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Low-Rank and Sparse Recovery of Human Gait Data

1
Department of Automated Manufacturing Engineering, École de Technologie Supérieure, Montreal, QC H3C1K3, Canada
2
Department of Mechanical Engineering and the Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
3
Department of Software and IT Engineering, École de Technologie Supérieure, Montreal, QC H3C1K3, Canada
4
Department of Applied Sciences, Université du Québec à Chicoutimi (UQAC), Chicoutimi, QC G7H2B1, Canada
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(16), 4525; https://doi.org/10.3390/s20164525
Received: 23 June 2020 / Revised: 7 August 2020 / Accepted: 7 August 2020 / Published: 13 August 2020
(This article belongs to the Section Wearables)
Due to occlusion or detached markers, information can often be lost while capturing human motion with optical tracking systems. Based on three natural properties of human gait movement, this study presents two different approaches to recover corrupted motion data. These properties are used to define a reconstruction model combining low-rank matrix completion of the measured data with a group-sparsity prior on the marker trajectories mapped in the frequency domain. Unlike most existing approaches, the proposed methodology is fully unsupervised and does not need training data or kinematic information of the user. We evaluated our methods on four different gait datasets with various gap lengths and compared their performance with a state-of-the-art approach using principal component analysis (PCA). Our results showed recovering missing data more precisely, with a reduction of at least 2 mm in mean reconstruction error compared to the literature method. When a small number of marker trajectories is available, our findings showed a reduction of more than 14 mm for the mean reconstruction error compared to the literature approach. View Full-Text
Keywords: human gait; recovery; low-rank matrix completion; group-sparsity; missing data human gait; recovery; low-rank matrix completion; group-sparsity; missing data
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Kamali, K.; Akbari, A.A.; Desrosiers, C.; Akbarzadeh, A.; Otis, M. .-D.; Ayena, J.C. Low-Rank and Sparse Recovery of Human Gait Data. Sensors 2020, 20, 4525.

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