Purpose -This paper aims to serve two main purposes. In the first instance it aims to it provide an overview addressing the state-of-the-art in the area of activity recognition, in particular, in the area of object-based activity recognition. This will provide the necessary material to inform relevant research communities of the latest developments in this area in addition to providing a reference for researchers and system developers who ware working towards the design and development of activity-based context aware applications. In the second instance this paper introduces a novel approach to activity recognition based on the use of ontological modeling, representation and reasoning, aiming to consolidate and improve existing approaches in terms of scalability, applicability and easy-of-use. Design/methodology/approach -The paper initially reviews the existing approaches and algorithms which have been used for activity recognition in a number of related areas. Form each of these their strengths and weaknesses are discussed with particular emphasis being placed on the application domain of sensor enabled intelligent pervasive environments. Based on an analysis of existing solutions, the paper then proposes an integrated ontology-based approach to activity recognition. The proposed approach adopts ontologies for modeling sensors, objects and activities, and exploits logical semantic reasoning for the purposes of activity recognition. This enables incremental progressive activity recognition at both coarse-grained and fine-grained levels. The approach has been considered within the realms of a real world activity recognition scenario in the context of assisted living within Smart Home environments. Findings -Existing activity recognition methods are mainly based on probabilistic reasoning, which inherently suffer from a number of limitations such as ad-hoc static models, data scarcity and scalability. Analysis of the state-of-the-art has helped to identify a major gap between existing approaches and the need for novel recognition approaches posed by the emerging multimodal sensor technologies and context-aware personalised activity-based applications in intelligent pervasive environments. The proposed ontology based approach to activity recognition is believed to be the first of its kind which provides an integrated framework based on the 2 Liming Chen, Chris Nugent unified conceptual backbone, i.e., activity ontologies, addressing the lifecycle of activity recognition. The approach allows easy incorporation of domain knowledge and machine understandability which facilitates interoperability, reusability and intelligent processing at a higher level of automation. Research limitations/implications -There are a number of challenges which have not been addressed within this paper. These mainly relate to the explicit modelling of the temporal dimension of the problem and managing uncertainty in both the sensor and decision making process. These will be key areas for consideration within our future work. Practical implications -We have used existing tools and APIs to illustrate the concepts of our activity recognition approach. Further work is required to support large-scale implementation and evaluation of the concepts in order for the approach to be deployed in real-world applications. Originality/value -The comprehensive overview and critiques on existing work on activity recognition provides a valuable reference for researchers and system developers in related research communities. The proposed ontology-based approach to activity recognition, in particular the recognition algorithm has been built upon description logic based semantic reasoning and offers a promising alternative to traditional probabilistic methods. In addition, Activities of Daily Living (ADL) activity ontologies in the context of smart homes have not been, to the best of our knowledge, been produced elsewhere.
The first approach, also called Data-Driven techniques are based on statistical and probabilistic reasoning and the second one, also called specification-based techniques, is based on logical modeling and reasoning algorithms REF .
Ontology‐based activity recognition in intelligent pervasive environments
IJWIS
IJWIS
Computer Science