Literature DB >> 28395854

Using the Microsoft Kinect™ to assess 3-D shoulder kinematics during computer use.

Xu Xu1, Michelle Robertson2, Karen B Chen3, Jia-Hua Lin4, Raymond W McGorry2.   

Abstract

Shoulder joint kinematics has been used as a representative indicator to investigate musculoskeletal symptoms among computer users for office ergonomics studies. The traditional measurement of shoulder kinematics normally requires a laboratory-based motion tracking system which limits the field studies. In the current study, a portable, low cost, and marker-less Microsoft Kinect™ sensor was examined for its feasibility on shoulder kinematics measurement during computer tasks. Eleven healthy participants performed a standardized computer task, and their shoulder kinematics data were measured by a Kinect sensor and a motion tracking system concurrently. The results indicated that placing the Kinect sensor in front of the participants would yielded a more accurate shoulder kinematics measurements then placing the Kinect sensor 15° or 30° to one side. The results also showed that the Kinect sensor had a better estimate on shoulder flexion/extension, compared with shoulder adduction/abduction and shoulder axial rotation. The RMSE of front-placed Kinect sensor on shoulder flexion/extension was less than 10° for both the right and the left shoulder. The measurement error of the front-placed Kinect sensor on the shoulder adduction/abduction was approximately 10° to 15°, and the magnitude of error is proportional to the magnitude of that joint angle. After the calibration, the RMSE on shoulder adduction/abduction were less than 10° based on an independent dataset of 5 additional participants. For shoulder axial rotation, the RMSE of front-placed Kinect sensor ranged between approximately 15° to 30°. The results of the study suggest that the Kinect sensor can provide some insight on shoulder kinematics for improving office ergonomics.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Motion tracking; Office ergonomics; Shoulder biomechanics; Workstation design

Mesh:

Year:  2017        PMID: 28395854     DOI: 10.1016/j.apergo.2017.04.004

Source DB:  PubMed          Journal:  Appl Ergon        ISSN: 0003-6870            Impact factor:   3.661


  10 in total

1.  Validity and Reliability of Upper Limb Functional Assessment Using the Microsoft Kinect V2 Sensor.

Authors:  Laisi Cai; Ye Ma; Shuping Xiong; Yanxin Zhang
Journal:  Appl Bionics Biomech       Date:  2019-02-11       Impact factor: 1.781

2.  Bilateral Tactile Feedback-Enabled Training for Stroke Survivors Using Microsoft KinectTM.

Authors:  Abbas Orand; Eren Erdal Aksoy; Hiroyuki Miyasaka; Carolyn Weeks Levy; Xin Zhang; Carlo Menon
Journal:  Sensors (Basel)       Date:  2019-08-08       Impact factor: 3.576

3.  The Reliability and Validity of Wearable Inertial Sensors Coupled with the Microsoft Kinect to Measure Shoulder Range-of-Motion.

Authors:  Peter Beshara; Judy F Chen; Andrew C Read; Pierre Lagadec; Tian Wang; William Robert Walsh
Journal:  Sensors (Basel)       Date:  2020-12-17       Impact factor: 3.576

4.  Validity and Reliability of Kinect v2 for Quantifying Upper Body Kinematics during Seated Reaching.

Authors:  Germain Faity; Denis Mottet; Jérôme Froger
Journal:  Sensors (Basel)       Date:  2022-04-02       Impact factor: 3.576

5.  A Simple Method to Optimally Select Upper-Limb Joint Angle Trajectories from Two Kinect Sensors during the Twisting Task for Posture Analysis.

Authors:  Pin-Ling Liu; Chien-Chi Chang; Li Li; Xu Xu
Journal:  Sensors (Basel)       Date:  2022-10-09       Impact factor: 3.847

6.  Simple benchmarking method for determining the accuracy of depth cameras in body landmark location estimation: Static upright posture as a measurement example.

Authors:  Pin-Ling Liu; Chien-Chi Chang; Jia-Hua Lin; Yoshiyuki Kobayashi
Journal:  PLoS One       Date:  2021-07-21       Impact factor: 3.240

7.  3D Analysis of Upper Limbs Motion during Rehabilitation Exercises Using the KinectTM Sensor: Development, Laboratory Validation and Clinical Application.

Authors:  Bruno Bonnechère; Victor Sholukha; Lubos Omelina; Serge Van Sint Jan; Bart Jansen
Journal:  Sensors (Basel)       Date:  2018-07-10       Impact factor: 3.576

8.  A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-cost Motion Capture Technique.

Authors:  Jianwei Niu; Xiai Wang; Dan Wang; Linghua Ran
Journal:  Sensors (Basel)       Date:  2020-02-18       Impact factor: 3.576

9.  An Evaluation of Posture Recognition Based on Intelligent Rapid Entire Body Assessment System for Determining Musculoskeletal Disorders.

Authors:  Ze Li; Ruiqiu Zhang; Ching-Hung Lee; Yu-Chi Lee
Journal:  Sensors (Basel)       Date:  2020-08-07       Impact factor: 3.576

10.  Accuracy Assessment of Joint Angles Estimated from 2D and 3D Camera Measurements.

Authors:  Izaak Van Crombrugge; Seppe Sels; Bart Ribbens; Gunther Steenackers; Rudi Penne; Steve Vanlanduit
Journal:  Sensors (Basel)       Date:  2022-02-23       Impact factor: 3.576

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.