Online Activity Recognition Combining Dynamic Segmentation and Emergent Modeling

Activity recognition is fundamental to many applications envisaged in pervasive computing, especially in smart environments where the resident’s data collected from sensors will be mapped to human activities. Previous research usually focuses on scripted or pre-segmented sequences related to activit...

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Vydané v:Sensors (Basel, Switzerland) Ročník 22; číslo 6; s. 2250
Hlavní autori: Xu, Zimin, Wang, Guoli, Guo, Xuemei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 14.03.2022
MDPI
Predmet:
Activities of daily living
Algorithms
Datasets
Deep learning
directed weighted network
dynamic segmentation
emergent modeling
Home environment
Human Activities
Humans
Machine learning
Methods
Monitoring systems
online activity recognition
Sensors
Ubiquitous computing
directed weighted network
dynamic segmentation
online activity recognition
emergent modeling
ISSN:1424-8220, 1424-8220
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Shrnutí:Activity recognition is fundamental to many applications envisaged in pervasive computing, especially in smart environments where the resident’s data collected from sensors will be mapped to human activities. Previous research usually focuses on scripted or pre-segmented sequences related to activities, whereas many real-world deployments require information about the ongoing activities in real time. In this paper, we propose an online activity recognition model on streaming sensor data that incorporates the spatio-temporal correlation-based dynamic segmentation method and the stigmergy-based emergent modeling method to recognize activities when new sensor events are recorded. The dynamic segmentation approach integrating sensor correlation and time correlation judges whether two consecutive sensor events belong to the same window or not, avoiding events from very different functional areas or with a long time interval in the same window, thus obtaining the segmented window for every single event. Then, the emergent paradigm with marker-based stigmergy is adopted to build activity features that are explicitly represented as a directed weighted network to define the context for the last sensor event in this window, which does not need sophisticated domain knowledge. We validate the proposed method utilizing the real-world dataset Aruba from the CASAS project and the results show the effectiveness.
Bibliografia:ObjectType-Article-1
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22062250