PhD defense : Marc Dupont: Glove-based gesture recognition for real-time outdoors robot control, 28th March, 2017.

Although gesture recognition has been studied for several decades, much research stays in the realm of
indoors laboratory experiments. In this thesis, we address the problem of designing a truly usable, real-
world gesture recognition system, focusing mainly on the real-time control of an outdoors robot for use by
military soldiers. The main contribution of this thesis is the development of a real-time gesture recognition
pipeline, which can be taught in a few minutes with: very sparse input (“small data”); freely user-invented
gestures; resilience to user mistakes during training; and low computation requirements. This is achieved
thanks to two key innovations: first, a stream-enabled, DTW-inspired technique to compute distances
between time series; and second, an efficient stream history analysis procedure to automatically determine
model hyperparameters without user intervention. Additionally, a custom, hardened data glove was built
and used to demonstrate successful gesture recognition and real-time robot control. We finally show this
work’s flexibility by furthermore using it beyond robot control to drive other kinds of controllable systems.

Jury members:
Dr Catherine ACHARD, Maître de Conférences, Habilitée à Diriger des Recherches, Université Pierre & Marie Curie, Paris 6
Prof. Fabien MOUTARDE, École Nationale Supérieure des Mines de Paris (ENSMP)
Prof. Éric ANQUETIL, INSA de Rennes
Prof. Pierre-François MARTEAU, Université Bretagne Sud
M. Philippe GOSSET, Responsable Industriel THALES OPTRONIQUE, Élancourt

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