Real-Time Sensor Observation Segmentation For Complex Activity Recognition Within Smart Environments

Date
2017
Authors
Triboan, Darpan
Chen, Liming
Chen, Feng
Fallmann, S.
Psychoula, Ismini
Journal Title
Journal ISSN
ISSN
DOI
Volume Title
Publisher
IEEE
Peer reviewed
Yes
Abstract
Activity Recognition (AR) is at the heart of any types of assistive living systems. One of the key challenges faced in AR is segmentation of the sensor events when inhabitant performs simple or composite activities of daily living (ADLs). In addition, each inhabitant may follow a particular ritual or a tradition in performing different ADLs and their patterns may change overtime. Many recent studies apply methods to segment and recognise generic ADLs performed in a composite manner. However, little has been explored in semantically distinguishing individual sensor events and directly passing it to the relevant ongoing/new atomic activities. This paper proposes to use the ontological model to capture generic knowledge of ADLs and methods which also takes inhabitant-specific preferences into considerations when segmenting sensor events. The system implementation was developed, deployed and evaluated against 84 use case scenarios. The result suggests that all sensor events were adequately segmented with 98% accuracy and the average classification time of 3971ms and 62183ms for single and composite ADL scenarios were recorded, respectively.
Description
The file attached to this record is the author's final peer reviewed version
Keywords
Sensor Segmentation, Activities of Daily Living (ADL), Composite Activities, Ontology Modelling, Web Ontology Language (OWL), Activity Recognition (AR)
Citation
Triboan, D. et al. (2017) Real-Time Sensor Observation Segmentation For Complex Activity Recognition Within Smart Environments. 14th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC), San Francisco, August 2017.
Research Institute
Cyber Technology Institute (CTI)