Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia

Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 hospital beds are occupied by a person with dementia, while about 22% of these hospital admissions are due to preventable causes. In this paper we discuss using Internet of Things (IoT) technologies and in-home sensory devices in combination with machine learning techniques to monitor health and well-being of people with dementia. This will allow us to provide more effective and preventative care and reduce preventable hospital admissions. One of the unique aspects of this work is combining environmental data with physiological data collected via low cost in-home sensory devices to extract actionable information regarding the health and well-being of people with dementia in their own home environment. We have worked with clinicians to design our machine learning algorithms where we focused on developing solutions for real-world settings. In our solutions, we avoid generating too many alerts/alarms to prevent increasing the monitoring and support workload. We have designed an algorithm to detect Urinary Tract Infections (UTI) which is one of the top five reasons of hospital admissions for people with dementia (around 9% of hospital admissions for people with dementia in the UK). To develop the UTI detection algorithm, we have used a Non-negative Matrix Factorisation (NMF) technique to extract latent factors from raw observation and use them for clustering and identifying the possible UTI cases. In addition, we have designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline in order to provide personalised and preventative care services. For this purpose, we have used an Isolation Forest (iForest) technique to create a holistic view of the daily activity patterns. This paper describes the algorithms and discusses the evaluation of the work using a large set of real-world data collected from a trial with people with dementia and their caregivers

Genuine Correlations of Like-Sign Particles in Hadronic Z0 Decays

Correlations among hadrons with the same electric charge produced in Z0 decays are studied using the high statistics data collected from 1991 through 1995 with the OPAL detector at LEP. Normalized factorial cumulants up to fourth order are used to measure genuine particle correlations as a function of the size of phase space domains in rapidity, azimuthal angle and transverse momentum. Both all-charge and like-sign particle combinations show strong positive genuine correlations. One-dimensional cumulants initially increase rapidly with decreasing size of the phase space cells but saturate quickly. In contrast, cumulants in two- and three-dimensional domains continue to increase. The strong rise of the cumulants for all-charge multiplets is increasingly driven by that of like-sign multiplets. This points to the likely influence of Bose-Einstein correlations. Some of the recently proposed algorithms to simulate Bose-Einstein effects, implemented in the Monte Carlo model PYTHIA, are found to reproduce reasonably well the measured second- and higher-order correlations between particles with the same charge as well as those in all-charge particle multiplets.Comment: 26 pages, 6 figures, Submitted to Phys. Lett.

Measurement of the B0 Lifetime and Oscillation Frequency using B0->D*+l-v decays

The lifetime and oscillation frequency of the B0 meson has been measured using B0->D*+l-v decays recorded on the Z0 peak with the OPAL detector at LEP. The D*+ -> D0pi+ decays were reconstructed using an inclusive technique and the production flavour of the B0 mesons was determined using a combination of tags from the rest of the event. The results t_B0 = 1.541 +- 0.028 +- 0.023 ps, Dm_d = 0.497 +- 0.024 +- 0.025 ps-1 were obtained, where in each case the first error is statistical and the second systematic.Comment: 17 pages, 4 figures, submitted to Phys. Lett.

IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services

With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT

The high-level overview of the interactions in TIHM.

<p>The high-level overview of the interactions in TIHM.</p

Illustration of correlation between the total number of detected anomalies and total number of validated notifications for 12 participants by the clinical monitoring team.

<p>Illustration of correlation between the total number of detected anomalies and total number of validated notifications for 12 participants by the clinical monitoring team.</p

Classification insights into decision fusion model.

<p>Classification insights into decision fusion model.</p

Receiver operating characteristic curves for multi-occupancy, participant-specific and decision fusion models for the detection of AIA.

<p>Receiver operating characteristic curves for multi-occupancy, participant-specific and decision fusion models for the detection of AIA.</p

Illustration of the training data.

<p>(a) Demonstration of data collected from an individual’s home over 39 training days. The data is aggregated in 10 minutes intervals and normalised to ensure that the activity level of each sensor is ranged between 0 to 10. The value 0 in the rightmost colour legend corresponding to a dark blue colour indicates no activity and value 10 corresponding to a yellow colour indicates high activity. (b) Aggregated data from 5 sensors; where each 10 minute interval is a five-dimensional array (top). Clustering the aggregated data to map each five-dimensional window to a single state (middle). Dividing the training data into low-active (LA) and high-active (HA) categories (below).</p