SPHERE: A Sensor Platform for Healthcare in a Residential Environment

Woznowski, Przemyslaw; Burrows, Alison; Diethe, Tom; Fafoutis, Xenofon; Hall, Jake; Hannuna, Sion; Camplani, Massimo; Twomey, Niall; Kozłowski, Michał; Tan, Bo; Nian, Zhu; Elsts, Atis; Vafeas, Antonis; Paiement, Adeline; Tao, Lili; Mirmehdi, Majid; Burghardt, Tilo; Damen, Dima; Flach, Peter A.; Piechocki, Robert J.; Craddock, Ian J; Oikonomou, George

doi:10.1007/978-3-319-44924-1_14

Cited by 65 publications

(42 citation statements)

References 42 publications

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“…Further to this, the positioning of sensors also plays an important role, and it is likely that this will be a limiting factor for many applications, since the positioning of sensors is often largely driven by user acceptance rather than optimality of ADL recognition performance [9]. It is worth mentioning here, however, that in some settings, such as in the scenario described in the Sensor Platform for HEalthcare in Residential Environment (SPHERE) project [16][17][18], we may not limited to the use of accelerometers alone, and other sensor modalities may be more appropriate for the activities that are hard to classify using (e.g., wrist-worn) accelerometers.…”

Section: Summary Of Research Directions and Open Questionsmentioning

confidence: 99%

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A Comprehensive Study of Activity Recognition Using Accelerometers

et al. 2018

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This paper serves as a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. The paper is particularly focused on long-term activity recognition in real-world settings. In these environments, data collection is not a trivial matter; thus, there are performance trade-offs between prediction accuracy, which is not the sole system objective, and keeping the maintenance overhead at minimum levels. We examine research that has focused on the selection of activities, the features that are extracted from the accelerometer data, the segmentation of the time-series data, the locations of accelerometers, the selection and configuration trade-offs, the test/retest reliability, and the generalisation performance. Furthermore, we study these questions from an experimental platform and show, somewhat surprisingly, that many disparate experimental configurations yield comparable predictive performance on testing data. Our understanding of these results is that the experimental setup directly and indirectly defines a pathway for context to be delivered to the classifier, and that, in some settings, certain configurations are more optimal than alternatives. We conclude by identifying how the main results of this work can be used in practice, specifically in experimental configurations in challenging experimental conditions.

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Section: Summary Of Research Directions and Open Questionsmentioning

confidence: 99%

“…However, the SPHERE project is endeavouring to capture and release these datasets [16,17]. One of the challenges that will need to be addressed in this setting is that of acquiring labelled data, since the cohort that contribute to the data collection campaigns occur in the homes of the participants.…”

Section: Comparison Between Datasets and Classifiersmentioning

confidence: 99%

A Comprehensive Study of Activity Recognition Using Accelerometers

et al. 2018

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“…The SPHERE system [34,38], developed primarily at the University of Bristol, uses three sensing modalities: environmental, video, and wearable, as well as collecting contextual information through questionnaires.…”

Section: The Sphere Systemmentioning

confidence: 99%

“…With the aim of addressing these issues, the UK-funded "Sensor Platform for HEalthcare in a Residential Environment (SPHERE)" project [34,35,38] has designed a multi-modal system driven by data analytics requirements. The system was first tested in a single test-bed house, now with deployment underway in a general population of 100 homes in Bristol (UK).…”

Section: Introductionmentioning

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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“…Applications of passive radio sensing are numerous and include, for instance, fall detection for ambient assisted living [8] and industrial workspaces [9], healthcare and elderly care applications [10], gait monitoring, for instance in Parkinson treatment [11], counting of crowd in social mass gatherings [12], as well as sleep monitoring [13]. Due to the high frequency regimes utilized, impressive accuracy can be reached, for instance for the recognition of typing and finger movement [14], lip movement [15], or respiration sensing [16].…”

Section: Introductionmentioning

confidence: 99%

A Cloud-IoT Platform for Passive Radio Sensing: Challenges and Application Case Studies

Kianoush

Raja

Savazzi

et al. 2018

IEEE Internet Things J.

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We propose a platform for the integration of passive radio sensing and vision technologies into a cloud-IoT framework that performs real-time channel quality information (CQI) time series processing and analytics. Radio sensing and vision technologies allow to passively detect and track objects or persons by using radio waves as probe signals that encode a 2D/3D view of the environment they propagate through. View reconstruction from the received radio signals, or CQI, is based on real-time data processing tools, that combine multiple radio measurements from possibly heterogeneous IoT networks. The proposed platform is designed to efficiently store and analyze CQI time series of different types and provides formal semantics for CQI data manipulation (ontology models). Post-processed data can be then accessible to third parties via JSON-REST calls. Finally, the proposed system supports the reconfiguration of CQI data collection based on the respective application. The performance of the proposed tools are evaluated through two experimental case studies that focus on assisted living applications in a smartspace environment and on driver behavior recognition for in-car control services. Both studies adopt and compare different CQI manipulation models and radio devices as supported by current and future (5G) standards. Index Terms-Passive radio sensing, cloud-assisted Internet of Things, real-time data analysis, semantic data models.

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SPHERE: A Sensor Platform for Healthcare in a Residential Environment

Cited by 65 publications

References 42 publications

A Comprehensive Study of Activity Recognition Using Accelerometers

A Comprehensive Study of Activity Recognition Using Accelerometers

Releasing eHealth Analytics into the Wild

A Cloud-IoT Platform for Passive Radio Sensing: Challenges and Application Case Studies

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