Literature DB >> 24122572

Validation of a method for real time foot position and orientation tracking with Microsoft Kinect technology for use in virtual reality and treadmill based gait training programs.

Gabriele Paolini, Agnese Peruzzi, Anat Mirelman, Andrea Cereatti, Stephen Gaukrodger, Jeffrey M Hausdorff, Ugo Della Croce.   

Abstract

The use of virtual reality for the provision of motor-cognitive gait training has been shown to be effective for a variety of patient populations. The interaction between the user and the virtual environment is achieved by tracking the motion of the body parts and replicating it in the virtual environment in real time. In this paper, we present the validation of a novel method for tracking foot position and orientation in real time, based on the Microsoft Kinect technology, to be used for gait training combined with virtual reality. The validation of the motion tracking method was performed by comparing the tracking performance of the new system against a stereo-photogrammetric system used as gold standard. Foot position errors were in the order of a few millimeters (average RMSD from 4.9 to 12.1 mm in the medio-lateral and vertical directions, from 19.4 to 26.5 mm in the anterior-posterior direction); the foot orientation errors were also small (average %RMSD from 5.6% to 8.8% in the medio-lateral and vertical directions, from 15.5% to 18.6% in the anterior-posterior direction). The results suggest that the proposed method can be effectively used to track feet motion in virtual reality and treadmill-based gait training programs.

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Year:  2013        PMID: 24122572     DOI: 10.1109/TNSRE.2013.2282868

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  8 in total

1.  Verification of gait analysis method fusing camera-based pose estimation and an IMU sensor in various gait conditions.

Authors:  Masataka Yamamoto; Koji Shimatani; Yuto Ishige; Hiroshi Takemura
Journal:  Sci Rep       Date:  2022-10-21       Impact factor: 4.996

2.  Development of a Smart Hallway for Marker-Less Human Foot Tracking and Stride Analysis.

Authors:  Vinod Gutta; Pascal Fallavollita; Natalie Baddour; Edward D Lemaire
Journal:  IEEE J Transl Eng Health Med       Date:  2021-03-29       Impact factor: 3.316

Review 3.  Validity of the Kinect for Gait Assessment: A Focused Review.

Authors:  Shmuel Springer; Galit Yogev Seligmann
Journal:  Sensors (Basel)       Date:  2016-02-04       Impact factor: 3.576

4.  Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors.

Authors:  Ahmad Jalal; Nida Khalid; Kibum Kim
Journal:  Entropy (Basel)       Date:  2020-07-26       Impact factor: 2.524

5.  Real-Time Foot Tracking and Gait Evaluation with Geometric Modeling.

Authors:  Ming Jeat Foo; Jen-Shuan Chang; Wei Tech Ang
Journal:  Sensors (Basel)       Date:  2022-02-20       Impact factor: 3.576

6.  Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model.

Authors:  Muhammad Hassan Khan; Manuel Schneider; Muhammad Shahid Farid; Marcin Grzegorzek
Journal:  Sensors (Basel)       Date:  2018-09-21       Impact factor: 3.576

7.  Marker-Based Movement Analysis of Human Body Parts in Therapeutic Procedure.

Authors:  Muhammad Hassan Khan; Martin Zöller; Muhammad Shahid Farid; Marcin Grzegorzek
Journal:  Sensors (Basel)       Date:  2020-06-10       Impact factor: 3.576

8.  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

  8 in total

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