golden standard, we tested twenty-one Kinect positions with three different distances and seven orientations. Upper limb joint angles, including shoulder flexion/extension, shoulder adduction/abduction, shoulder internal/external rotation, and elbow flexion/extension angles, are calculated via our developed Kinect kinematic model and the UWA kinematic model for both the Kinect-based system and the 3D motion capture system. We extracted the angles at the point of the target achieved (PTA). The mean-absolute-error (MEA) with the standard represents the Kinect-based system’s performance. We conducted a two-way repeated measure ANOVA to explore the impacts of distance and orientation on the MEAs for all upper limb angles. There is a significant main effect for orientation. The main effects for distance and the interaction effects do not reach statistical significance. The post hoc test using LSD test for orientation shows that the effect of orientation is joint-dependent and plane-dependent. For a complex task (e.g., drinking), which involves body occlusions, placing a Kinect sensor right in front of a subject is not a good choice. We suggest that place a Kinect sensor at the contralateral side of a subject with the orientation around 30∘ to 45∘ for upper limb functional tasks. For all kinds of dynamic tasks, we put forward the following recommendations for the placement of a Kinect sensor. First, set an optimal sensor position for capture, making sure that all investigated joints are visible during the whole task. Second, sensor placement should avoid body occlusion at the maximum extension. Third, if an optimal location cannot be achieved in an out-of-the-lab environment, researchers could put the Kinect sensor at an optimal orientation by trading off the factor of distance. Last, for those need to assess functions of both limbs, the users can relocate the sensor and re-evaluate the functions of the other side once they finish evaluating functions of one side of a subject.

An Apparatus and Protocol to Measure Shoulder Girdle Strength

2007 ◽  
Vol 1 (4) ◽  
pp. 246-253
Author(s):  
Brian A. Garner ◽  
Jaeho Shim ◽  
Scott Wilson
Keyword(s):  
Motion Capture ◽  
Isometric Force ◽  
Intraclass Correlation ◽  
Elevation Angle ◽  
Shoulder Girdle ◽  
Total Force ◽  
Motion Capture System ◽  
Clinical Tool ◽  
Horizontal Beam ◽  
Flexion Extension

Muscles actuating the shoulder girdle are important for stabilizing the scapula and coordinating phased kinematics of the shoulder complex. If these muscles become weak or imbalanced, joint instability and injury may result. Reliable measurement of shoulder strength is thus important for prevention, diagnosis, and rehabilitation of shoulder problems. To date, studies quantifying the strength of the shoulder girdle are limited. The purpose of this work was to design and evaluate a custom apparatus and corresponding protocol for measuring maximal, voluntary, isometric strength of the shoulder girdle during various forms of shrugging exercise. A custom apparatus was constructed as a rigid frame with a vertical post supporting a seat, seat back, and horizontal beam. The beam extends laterally on either side beyond and around the shoulders of a seated subject. A pair of arm extension members pivots on the beam about an axis aligned with the shoulder flexion-extension axis. These members can be locked in place at any angle. Between them is mounted a force-sensing grip assembly, which can be adjusted proximally or distally to accommodate varying shoulder girdle positions. Subjects grasp the grip assembly handles with extended elbows and push or pull as forcefully as possible. Nine female and ten male subjects participated in a protocol using the apparatus to measure maximum isometric force generated at three positions each for elevation, depression, protraction, and retraction of the shoulder girdle (3positions×4modes=12tests). A video motion capture system was used to measure shoulder girdle angles. The reliability of the approach was evaluated based on the repeatability of measured shoulder elevation angle, protraction angle, and total force over three days of testing. The apparatus performed well during the tests, providing a stable, rigid, yet adjustable platform for measuring shoulder girdle strength. Repeatability of force measurements was interpreted as very good to excellent, with intraclass correlation coefficient (ICC) (2,1) values ranging from 0.83 to 0.95 for all tests except one (ICC=0.79). Repeatability of angle measurements was interpreted as good to excellent. For tests measuring elevation and depression strength, the ICC of elevation angle ranged from 0.85 to 0.89. For tests measuring protraction and retraction strength, the ICC of protraction angle ranged from 0.68 to 0.88. This type of apparatus could be an effective clinical tool for measuring strength in the shoulder girdle muscles. Use of the video motion capture system is optional.

scholarly journals Accuracy of a Low-Cost 3D-Printed Wearable Goniometer for Measuring Wrist Motion

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4799
Author(s):  
Calvin Young ◽  
Sarah DeDecker ◽  
Drew Anderson ◽  
Michele L. Oliver ◽  
Karen D. Gordon
Keyword(s):  
Motion Capture ◽  
Low Cost ◽  
Wearable Device ◽  
Matching Task ◽  
Motion Capture System ◽  
Wrist Motion ◽  
Flexion Extension ◽  
Optical Motion ◽  
3D Printed ◽  
Optical Motion Capture

Wrist motion provides an important metric for disease monitoring and occupational risk assessment. The collection of wrist kinematics in occupational or other real-world environments could augment traditional observational or video-analysis based assessment. We have developed a low-cost 3D printed wearable device, capable of being produced on consumer grade desktop 3D printers. Here we present a preliminary validation of the device against a gold standard optical motion capture system. Data were collected from 10 participants performing a static angle matching task while seated at a desk. The wearable device output was significantly correlated with the optical motion capture system yielding a coefficient of determination (R2) of 0.991 and 0.972 for flexion/extension (FE) and radial/ulnar deviation (RUD) respectively (p < 0.0001). Error was similarly low with a root mean squared error of 4.9° (FE) and 3.9° (RUD). Agreement between the two systems was quantified using Bland–Altman analysis, with bias and 95% limits of agreement of 3.1° ± 7.4° and −0.16° ± 7.7° for FE and RUD, respectively. These results compare favourably with current methods for occupational assessment, suggesting strong potential for field implementation.

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