Using virtual humans and computer animations to learn complex motor skills: a case study in karate

Burns, Anne-Marie; Kulpa, Richard; Durny, Annick; Spanlang, Bernhard; Slater, Mel; Multon, Franck

doi:10.1051/bioconf/20110100012

Cited by 16 publications

(18 citation statements)

References 2 publications

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“…In the same time NA have generated similar value for both (5) and (9), however the arm movement is smooth. That is because there is a relatively large variability of data within the source dataset and signal averaging cannot overcome the highest differences.…”

Section: Discussionmentioning

confidence: 63%

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Averaging of motion capture recordings for movements’ templates generation

Hachaj

Koptyra

Ogiela

2018

Multimed Tools Appl

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In this paper we propose, describe and evaluate the novel motion capture (MoCap) data averaging framework. It incorporates hierarchical kinematic model, angle coordinates' preprocessing methods, that recalculate the original MoCap recording making it applicable for further averaging algorithms, and finally signals averaging processing. We have tested two signal averaging methods namely Kalman Filter (KF) and Dynamic Time Warping barycenter averaging (DBA). The propose methods have been tested on MoCap recordings of elite Karate athlete, multiple champion of Oyama karate knockdown kumite who performed 28 different karate techniques repeated 10 times each. The proposed methods proved to have not only high effectiveness measured with root-mean-square deviation (4.04 ± 5.03 degrees for KF and 5.57 ± 6.27 for DBA) and normalized Dynamic Time Warping distance (0.90 ± 1.58 degrees for KF and 0.93 ± 1.23 for DBA), but also the reconstruction and visualization of those recordings persists all crucial aspects of those complicated actions. The proposed methodology has many important applications in classification, clustering, kinematic analysis and coaching. Our approach generates an averaged full body motion template that can be practically used for example for human actions Multimed Tools Appl (2018) 77:30353-30380 recognition. In order to prove it we have evaluated templates generated by our method in human action classification tasks using DTW classifier. We have made two experiments. In first leave -one -out cross -validation we have obtained 100% correct recognitions. In second experiment when we classified recordings of one person using templates of another recognition rate 94.2% was obtained.

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Section: Discussionmentioning

confidence: 63%

“…With them a trainer can visualize averaged performance of an athlete and evaluate his or her kinematic parameters [9,38]. -Many up-to-date researches evaluate only statistical kinematic parameters of actions like mean displacement, velocity or acceleration and their angular analogs.…”

Section: Resultsmentioning

confidence: 99%

Averaging of motion capture recordings for movements’ templates generation

Hachaj

Koptyra

Ogiela

2018

Multimed Tools Appl

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“…For this reason, some authors proposed to add knowledge to the motion evaluation process to know in advance the features to analyze. For instance, Burns et al defined a set of rules that characterizes some kata in karate, such as the linear trajectory the kicking wrist must follow (Burns et al, 2011). Komura et al based their evaluation on the minimization of the global movement since they considered that the defender can better counteract an attack if he does not move too much just before the action (Komura et al, 2006).…”

Section: Related Workmentioning

confidence: 99%

Automatic and Generic Evaluation of Spatial and Temporal Errors in Sport Motions

Morel

Kulpa

Sorel

et al. 2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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Abstract:Automatically evaluating and quantifying the performance of a player is a complex task since the important motion features to analyze depend on the type of performed action. But above all, this complexity is due to the variability of morphologies and styles of both the experts who perform the reference motions and the novices. Only based on a database of experts' motions and no additional knowledge, we propose an innovative 2-level DTW (Dynamic Time Warping) approach to temporally and spatially align the motions and extract the imperfections of the novice's performance for each joints. In this study, we applied our method on tennis serve but since it is automatic and morphology-independent, it can be applied to any individual motor performance.

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“…To date, several studies have used VR to investigate the influence of observing one's own and/or a skilled performance on subsequent motor performance and motor learning (Todorov et al, 1997;Chua et al, 2003;Burns et al, 2011;Anderson et al, 2013;Covaci et al, 2014;Sigrist et al, 2015;Tang et al, 2015;Hoang et al, 2016). In these studies, the skilled performance has either been visualized as an overlay on top of the participant's movement (e.g., Sigrist et al, 2015) and/or has been visualized on a virtual character next to the participant (e.g., Chua et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Superimposed Skilled Performance in a Virtual Mirror Improves Motor Performance and Cognitive Representation of a Full Body Motor Action

Hülsmann

Frank

Senna³

et al. 2019

Front. Robot. AI

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Feedback is essential for skill acquisition as it helps identifying and correcting performance errors. Nowadays, Virtual Reality can be used as a tool to guide motor learning, and to provide innovative types of augmented feedback that exceed real world opportunities. Concurrent feedback has shown to be especially beneficial for novices. Moreover, watching skilled performances helps novices to acquire a motor skill, and this effect depends on the perspective taken by the observer. To date, however, the impact of watching one's own performance together with full body superimposition of a skilled performance, either from the front or from the side, remains to be explored. Here we used an immersive, state-of-the-art, low-latency cave automatic virtual environment (CAVE), and we asked novices to perform squat movements in front of a virtual mirror. Participants were assigned to one of three concurrent visual feedback groups: participants either watched their own avatar performing full body movements or were presented with the movement of a skilled individual superimposed on their own performance during movement execution, either from a frontal or from a side view. Motor performance and cognitive representation were measured in order to track changes in movement quality as well as motor memory across time. Consistent with our hypotheses, results showed an advantage of the groups that observed their own avatar performing the squat together with the superimposed skilled performance for some of the investigated parameters, depending on perspective. Specifically, for the deepest point of the squat, participants watching the squat from the front adapted their height, while those watching from the side adapted their backward movement. In a control experiment, we ruled out the possibility that the observed improvements were due to the mere fact of performing the squat movements—irrespective of the type of visual feedback. The present findings indicate that it can be beneficial for novices to watch themselves together with a skilled performance during execution, and that improvement depends on the perspective chosen.

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Using virtual humans and computer animations to learn complex motor skills: a case study in karate

Abstract: Learning motor skills is a complex task involving a lot of cognitive issues. One of the main issues consists (traditional group, video and VR).

Cited by 16 publications

References 2 publications

Averaging of motion capture recordings for movements’ templates generation

Averaging of motion capture recordings for movements’ templates generation

Automatic and Generic Evaluation of Spatial and Temporal Errors in Sport Motions

Superimposed Skilled Performance in a Virtual Mirror Improves Motor Performance and Cognitive Representation of a Full Body Motor Action

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