A consensus novelty detection ensemble approach for anomaly detection in activities of daily living

A new approach to creating an ensemble of novelty detection algorithms is proposed in this paper. The novelty detection process identifies new or unknown data by detecting if a test data differs significantly from the data available during training. It is applicable for anomaly detection in a situation where there is sufficiently large training data representing the normal class and little or no training data for the anomalous (abnormal) class. Abnormality in Activities of Daily Living (ADL) is identified as any significant deviation from an individual’s usual behavioural routine. Novelty detection is relevant to ADL anomaly detection since abnormalities in ADL are rare and data representing the anomalous cases are not readily available. The proposed Consensus Novelty Detection Ensemble approach is based on the concept of internal and external consensus. The internal consensus is an internal voting scheme within each model in the ensemble while the external consensus is an external voting scheme among the ensemble models. The weight of each model is estimated based on its performance and a score, called “Normality Score”. Computed score is used in classifying the data as abnormal (anomalous) based on certain threshold crossing, normal otherwise. Experimental evaluation is conducted to detect abnormalities in ADL data obtained from CASAS repository as well as experimental dataset collected for this research. The obtained results show that the proposed approach is able to identify anomalous instances. The proposed approach offers more flexibility compared with the existing approaches by allowing the Normality Score threshold to be adjusted without retraining the models

User-centric anomaly detection in activities of daily living

The current system for providing care to older adults is not sustainable due to its excessive cost. It places an unbearable financial burden on the government and families and pressure on the workforce due to the demand for human carers. Studies have also shown that older adults prefer to be looked after in their homes rather than in a care facility. An automated system of monitoring can provide much-needed support at a lower cost and give peace of mind to relatives. The focus of the research reported in this thesis is to investigate the concept of abnormality detection in activities of daily living. More precisely, this work is aimed at proposing a dynamic approach for anomaly detection capable of adapting to changes in human behaviour. Abnormalities in daily activities can be an early indication of health decline. Therefore, early detection can inform the families of the need for intervention. Anomalies are often detected by modelling the existing activity data representing the usual behavioural routine of an individual to serve as a baseline model. Subsequent activities deviating from the baseline are then classified as outliers or anomalies. However, existing approaches suffer from a high rate of false prediction due to the static nature and the inability of the approaches to adapt to the changing human behaviour. The contributions of the research are reported in four main categories. First, a novel ensemble approach termed "Consensus Novelty Detection Ensemble" is proposed. The outlying activities are predicted by computing their normality score using the internal and external consensus vote and the estimated weights of the models in the ensemble. Activities with a score exceeding a threshold estimated using a statistical method based on data distribution are predicted as outliers and vice versa. Secondly, a similarity measure approach for identifying the likely sources of the ADL anomalies is proposed. While the models can detect anomalous activities, they are unable to identify the source (cause) of the anomaly. Identifying the anomaly source allows for the development of an adaptive system. The approach is based on a pairwise distance measurement of the features extracted from the activity data. Two approaches for performing the similarity measures are presented, namely, One vs One Similarity Measure (OOSM) and One vs All Similarity Measure (OASM). Features of the data with a higher dissimilarity rate are predicted as the source. To make the proposed model adaptive to the changes in human behaviour, a novel adaptive approach is proposed based on the concept of forgetting factors. This allows the model to forget (discard) outdated activity data and adapt to the current behavioural patterns by incorporating newly verified data. The data verification can be performed by incorporating human feedback into the system. Two forgetting factor approaches are proposed namely; Forgetting Factor based on Data Ageing (FFDD) and Forgetting Factor based on Data Dissimilarity (FFDA). The data ageing forgetting factor discard old behavioural routine based on the age of the activity data, while in the data dissimilarity approach, this is achieved by measuring the similarity of the activity data. Lastly, the means of utilising an assistive robot as a communication intermediary is explored for incorporating human feedback into the learning process using hand gestures as a communication modality. Experimental data used for the gesture recognition model is collected using a wearable sensor and a 2D camera. The feasibility of utilising the robotic platform as an exercise coach to encourage physical activity and promote a healthy lifestyle is explored. To this end, an exercise training solution is developed for the robotic platform to coach, motivate and assess the older adults in the recommended physical activities

EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes

Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones. This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances. The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises

Chilean wildfires: probabilistic prediction, emergency response and public communication

