Semantic segmentation of real-time sensor data stream for complex activity recognition

Date

2017-02-18

Advisors

Journal Title

Journal ISSN

ISSN

1617-4909

Volume Title

Publisher

Springer

Type

Article

Peer reviewed

Yes

Abstract

Data segmentation plays a critical role in performing human activity recognition in the ambient assistant living systems. It is particularly important for complex activity recognition when the events occur in short bursts with attributes of multiple sub-tasks. Although substantial efforts have been made in segmenting the real-time sensor data stream such as static/dynamic window sizing approaches, little has been explored to exploit object semantic for discerning sensor data into multiple threads of activity of daily living. This paper proposes a semantic-based approach for segmenting sensor data series using ontologies to perform terminology box and assertion box reasoning, along with logical rules to infer whether the incoming sensor event is related to a given sequences of the activity. The proposed approach is illustrated using a use-case scenario which conducts semantic segmentation of a real-time sensor data stream to recognise an elderly persons complex activities.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Smart home, Semantic object modelling, Ontology-based segmentation and separation, Complex activity recognition, Activities of daily living (ADL)

Citation

Triboan, D. et al. (2017) Semantic segmentation of real-time sensor data stream for complex activity recognition. Personal and Ubiquitous Computing, 21 (3), pp. 411-425

Rights

Research Institute

Cyber Technology Institute (CTI)