Please use this identifier to cite or link to this item:
Title: Hand/arm Gesture Segmentation by Motion Using IMU and EMG Sensing
Authors: Lopes, João 
Simão, Miguel 
Mendes, Nuno 
Safeea, Mohammad 
Afonso, José 
Neto, Pedro 
Keywords: Gestures; Segmentation; Motion; IMU; EMG
Issue Date: 2017
Project: Portugal 2020 project DM4Manufacturing POCI-01-0145-FEDER-016418 by UE/FEDER through the program COMPETE2020 
Serial title, monograph or event: Procedia Manufacturing
Volume: 11
Abstract: Gesture recognition is more reliable with a proper motion segmentation process. In this context we can distinguish if gesture patterns are static or dynamic. This study proposes a gesture segmentation method to distinguish dynamic from static gestures, using (Inertial Measurement Units) IMU and Electromyography (EMG) sensors. The performance of the sensors, individually as well as their combination, was evaluated by different users. It was concluded that when considering gestures which only contain arm movement, the lowest error obtained was by the IMU. However, as expected, when considering gestures which have only hand motion, the combination of the 2 sensors achieved the best performance. Results of the sensor fusion modality varied greatly depending on user. The application of different filtering method to the EMG data as a solution to the limb position resulted in a significative reduction of the error.
ISSN: 23519789
DOI: 10.1016/j.promfg.2017.07.158
Rights: openAccess
Appears in Collections:FCTUC Eng.Mecânica - Artigos em Revistas Internacionais

Files in This Item:
File Description SizeFormat
1-s2.0-S2351978917303645-main.pdf646.81 kBAdobe PDFView/Open
Show full item record


checked on Nov 17, 2022

Page view(s)

checked on Mar 20, 2023


checked on Mar 20, 2023

Google ScholarTM




This item is licensed under a Creative Commons License Creative Commons