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@InProceedings{Beltr„oNazaSchw:2018:AuGyWo,
               author = "Beltr{\~a}o, Davi Faria de Assis and Nazare, Ant{\^o}nio Carlos 
                         and Schwartz, William Robson",
          affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal 
                         de Minas Gerais} and {Universidade Federal de Minas Gerais}",
                title = "Automatic Gym Workout Recognition using Wearable Devices",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "gym recognition, smartwatch.",
             abstract = "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.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
                  ibi = "8JMKD3MGPAW/3S4Q5ES",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3S4Q5ES",
           targetfile = "2018_wip_gymsensors.pdf",
        urlaccessdate = "2020, Dec. 02"
}


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