Payam Barnaghi

• BSc, MSc, PhD
• Professor at Imperial College London

Objectives To navigate the field of digital cancer care and define and discuss key aspects and applications of big data analytics, artificial intelligence (AI), and data-driven interventions. Data Sources Peer-reviewed scientific publications and expert opinion. Conclusion The digital transformation of cancer care, enabled by big data analytics,...

Current methods for pattern analysis in time series mainly rely on statistical features or probabilistic learning and inference methods to identify patterns and trends in the data. Such methods do not generalize well when applied to multivariate, multi-source, state-varying, and noisy time-series data. To address these issues, we propose a highly g...

When data is streaming from multiple sources, conventional training methods update model weights often assuming the same level of reliability for each source; that is: a model does not consider data quality of each source during training. In many applications, sources can have varied levels of noise or corruption that has negative effects on the le...

Malnutrition and dehydration are strongly associated with increased cognitive and functional decline in people living with dementia (PLWD), as well as an increased rate of hospitalisations in comparison to their healthy counterparts. Extreme changes in eating and drinking behaviours can often lead to mal- nutrition and dehydration, accelerating the...

The COVID-19 pandemic has dramatically altered the behaviour of most of the world's population, particularly affecting the elderly, including people living with dementia (PLwD). Here we use remote home monitoring technology deployed into 31 homes of PLwD living in the UK to investigate the effects of COVID-19 on behaviour within the home, including...

Oncology patients experience numerous co-occurring symptoms during their treatment. The identification of sentinel/core symptoms is a vital prerequisite for therapeutic interventions. In this study, using Network Analysis, we investigated the inter-relationships among 38 common symptoms over time (i.e., a total of six time points over two cycles of...

In this work, we apply information theory inspired methods to quantify changes in daily activity patterns. We use in-home movement monitoring data and show how they can help indicate the occurrence of healthcare-related events. Three different types of entropy measures namely Shannon's entropy, entropy rates for Markov chains, and entropy productio...

Introduction Traumatic Brain Injury (TBI) is common, and increasing in older adults, in whom functional outcomes can be particularly poor. We studied post-TBI recovery using Minder, a remote home monitor- ing system that records sleep and activity data. Methods We installed Minder in recently discharged patients >60years with moderate-severe TBI....

Introduction Disturbances of sleep/wake behaviour are amongst the most disabling symptoms of dementia, leading to increased carers’ burden and institutionalisation. The lack of unobtrusive, low- burden technologies validated to monitor sleep in patients living with dementia (PLWD) has prevented longitudinal studies of nocturnal disturbances and the...

It has now become a realistic prospect for smart care to be provided at home for those living with long-term conditions such as dementia. In the contemporary smart care scenario, homes are fitted with an array of sensors for remote monitoring providing data that feed into intelligent systems developed to highlight concerning patterns of behaviour o...

We introduce a set of input models for fusing information from ensembles of wearable sensors supporting human performance and telemedicine. Veracity is demonstrated in action classification related to sport, specifically strikes in boxing and taekwondo. Four input models, formulated to be compatible with a broad range of classifiers, are introduced...

Background: People living with dementia (PLWD) have an increased susceptibility to developing adverse physical and psychological events. Internet of Things (IoT) technologies provides new ways to remotely monitor patients within the comfort of their homes, particularly important for the timely delivery of appropriate healthcare. Presented here is...

Background Behavioural 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) usin...

Background Social robots are anthropomorphised platforms developed to interact with humans, using natural language, offering an accessible and intuitive interface suited to diverse cognitive abilities. Social robots can be used to support people with dementia (PwD) and carers in their homes managing medication, hydration, appointments, and evaluati...

Background: People with dementia (PwD) are at increased risk of adverse medical events (e.g. infections and falls). These often cause clinical deterioration, and potentially preventable admissions. Remote home monitoring of vital signs using internet-of-things technology can identify risk factors for these events - something particular pertinent d...

Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia which can negatively impact the Activities of Daily Living (ADL) and the independence of individuals. Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions. Analysing agitation episodes will also h...

Agitation is one of the neuropsychiatric symptoms with high prevalence in de-mentia which can negatively impact the Activities of Daily Living (ADL) and the independence of individuals. Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions. Analysing agitation episodes will also...

