Low energy physical activity recognition system on smartphones.
An innovative approach to physical activity recognition based on the use of discrete variables obtained from accelerometer sensors is presented. The system first performs a discretization process for each variable, which allows efficient recognition of activities performed by users using as little energy as possible. To this end, an innovative discretization and classification technique is presented based on the χ2 distribution. Furthermore, the entire recognition process is executed on the smartphone, which determines not only the activity performed, but also the frequency at which it is carried out. These techniques and the new classification system presented reduce energy consumption caused by the activity monitoring system. The energy saved increases smartphone usage time to more than 27 h without recharging while maintaining accuracy.
Computer Languages and Systems Department, University of Seville, 41012 Seville, Spain. lsoria@us.es.
Luis Miguel
LM
Soria Morillo
Applied Economics I Department, University of Seville, 41018 Seville, Spain. luisgon@us.es.
Luis
L
Gonzalez-Abril
Computer Languages and Systems Department, University of Seville, 41012 Seville, Spain. jortega@us.es.
Juan Antonio
JA
Ortega Ramirez
Computer Languages and Systems Department, University of Seville, 41012 Seville, Spain. maalvarez@us.es.
Miguel Angel Alvarez
MA
de la Concepcion
D040421:Cell Phone; D006801:Humans; D008991:Monitoring, Physiologic; D009043:Motor Activity / Q000502:physiology*