+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Accuracy of a novel marker tracking approach based on the low-cost Microsoft Kinect v2 sensor

Accuracy of a novel marker tracking approach based on the low-cost Microsoft Kinect v2 sensor

Medical Engineering and Physics 59: 63-69

Microsoft Kinect for Windows v2 is a motion analysis system that features a markerless human pose estimation algorithm. Given its affordability and portability, Kinect v2 has potential for use in biomechanical research and within clinical settings; however, recent studies suggest high inaccuracy of the markerless algorithm compared to marker-based motion capture systems. A novel tracking method was developed using Kinect v2, employing custom-made colored markers and computer vision techniques. The aim of this study was to test the accuracy of this approach relative to a conventional Vicon motion analysis system, performing a Bland-Altman analysis of agreement. Twenty participants were recruited, and markers placed on bony prominences near hip, knee and ankle. Three-dimensional coordinates of the markers were recorded during treadmill walking and running. The limits of agreement (LOA) of marker coordinates were narrower than - 10 and 10 mm in most conditions, however a negative relationship between accuracy and treadmill speed was observed along Kinect depth direction. LOA of the surrogate knee angles were within - 1.8°, 1.7° for flexion in all conditions and - 2.9°, 1.7° for adduction during fast walking. The proposed methodology exhibited good agreement with a marker-based system over a range of gait speeds and, for this reason, may be useful as low-cost motion analysis tool for selected biomechanical applications.

Please choose payment method:

(PDF emailed within 0-6 h: $19.90)

Accession: 043746755

Download citation: RISBibTeXText

PMID: 29983277

DOI: 10.1016/j.medengphy.2018.04.020

Related references

Development and evaluation of low cost game-based balance rehabilitation tool using the Microsoft Kinect sensor. Conference Proceedings 2011: 1831-1834, 2011

Comparative analysis of respiratory motion tracking using Microsoft Kinect v2 sensor. Journal of Applied Clinical Medical Physics 19(3): 193-204, 2018

Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease. Gait and Posture 39(4): 1062-1068, 2014

Statistical analysis-based error models for the Microsoft Kinect(TM) depth sensor. Sensors 14(9): 17430-17450, 2014

Feasibility of a Customized, In-Home, Game-Based Stroke Exercise Program Using the Microsoft Kinect® Sensor. International Journal of Telerehabilitation 7(2): 23-34, 2015

Microsoft Kinect based head tracking for Life Size Collaborative Surgical Simulation Environments (LS-CollaSSLE). Studies in Health Technology and Informatics 184: 109-113, 2013

Developing movement recognition application with the use of Shimmer sensor and Microsoft Kinect sensor. Studies in Health Technology and Informatics 217: 767-772, 2015

Design and Test of a Closed-Loop FES System for Supporting Function of the Hemiparetic Hand Based on Automatic Detection using the Microsoft Kinect sensor. IEEE Transactions on Neural Systems and Rehabilitation Engineering 25(8): 1249-1256, 2017

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. IEEE Transactions on Neural Systems and Rehabilitation Engineering 22(5): 997-1002, 2015

Enhanced computer vision with Microsoft Kinect sensor: a review. IEEE Transactions on Cybernetics 43(5): 1318-1334, 2013

Rapid characterization of vegetation structure with a Microsoft Kinect sensor. Sensors 13(2): 2384-2398, 2013

Automated Tracking and Quantification of Autistic Behavioral Symptoms Using Microsoft Kinect. Studies in Health Technology and Informatics 220: 167-170, 2016

SU-E-I-92: Accuracy Evaluation of Depth Data in Microsoft Kinect. Medical Physics 39(6part5): 3646, 2012

Implementation of facial recognition with Microsoft Kinect v2 sensor for patient verification. Medical Physics 44(6): 2391-2399, 2017

Repurposing the Microsoft Kinect for Windows v2 for external head motion tracking for brain PET. Physics in Medicine and Biology 60(22): 8753-8766, 2015