Literature DB >> 28269481

Accuracy evaluation of the Kinect v2 sensor during dynamic movements in a rehabilitation scenario.

M Capecci, M G Ceravolo, F Ferracuti, S Iarlori, S Longhi, L Romeo, S N Russi, F Verdini.   

Abstract

In this paper, the accuracy evaluation of the Kinect v2 sensor is investigated in a rehabilitation scenario. The accuracy analysis is provided in terms of joint positions and angles during dynamic postures used in low-back pain rehabilitation. Although other studies have focused on the validation of the accuracy in terms of joint angles and positions, they present results only considering static postures whereas the rehabilitation exercise monitoring involves to consider dynamic movements with a wide range of motion and issues related to the joints tracking. In this work, joint positions and angles represent clinical features, chosen by medical staff, used to evaluate the subject's movements. The spatial and temporal accuracy is investigated with respect to the gold standard, represented by a stereophotogrammetric system, characterized by 6 infrared cameras. The results provide salient information for evaluating the reliability of Kinect v2 sensor for dynamic postures.

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Year:  2016        PMID: 28269481     DOI: 10.1109/EMBC.2016.7591950

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  9 in total

Review 1.  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

2.  Kinect V2 Performance Assessment in Daily-Life Gestures: Cohort Study on Healthy Subjects for a Reference Database for Automated Instrumental Evaluations on Neurological Patients.

Authors:  Alessandro Scano; Andrea Chiavenna; Matteo Malosio; Lorenzo Molinari Tosatti
Journal:  Appl Bionics Biomech       Date:  2017-11-22       Impact factor: 1.781

3.  Low-Cost Tracking Systems Allow Fine Biomechanical Evaluation of Upper-Limb Daily-Life Gestures in Healthy People and Post-Stroke Patients.

Authors:  Alessandro Scano; Franco Molteni; Lorenzo Molinari Tosatti
Journal:  Sensors (Basel)       Date:  2019-03-11       Impact factor: 3.576

4.  Kinect and wearable inertial sensors for motor rehabilitation programs at home: state of the art and an experimental comparison.

Authors:  Bojan Milosevic; Alberto Leardini; Elisabetta Farella
Journal:  Biomed Eng Online       Date:  2020-04-23       Impact factor: 2.819

5.  Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training.

Authors:  Elise Klæbo Vonstad; Xiaomeng Su; Beatrix Vereijken; Kerstin Bach; Jan Harald Nilsen
Journal:  Sensors (Basel)       Date:  2020-12-04       Impact factor: 3.576

Review 6.  A Review on the Use of Microsoft Kinect for Gait Abnormality and Postural Disorder Assessment.

Authors:  Anthony Bawa; Konstantinos Banitsas; Maysam Abbod
Journal:  J Healthc Eng       Date:  2021-11-01       Impact factor: 2.682

Review 7.  A SWOT Analysis of Portable and Low-Cost Markerless Motion Capture Systems to Assess Lower-Limb Musculoskeletal Kinematics in Sport.

Authors:  Cortney Armitano-Lago; Dominic Willoughby; Adam W Kiefer
Journal:  Front Sports Act Living       Date:  2022-01-25

8.  Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard: A Pilot Study.

Authors:  Justin Amadeus Albert; Victor Owolabi; Arnd Gebel; Clemens Markus Brahms; Urs Granacher; Bert Arnrich
Journal:  Sensors (Basel)       Date:  2020-09-08       Impact factor: 3.576

9.  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

  9 in total

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