VI-Net—View-Invariant Quality of Human Movement Assessment

We propose a view-invariant method towards the assessment of the quality of human movements which does not rely on skeleton data. Our end-to-end convolutional neural network consists of two stages, where at first a view-invariant trajectory descriptor for each body joint is generated from RGB images...

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Vydáno v:	Sensors (Basel, Switzerland) Ročník 20; číslo 18; s. 5258
Hlavní autoři:	Sardari, Faegheh, Paiement, Adeline, Hannuna, Sion, Mirmehdi, Majid
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI 15.09.2020 MDPI AG
Témata:	Artificial Intelligence Computer Science Computer Vision and Pattern Recognition health monitoring Humans Image Processing Machine Learning Movement movement analysis Neural and Evolutionary Computing Neural Networks, Computer Signal and Image Processing view-invariant convolutional neural network (CNN) health monitoring view-invariant convolutional neural network (CNN) movement analysis
ISSN:	1424-8220, 1424-8220
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Abstract	We propose a view-invariant method towards the assessment of the quality of human movements which does not rely on skeleton data. Our end-to-end convolutional neural network consists of two stages, where at first a view-invariant trajectory descriptor for each body joint is generated from RGB images, and then the collection of trajectories for all joints are processed by an adapted, pre-trained 2D convolutional neural network (CNN) (e.g., VGG-19 or ResNeXt-50) to learn the relationship amongst the different body parts and deliver a score for the movement quality. We release the only publicly-available, multi-view, non-skeleton, non-mocap, rehabilitation movement dataset (QMAR), and provide results for both cross-subject and cross-view scenarios on this dataset. We show that VI-Net achieves average rank correlation of 0.66 on cross-subject and 0.65 on unseen views when trained on only two views. We also evaluate the proposed method on the single-view rehabilitation dataset KIMORE and obtain 0.66 rank correlation against a baseline of 0.62.
AbstractList	We propose a view-invariant method towards the assessment of the quality of human movements which does not rely on skeleton data. Our end-to-end convolutional neural network consists of two stages, where at first a view-invariant trajectory descriptor for each body joint is generated from RGB images, and then the collection of trajectories for all joints are processed by an adapted, pre-trained 2D convolutional neural network (CNN) (e.g., VGG-19 or ResNeXt-50) to learn the relationship amongst the different body parts and deliver a score for the movement quality. We release the only publicly-available, multi-view, non-skeleton, non-mocap, rehabilitation movement dataset (QMAR), and provide results for both cross-subject and cross-view scenarios on this dataset. We show that VI-Net achieves average rank correlation of 0.66 on cross-subject and 0.65 on unseen views when trained on only two views. We also evaluate the proposed method on the single-view rehabilitation dataset KIMORE and obtain 0.66 rank correlation against a baseline of 0.62.We propose a view-invariant method towards the assessment of the quality of human movements which does not rely on skeleton data. Our end-to-end convolutional neural network consists of two stages, where at first a view-invariant trajectory descriptor for each body joint is generated from RGB images, and then the collection of trajectories for all joints are processed by an adapted, pre-trained 2D convolutional neural network (CNN) (e.g., VGG-19 or ResNeXt-50) to learn the relationship amongst the different body parts and deliver a score for the movement quality. We release the only publicly-available, multi-view, non-skeleton, non-mocap, rehabilitation movement dataset (QMAR), and provide results for both cross-subject and cross-view scenarios on this dataset. We show that VI-Net achieves average rank correlation of 0.66 on cross-subject and 0.65 on unseen views when trained on only two views. We also evaluate the proposed method on the single-view rehabilitation dataset KIMORE and obtain 0.66 rank correlation against a baseline of 0.62. We propose a view-invariant method towards the assessment of the quality of human movements which does not rely on skeleton data. Our end-to-end convolutional neural network consists of two stages, where at first a view-invariant trajectory descriptor for each body joint is generated from RGB images, and then the collection of trajectories for all joints are processed by an adapted, pre-trained 2D convolutional neural network (CNN) (e.g., VGG-19 or ResNeXt-50) to learn the relationship amongst the different body parts and deliver a score for the movement quality. We release the only publicly-available, multi-view, non-skeleton, non-mocap, rehabilitation movement dataset (QMAR), and provide results for both cross-subject and cross-view scenarios on this dataset. We show that VI-Net achieves average rank correlation of 0.66 on cross-subject and 0.65 on unseen views when trained on only two views. We also evaluate the proposed method on the single-view rehabilitation dataset KIMORE and obtain 0.66 rank correlation against a baseline of 0.62. We propose a view-invariant method towards the assessment of the quality of human 1 movements which does not rely on skeleton data. Our end-to-end convolutional neural network 2 consists of two stages, where at first a view-invariant trajectory descriptor for each body joint is 3 generated from RGB images, and then the collection of trajectories for all joints are processed by an 4 adapted, pre-trained 2D CNN (e.g. VGG-19 or ResNeXt-50) to learn the relationship amongst 5 the different body parts and deliver a score for the movement quality. We release the only 6 publicly-available, multi-view, non-skeleton, non-mocap, rehabilitation movement dataset (QMAR), 7 and provide results for both cross-subject and cross-view scenarios on this dataset. We show that 8 VI-Net achieves average rank correlation of 0.66 on cross-subject and 0.65 on unseen views when 9 trained on only two views. We also evaluate the proposed method on the single-view rehabilitation 10 dataset KIMORE and obtain 0.66 rank correlation against a baseline of 0.62.
Author	Hannuna, Sion Sardari, Faegheh Mirmehdi, Majid Paiement, Adeline
AuthorAffiliation	2 Université de Toulon, Aix Marseille Univ, CNRS, LIS, Marseille, France; adeline.paiement@univ-tln.fr 1 Department of Computer Science, University of Bristol, Bristol BS8 1UB, UK; sh1670@bristol.ac.uk (S.H.); m.mirmehdi@bristol.ac.uk (M.M.)
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SubjectTerms	Artificial Intelligence Computer Science Computer Vision and Pattern Recognition health monitoring Humans Image Processing Machine Learning Movement movement analysis Neural and Evolutionary Computing Neural Networks, Computer Signal and Image Processing view-invariant convolutional neural network (CNN)
Title	VI-Net—View-Invariant Quality of Human Movement Assessment
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