Socially assistive robots (SAR) hold significant potential to assist older adults and people with dementia in human engagement and clinical contexts by supporting mental health and independence at home. While SAR research has recently experienced prolific growth, long-term trust, clinical translation and patient benefit remain immature. Affective h...

Smart technologies promise a future in which the care needed by vulnerable people can be delivered at a distance, informed by Internet of Things-enabled remote sensing and by artificial intelligence used to identify problematic patterns in physiological readings and behavioural data. In this context, surveillance is widely portrayed as a means to m...

Federated learning is proposed as an alternative to centralized machine learning since its client-server structure provides better privacy protection and scalability in real-world applications. In many applications, such as smart homes with IoT devices, local data on clients are generated from different modalities such as sensory, visual, and audio...

We present the conceptual formulation, design, fabrication, control and commercial translation of an IoT enabled social robot as mapped through validation of human emotional response to its affective interactions. The robot design centres on a humanoid hybrid-face that integrates a rigid faceplate with a digital display to simplify conveyance of co...

Today, the Internet of Things (IoT) is increasingly flourishing with establishing ubiquitous connections between smart devices and objects, and by 2020, there will be a total of 30 billion connected things reported by IDC. The unprecedented data explosion provides immense opportunities for valuable information mining. At the same time, it also floo...

Interpreting the environmental, behavioural and psychological data from in-home sensory observations and measurements can provide valuable insights into the health and well-being of individuals. Presents of neuropsychiatric and psychological symptoms in people with dementia have a significant impact on their well-being and disease prognosis. Agitat...

Given that oncology patients experience an average of 14 co-occurring symptoms, the identification of sentinel or core symptoms is a critical need for effective symptom management. However, this task is an extremely challenging one. To address this need, we used Bayesian Network Analysis (BNA) approaches on a comprehensive dataset of 38 distinct an...

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), Artificial Intelligence (AI) — including Machine Learning (ML) and Big Data analytics — as well as Robotics and Blockchain, are the four de...

We present an IoT-based intelligent bed sensor system that collects and analyses respiration-associated signals for unobtrusive monitoring in the home, hospitals and care units. A contactless device is used, which contains four load sensors mounted under the bed and one data processing unit (data logger). Various machine learning methods are applie...

Missing values exist in nearly all clinical studies because data for a variable or question are not collected or not available. Inadequate handling of missing values can lead to biased results and loss of statistical power in analysis. Existing models usually do not consider privacy concerns or do not utilise the inherent correlations across multip...

Behavioural symptoms and urinary tract infections (UTI) are among the most common problems faced by people with dementia. One of the key challenges in the management of these conditions is early detection and timely intervention in order to reduce distress and avoid unplanned hospital admissions. Using in-home sensing technologies and machine learn...

This book constitutes the proceedings of the 20th International Semantic Web Conference, ISWC 2021, which took place in October 2021. Due to COVID-19 pandemic the conference was held virtually. The papers included in this volume deal with the latest advances in fundamental research, innovative technology, and applications of the Semantic Web, linke...

We introduce the conceptual formulation, design, fabrication, control and commercial translation with IoT connection of a hybrid-face social robot and validation of human emotional response to its affective interactions. The hybrid-face robot integrates a 3D printed faceplate and a digital display to simplify conveyance of complex facial movements...

The Urinary Tract Infections (UTIs) are one of the top reasons for unplanned hospital admissions in people with dementia, and if detected early, they can be timely treated. However, the standard UTI diagnosis tests, e.g. urine tests, will be only taken if the patients are clinically suspected of having UTIs. This causes a delay in diagnosis and tre...

The proliferation of IoT sensors and edge devices makes it possible to use deep learning models to recognise daily activities locally using in-home monitoring technologies. Recently, federated learning systems that use edge devices as clients to collect and utilise IoT sensory data for human activity recognition have been commonly used as a new way...

The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs, which mislead a classifier to incorrect outputs in high confidence. Limited by the extreme difficulty in examining a high-dimensional image space thoroughly, research on explaining and justifying the causes of adversari...

Continual learning models allow them to learn and adapt to new changes and tasks over time. However, in continual and sequential learning scenarios, in which the models are trained using different data with various distributions, neural networks (NNs) tend to forget the previously learned knowledge. This phenomenon is often referred to as catastrop...

The thirteen articles in this special section focus on big data analytics for intelligent networking. The Internet of Things (IoT) is likely to have a significant impact on human lives as new services and applications are developed through integration of the physical and digital worlds. IoT is an umbrella term referring to a large number of sensing...

