Event-driven daily activity recognition with enhanced emergent modeling. (March 2023)

• Record Type: Journal Article
• Authors: Xu, Zimin; Wang, Guoli; Guo, Xuemei
• Is Part Of: Pattern recognition. Volume 135(2023)
• Publication Date: 2023-03
• Subjects: Emergent paradigm -- Marker-based stigmergy -- Directed-weighted network -- Activity modeling -- Daily activity recognition; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.109149
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: The activity modeling method based on the emergent paradigm and the directed-weighted network can fully extract feature structure. The additional context-aware information of each whole activity settles the issue of activity ambiguity. The heterogeneous mechanism of pheromone keeps the balance between the new and old context-aware information. Abstract: With the population aging, elderly health monitoring is triggering more studies on daily activity recognition as the fundamental of ambient assisted living. It is remarkable that activity recognition remains difficulties including how to adequately extract feature structure and settle the issue of activity confusion. To address these challenges, we propose a novel activity modeling method under the emergent paradigm with marker-based stigmergy and the directed-weighted network with additional context-aware information. In the modeling process, stigmergy is first introduced to aggregate the context information at the low level for generating activity pheromone trails, and then the constructed stigmergic trails are represented in form of directed-weighted network with distinguishability of individual pheromone source corresponding to location. The potential advantage is that the robust trails with distinguishable individual initial positions are feasible to supplement user's daily habits and thus both inter-class and intra-class distances can be kept at acceptable levels. Experiments on Aruba demonstrates that the proposed emergent modeling method can effectively deal with the problems of feature extraction and activity ambiguity and achieve good classification performance.