Combined Learning Models for AnalysingTime­series Data in Healthcare

"The developments in data collection technologies and their growing availability lead to an extensive amount of time-series data. The collected data contain information from people's day-to-day lives, health status or activities. However, this huge amount of data need to be analysed to extract useful information. These insights about human lives and health can be helpful in different fields and areas such as marketing, transportation, health tracking and improving quality of life, education and safety.There are extensive research and studies to improve the time-series analysis. One area which has gained more attention is machine learning and deep learning. Machine learning can be useful in gathering insight from massive datasets and developing models for predictive reasons. However, there are challenges in applying machine learning methods and techniques to time-series data.This research proposes a number of methods to tackle some of the challenges and issues in this area. This work proposes a novel representation model to deal with high-dimensionality of time-series data. The model preserves the principal information of data and we demonstrate how this data representation can be used in different time-series data analysis such as clustering, classification and change point detection. We also compared the proposed model with other representation models in clustering which showed 20% higher clustering quality in Silhouette coefficient measure.Healthcare and especially remote healthcare is an area that can benefit from time-series data analysis. An attention-based model has also been implemented in a dementia care project. Attention-based model is a deep learning model that can find important features in data and give higher weight to them in the prediction. The proposed model is able to detect the risk of adverse health related incidents at the homes of people with dementia with a recall of 91% and precision of 83%.Finally, to further improve robustness of the analysis methods, a semi-supervised model has been developed. The model can use a limited amount of labelled data and improve the performance compared to other state-of-the-art models. The model shows 27% higher recall in average compared with other models in detecting agitation in people with dementia.

Lagrangian-based Pattern Extraction for Edge Computing in the Internet of Things

Edge computing can improve the scalability and efficiency of IoT systems by performing some of the analysis and operations on the nodes or on intermediary edge devices. This will reduce the energy consumption, data transmission load and latency by shifting some of the processes to the edge devices. In this paper, we introduce a pattern extraction method which uses both the Lagrangian Multiplier and the Principal Component Analysis (PCA) to create patterns from raw sensory data. We have evaluated our method by applying a clustering method on constructed patterns. The results show that by using our proposed Lagrangian-based pattern extraction method, the existing clustering algorithms perform more accurately - by up to 20% higher compared with the state-of-the-art methods, especially in dealing with dynamic real-world data. We have conducted our evaluations based on synthetic and real-world data sets and have compared the results to the existing state-of-the-art approaches. We also discuss how the proposed methods can be embedded into the edge computing devices in IoT systems and applications

A New Pattern Representation Method for Time-series Data

The rapid growth of Internet of Things (IoT) and sensing technologies has led to an increasing interest in time-series data analysis. In many domains, detecting patterns of IoT data and interpreting these patterns are challenging issues. There are several methods in time-series analysis that deal with issues such as volume and velocity of IoT data streams. However, analysing the content of the data streams and extracting insights from dynamic IoT data is still a challenging task. In this paper, we propose a pattern representation method which represents time-series frames as vectors by first applying Piecewise Aggregate Approximation (PAA) and then applying Lagrangian Multipliers. This method allows representing continuous data as a series of patterns that can be used and processed by various higher-level methods. We introduce a new change point detection method which uses the constructed patterns in its analysis. We evaluate and compare our representation method with Blocks of Eigenvalues Algorithm (BEATS) and Symbolic Aggregate approXimation (SAX) methods to cluster various datasets. We have also evaluated our proposed change detection method. We have evaluated our algorithm using UCR time-series datasets and also a healthcare dataset. The evaluation results show significant improvements in analysing time-series data in our proposed method

Analysing behavioural changes in people with dementia using in‐home monitoring technologies

BackgroundBehavioural changes and neuropsychiatric symptoms such as agitation are common in people with dementia. These symptoms impact the quality of life of people with dementia and can increase the stress on caregivers. This study aims to identify the likelihood of having agitation in people affected by dementia (i.e., patients and carers) using routinely collected data from in‐home monitoring technologies. We have used a digital platform and analytical methods, developed in our previous study, to generate alerts when changes occur in the digital markers collected using in‐home sensing technologies (i.e., vital signs, environmental and activity data). A care monitoring team use the platform and interact with participants and caregivers when an alert is generated.MethodWe have used connected sensory devices to collect environmental markers, including Passive Infra‐Red (PIR), smart power plugs for monitoring home appliance use, motion and door sensors. The environmental marker data have been aggregated within each hour and used to train an agitation risk analysis model. We have trained a model using data collected from 88 homes (∼6 months of data from each home). The proposed model has two components: a self‐supervised transformation learning and an ensemble classification model for agitation likelihood. Ten different neural network encoders are learned to create pseudo‐labels using the samples from the unlabelled data. We use these pseudo‐labels to train a classification model with a convolutional block and a decision layer. The trained convolutional block is then used to learn a latent representation of the data for an ensemble classification block.ResultsComparing with baseline models such as LSTM network, Bidirectional LSTM (BiLSTM) network, VGG, ResNet, Inception, Random Forest (RF), Support Vector Machine (SVM) and Gaussian Process (GP) classifiers, the proposed model performs better in sensitivity (recall) and area under the precision‐recall curve with at most 40% improvement. The recall measure using the 10‐fold cross‐validation technique is 61%.ConclusionThis method can support early interventions and help develop new pathways to support people affected by dementia. A limitation in our current study is that the environmental and movement data is at the home level and not personalised

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