Bridging e-Health and the Internet of Things: The SPHERE Project

Nian, Zhu; Diethe, Tom; Camplani, Massimo; Tao, Lili; Burrows, Alison; Twomey, Niall; Kaleshi, Dritan; Mirmehdi, Majid; Flach, Peter A.; Craddock, Ian J

doi:10.1109/mis.2015.57

Cited by 221 publications

(151 citation statements)

References 29 publications

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“…Thus, the proposed method can readily fit into real-time monitoring systems. Indeed, the proposed method has been successfully tested in the real-time multi-camera video platform of the SPHERE sensor network system [25].…”

Section: Quantitative Evaluationmentioning

confidence: 99%

Real-time Estimation of Physical Activity Intensity for Daily Living

Tao

Burghardt

Mirmehdi

et al. 2016

2nd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2016)

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Section: Quantitative Evaluationmentioning

confidence: 99%

Real-time Estimation of Physical Activity Intensity for Daily Living

Tao

Burghardt

Mirmehdi

et al. 2016

2nd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2016)

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“…The Sensor Platform for HEalthcare in a Residential Environment (SPHERE) Interdisciplinary Research Collaboration (IRC) has developed a multi-modality sensing platform for collecting data from 100 houses in the Bristol area [1,2,3]. The overall architecture, which follows a clustered-sensor approach, is currently installed and running in a real house in Bristol (the SPHERE House).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised learning of sensor topologies for improving activity recognition in smart environments

et al. 2017

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. AbstractThere has been significant recent interest in sensing systems and 'smart environments', with a number of longitudinal studies in this area. Typically the goal of these studies is to develop methods to predict, at any one moment of time, the activity or activities that the resident(s) of the home are engaged in, which may in turn be used for determining normal or abnormal patterns of behaviour (e.g. in a health-care setting). Classification algorithms, such as Conditional Random Fields (CRFs), typically consider sensor activations as features but these are often treated as if they were independent, which in general they are not. Our hypothesis is that learning patterns based on combinations of sensors will be more powerful than single sensors alone. The exhaustive approach -to take all possible combinations of sensors and learn classifier weights for each combination -is clearly computationally prohibitive. We show that through the application of signal processing and informationtheoretic techniques we can learn about the sensor topology in the home (i.e. learn an adjacency matrix) which enables us to determine the combinations of sensors that will be useful for classification ahead of time. As a result we can achieve classification performance better than that of the exhaustive approach, whilst only incurring a small cost in terms of computational resources. We demonstrate our results on several datasets, showing that our method is robust in terms of variations in the layout and the number of residents in the house. Furthermore, we have incorporated the adjacency matrix into the CRF learning framework and have shown that it can improve performance over multiple baselines.

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“…Research interest in smart homes for eHealth and Active and Assisted Living (AAL) has continuously grown in the recent years. This research has examined different diseases and target groups [19,4] and technologies ranging from wearable devices gathering movement information [27] and vital signals [4], to environmental sensors [1] and video based platforms [9].…”

Section: Introductionmentioning

confidence: 99%

“…More flexible approaches based on smart cameras and distributed processing have been also employed, for example in [14,15], where colour data was processed in each node and processing results transmitted to central storage nodes. As previously mentioned, RGBD cameras are becoming increasingly more deployed in AAL applications, such as [9,20,27,21,22,11]. For a review of video based AAL systems, the reader is referred to [7,8].…”

Section: Introductionmentioning

confidence: 99%