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Activity Recognition With Multiple Wearable Sensors for Industrial Applications

Adrien Malaisé 1 Pauline Maurice 1 Francis Colas 1 François Charpillet 1 Serena Ivaldi 1 
1 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
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 activities 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.
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Submitted on : Tuesday, February 6, 2018 - 1:57:14 PM
Last modification on : Saturday, June 25, 2022 - 7:43:23 PM
Long-term archiving on: : Tuesday, May 8, 2018 - 7:10:25 AM


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  • HAL Id : hal-01701996, version 1



Adrien Malaisé, Pauline Maurice, Francis Colas, François Charpillet, Serena Ivaldi. Activity Recognition With Multiple Wearable Sensors for Industrial Applications. ACHI 2018 - Eleventh International Conference on Advances in Computer-Human Interactions, Mar 2018, Rome, Italy. ⟨hal-01701996⟩



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