A multi-modal sensor infrastructure for healthcare in a residential environment

Woznowski, Przemyslaw; Fafoutis, Xenofon; Song, Terence; Hannuna, Sion; Camplani, Massimo; Tao, Lili; Paiement, Adeline; Mellios, Evangelos; Haghighi, Mo; Nian, Zhu; Hilton, Geoff; Damen, Dima; Burghardt, Tilo; Mirmehdi, Majid; Piechocki, Robert J.; Kaleshi, Dritan; Craddock, Ian J

doi:10.1109/iccw.2015.7247190

Cited by 59 publications

(46 citation statements)

References 28 publications

(28 reference statements)

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“…1 provides a high-level view of the SPHERE hub and data sharing system. A detailed description of the system architecture and deployed sensors can be found in [14], along with a comparative analysis of similar multi-modal sensing platforms.…”

Section: Experimental Testbedmentioning

confidence: 99%

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Rule-Based Real-Time ADL Recognition in a Smart Home Environment

Baryannis

Woznowski

Antoniou

2016

Rule Technologies. Research, Tools, and Applications

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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. Abstract. This paper presents a rule-based approach for both offline and real-time recognition of Activities of Daily Living (ADL), leveraging events produced by a non-intrusive multi-modal sensor infrastructure deployed in a residential environment. Novel aspects of the approach include: the ability to recognise arbitrary scenarios of complex activities using bottom-up multi-level reasoning, starting from sensor events at the lowest level; an effective heuristics-based method for distinguishing between actual and ghost images in video data; and a highly accurate indoor localisation approach that fuses different sources of location information. The proposed approach is implemented as a rule-based system using Jess and is evaluated using data collected in a smart home environment. Experimental results show high levels of accuracy and performance, proving the effectiveness of the approach in real world setups.

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Section: Experimental Testbedmentioning

confidence: 99%

“…1. An overview of the SPHERE system architecture [14] multi-modal AAL environments. The SPHERE (Sensor Platform for HEalthcare in Residential Environment) architecture attempts to combine different sensing technologies to provide a generic platform for ADL recognition.…”

Section: Experimental Testbedmentioning

confidence: 99%

Rule-Based Real-Time ADL Recognition in a Smart Home Environment

Baryannis

Woznowski

Antoniou

2016

Rule Technologies. Research, Tools, and Applications

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“…There are also projects which link across these categories and analyse sensing data originating from di↵erent sensing modalities e.g. the SPHERE project [126]. The presented taxonomy is based on the means of perceiving the physical environment by the systems i.e.…”

Section: Activity Recognition and Taxonomymentioning

confidence: 99%

Classification and suitability of sensing technologies for activity recognition

Woznowski

Kaleshi

Oikonomou

et al. 2016

Computer Communications

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Wider availability of sensors and sensing systems has pushed research in the direction of automatic activity recognition (AR) either for medical or other personal benefits e.g. wellness or fitness monitoring. Researchers apply di↵erent AR techniques/algorithms and use a wide range of sensors to discover home activities. However, it seems that the AR algorithms are purely technology-driven rather than informing studies on the type and quality of input required. There is an expectation to over-instrument the environment or the subjects and then develop AR algorithms, where instead the problem should be approached from a di↵erent angle i.e. what sensors (type, quality and quantity) a given algorithm requires to infer particular activities with a certain confidence? This paper introduces the concept of activity recognition, its taxonomy and familiarises the reader with sub-classes of sensor-based AR. Furthermore, it presents an overview of existing health services Telecare and Telehealth solutions, and introduces the hierarchical taxonomy of human behaviour analysis tasks. This work is a result of a systematic literature review and it presents the reader with a comprehensive set of home-based activities of daily living (ADL) and sensors proven to recognise these activities. Apart from reviewing usefulness of various sensing technologies for homebased AR algorithms, it highlights the problem of technology-driven cycle of development in this area. IntroductionIn the last two decades sensors have become cheaper, smaller and widely available, residing at the edge of the Internet. Some such examples are wearable personal activity (PA) trackers (e.g. Fitbit, Nike+ FuelBand, etc.). However, the available commercial o↵-the-shelf (COTS) sensors are only capable of 'sensing' a small subset of user activities -mostly outdoor sport activities (type of activity, distance covered, time taken, etc.) and estimation of additional information such as energy expenditure (either kcal or self-crafted metrics e.g. Nike's fuel-points). However, a large part of our lives, and increasingly so in the advanced age, is spent in the home, yet very little is known about our activities and behaviour in there.We are surrounded by a multitude of sensing devices and Mark Weiser's vision of ubiquitous computing [120] is starting to materialise in the advances made in embedded networked systems currently addressed as the Internet of Things (IoT). The significant increase in devices streaming low-level information over the Web presents many new challenges. Whilst many researchers present this as a big data challenge, we believe that many of the environments and applications will require to justify the value and process relatively small data, making this a two-faceted problem requiring to consider the highly distributed, non-interoperable, small and relatively "lonely" data. E cient and accurate activity recognition (AR) algorithms are needed in order to make sense of this data and provide useful/actionable information and services in the human...

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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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A multi-modal sensor infrastructure for healthcare in a residential environment

Cited by 59 publications

References 28 publications

Rule-Based Real-Time ADL Recognition in a Smart Home Environment

Rule-Based Real-Time ADL Recognition in a Smart Home Environment

Classification and suitability of sensing technologies for activity recognition

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

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