Diethe, Tom

Flach, Peter

Twomey, Niall

Obesity, depression, stroke, falls, cardiovascular and musculoskeletal disease are some of the biggest health issues and fastest-rising categories of healthcare costs. The associated expenditure is widely regarded as unsustainable and the impact on quality of life is felt by millions of people in the UK each day. The vision of the SPHERE IRC is not to develop fundamentally-new sensors for individual health conditions but rather to impact all these healthcare needs simultaneously through data-fusion and pattern-recognition from a common platform of non-medical/environmental sensors at home. The system will be general-purpose, low-cost and scalable. Sensors will be entirely passive, requiring no action by the user and hence suitable for all patients including the most vulnerable. A central hypothesis is that deviations from a user's established pattern of behaviour in their own home have particular, unexploited, diagnostic value

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                          Diethe, T., Twomey, N., & Flach, P. (2014). SPHERE-a Sensor Platform forHealthcare in a Residential Environment.Peer reviewed versionLink to publication record in Explore Bristol ResearchPDF-documentUniversity of Bristol - Explore Bristol ResearchGeneral rightsThis document is made available in accordance with publisher policies. Please cite only the publishedversion using the reference above. Full terms of use are available:http://www.bristol.ac.uk/pure/about/ebr-terms.htmlSPHERE - a Sensor Platform for HEalthcare in aResidential EnvironmentTom R. Diethe, Niall Twomey, Peter FlachDepartment of Electrical and Electronic EngineeringUniversity of BristolBristol, BS8 1UB, UK{tom.diethe, niall.twomey, peter.flach}@bristol.ac.ukAbstractObesity, depression, stroke, falls, cardiovascular and musculoskeletal disease aresome of the biggest health issues and fastest-rising categories of healthcare costs.The associated expenditure is widely regarded as unsustainable and the impacton quality of life is felt by millions of people in the UK each day. The visionof the SPHERE IRC is not to develop fundamentally-new sensors for individ-ual health conditions but rather to impact all these healthcare needs simultane-ously through data-fusion and pattern-recognition from a common platform ofnon-medical/environmental sensors at home. The system will be general-purpose,low-cost and scalable. Sensors will be entirely passive, requiring no action by theuser and hence suitable for all patients including the most vulnerable. A centralhypothesis is that deviations from a user’s established pattern of behaviour in theirown home have particular, unexploited, diagnostic value.1 The SPHERE IRCThe “SPHERE” Inter-disciplinary Research Collaboration (IRC) has been developed with clinicians,social workers and clinical scientists from internationally recognised institutes including the BristolHeart Institute, Southampton’s Rehabilitation and Health Technologies Group, the NIHR Biomedi-cal Research Unit in Diet and Nutrition and the Orthopaedic Surgery Group at Southmead hospitalin Bristol. United by a shared vision, the IRC also includes a local authority that is a UK leader in thefield of “Smart Cities” (Bristol City Council, BCC), a local charity with an impressive track recordof community-based technology pilots (Knowle West Media Centre, KWMC) and a unique lon-gitudinal study (the world-renowned Avon Longitudinal Study of Parents and Children (ALSPAC),a.k.a. “The Children of the Nineties”). SPHERE draws upon expertise from the UK’s leading groupsin Communications, Machine Vision, Cybernetics, Data Mining and Energy Harvesting, and fromtwo corporations with world-class reputations for research and development (IBM, Toshiba).Working hand-in-hand with the local community through BCC and its partners at KWMC, SPHEREis developing practical, user-accepted technologies and pilot systems in a large number of homesover extended periods of time. Leading clinicians in Heart Surgery, Orthopaedics, Stroke andParkinson’s Disease, and recognised authorities on Depression and Obesity are embedded withinthe IRC.The proposal, led by the University of Bristol in partnership with Reading and Southampton wasfunded in full (∼£12M) and also attracted additional investment from the Universities and industryof∼£3M. The total resource of∼15M is supporting a team of mostly postdoctoral researchers (withsome postgraduate students). In total, including the academic co-investigators actively managingelements of the research programme, SPHERE comprises approximately 60 people.12 Machine Learning and Data Mining for SPHEREInvestigations are underway into a wide variety of sensor technologies, including cameras, motionsensors, ambient climatic sensors and a variety of on-body sensors. Based on their performance (interms of