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Window Size Impact in Human Activity Recognition

Department of Computer Architecture and Computer Technology, Research Center for Information and Communications Technologies—University of Granada (CITIC-UGR), C/Calle Periodista Rafael Gomez Montero 2, Granada E18071, Spain
Author to whom correspondence should be addressed.
Sensors 2014, 14(4), 6474-6499;
Received: 10 December 2013 / Revised: 19 March 2014 / Accepted: 26 March 2014 / Published: 9 April 2014
(This article belongs to the Special Issue Wearable Gait Sensors)
Signal segmentation is a crucial stage in the activity recognition process; however, this has been rarely and vaguely characterized so far. Windowing approaches are normally used for segmentation, but no clear consensus exists on which window size should be preferably employed. In fact, most designs normally rely on figures used in previous works, but with no strict studies that support them. Intuitively, decreasing the window size allows for a faster activity detection, as well as reduced resources and energy needs. On the contrary, large data windows are normally considered for the recognition of complex activities. In this work, we present an extensive study to fairly characterize the windowing procedure, to determine its impact within the activity recognition process and to help clarify some of the habitual assumptions made during the recognition system design. To that end, some of the most widely used activity recognition procedures are evaluated for a wide range of window sizes and activities. From the evaluation, the interval 1–2 s proves to provide the best trade-off between recognition speed and accuracy. The study, specifically intended for on-body activity recognition systems, further provides designers with a set of guidelines devised to facilitate the system definition and configuration according to the particular application requirements and target activities. View Full-Text
Keywords: activity recognition; segmentation; windowing; window size; wearable sensors; inertial sensing; human behavior inference activity recognition; segmentation; windowing; window size; wearable sensors; inertial sensing; human behavior inference
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MDPI and ACS Style

Banos, O.; Galvez, J.-M.; Damas, M.; Pomares, H.; Rojas, I. Window Size Impact in Human Activity Recognition. Sensors 2014, 14, 6474-6499.

AMA Style

Banos O, Galvez J-M, Damas M, Pomares H, Rojas I. Window Size Impact in Human Activity Recognition. Sensors. 2014; 14(4):6474-6499.

Chicago/Turabian Style

Banos, Oresti, Juan-Manuel Galvez, Miguel Damas, Hector Pomares, and Ignacio Rojas. 2014. "Window Size Impact in Human Activity Recognition" Sensors 14, no. 4: 6474-6499.

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