期刊
ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2019, PT I
卷 11506, 期 -, 页码 360-367出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-20521-8_30
关键词
Activity recognition; Segmentation; Data window; Data fusion; Wearable sensors
资金
- Spanish Ministry of Economy and Competitiveness (MINECO) [TIN2015-71873-R, TIN201567020-P]
- European Fund for Regional Development (FEDER)
- User Behaviour Sensing, Modelling and Analysis contract [OTRI-UGR-4071]
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.
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