reliability, discriminative ability, monetary and energy costs), some subset of these sensorswill be incorporated into systems to deployed in houses.The information from the individual sensors must be preprocessed, integrated and mined to providea most likely model of activity which maximises information content in a health monitoring context.Moreover, the decision-making process (e.g., distinguishing between regular activities and medicalemergencies) needs to be implemented and fine-tuned taking into account the specific characteristicsof sensors and people.2.1 Feature Construction and SelectionThe interpretation of the preprocessed sensor data to identify meaningful features in the data requiresunderstanding of the data generation process and hence will be closely coupled to the developmentof the individual sensing modalities in the initial phase of the project. The approach being takenselects features through the discovery of subgroups that behave significantly differently with respectto a particular target [1] and exploits those features in modelling customer behaviour through userprofiles.One of the main hypotheses underlying the SPHERE IRC is that many weak signals from particularsensors can be fused into a strong signal allowing meaningful health-related interventions. A nec-essary step here is signal calibration, for which the team will apply a range of techniques includingpowerful nonparametric methods derived from the ROC convex hull [2]. Further fusion arises fromthe combination of multiple (homogeneous or heterogeneous) sensor-level signals with knowledgeof the kinematics and bio-mechanics of movement [3, 4], and with information regarding the widercontext such as the time of the day etc.Based on the calibrated and fused signals, the system must decide when to intervene and whichintervention to recommend; interventions will need to be information-gathering as well as healthproviding. Existing approaches to this exploitation-exploration dilemma of machine learning will beextended to address the challenges of costly interventions and complex data-structures [5]. Interfaceswill need to communicate (visualise) the data, such that it is informative, assists in decision-makingand helps to influence improved health behaviour; this includes communicating uncertainty andconflicts within data.2.2 Background Knowledge and Operating ContextsData mining and decision making needs to be contextualised and in this case will be situated withina wide body of non-trivial health-related background knowledge including knowledge of the humansubjects, of their likely activities and of their medical condition(s). We will draw on previous workin incorporating and utilising structured background knowledge into the data mining process to buildbetter models that are highly explanatory [6]. The operating context will however inevitably varyfrom training to deployment and between different deployment situations; an example is the use ofsignal detection theory to adapt the false positive rate to varying costs.2.3 Quantification of UncertaintyWhen dealing with multiple sensors in a real-world environment, we are faced with many differentsources of uncertainty. At a basic level, we might have sensors that are simply not working, or thatare giving incorrect readings. More generally, a given sensor will at any given time have a particularsignal to noise ratio, and the types of noise that are corrupting the signal might also vary. Oneapproach to solving this is to use standard signal processing techniques to try to clean up the signalbefore performing learning. However, this means that potentially useful information about the levelsof uncertainty in the information has been effectively thrown away.As a result we need to be able to handle quantities whose values are uncertain, and we need aprincipled framework for quantifying uncertainty which will allow us to build solutions in ways that2Figure 1: Demonstration of the ground truth activity labels (top) and predictions from the CRF(bottom). Co-occurring activities are seen when multiple colours are seen the upper image. Regionsmarked with a and c identifies co-occurring activities, b shows mislabelled activities and d marks anfalse negative activity.can represent and process uncertain values. The approach we will take will be to build a model ofthe data-generating process, which directly incorporates the noise models for each of the sensors.Probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexiblefoundation for such model-based machine learning [7]. The probabilistic programming languageInfer.NET [8], which has been widely used in practical applications, will allow us to construct anddeploy these models.2.4 Temporal Patterns and PersonalisationMany sensors provide continuous streams of data that can be mined for temporal patterns concerningbehaviour that varies from individual to