Literature DB >> 25766422

The validity of the first and second generation Microsoft Kinect™ for identifying joint center locations during static postures.

Xu Xu1, Raymond W McGorry2.   

Abstract

The Kinect™ sensor released by Microsoft is a low-cost, portable, and marker-less motion tracking system for the video game industry. Since the first generation Kinect sensor was released in 2010, many studies have been conducted to examine the validity of this sensor when used to measure body movement in different research areas. In 2014, Microsoft released the computer-used second generation Kinect sensor with a better resolution for the depth sensor. However, very few studies have performed a direct comparison between all the Kinect sensor-identified joint center locations and their corresponding motion tracking system-identified counterparts, the result of which may provide some insight into the error of the Kinect-identified segment length, joint angles, as well as the feasibility of adapting inverse dynamics to Kinect-identified joint centers. The purpose of the current study is to first propose a method to align the coordinate system of the Kinect sensor with respect to the global coordinate system of a motion tracking system, and then to examine the accuracy of the Kinect sensor-identified coordinates of joint locations during 8 standing and 8 sitting postures of daily activities. The results indicate the proposed alignment method can effectively align the Kinect sensor with respect to the motion tracking system. The accuracy level of the Kinect-identified joint center location is posture-dependent and joint-dependent. For upright standing posture, the average error across all the participants and all Kinect-identified joint centers is 76 mm and 87 mm for the first and second generation Kinect sensor, respectively. In general, standing postures can be identified with better accuracy than sitting postures, and the identification accuracy of the joints of the upper extremities is better than for the lower extremities. This result may provide some information regarding the feasibility of using the Kinect sensor in future studies.
Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Entities:  

Keywords:  Daily activities; Kinect v2; Reference frame alignment

Mesh:

Year:  2015        PMID: 25766422     DOI: 10.1016/j.apergo.2015.01.005

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


  21 in total

1.  Automating the Clinical Assessment of Independent Wheelchair Sitting Pivot Transfer Techniques.

Authors:  Lin Wei; Cheng-Shiu Chung; Alicia M Koontz
Journal:  Top Spinal Cord Inj Rehabil       Date:  2021-08-13

2.  Autonomous modeling of repetitive movement for rehabilitation exercise monitoring.

Authors:  Prayook Jatesiktat; Guan Ming Lim; Christopher Wee Keong Kuah; Dollaporn Anopas; Wei Tech Ang
Journal:  BMC Med Inform Decis Mak       Date:  2022-07-03       Impact factor: 3.298

Review 3.  A review of computational approaches for evaluation of rehabilitation exercises.

Authors:  Yalin Liao; Aleksandar Vakanski; Min Xian; David Paul; Russell Baker
Journal:  Comput Biol Med       Date:  2020-03-04       Impact factor: 4.589

4.  Accuracy and Reliability of the Kinect Version 2 for Clinical Measurement of Motor Function.

Authors:  Karen Otte; Bastian Kayser; Sebastian Mansow-Model; Julius Verrel; Friedemann Paul; Alexander U Brandt; Tanja Schmitz-Hübsch
Journal:  PLoS One       Date:  2016-11-18       Impact factor: 3.240

5.  Evaluation of Kinect 3D Sensor for Healthcare Imaging.

Authors:  Stefanie T L Pöhlmann; Elaine F Harkness; Christopher J Taylor; Susan M Astley
Journal:  J Med Biol Eng       Date:  2016-12-09       Impact factor: 1.553

6.  Assessment of a markerless motion analysis system for manual wheelchair application.

Authors:  Jacob Rammer; Brooke Slavens; Joseph Krzak; Jack Winters; Susan Riedel; Gerald Harris
Journal:  J Neuroeng Rehabil       Date:  2018-11-06       Impact factor: 4.262

7.  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

8.  Statistical Validation for Clinical Measures: Repeatability and Agreement of Kinect™-Based Software.

Authors:  Natalia Lopez; Elisa Perez; Emanuel Tello; Alejandro Rodrigo; Max E Valentinuzzi
Journal:  Biomed Res Int       Date:  2018-03-20       Impact factor: 3.411

9.  Validation of Foot Placement Locations from Ankle Data of a Kinect v2 Sensor.

Authors:  Daphne Geerse; Bert Coolen; Detmar Kolijn; Melvyn Roerdink
Journal:  Sensors (Basel)       Date:  2017-10-10       Impact factor: 3.576

10.  Usability Test of Exercise Games Designed for Rehabilitation of Elderly Patients After Hip Replacement Surgery: Pilot Study.

Authors:  Yun Ling; Louis P Ter Meer; Zerrin Yumak; Remco C Veltkamp
Journal:  JMIR Serious Games       Date:  2017-10-12       Impact factor: 4.143

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.