Conventional deep learning models have limited capacity in learning multiple tasks sequentially. The issue of forgetting the previously learned tasks in continual learning is known as catastrophic forgetting or interference. When the input data or the goal of learning change, a continual model will learn and adapt to the new status. However, the mo...

This article proposes a novel approach for enhancing the video popularity prediction models. Using the proposed approach, we enhance three popularity prediction techniques that outperform the accuracy of the prior state-of-the-art solutions. The major components of the proposed approach are two novel mechanisms for ” user grouping ” and ” content c...

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...

Continual learning models allow to learn and adapt to new changes and tasks over time. However, in continual and sequential learning scenarios in which the models are trained using different data with various distributions, neural networks tend to forget the previously learned knowledge. This phenomenon is often referred to as catastrophic forgetti...

With dementia on the rise and care staff already overstretched, solutions need to be found to ensure the health and wellbeing of people living with the condition. Helen Rostill, Ramin Nilforooshan and Payam Barnaghi ran a trial with an IoT-led monitoring system.

Large volumes of real-world observation and measurement data are collected from sensory devices in the Internet of Things (IoT) networks. IoT data is often generated in highly distributed and dynamic environments. Continuous transmission of large volumes of data collected between sensor and head/sink nodes induces a high communication cost for indi...

There are currently around 46.8 million people living with dementia around the world and this number is estimated to increase to 74.7 million by 2030 and to 131.5 million by 2050. Currently there is no definite cure for dementia and the cost of care for this condition is around £26 billion a year in the UK and soring dramatically. Being able to slo...

In recent years, the development and deployment of Internet of Things (IoT) devices has led to the generation of large volumes of real world data. Analytical models can be used to extract meaningful insights from this data. However, most of IoT data is not fully utilised, which is mainly due to interoperability issues and the difficulties to analys...

Learning and adapting to new distributions or learning new tasks sequentially without forgetting the previously learned knowledge is a challenging phenomenon in continual learning models. Most of the conventional deep learning models are not capable of learning new tasks sequentially in one model without forgetting the previously learned ones. We a...

Oncology patients undergoing cancer treatment experience an average of fifteen unrelieved symptoms that are highly variable in both their severity and distress. Recent advances in Network Analysis (NA) provide a novel approach to gain insights into the complex nature of co-occurring symptoms and symptom clusters and identify core symptoms. We prese...

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 an...

Decomposition-based profiling of user behaviour. (PDF)

Cluster categorisation. (PDF)

Two level rule-based algorithm to analyse environmental data and extract night-time sleep pattern. (PDF)

Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the potential to assist oncology clinicians to personalize the patient’s treatment regimen more efficiently and provide more aggressive and timely interventions. Three common and inter-related sym...

Predictor variables for the Support Vector Regression (SVR) and the Non-linear Canonical Correlation Analysis by neural networks models. (TIF)

Learning in a non-stationary environment is an inevitable problem when applying machine learning algorithm to real world environment. Learning new tasks without forgetting the previous knowledge is a challenge issue in machine learning. We propose a Kalman Filter based modifier to maintain the performance of Neural Network models under non-stationa...

Pioneering advances have been made in Internet of Things technologies (IoT) in healthcare. This article describes the development and testing of a bespoke IoT system for dementia care. Technology integrated health management (TIHM) for dementia is part of the NHS England National Test Bed Programme and has involved trailing the deployment of networ...

There has been a keen interest in detecting abrupt sequential changes in streaming data obtained from sensors in Wireless Sensor Networks (WSNs) for Internet of Things (IoT) applications such as fire/fault detection, activity recognition and environmental monitoring. Such applications require (near) online detection of instantaneous changes. This p...

The chapter presents an overview of the eight that are part of the European IoT Security and Privacy Projects initiative (IoT-ESP) addressing advanced concepts for end-to-end security in highly distributed, heteroge- neous and dynamic IoT environments. The approaches presented are holistic and include identification and authentication, data prote...

An increasing number of cities are confronted with challenges resulting from the rapid urbanisation and new demands that a rapidly growing digital economy imposes on current applications and information systems. Smart city applications enable city authorities to monitor, manage and provide plans for public resources and infrastructures in city envi...

The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study tha...

