Published March 30, 2020 | Version v1
Conference paper Open

Towards a Professional Gesture Recognition with RGB-D from Smartphone

  • 1. Centre for Robotics, MINES ParisTech, PSL Université

Description

Abstract. The goal of this work is to build the basis for a smartphone application that provides functionalities for recording human motion data, train machine learning algorithms and recognize professional gestures. First, we take advantage of the new mobile phone cameras, either infrared or stereoscopic, to record RGB-D data. Then, a bottom-up pose estimation algorithm based on Deep Learning extracts the 2D human skeleton and exports the 3rd dimension using the depth. Finally, we use a gesture recognition engine, which is based on K-means and Hidden Markov Models (HMMs). The performance of the machine learning algorithm has been tested with professional gestures using a silk-weaving and a TV-assembly datasets.

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Funding

Mingei – Representation and Preservation of Heritage Crafts 822336
European Commission
CoLLaboratE – Co-production CeLL performing Human-Robot Collaborative AssEmbly 820767
European Commission