Window Size Impact in Human Activity Recognition
By Oresti Banos, Juan Manuel Galvez, Miguel Damas, Héctor Pomares, Ignacio Rojas. Italian National Conference on Sensors, 2014
615 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/615a72aadb94a994f9d49548c0dffbeed1631ba2
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In Biomedical Engineering
In Computer Vision and Pattern Recognition
Citations in the corpus
- Inertial Sensor-Based Gait Recognition: A Review (2015, background): “At this point is it necessary to explain that this procedure is closely related with the problem of activity recognition that can be performed on the basis of inertial data, mostly relying on window-based approaches [59].”