Browsing by Author "Sterritt, Roy"

Now showing 1 - 7 of 7
  • Thumbnail Image
    ItemOpen Access
    Dynamic Sensor Data Segmentation for Real time Activity Recognition
    (Elsevier, 2012-12-03) Okeyo, George; Chen, Liming; Wang, H.; Sterritt, Roy
    Approaches and algorithms for activity recognition have recently made substantial progress due to advancements in pervasive and mobile computing, smart environments and ambient assisted living. Nevertheless, it is still difficult to achieve real-time continuous activity recognition as sensor data segmentation remains a challenge. This paper 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 paper first analyzes 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 knowledge-driven activity recognition based on ontologies. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. Results have shown average recognition accuracy above 83% in all experiments for real time activity recognition, which proves the approach and the underlying model.
  • Thumbnail Image
    ItemOpen Access
    A Hybrid Ontological and Temporal Approach for Composite Activity Modelling
    (IEEE, 2012-06) Okeyo, George; Chen, Liming; Wang, H.; Sterritt, Roy
    Activity modelling is required to support activity recognition and further to provide activity assistance for users in smart homes. Current research in knowledge-driven activity modelling has mainly focused on single activities with little attention being paid to the modelling of composite activities such as interleaved and concurrent activities. This paper presents a hybrid approach to composite activity modelling by combining ontological and temporal knowledge modelling formalisms. Ontological modelling constructors, i.e. concepts and properties for describing composite activities, have been developed and temporal modelling operators have been introduced. As such, the resulting approach is able to model both static and dynamic characteristics of activities. Several composite activity models have been created based on the proposed approach. In addition, a set of inference rules has been provided for use in composite activity recognition. A concurrent meal preparation scenario is used to illustrate both the proposed approach and associated reasoning mechanisms for composite activity recognition.
  • No Thumbnail Available
    ItemMetadata only
    A Knowledge-driven Approach to Composite Activity Recognition in Smart Environments
    (Springer, 2012-12) Chen, Liming; Wang, H.; Sterritt, Roy; Okeyo, George
    Knowledge-driven activity recognition has recently attracted increasing attention but mainly focused on simple activities. This paper extends previous work to introduce a knowledge-driven approach to recognition of composite activities such as interleaved and concurrent activities. The approach combines ontological and temporal knowledge modelling formalisms for composite activity modelling. It exploits ontological reasoning for simple activity recognition and rule-based temporal inference to support composite activity recognition. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. The initial experimental results have shown that average recognition accuracy for simple and composite activities is 100% and 88.26%, respectively.
  • Thumbnail Image
    ItemEmbargo
    Ontology-based Learning Framework for Activity Assistance in an Adaptive Smart Home
    (Atlantis Press, 2011-05) Okeyo, George; Chen, Liming; Wang, H.; Sterritt, Roy
    Activity and behaviour modelling are significant for activity recognition and personalized assistance, respectively, in smart home based assisted living. Ontology-based activity and behaviour modelling is able to leverage domain knowledge and heuristics to create Activities of Daily Living (ADL) and behaviour models with rich semantics. However, they suffer from incompleteness, inflexibility, and lack of adaptation. In this article, we propose a novel approach for learning and evolving activity and behaviour models. The approach uses predefined “seed” ADL ontologies to identify activities from sensor activation streams. Similarly, we provide predefined, but initially unpopulated behaviour ontologies to aid behaviour recognition. First, we develop algorithms that analyze logs of activity data to discover new activities as well as the conditions for evolving the seed ADL ontologies. Consequently, we provide an algorithm for learning and evolving behaviours (or life habits) from these logs. We illustrate our approach through scenarios. The first scenario shows how ADL models can be evolved to accommodate new ADL activities and peculiarities of individual smart home’s inhabitants. The second scenario describes how, subsequent to ADL learning and evolution, behaviours can be learned and evolved.
  • No Thumbnail Available
    ItemMetadata only
    Ontology-Enabled Activity Learning and Model Evolution in Smart Homes
    (Springer, Berlin, Heidelberg, 2010-10-26) Sterritt, Roy; Wang, H.; Chen, Liming; Okeyo, George
    Activity modelling plays a critical role in activity recognition and assistance in smart home based assisted living. Ontology-based activity modelling is able to leverage domain knowledge and heuristics to create Activities of Daily Living (ADL) models with rich semantics. However, they suffer from incompleteness, inflexibility, and lack of adaptation. In this paper, we propose a novel approach for learning and evolving activity models. The approach uses predefined ”seed” ADL ontologies to identify activities from sensor activation streams. We develop algorithms that analyze logs of activity data to discover new activities as well as the conditions for evolving the seed ADL ontologies. We illustrate our approach through a scenario that shows how ADL models can be evolved to accommodate new ADL activities and preferences of individual smart home’s inhabitants.
  • No Thumbnail Available
    ItemMetadata only
    A Systematic Approach to Adaptive Activity Modeling and Discovery in Smart Homes
    (IEEE, 2011-12-12) Chen, Liming; Okeyo, George; Wang, H.; Sterritt, Roy; Nugent, Chris
    Activity modelling and discovery plays a critical role in smart home based assisted living. Existing approaches to pattern recognition using data-intensive analysis suffers from various drawbacks. To address these shortcomings, this paper introduces a novel ontology-based approach to activity modelling, activity discovery and evolution. In this approach, activity modelling is undertaken through ontological engineering by leveraging domain knowledge and heuristics. The generated activity models evolve from the initial “seed” activity models through continuous activity discovery and learning. Activity discovery is performed through ontological reasoning. The paper describes the approach in the context of smart home with special emphases placed on activity discovery algorithms and evolution mechanism. The approach has been implemented in a feature-rich assistive living system in which new daily activities can be detected and further used to evolve the underlying activity models.
  • No Thumbnail Available
    ItemMetadata only
    Time Handling for Real-time Progressive Activity Recognition
    (ACM, 2011-09-18) Sterritt, Roy; Wang, H.; Chen, Liming; Okeyo, George
    In a dense sensor-based smart home (SH), a significant challenge is to segment the sensor data stream in real-time to continuously support progressive activity recognition (AR). In this paper, we evaluate an approach that supports the segmentation of the sensor data stream used for knowledge-driven AR. The approach is based on the notion of dynamically varied sliding time windows, where data segments are formally modeled as time windows and ontological reasoning used to infer the ongoing activities of daily living (ADLs). We then present an algorithm that supports perpetual, real-time activity recognition and provide an implementation of both the proposed approach and the ADL ontology it uses. For evaluation, we developed a synthetic data generator and generated a set of synthetic ADLs. In addition, we implemented a real-time activity recognition system and a simple simulator that plays back a synthetic ADL as if the sensors are activated in real-time. We evaluated the real-time activity recognition algorithm, and obtained 99.2% average recognition accuracy on the synthetic ADLs tested. The ability of the algorithm to discriminate sensors that are activated in error was evaluated for selected ADLs and impressive results obtained.