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Image Generation for 2D-CNN Using Time-Series Signal Features from Foot Gesture Applied to Select Cobot Operating Mode

El Aswad Fadwa, Tchane Djogdom Gilde Vanel, Otis Martin J.-D., Ayena Johannes C. et Meziane Ramy. (2021). Image Generation for 2D-CNN Using Time-Series Signal Features from Foot Gesture Applied to Select Cobot Operating Mode. Sensors, 21, (17), p. 5743.

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Advances in robotics are part of reducing the burden associated with manufacturing tasks in workers. For example, the cobot could be used as a “third-arm” during the assembling task. Thus, the necessity of designing new intuitive control modalities arises. This paper presents a foot gesture approach centered on robot control constraints to switch between four operating modalities. This control scheme is based on raw data acquired by an instrumented insole located at a human’s foot. It is composed of an inertial measurement unit (IMU) and four force sensors. Firstly, a gesture dictionary was proposed and, from data acquired, a set of 78 features was computed with a statistical approach, and later reduced to 3 via variance analysis ANOVA. Then, the time series collected data were converted into a 2D image and provided as an input for a 2D convolutional neural network (CNN) for the recognition of foot gestures. Every gesture was assimilated to a predefined cobot operating mode. The offline recognition rate appears to be highly dependent on the features to be considered and their spatial representation in 2D image. We achieve a higher recognition rate for a specific representation of features by sets of triangular and rectangular forms. These results were encouraging in the use of CNN to recognize foot gestures, which then will be associated with a command to control an industrial robot.

Type de document:Article publié dans une revue avec comité d'évaluation
Pages:p. 5743
Version évaluée par les pairs:Oui
Date:26 Août 2021
Sujets:Sciences naturelles et génie > Génie
Sciences naturelles et génie > Génie > Génie informatique et génie logiciel
Sciences naturelles et génie > Sciences appliquées
Département, module, service et unité de recherche:Départements et modules > Département des sciences appliquées > Module d'ingénierie
Mots-clés:human–robot collaboration, instrumented insole, foot gesture recognition, convolutional neural network, collaboration homme-robot, semelle instrumentée, reconnaissance des gestes du pied, réseau de neurones convolutifs
Déposé le:31 août 2021 20:41
Dernière modification:31 août 2021 20:41
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