Ponencia
Online motions recognition method using mobile phone accelerometer
Autor/es | Fuentes Brenes, Daniel
González Abril, Luis Parera Giro, Jordi Angulo, Cecilio Ortega Ramírez, Juan Antonio |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos Universidad de Sevilla. Departamento de Economía Aplicada I |
Fecha de publicación | 2009-06 |
Fecha de depósito | 2023-05-24 |
Publicado en |
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ISBN/ISSN | 978-84-613-71-587 |
Resumen | Accelerometers can obtain data about the movements of sorne person. Ihe continued study of this information can help to a doctor to establish a correct diagnosis or a rehabilitation plan for a person with mobility problems. ... Accelerometers can obtain data about the movements of sorne person. Ihe continued study of this information can help to a doctor to establish a correct diagnosis or a rehabilitation plan for a person with mobility problems. This paper focuses on a new method to implement a motion recognition process with accelerometer sensor data contained in a mobile device. Ali the steps are described, from the data collection to motion recognition through statistical study data and machine leaming algorithms. Nowadays, mobile phones have big processing capacity to execute complex programs. In the last step of this process, a classification function is implemented in a mobile phone for online motion recognition. The practical experience showed an overall accuracy of 91 % when recognizing four activities. |
Agencias financiadoras | Ministerio de Ciencia e Innovación (MICIN). España Junta de Andalucía Ministerio de Educación y Ciencia (MEC). España |
Identificador del proyecto | TIN2009-14378-C02-01
TIC2141 TS12006-13390-C02-02 |
Cita | Fuentes Brenes, D., González Abril, L., Parera Giro, J., Angulo, C. y Ortega Ramírez, J.A. (2009). Online motions recognition method using mobile phone accelerometer. En XI Jornadas de ARCA. Sistemas Cualitativos, Diagnosis, Robótica, Sistemas Domóticos y Computación Ubicua (JARCA 2009) (23-27), Almuñecar, Granada: Universidad de Sevilla. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Online motions recognition.pdf | 2.325Mb | [PDF] | Ver/ | |