Window Size Impact in Human Activity Recognition Baños Legrán, Oresti Gálvez Gómez, Juan Manuel Damas Hermoso, Miguel Pomares Cintas, Héctor Emilio Rojas Ruiz, Ignacio activity recognition segmentation windowing 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. 2024-10-01T11:13:11Z 2024-10-01T11:13:11Z 2014-04-09 journal article Baños Legrán, O. et. al. Sensors 2014, 14, 6474-6499. [https://doi.org/10.3390/s140406474] https://hdl.handle.net/10481/95359 10.3390/s140406474 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI