Gesture recognition for humanoid robot teleoperation

Abstract

Interactive robotics is a vast and expanding research field. Interactions must be sufficiently natural, with robots having socially acceptable behavior by humans, adaptable to user expectations. Thus allowing easy integration in our daily lives in various fields (science, industry, health ...). Natural interaction during human-robot collaborative action needs suitable interaction techniques. In our paper we develop an online gesture recognition system for natural and intuitive communication between Human and NAO robot. However recognizing meaningful gesture patterns from whole-body gestures is a complex task. That is why we used the Laban Movement Analysis technique to describe high level gestures for NAO teleoperation. The major contributions of the present work is: (1) an efficient preprocessing step based on view invariant human motion, (2) a robust descriptor vector based on Laban Movement Analysis technique to generate compact and informative representations of Human movement, and (3) an online gesture recognition with Hidden Markov Model method was applied to teleoperate NAO based on our proper data base dedicated to the teleoperation of NAO. Our approach was evaluated with two challenging datasets, Microsoft Research Cambridge-12 (MSRC-12) and UTKinect-Action. Experimental results show that our approach outperforms the state-of-the-art methods.

Human motions and emotions recognition inspired by LMA qualities

“…It is than essential to find reliable and discriminative features to represent expressive human motions. In this paper, which extends our preliminary work [6,7], we propose a suitable representation of motions that captures both the quantitative and qualitative aspects of the movement. Two experimental parts are performed: in the first one we evaluate our descriptor vector for motion recognition using three public datasets and our own dataset of control motions.…”

10 Years of Human-NAO Interaction Research: A Scoping Review

“…In another study, a speech recognition system called Cloud technology was used to assess the correct understanding of Chinese language words that were transmitted to NAO (Han et al, 2018). Other researchers use gesture recognition system based on external cameras (Ajili et al, 2017) and object detection algorithm to recognize the face from NAO's main camera (Cheng et al, 2012;Cuijpers and van der Pol, 2013). These advancements are significant as service robots are becoming popular in our domestic and social lives.…”

Motion recognition using deep convolutional neural network for Kinect-based NAO teleoperation

“…Several researchers presented human motion recognition in their literatures which used Kinect sensors [21,22,23,24,25]. These studies either used depth images [26,27,28] or skeleton data [3,29,30] to classify the arbitrary motions. It serves reliable teleoperated systems where a robot predicts the human behaviour towards them to perform the associated tasks.…”