This paper presents an approach to the recognition of human gestures in the context of the “Seamless” project. In the project, data collected by IoT sensors can be navigated through gesture recognition algorithms. The aim of recognizing human gestures in Seamless is to give commands through a wearable control device. The paper outlines the Seamless project and then concentrates on our approach to understand a set of gestures using Machine Learning. The employed model and the results are illustrated.

Gesture Recognition using Dynamic Time Warping

M. Fugini;J. Finocchi;
2020-01-01

Abstract

This paper presents an approach to the recognition of human gestures in the context of the “Seamless” project. In the project, data collected by IoT sensors can be navigated through gesture recognition algorithms. The aim of recognizing human gestures in Seamless is to give commands through a wearable control device. The paper outlines the Seamless project and then concentrates on our approach to understand a set of gestures using Machine Learning. The employed model and the results are illustrated.
2020
2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)
978-1-7281-6975-0
978-1-7281-6976-7
Gesture Recognition, Human-Machine Interaction, Inertial Measurement Unit, Dynamic Time Warping, Supervised Machine Learning.
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Descrizione: 2020 IEEE 29th WETICE Conference, Bayonne, France
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1141040
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