Decision scoring by the expert system. (PDF)

Anomaly detection algorithm. (PDF)

Label sequence generation by categorisation block. (PDF)

Applications in different domains require reactive processing of massive, dynamically generated streams of data. This trend is increasingly visible also on the Web, where more and more streaming sources are becoming available. These originate from social networks, sensor networks, the Internet of Things (IoT) and many other technologies that use th...

When dealing with a large number of devices, the existing indexing solutions for the discovery of IoT sources often fall short to provide an adequate scalability. This is due to the high computational complexity and communication overhead that is required to create and maintain the indices of the IoT sources particularly when their attributes are d...

The data gathered from smart cities can help citizens and city manager planners know where and when they should be aware of the repercussions regarding events happening in different parts of the city. Most of the smart city data analysis solutions are focused on the events and occurrences of the city as a whole, making it difficult to discern th...

Smart Cities use different Internet of Things (IoT) data sources and rely on big data analytics to obtain information or extract actionable knowledge crucial for urban planners for efficiently use and plan the construction infrastructures. Big data analytics algorithms often consider the correlation of different patterns and various data types. How...

The massive collection of data via emerging technologies like the Internet of Things (IoT) requires finding optimal ways to reduce the observations in the time series analysis domain. The IoT time series require aggregation methods that can preserve and represent the key characteristics of the data. In this paper, we propose a segmentation algorith...

Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber world. The Internet of Things (IoT) consists of the network-enabled devices and communication technologies that allow connectivity and integration of physical objects (Things) into the digital world (Internet). Enormous amounts of dynamic IoT d...

Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As the numbers gro...

Enormous amounts of dynamic observation and measurement data are collected from sensors in Wireless Sensor Networks (WSNs) for the Internet of Things (IoT) applications such as environmental monitoring. However, continuous transmission of the sensed data requires high energy consumption. Data transmission between sensor nodes and cluster heads (sin...

In this paper we discuss a technical design and an ongoing trial that is being conducted in the UK, called TIHM. TIHM uses the Internet of Things (IoT) enabled solutions provided by various companies in a collaborative project. The IoT devices and solutions are integrated in a common platform that supports interoperable and open standards. A set of...

Data owners are creating an ever richer set of information resources online, and these are being used for more and more applications. Spatial data on the Web is becoming ubiquitous and voluminous with the rapid growth of location-based services, spatial technologies, dynamic location-based data and services published by different organizations. How...

This work addresses the problem of segmentation in time series data with respect to a statistical parameter of interest in Bayesian models. It is common to assume that the parameters are distinct within each segment. As such, many Bayesian change point detection models do not exploit the segment parameter patterns, which can improve performance. Th...

Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As the numbers gro...

An important field in exploratory sensory data analysis is the segmentation of time-series data to identify activities of interest. In this work, we analyse the performance of univariate and multi-sensor Bayesian change detection algorithms in segmenting accelerometer data. In particular, we provide theoretical analysis and also performance evaluat...

This work develops a class of techniques for the sequential detection of transient changes in the variance of time series data. In particular, we introduce a class of change detection algorithms based on the windowed volatility filter. The first method detects changes by employing a convex combination of two such filters with differing window sizes...

Context: Risk profiling of oncology patients based on their symptom experience assists clinicians to provide more personalized symptom management interventions. Recent findings suggest that oncology patients with distinct symptom profiles can be identified using a variety of analytic methods. Objectives: To evaluate the concordance between the n...

Recent advancements in sensing, networking technologies, and collecting real-world data on a large scale and from various environments have created an opportunity for new forms of real-world services and applications. This is known under the umbrella term of the Internet of Things (IoT). Physical sensor devices constantly produce very large amounts...

Over the past few years, the semantics community has developed several ontologies to describe concepts and relationships for internet of things (IoT) applications. A key problem is that most of the IoT-related semantic descriptions are not as widely adopted as expected. One of the main concerns of users and developers is that semantic techniques in...

The Internet-of-Things (IoT) [61] has been identified as one of the main pillars of the world’s economies and the technology enabler for the evolution of the societies and for the future developments and improvement of the Internet [4]. A large number of research activities in Europe have been working in this direction i.e. FP7 projects in the cont...

Cities have been a thriving place for citizens over the centuries due to their complex infrastructure. The emergence of the Cyber-Physical-Social Systems (CPSS) and context-aware technologies boost a growing interest in analysing, extracting and eventually understanding city events which subsequently can be utilised to leverage the citizen observat...