Validity of Linear Position Transducers Versus the Optotrak 3D Motion Capture System

2017 ◽  
Vol 49 (5S) ◽  
pp. 757
Author(s):  
Jessica L. Halle ◽  
Jacob A. Goldsmith ◽  
Cameron Trepeck ◽  
Ryan K. Byrnes ◽  
Daniel M. Cooke ◽  
...  
Keyword(s):  
Motion Capture ◽  
Motion Capture System ◽  
3D Motion ◽  
3D Motion Capture

Gender differences in the Achilles tendon load during the fencing lunge

2014 ◽  
Vol 6 (3) ◽  
Author(s):  
Jonathan Kenneth Sinclair ◽  
Lindsay Bottoms
Keyword(s):  
Gender Differences ◽  
Achilles Tendon ◽  
Motion Capture ◽  
Tendon Injury ◽  
Force Platform ◽  
Motion Capture System ◽  
3D Motion ◽  
Loading Rates ◽  
Tendon Pathology ◽  
3D Motion Capture

AbstractRecent epidemiological analyses in fencing have shown that injuries and pain linked specifically to fencing training/competition were evident in 92.8% of fencers. Specifically the prevalence of Achilles tendon pathology has increased substantially in recent years, and males have been identified as being at greater risk of Achilles tendon injury compared to their female counterparts. This study aimed to examine gender differences in Achilles tendon loading during the fencing lunge.Achilles tendon load was obtained from eight male and eight female club level epee fencers using a 3D motion capture system and force platform information as they completed simulated lunges. Independent t-tests were performed on the data to determine whether differences existed.The results show that males were associated with significantly greater Achilles tendon loading rates in comparison to females.This suggests that male fencers may be at greater risk from Achilles tendon pathology as a function of fencing training/ competition.

scholarly journals Exploring the effects of deep brain stimulation and vision on tremor in Parkinson’s disease - benefits from objective methods

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Per-Anders Fransson ◽  
Maria H. Nilsson ◽  
Diederick C. Niehorster ◽  
Marcus Nyström ◽  
Stig Rehncrona ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Motion Capture ◽  
Brain Stimulation ◽  
The Body ◽  
Motion Capture System ◽  
3D Motion ◽  
3D Motion Capture ◽  
Deep Brain

Abstract Background Tremor is a cardinal symptom of Parkinson’s disease (PD) that may cause severe disability. As such, objective methods to determine the exact characteristics of the tremor may improve the evaluation of therapy. This methodology study aims to validate the utility of two objective technical methods of recording Parkinsonian tremor and evaluate their ability to determine the effects of Deep Brain Stimulation (DBS) of the subthalamic nucleus and of vision. Methods We studied 10 patients with idiopathic PD, who were responsive to L-Dopa and had more than 1 year use of bilateral subthalamic nucleus stimulation. The patients did not have to display visible tremor to be included in the study. Tremor was recorded with two objective methods, a force platform and a 3 dimensional (3D) motion capture system that tracked movements in four key proximal sections of the body (knee, hip, shoulder and head). They were assessed after an overnight withdrawal of anti-PD medications with DBS ON and OFF and with eyes open and closed during unperturbed and perturbed stance with randomized calf vibration, using a randomized test order design. Results Tremor was detected with the Unified Parkinson’s Disease Rating Scale (UPDRS) in 6 of 10 patients but only distally (hands and feet) with DBS OFF. With the force platform and the 3D motion capture system, tremor was detected in 6 of 10 and 7 of 10 patients respectively, mostly in DBS OFF but also with DBS ON in some patients. The 3D motion capture system revealed that more than one body section was usually affected by tremor and that the tremor amplitude was non-uniform, but the frequency almost identical, across sites. DBS reduced tremor amplitude non-uniformly across the body. Visual input mostly reduced tremor amplitude with DBS ON. Conclusions Technical recording methods offer objective and sensitive detection of tremor that provide detailed characteristics such as peak amplitude, frequency and distribution pattern, and thus, provide information that can guide the optimization of treatments. Both methods detected the effects of DBS and visual input but the 3D motion system was more versatile in that it could detail the presence and properties of tremor at individual body sections.

