Expert Gesture Analysis through Motion Capture using Statistical Modeling and Machine Learning par M. Mickaël TITS

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Le 23 octobre 2018 À 10:00
Où ?
Salle d'Automatique

Organisé par

Secrétariat des études

Promoteur : Prof. Thierry Dutoit

Résumé :

The present thesis is a contribution to the field of human motion analysis. It studies
the possibilities for a computer to interpret human gestures, and more specifically to
evaluate the quality of expert gestures. These gestures are generally learned through
an empirical process, limited to the subjectivity and own perception of the teacher. In
order to objectify the evaluation of the quality of these gestures, researchers have proposed
various measurable criteria. However, these measurements are still generally
based on human observation.
Enabled by significant steps in the development of Motion Capture (MoCap) and artificial
intelligence technologies, research on automatic gesture evaluation has sparked
a new interest, due to its applications in education, health and entertainment. This
research field is, however, recent and sparsely explored. The few studies on the subject
generally focus on a small dataset, limited to a specific type of gestures, and a
data representation specific to the studied discipline, hereby limiting the validity of
their results. Moreover, the few proposed methods are rarely compared, due to the
lack of available benchmark datasets and of reproducibility on other types of data.
The aim of this thesis is therefore to develop a generic framework for the development
of an evaluation model for the expertise of a gesture. The methods proposed
in this framework are designed to be reusable on various types of data and in various
contexts. The framework consists of six sequential steps, for each of which an
original contribution is proposed in the present thesis:
Firstly, a benchmark dataset is proposed to promote further research in the domain
and allow method comparison. The dataset consists of repetitions of 13 Taijiquan
techniques by 12 participants of various levels from novice to expert, resulting in a
total of 2200 gestures.
Secondly, the MoCap data must be processed, in order to ensure the use of highquality
data for the design of an evaluation model. To that end, an original method
is proposed for automatic and robust recovery of optical MoCap data, based on a
probabilistic averaging of different individual recovery models, and the application
of automatic skeleton constraints. In an experiment where missing data were simulated
into a MoCap dataset, the proposed method outperforms various methods of
the literature, independently of gap length, sequence duration and the number of
simultaneous gaps.

Thirdly, various motion features are proposed for the representation of various aspects
of motion, potentially correlated with different components of expertise. Additionally,
a new set of features is proposed, inspired by Taijiquan ergonomic principles.
In this respect, 36 new motion features, representing aspects of stability, joint
alignments, joint optimal angles and fluidity are presented.
Fourthly, the features must be processed to provide a more relevant representation
of expertise. In the present work, the morphology influence on motion is addressed.
Morphology is an individual factor that has a great influence on motion, but is not
related to expertise. A novel method is therefore proposed for the extraction of
motion features independent of the morphology. From the linear modeling of the
relation of each feature with a morphological factor, residues are extracted, providing
a morphology-independent version of the motion features. As a consequence,
the resulting features are (i) less correlated between each other, and (ii) enable a
more relevant comparison between the gestures of various individuals, hereby allowing
a more relevant modeling of expertise. Results show that the method, termed
as Morphology-Independent Residual Feature Extraction (MIRFE) outperforms a
baseline method (skeleton scaling) in (i) reducing the correlation with the morphological
factor, and in (ii) improving the correlation with skill, for various gestures of
the Taijiquan MoCap dataset, and for a large set of motion features.
Fifthly, an evaluation model must be developed from these features, allowing the
prediction of the expertise level on a new gesture performed by a new user. A model
based on feature statistics, dimension reduction and regression is proposed. The
model is designed to be used with any motion feature, in order to be generic and
relevant in different contexts, including various users and various types of gestural
disciplines. Trained on the Taijiquan MoCap dataset, the model outperforms two
methods of the literature for the evaluation of gestures of a new user, with a mean
relative prediction error of 10% (R = 0.909).
Additionally, a first exploration of the use of deep learning for gesture evaluation is
proposed. To that end, MoCap sequences are represented as abstract RGB images,
and used for transfer learning on a pre-trained image classification convolutional
neural network. Despite a lower performance (R = 0.518), an analysis of the results
suggests that the model could achieve better performance given a larger dataset,
including a larger number of novices and experts.
Sixthly, and finally, to allow a practical use of the evaluation model, a feedback
system must provide an intuitive interpretation of the predicted level, allowing an
effective understanding and assimilation by the user of the system. In the present
work, an original and generic feedback system is proposed, based on the synthesis
of an improved gesture, and its comparison to the user’s original gesture. Both
intuitive and precise feedback are proposed, based on (i) synchronized visualization
of both gestures, and (ii) striped images highlighting the motion features that need
improvement. As a validation of the proposed method, examples of feedback are
proposed for various sequences of the Taijiquan MoCap dataset, showing its practical
interest for objective and automated supervision.

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