Sensors 2014, 14(4), 6474-6499; doi:10.3390/s140406474
Article

Window Size Impact in Human Activity Recognition

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Received: 10 December 2013; in revised form: 19 March 2014 / Accepted: 26 March 2014 / Published: 9 April 2014
(This article belongs to the Special Issue Wearable Gait Sensors)
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Abstract: 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.
Keywords: activity recognition; segmentation; windowing; window size; wearable sensors; inertial sensing; human behavior inference
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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


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