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Erschienen in:

2019 | OriginalPaper | Buchkapitel

5. Time-Window Based Data Segmentation

verfasst von : Liming Chen, Chris D. Nugent

Erschienen in: Human Activity Recognition and Behaviour Analysis

Verlag: Springer International Publishing

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Abstract

This chapter presents a novel approach to real-time sensor data segmentation for continuous activity recognition. Central to the approach is a dynamic segmentation model, based on the notion of varied time windows, which can shrink and expand the segmentation window size by using temporal information of sensor data and activities as well as the state of activity recognition. The chapter first analyses the characteristics of activities of daily living from which the segmentation model that is applicable to a wide range of activity recognition scenarios is motivated and developed. It then describes the working mechanism and relevant algorithms of the model in the context of ontology-based activity recognition. An example case study has been undertaken in a number of experiments to evaluate and demonstrate the developed approach in a prototype system.

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Vorheriges Kapitel A Hybrid Approach to Activity Modelling

Nächstes Kapitel Semantic-Based Sensor Data Segmentation

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van Kasteren T, Noulas A, Englebienne G, Kröse B (2008) Accurate activity recognition in a home setting. In: Proceedings of the 10th international conference on Ubiquitous computing, UbiComp’08

Akdemir U, Turaga P, Chellappa R (2008) An ontology based approach for activity recognition from video. In: Proceeding of the 16th ACM international conference on multimedia, MM’08

Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: Ferscha A, Mattern F (eds) Pervasive Computing. Springer, Berlin, pp 1–17

Huỳnh T, Blanke U, Schiele B (2007) Scalable recognition of daily activities with wearable sensors. In: Hightower J, Schiele B, Strang T (eds) Location- and context-awareness. Springer, Berlin, pp 50–67CrossRef

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Stardog-union: Pellet: OWL 2 Reasoner for Java. https://github.com/stardog-union/pellet

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Okeyo G, Chen L, Wang H, Sterritt R (2010) Ontology-enabled activity learning and model evolution in smart homes. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)CrossRef

Titel: Time-Window Based Data Segmentation
verfasst von: Liming Chen
Chris D. Nugent
Verlag: Springer International Publishing
Buch: Human Activity Recognition and Behaviour Analysis
Print ISBN: 978-3-030-19407-9

Electronic ISBN: 978-3-030-19408-6

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-19408-6_5

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