Improving Wearable Activity Recognition via Fusion of Multiple Equally-Sized Data Subwindows
Autor:
Banos, Oresti
; Gálvez, Juan Manuel
; Damas, Miguel
; Guillén, Alberto
; Herrera, Luis Javier
; Pomares, Héctor
; Rojas, Ignacio
; Villalonga, Claudia (1)
Fecha:
2019Palabra clave:
Tipo de Ítem:
conferenceObjectResumen:
The automatic recognition of physical activities typically involves various signal processing and machine learning steps used to transform raw sensor data into activity labels. One crucial step has to do with the segmentation or windowing of the sensor data stream, as it has clear implications on the eventual accuracy level of the activity recogniser. While prior studies have proposed specific window sizes to generally achieve good recognition results, in this work we explore the potential of fusing multiple equally-sized subwindows to improve such recognition capabilities. We tested our approach for eight different subwindow sizes on a widely-used activity recognition dataset. The results show that the recognition performance can be increased up to 15% when using the fusion of equally-sized subwindows compared to using a classical single window.
Descripción:
Ponencia de la conferencia "15th International Work-Conference on Artificial Neural Networks, IWANN 2019; Gran Canaria; Spain; 12 June 2019 through 14 June 2019"
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