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Sensors 2017, 17(11), 2698; doi:10.3390/s17112698

Markerless Knee Joint Position Measurement Using Depth Data during Stair Walking

1
School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
2
Department of System Design Engineering, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
3
Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
*
Author to whom correspondence should be addressed.
Received: 4 September 2017 / Revised: 28 October 2017 / Accepted: 21 November 2017 / Published: 22 November 2017
(This article belongs to the Section Physical Sensors)
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Abstract

Climbing and descending stairs are demanding daily activities, and the monitoring of them may reveal the presence of musculoskeletal diseases at an early stage. A markerless system is needed to monitor such stair walking activity without mentally or physically disturbing the subject. Microsoft Kinect v2 has been used for gait monitoring, as it provides a markerless skeleton tracking function. However, few studies have used this device for stair walking monitoring, and the accuracy of its skeleton tracking function during stair walking has not been evaluated. Moreover, skeleton tracking is not likely to be suitable for estimating body joints during stair walking, as the form of the body is different from what it is when it walks on level surfaces. In this study, a new method of estimating the 3D position of the knee joint was devised that uses the depth data of Kinect v2. The accuracy of this method was compared with that of the skeleton tracking function of Kinect v2 by simultaneously measuring subjects with a 3D motion capture system. The depth data method was found to be more accurate than skeleton tracking. The mean error of the 3D Euclidian distance of the depth data method was 43.2 ± 27.5 mm, while that of the skeleton tracking was 50.4 ± 23.9 mm. This method indicates the possibility of stair walking monitoring for the early discovery of musculoskeletal diseases. View Full-Text
Keywords: stair climbing; stair descending; knee joint position; gait measurement; depth data; skeleton tracking; markerless measurement; Kinect v2; VICON; 3D motion capture system stair climbing; stair descending; knee joint position; gait measurement; depth data; skeleton tracking; markerless measurement; Kinect v2; VICON; 3D motion capture system
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ogawa, A.; Mita, A.; Yorozu, A.; Takahashi, M. Markerless Knee Joint Position Measurement Using Depth Data during Stair Walking. Sensors 2017, 17, 2698.

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