Literature DB >> 30097314

Validity of the Microsoft Kinect in assessing spatiotemporal and lower extremity kinematics during stair ascent and descent in healthy young individuals.

Jeonghoon Oh1, Christopher Kuenze2, Marco Jacopetti3, Joseph F Signorile4, Moataz Eltoukhy1.   

Abstract

Stair negotiation is one of the most challenging, yet frequently encountered, locomotor tasks in daily life. This study is the first attempt to investigate the capacity of the Kinect™ sensor to assess stair negotiation spatiotemporal and sagittal plane kinematic variables. The goal of this study was to examine the validity of the Kinect™ v2 sensor in assessing lower extremity kinematics and spatiotemporal parameters in healthy young individuals; and to demonstrate its potential as a low-cost stair gait analysis tool. Twelve healthy participants ascended and descended a 3-step custom-built staircase at their preferred speed, as spatiotemporal parameters and kinematics were extracted simultaneously using the Kinect™ and a three-dimensional motion analysis. Spatiotemporal measures included gait speed, swing phase time, and double stance time. Kinematic outcomes included hip, knee, and ankle joint angles in the sagittal plane. Consistency (ICC2,1) and absolute agreement (ICC3,1) between the two systems were assessed using separate interclass correlations coefficients. In addition, ensemble curves and associated 90% confidence intervals (CI90) were generated for the hip, knee, and ankle kinematics to enable between system comparisons throughout the gait cycle. Results showed that the Kinect™ has the potential to be an effective clinical assessment device for sagittal plane hip and knee joint kinematics and for some spatiotemporal parameters during the stair gait negotiation.
Copyright © 2018 IPEM. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Gait analysis; Kinect; Motion capture; Stair negotiation

Mesh:

Year:  2018        PMID: 30097314     DOI: 10.1016/j.medengphy.2018.07.011

Source DB:  PubMed          Journal:  Med Eng Phys        ISSN: 1350-4533            Impact factor:   2.242


  4 in total

1.  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

2.  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

3.  Automatic Ankle Angle Detection by Integrated RGB and Depth Camera System.

Authors:  Guillermo Díaz-San Martín; Luis Reyes-González; Sergio Sainz-Ruiz; Luis Rodríguez-Cobo; José M López-Higuera
Journal:  Sensors (Basel)       Date:  2021-03-09       Impact factor: 3.576

4.  Kinect V2-Based Gait Analysis for Children with Cerebral Palsy: Validity and Reliability of Spatial Margin of Stability and Spatiotemporal Variables.

Authors:  Yunru Ma; Kumar Mithraratne; Nichola Wilson; Yanxin Zhang; Xiangbin Wang
Journal:  Sensors (Basel)       Date:  2021-03-17       Impact factor: 3.576

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.