The 2016/17 wildfire season in Chile was the worst on record, burning more than 600,000 hectares. Whilst wildfires are an important natural process in some areas of Chile supporting its diverse ecosystems, wildfires are also one of the biggest threats to Chile’s unique biodiversity and it’s timber and wine industries. They also pose a danger to human life and property due to the sharp wildland-urban interface that exists in many Chilean towns and cities. Wildfires are however difficult to predict due to the combination of physical (meteorology, vegetation and fuel condition), and human (population density and awareness level) factors. Most Chilean wildfires are started due to accidental ignition by humans. This accidental ignition could be minimized if an effective wildfire warning system alerted the population to the heightened danger of wildfires in certain locations and meteorological conditions. Here we demonstrate the design of a novel probabilistic wildfire prediction system. The system uses ensemble forecast meteorological data together with a longtime series of fire products derived from Earth Observation to predict not only fire occurrence, but in addition, how intense wildfires could be. The system provides wildfire risk estimation and associated uncertainty for up to 6 days in advance, and communicates it to a variety of end users. The advantage of this probabilistic wildfire warning system over deterministic systems is that it allows users to assess the confidence of a forecast and thus make more informed decisions regarding resource allocation and forest management. The approach used in this study could easily be adapted to communicate other probabilistic forecasts of natural hazards

Entropy measures for anomaly detection

Human activity recognition methods are used to support older adults to live independently in their own homes by monitoring their Activities of Daily Living (ADL). The gathered data and information representing different activities will be used to identify anomalous activities in comparison with the routine activities. In the related research in this area, the most recent studies have mainly focused on detecting anomalies in a single occupant environment. Although older adults often receive visits from family members or health care workers, representing a multi-occupancy environment. This research is focused on the application of entropy measures for anomaly detection in ADLs in a single-occupancy and multi-occupancy environment. In many applications, entropy measures are used to detect the irregularities and the degree of randomness in data. However, this has rarely been applied in the context of activities of daily living. To address the research questions identified in the thesis, three novel contributions of the thesis are; Firstly, a novel method based on different entropy measures is investigated to detect anomalies in ADLs, specifically in sleeping routine and human falls. Secondly, a novel entropy-based method is explored to detect anomalies in ADLs in the presence of a visitor, solely based on information gathered from ambient sensors. Finally, entropy measures are applied to investigate their effectiveness in identifying a visitor in a single home environment based on data gathered from ambient sensors. The results presented in this thesis show that entropy measures could be used to detect abnormality (here, irregular sleep, human fall and a visitor) in ADLs

Novelty, distillation, and federation in machine learning for medical imaging

The practical application of deep learning methods in the medical domain has many challenges. Pathologies are diverse and very few examples may be available for rare cases. Where data is collected it may lie in multiple institutions and cannot be pooled for practical and ethical reasons. Deep learning is powerful for image segmentation problems but ultimately its output must be interpretable at the patient level. Although clearly not an exhaustive list, these are the three problems tackled in this thesis. To address the rarity of pathology I investigate novelty detection algorithms to find outliers from normal anatomy. The problem is structured as first finding a low-dimension embedding and then detecting outliers in that embedding space. I evaluate for speed and accuracy several unsupervised embedding and outlier detection methods. Data consist of Magnetic Resonance Imaging (MRI) for interstitial lung disease for which healthy and pathological patches are available; only the healthy patches are used in model training. I then explore the clinical interpretability of a model output. I take related work by the Canon team — a model providing voxel-level detection of acute ischemic stroke signs — and deliver the Alberta Stroke Programme Early CT Score (ASPECTS, a measure of stroke severity). The data are acute head computed tomography volumes of suspected stroke patients. I convert from the voxel level to the brain region level and then to the patient level through a series of rules. Due to the real world clinical complexity of the problem, there are at each level — voxel, region and patient — multiple sources of “truth”; I evaluate my results appropriately against these truths. Finally, federated learning is used to train a model on data that are divided between multiple institutions. I introduce a novel evolution of this algorithm — dubbed “soft federated learning” — that avoids the central coordinating authority, and takes into account domain shift (covariate shift) and dataset size. I first demonstrate the key properties of these two algorithms on a series of MNIST (handwritten digits) toy problems. Then I apply the methods to the BraTS medical dataset, which contains MRI brain glioma scans from multiple institutions, to compare these algorithms in a realistic setting

Machine learning based anomaly detection for industry 4.0 systems.

223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users

Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field