Next Article in Journal
Associations between Commonly Used Characteristics in Frailty Assessment and Mental State in Frail Elderly People
Previous Article in Journal
Real Time Water Quality Monitoring Boat
Proceedings

A Comparative Analysis of Windowing Approaches in Dense Sensing Environments

Pervasive Computing Research Group, School of Computing, Ulster University, Coleraine BT37 0QB, Northern Ireland
*
Author to whom correspondence should be addressed.
Presented at the 12th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2018), Punta Cana, Dominican Republic, 4–7 December 2018.
Proceedings 2018, 2(19), 1245; https://doi.org/10.3390/proceedings2191245
Published: 17 October 2018
(This article belongs to the Proceedings of UCAmI 2018)
Windowing is an established technique employed within dense sensing environments to extract relevant features from sensor data streams. Among the established approaches of Explicit, Time-based and Sensor-Event based windowing, Dynamic windowing approaches are beginning to emerge. These dynamic approaches claim to address the inherent shortcomings of the aforementioned established approaches by determining the appropriate window length for live sensor data streams in real-time, thereby offering the potential to optimize and increase the recognition of these sensor represented activities. Beyond these potential benefits, dynamic approaches can also support anomaly detection by actively uncovering new, unknown window patterns within a trained model. This paper presents findings from a study which utilizes data from a single source dataset, towards benchmarking and comparing more traditional windowing approaches against a dynamic windowing approach. The experiments conducted on a real-world smart home dataset suggest Time-based windowing is the best approach. Through evaluation of results, Dynamic windowing approaches may benefit from carefully annotated datasets.
Keywords: windowing; segmentation; human activity recognition; smart home; dynamic; sensor event; time windowing; segmentation; human activity recognition; smart home; dynamic; sensor event; time
MDPI and ACS Style

Quigley, B.; Donnelly, M.; Moore, G.; Galway, L. A Comparative Analysis of Windowing Approaches in Dense Sensing Environments. Proceedings 2018, 2, 1245. https://doi.org/10.3390/proceedings2191245

AMA Style

Quigley B, Donnelly M, Moore G, Galway L. A Comparative Analysis of Windowing Approaches in Dense Sensing Environments. Proceedings. 2018; 2(19):1245. https://doi.org/10.3390/proceedings2191245

Chicago/Turabian Style

Quigley, Bronagh, Mark Donnelly, George Moore, and Leo Galway. 2018. "A Comparative Analysis of Windowing Approaches in Dense Sensing Environments" Proceedings 2, no. 19: 1245. https://doi.org/10.3390/proceedings2191245

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop