%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2018/10.26.04.25
%2 sid.inpe.br/sibgrapi/2018/10.26.04.25.12
%T Automatic Gym Workout Recognition using Wearable Devices
%D 2018
%A Beltrão, Davi Faria de Assis,
%A Nazare, Antônio Carlos,
%A Schwartz, William Robson,
%@affiliation Universidade Federal de Minas Gerais
%@affiliation Universidade Federal de Minas Gerais
%@affiliation Universidade Federal de Minas Gerais
%E Ross, Arun,
%E Gastal, Eduardo S. L.,
%E Jorge, Joaquim A.,
%E Queiroz, Ricardo L. de,
%E Minetto, Rodrigo,
%E Sarkar, Sudeep,
%E Papa, João Paulo,
%E Oliveira, Manuel M.,
%E Arbeláez, Pablo,
%E Mery, Domingo,
%E Oliveira, Maria Cristina Ferreira de,
%E Spina, Thiago Vallin,
%E Mendes, Caroline Mazetto,
%E Costa, Henrique Sérgio Gutierrez,
%E Mejail, Marta Estela,
%E Geus, Klaus de,
%E Scheer, Sergio,
%B Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)
%C Foz do Iguaçu, PR, Brazil
%8 29 Oct.-1 Nov. 2018
%I Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K gym recognition, smartwatch.
%X It 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.
%@language en
%3 2018_wip_gymsensors.pdf