Identity statement area
Reference TypeConference Proceedings
Last Update2018:
Metadata Last Update2020: administrator
Citation KeyBeltrãoNazaSchw:2018:AuGyWo
TitleAutomatic Gym Workout Recognition using Wearable Devices
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 04
Number of Files1
Size1079 KiB
Context area
Author1 Beltrão, Davi Faria de Assis
2 Nazare, Antônio Carlos
3 Schwartz, William Robson
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
3 Universidade Federal de Minas Gerais
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
History2018-10-26 04:25:12 :: -> administrator ::
2020-02-20 22:06:51 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Tertiary TypeWork in Progress
Keywordsgym recognition, smartwatch.
AbstractIt is well known among people that sports practice leads to a better quality of life and prevent diseases. Furthermore, according to some sources, the use of smartwatches is spreading worldwide, reaching almost 20% of U.S. population nowadays. Aiming at helping people at gym, we proposed a work that employs smartwatches to recognize and classify activities executed by the users, allowing users to exercise properly and easily. This way, the users will be able to control their exercise series more precisely, for instance. We develop a new open source application capable of capturing and providing data easily. We use all sensors available (e.g., accelerometer, gyroscope, magnetometer, barometer and linear acceleration) to capture as much data as possible to perform exercise classification after performing feature extraction.
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Target File2018_wip_gymsensors.pdf
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Next Higher Units8JMKD3MGPAW/3RPADUS
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