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Woznowski, Przemyslaw; Tonkin, Emma L.; Laskowski, Pawel; Twomey, Niall; Yordanova, Kristina; Burrows, Alison

doi:10.1109/percomw.2017.7917544

Cited by 12 publications

(17 citation statements)

References 27 publications

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“…We then demonstrate how case studies in the use of the SPHERE ADL ontology have been used to aggregate data about the ontology in use, thus allowing us to perform a series of data-and application-based evaluations of the ontology [ 12 ]—that is, through evaluation of the performance of the applications powered by the ontology, and of the data collected through those applications, strengths and weaknesses of the ontology itself are identified. This article extends preliminary work previously published by Woznowski et al [ 13 , 14 ] by describing the development of the SPHERE ADL ontology, expressing its relationship to related knowledge structures and critically evaluating the ontology by reference to experience gained in its use in two case studies.…”

Section: Introductionmentioning

confidence: 54%

“…We begin with the most straightforward of these, the use of the ontology within an annotation tool used by a post-hoc observer working from scripted, recorded data, in this case video data [ 42 ]. The second case is the use of the ontology within an annotation tool [ 14 ] intended to support unscripted annotation in free living within a smart home environment, in this case a home in which the SPHERE system is deployed.…”

Section: The Role Of Case Studies In Validation and Revision Of Thmentioning

confidence: 99%

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Activities of Daily Living Ontology for Ubiquitous Systems: Development and Evaluation

Woznowski

Tonkin

Flach

2018

Sensors

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Ubiquitous eHealth systems based on sensor technologies are seen as key enablers in the effort to reduce the financial impact of an ageing society. At the heart of such systems sit activity recognition algorithms, which need sensor data to reason over, and a ground truth of adequate quality used for training and validation purposes. The large set up costs of such research projects and their complexity limit rapid developments in this area. Therefore, information sharing and reuse, especially in the context of collected datasets, is key in overcoming these barriers. One approach which facilitates this process by reducing ambiguity is the use of ontologies. This article presents a hierarchical ontology for activities of daily living (ADL), together with two use cases of ground truth acquisition in which this ontology has been successfully utilised. Requirements placed on the ontology by ongoing work are discussed.

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Section: Introductionmentioning

confidence: 54%

Section: The Role Of Case Studies In Validation and Revision Of Thmentioning

confidence: 99%

Activities of Daily Living Ontology for Ubiquitous Systems: Development and Evaluation

Woznowski

Tonkin

Flach

2018

Sensors

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“…This allows for producing very precise high quality annotation as the annotator can go back and re-annotate problematic parts of the log; 2. by directly observing the experiment participant and manually labelling their behaviour [ 16 ]. This allows for producing annotation on the run, which saves time, but, at the same time, produces annotation with relatively low reliability; 3. by self-annotating one’s own behaviour during the experiment [ 17 , 18 , 19 ]. This reduces the costs for external observer or offline annotation based on video logs.…”

Section: Annotation Of Human Behaviourmentioning

confidence: 99%

Creating and Exploring Semantic Annotation for Behaviour Analysis

Yordanova

2018

Sensors

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Providing ground truth is essential for activity recognition and behaviour analysis as it is needed for providing training data in methods of supervised learning, for providing context information for knowledge-based methods, and for quantifying the recognition performance. Semantic annotation extends simple symbolic labelling by assigning semantic meaning to the label, enabling further reasoning. In this paper, we present a novel approach to semantic annotation by means of plan operators. We provide a step by step description of the workflow to manually creating the ground truth annotation. To validate our approach, we create semantic annotation of the Carnegie Mellon University (CMU) grand challenge dataset, which is often cited, but, due to missing and incomplete annotation, almost never used. We show that it is possible to derive hidden properties, behavioural routines, and changes in initial and goal conditions in the annotated dataset. We evaluate the quality of the annotation by calculating the interrater reliability between two annotators who labelled the dataset. The results show very good overlapping (Cohen’s κ of 0.8) between the annotators. The produced annotation and the semantic models are publicly available, in order to enable further usage of the CMU grand challenge dataset.

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“…As a fundamental step towards intelligent healthcare, activity recognition and indoor localisation lie at the core of the ML component within the SPHERE system. However, as described in the previous section, ground truth for activities and locations are particularly hard to obtain in a smart home environment [36]. The difficulties can be seen from two perspectives.…”

Section: Annotation Collectionmentioning

confidence: 99%

Releasing eHealth Analytics into the Wild

Diethe

Holmes

Kull

et al. 2018

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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The SPHERE project is devoted to advancing eHealth in a smarthome context, and supports full-scale sensing and data analysis to enable a generic healthcare service. We describe, from a datascience perspective, our experience of taking the system out of the laboratory into more than thirty homes in Bristol, UK. We describe the infrastructure and processes that had to be developed along the way, describe how we train and deploy Machine Learning systems in this context, and give a realistic appraisal of the state of the deployed systems. CCS CONCEPTS • Computing methodologies → Machine learning; • Applied computing → Health informatics; • Hardware → Sensor applications and deployments; • Information systems → Sensor networks; Data streaming; • Social and professional topics → Remote medicine;

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Talk, text or tag?

Cited by 12 publications

References 27 publications

Activities of Daily Living Ontology for Ubiquitous Systems: Development and Evaluation

Activities of Daily Living Ontology for Ubiquitous Systems: Development and Evaluation

Creating and Exploring Semantic Annotation for Behaviour Analysis

Releasing eHealth Analytics into the Wild

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