individual. For example, a period of inactivity at a certaintime might mean an afternoon nap for one person but a medical emergency for another. Following onfrom previous work in trend analysis of stream data (e.g., identifying flu epidemics from Twitter [9])will allow the system to be adapted to personal habits and circumstances. These temporal patternscan be directly built in to our model-based framework, and additionally can be learnt on both agroup-wide and individual level to learn context sensitive and specific patterns.2.5 Initial Work: Activity ClassificationThe Sphere project is in the process of populating a house with sensors so that, in collaboration withour clinical partners, novel data, characteristic of important medical episodes, will be recorded forlater analysis. In parallel with the collection of these data, we have already begun to investigatethe efficacy of employing probabilistic approaches for activity recognition on third-party on-bodyand smart home sensor datasets. In particular we have investigated datasets from the CASAS re-search group 1 [10], and our results convey the utility of conditional random fields (CRFs) [11] forclassification of activities of daily living in smart home environments [12].Figure 1 shows an example of activity prediction on smart home sensor data, where the activities be-ing performed can be identified by the indicated colours. In this figure the ground truth and predictedactivities are shown in the upper and lower images respectively. As multiple residents inhabited thetestbed at the time of recording, multiple activities can be labelled as occurring concurrently; this isillustrated in Figure 1 (upper) by regions a, b, c, and d. While multiple activities may co-occur inthe dataset, only one activity from the set is, in general, responsible for the individual sensor events,and so the predictions in the lower image predict only one activity at all time points. Our approachelicits good visual correspondence between ground truth and predicted activities, and we obtained aper-instance precision of ≈ 85% with false negative rates of ≈ 6% (see [12] for more details).1http://ailab.wsu.edu/casas/datasets/3AcknowledgmentsThis work was performed under the SPHERE IRC funded by the UK Engineering and PhysicalSciences Research Council (EPSRC), Grant EP/K031910/1.References[1] Tarek Abudawood, Peter Flach, Masashi Sugiyama, and Qiang Yang. Exploiting the high predictivepower of multi-class subgroups. In JMLR Workshop and Conference Proceedings Volume 13: 2nd AsianConference on Machine Learning (ACML’10), pages 177–192, 2010.[2] Jose´ Herna´ndez-Orallo, Peter Flach, and Ce`sar Ferri. A unified view of performance metrics: Translatingthreshold choice into expected classification loss. J. Mach. Learn. Res., 13(1):2813–2869, October 2012.[3] William Neil L Browne, K Kawamura, J Krichmar, W Harwin, and H Wagatsuma. Cognitive robotics:new insights into robot and human intelligence by reverse engineering brain functions. IEEE Roboticsand Automation Magazine, 16:17 – 18, 2009.[4] V.M. Becerra, F.R. Garces, S.J. Nasuto, and W. Holderbaum. An efficient parameterization of dynamicneural networks for nonlinear system identification. Neural Networks, IEEE Transactions on, 16(4):983–988, July 2005.[5] Benedict C. May, Nathan Korda, Anthony Lee, and David S. Leslie. Optimistic bayesian sampling incontextual-bandit problems. J. Mach. Learn. Res., 13:2069–2106, June 2012.[6] Thomas Ga¨rtner, John W. Lloyd, and Peter A. Flach. Kernels and distances for structured data. MachineLearning, 2004.[7] Bishop C.M. Model-based machine learning. Phil Trans R Soc A, 371, 2013.[8] T. Minka, J.M. Winn, J.P. Guiver, and D.A. Knowles. Infer.NET 2.5, 2012. Microsoft Research Cam-bridge. http://research.microsoft.com/infernet.[9] Vasileios Lampos and Nello Cristianini. Nowcasting events from the social web with statistical learning.ACM Trans. Intell. Syst. Technol., 3(4):72:1–72:22, September 2012.[10] Diane J Cook and M Schmitter-Edgecombe. Assessing the quality of activities in a smart environment.Methods Inf Med, 48(5):480–5, 2009.[11] John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. Conditional random fields: Probabilis-tic models for segmenting and labeling sequence data. In Proceedings of the Eighteenth InternationalConference on Machine Learning, ICML ’01, pages 282–289, San Francisco, CA, USA, 2001. MorganKaufmann Publishers Inc.[12] N. Twomey and P. Flach. Context modulation of sensor data applied to activity recognition in smarthomes. In Workshop on Learning over Multiple Contexts, European Conference on Machine Learning(ECML’14), in press.4

SPHERE-a Sensor Platform for Healthcare in a Residential Environment

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SPHERE-a Sensor Platform for Healthcare in a Residential Environment

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