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Activity Recognition With Multiple Wearable Sensors for Industrial Applications

Authors:
Adrien Malaisé
Pauline Maurice
Francis Colas
François Charpillet
Serena Ivaldi

Keywords: Activity recognition; Hidden Markov Model; Wearable sensors.

Abstract:
In this paper, we address the problem of recognizing the current activity performed by a human operator, providing an information useful for automatic ergonomic evaluation for industrial applications. While the majority of research in activity recognition relies on cameras observing the human, here we explore the use of wearable sensors, which are more suitable in industrial environments. We use a wearable motion tracking suit and a sensorized glove. We describe our approach for activity recognition with a probabilistic model based on Hidden Markov Models, applied to the problem of recognizing elementary acti-vities during a pick-and-place task inspired by a manufacturing scenario. We show that our model is able to correctly recognize the activities with 96% of precision if both sensors are used.

Pages: 229 to 234

Copyright: Copyright (c) IARIA, 2018

Publication date: March 25, 2018

Published in: conference

ISSN: 2308-4138

ISBN: 978-1-61208-616-3

Location: Rome, Italy

Dates: from March 25, 2018 to March 29, 2018