scholarly journals High Speed 3D Motion Capture System for Flying Golf Ball

2011 ◽  
Vol 19 ◽  
pp. 214-219 ◽  
Author(s):  
Yi-Hong Lin ◽  
Wen-Hong Wu ◽  
Wei-Zhe Huang
Keyword(s):  
Motion Capture ◽  
High Speed ◽  
Golf Ball ◽  
Motion Capture System ◽  
3D Motion ◽  
3D Motion Capture

Evaluation of the Reproducibility of Nonverbal Facial Expressions Using a 3D Motion Capture System

2016 ◽  
Vol 53 (1) ◽  
pp. 22-29 ◽  
Author(s):  
Xiangyang Ju ◽  
Emer O'leary ◽  
Matthew Peng ◽  
Thamer Al-Anezi ◽  
Ashraf Ayoub ◽  
...  
Keyword(s):  
Facial Expressions ◽  
Motion Capture ◽  
Motion Capture System ◽  
3D Motion ◽  
3D Motion Capture

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.

scholarly journals Validity of the Center of Mass Assessed by an Accelerometer: Comparison with a 3D Motion Capture System

2016 ◽  
Vol 31 (4) ◽  
pp. 591-596 ◽  
Author(s):  
Yu HORIMIZU ◽  
Minoru KIMOTO ◽  
Yoshino TERUI ◽  
Akiho TAKAHASHI ◽  
Tomoko FUKUI ◽  
...  
Keyword(s):  
Motion Capture ◽  
Center Of Mass ◽  
Motion Capture System ◽  
3D Motion ◽  
3D Motion Capture

scholarly journals A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3287 ◽  
Author(s):  
Satyabrata Aich ◽  
Pyari Pradhan ◽  
Jinse Park ◽  
Nitin Sethi ◽  
Vemula Vathsa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Motion Capture ◽  
Error Rate ◽  
Freezing Of Gait ◽  
Motion Capture System ◽  
3D Motion ◽  
Mean Error ◽  
Gait Parameters ◽  
Fog Prediction ◽  
3D Motion Capture

One of the most common symptoms observed among most of the Parkinson’s disease patients that affects movement pattern and is also related to the risk of fall, is usually termed as “freezing of gait (FoG)”. To allow systematic assessment of FoG, objective quantification of gait parameters and automatic detection of FoG are needed. This will help in personalizing the treatment. In this paper, the objectives of the study are (1) quantification of gait parameters in an objective manner by using the data collected from wearable accelerometers; (2) comparison of five estimated gait parameters from the proposed algorithm with their counterparts obtained from the 3D motion capture system in terms of mean error rate and Pearson’s correlation coefficient (PCC); (3) automatic discrimination of FoG patients from no FoG patients using machine learning techniques. It was found that the five gait parameters have a high level of agreement with PCC ranging from 0.961 to 0.984. The mean error rate between the estimated gait parameters from accelerometer-based approach and 3D motion capture system was found to be less than 10%. The performances of the classifiers are compared on the basis of accuracy. The best result was accomplished with the SVM classifier with an accuracy of approximately 88%. The proposed approach shows enough evidence that makes it applicable in a real-life scenario where the wearable accelerometer-based system would be recommended to assess and monitor the FoG.

scholarly journals 3D motion capture system for assessing patient motion during Fugl‐Meyer stroke rehabilitation testing

2018 ◽  
Vol 12 (7) ◽  
pp. 963-975 ◽  
Author(s):  
Nadav Eichler ◽  
Hagit Hel‐Or ◽  
Ilan Shimshoni ◽  
Dorit Itah ◽  
Bella Gross ◽  
...  
Keyword(s):  
Motion Capture ◽  
Stroke Rehabilitation ◽  
Motion Capture System ◽  
3D Motion ◽  
Patient Motion ◽  
3D Motion Capture
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