The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Window Size Impact in Human Activity Recognition
Open accessAbstract
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.
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Citations
Cites
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Feature extraction for robust physical activity recognition
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Prediction of gait trajectories based on the Long Short Term Memory neural networks
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Analysis of Multi-Sensor Fusion for Mobile and Wearable Sensor Based Human Activity Recognition
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Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor
Boborzi, L., Decker, J., Rezaei, R., Schniepp, R., & Wuehr, M. (2024) · Sensors · doi:10.3390/s24092665 · Cited by 9
Across-Sensor Feature Learning for Energy-Efficient Activity Recognition on Mobile Devices
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Temporal Distance Based Self-Supervised Learning for Human Activity Recognition
Bigelli, L., Contoli, C., Kania, N., & Lattanzi, E. (2025) · 2025 Sixth International Conference on Intelligent Data Science Technologies and Applications (IDSTA) · doi:10.1109/idsta66210.2025.11202839
Metadata
- Authors
- Banos, O., Galvez, J.-M., Damas, M., Pomares, H., & Rojas, I.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2014-04-09
- Volume
- 14
- Issue
- 4
- Pages
- 6474-6499
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2140944144
- Cited by
- 648
- References
- 7
- FWCI
- 23.15
- Type
- journal article
- Language
- en
- Open access
- gold
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