Mickaël Tits

Mickaël Tits
Centre d'Excellence en Technologies de l'Information et de la Communication | CETIC · D-SIDE

Doctor of Engineering

About

14
Publications
14,836
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72
Citations

Publications

Publications (14)
Conference Paper
Full-text available
Recently, most European distribution systems (DS) are overwhelmed by the coupled growth of decentralized production and residential appliance volatility. To cope with this issue, new solutions are emerging, such as local energy storage and energetic community management. The latter aims for the collective self-consumption maximization of the locall...
Article
Full-text available
Motion capture allows accurate recording of human motion, with applications in many fields, including entertainment, medicine, sports science and human computer interaction. A common difficulty with this technology is the occurrence of missing data, due to occlusions, or recording conditions. Various models have been proposed to estimate missing da...
Article
Full-text available
In this article, we present a large 3D motion capture dataset of Taijiquan martial art gestures (n = 2200 samples) that includes 13 classes (relative to Taijiquan techniques) executed by 12 participants of various skill levels. Participants levels were ranked by three experts on a scale of [0-10]. The dataset was captured using two motion capture s...
Thesis
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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 or...
Conference Paper
Full-text available
In the recent domain of motion capture and analysis, a new challenge has been the automatic evaluation of skill in gestures. Many methods have been proposed for gesture evaluation based on feature extraction, skill modeling and gesture comparison. However, movements can be influenced by many factors other than skill, including morphology. All these...
Experiment Findings
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Data
Matlab code. Matlab script containing a Matlab implementation of the proposed method. Note that the maintained version of the code can be found on the Github repository: https://github.com/numediart/MocapRecovery. (M)
Article
Full-text available
The 11th Summer Workshop on Multimodal Interfaces eNTERFACE 2015 was hosted by the Numediart Institute of Creative Technologies of the University of Mons from August 10th to September 2015. During the four weeks, students and researchers from all over the world came together in the Numediart Institute of the University of Mons to work on eight sele...
Conference Paper
Full-text available
The recent arise of Motion Capture (MoCap) technologies provides new possibilities, but also new challenges in human motion analysis. Indeed, the analysis of a motion database is a complex task, due to the high dimensionality of motion data, and the number of independent factors that can affect movements. We addressed the first issue in some of our...
Conference Paper
In this paper, we introduce the i-Treasures Intangible Cultural Heritage (ICH) dataset, a freely available collection of multimodal data captured from different forms of rare ICH. More specifically, the dataset contains video, audio, depth, motion capture data and other modalities, such as EEG or ultrasound data. It also includes (manual) annotatio...
Technical Report
Full-text available
This deliverable entitled “ICH Indexing by Stylistic Factors and Locality Variations” presents different approaches developed for the stylistic analysis of dance performances and pottery construction sessions. Motion Machine, a new framework for stylistic analysis and comparison of full body gestures is presented, along with a set of stylistic moti...
Conference Paper
Full-text available
This paper investigates the analysis of expert piano playing gestures. It aims to extract quantitative and objective features to represent pianists' hands gestures, and more specifically to enable characterization of the expertise level of pianists. To do so, four pianists with different expertise levels were recorded with a marker-based optical mo...
Article
Full-text available
Skeletal data acquisition generates a huge amount of high-dimensionality data. In many fields where motion capture techniques are now used, practitioners would greatly benefit from high-level representations of these motion sequences. However meaningful motion data dimensionality reduction is not a trivial task and the selection of the best set of...

Questions

Question (1)
Question
I find the GRNN algorithm quite interesting, as it is intuitive, fast and quite effective for small datasets with non-linear relations. The only trouble is the hyper-parameter called smoothness, or smooth or spread parameter. I wonder if there is a way to define it automaticcally (without needing a cross-validation), based on a priori knowledge on the dataset, somewhat based on data sparsity or empirical distribution for instance?

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Projects

Projects (2)
Project
Motion capture is an emerging area of current scientific research. Recent progress in this technology has led to great perspectives for the development in many fields including physical (re)education, ergonomics, animation, and human-computer interface. Recently, it has become possible to capture highly complex gestures, such as “expert gestures”. This term covers all gestures that require a high level of motor control acquired through training. In several recent studies, different signal processing and learning algorithms were applied to signals recorded by motion capture, and allowed decomposition, analysis and resynthesis of several movements such as the human gait and dance. The research proposed in the framework of a doctoral thesis aims at adaptation and application of these algorithms to expert gestures in various fields such as sports, music or games. A previous study conducted as part of a Master’s thesis showed that the algorithm of principal component analysis allowed assessment of the quality of pianists’ expert gestures. The aim of the proposed research is the development of an assessment tool for the quality of expert gestures in different fields based on machine learning and statistical modeling. A such tool would help objectifying diagnoses given by physiotherapists and help them develop effective rehabilitation exercises. It would also help measuring the effectiveness of learning techniques taught by teachers in physical education. Such a tool could also present a great interest in the development of new “serious games”.