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Sensors
Volume 20
Issue 23
10.3390/s20236940
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Article

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

by
Elise Klæbo Vonstad
1,*,
Xiaomeng Su
1,
Beatrix Vereijken
2,
Kerstin Bach
1 and
Jan Harald Nilsen
1
1
Department of Computer Science, Norwegian University of Science and Technology, 7034 Trondheim, Norway
2
Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, 7030 Trondheim, Norway
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(23), 6940; https://doi.org/10.3390/s20236940
Received: 26 October 2020 / Revised: 20 November 2020 / Accepted: 1 December 2020 / Published: 4 December 2020
(This article belongs to the Section Biosensors)
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Abstract

Using standard digital cameras in combination with deep learning (DL) for pose estimation is promising for the in-home and independent use of exercise games (exergames). We need to investigate to what extent such DL-based systems can provide satisfying accuracy on exergame relevant measures. Our study assesses temporal variation (i.e., variability) in body segment lengths, while using a Deep Learning image processing tool (DeepLabCut, DLC) on two-dimensional (2D) video. This variability is then compared with a gold-standard, marker-based three-dimensional Motion Capturing system (3DMoCap, Qualisys AB), and a 3D RGB-depth camera system (Kinect V2, Microsoft Inc). Simultaneous data were collected from all three systems, while participants (N = 12) played a custom balance training exergame. The pose estimation DLC-model is pre-trained on a large-scale dataset (ImageNet) and optimized with context-specific pose annotated images. Wilcoxon’s signed-rank test was performed in order to assess the statistical significance of the differences in variability between systems. The results showed that the DLC method performs comparably to the Kinect and, in some segments, even to the 3DMoCap gold standard system with regard to variability. These results are promising for making exergames more accessible and easier to use, thereby increasing their availability for in-home exercise. View Full-Text
Keywords: motion capture; image analysis; markerless motion capture; exergaming; segment lengths; kinect; deep learning; human movement motion capture; image analysis; markerless motion capture; exergaming; segment lengths; kinect; deep learning; human movement
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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.
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MDPI and ACS Style

Vonstad, E.K.; Su, X.; Vereijken, B.; Bach, K.; Nilsen, J.H. Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training. Sensors 2020, 20, 6940. https://doi.org/10.3390/s20236940

AMA Style

Vonstad EK, Su X, Vereijken B, Bach K, Nilsen JH. Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training. Sensors. 2020; 20(23):6940. https://doi.org/10.3390/s20236940

Chicago/Turabian Style

Vonstad, Elise Klæbo, Xiaomeng Su, Beatrix Vereijken, Kerstin Bach, and Jan Harald Nilsen. 2020. "Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training" Sensors 20, no. 23: 6940. https://doi.org/10.3390/s20236940

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