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Paper ThA2ML.4

Bargellesi, Nicoḷ (University of Padova), Carletti, Mattia (University of Padova), Cenedese, Angelo (University of Padova), Susto, Gian Antonio (University of Padova), Terzi, Matteo (University of Padova)

A Random Forest-Based Approach for Hand Gesture Recognition with Wireless Wearable Motion Capture Sensors

Scheduled for presentation during the Regular Session "Machine Learning and Human-Centric Applications" (ThA2ML), Thursday, August 22, 2019, 11:50−12:10,

5th IFAC International Conference on Intelligent Control and Automation Sciences, August 21-23, 2019, Queen’s University Belfast, Northern Ireland

This information is tentative and subject to change. Compiled on November 29, 2021

Keywords Emerging areas, Learning, adaptation and evaluation, Modeling and identification

Abstract

Gesture Recognition has a prominent importance in smart environment and home automation. Thanks to the availability of Machine Learning approaches it is possible for users to define gestures that can be associated with commands for the smart environment. In this paper we propose a Random Forest-based approach for Gesture Recognition of hand movements starting from wireless wearable motion capture data. In the presented approach, we evaluate different feature extraction procedures to handle gestures and data with different duration. To enhance reproducibility of our results and to foster research in the Gesture Recognition area, we share the dataset that we have collected and exploited for the present work.

 

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