A Comparative Analysis of Windowing Approaches in Dense Sensing Environments

Proceedings

Donnelly, Mark¹  Quigley, Bronagh²  Moore, George³ 

[1] Author to whom correspondence should be addressed.;Pervasive Computing Research Group, School of Computing, Ulster University, Coleraine BT37 0QB, Northern Ireland;Presented at the 12th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2018), Punta Cana, Dominican Republic, 4–7 December 2018

关键词: windowing;    segmentation;    human activity recognition;    smart home;    dynamic;    sensor event;    time;   

DOI  :  10.3390/proceedings2191245

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY