Artificial intelligence (AI) Large Language Models (LLMs) are increasingly being used in mental health training and therapeutic interventions. This narrative review explores how LLMs can support simulation-based training for mental health practitioners by simulating patient interactions. Key topics include the use of co-production techniques, cost considerations, and the role of Retrieval-Augmented Generation (RAG) in enhancing training accuracy. The review highlights the importance of evaluation methods and expert-guided enhancements. Although LLMs offer valuable training opportunities, they should complement, rather than replace traditional face-to-face training methods. Collaboration across disciplines is essential in refining AI chatbots and enhancing their effectiveness in therapeutic training. In conclusion, the integration of AI in mental health practitioner training shows promise for addressing the growing demands within mental health care. However further research is required to develop standardized approaches and improve the realism of AI simulations.

This editorial introduces a special issue in digital mental health and wellbeing and includes topics such as digital wellbeing, chatbots, virtual reality and youth mental health. This editorial also discusses the potential adverse psychological effects of an artificial intelligence (AI) centric future. We emphasise the need to be ‘wellbeing centric’ and not technology centric. AI can expedite tasks and ‘think’ on our behalf, but if the goal of humanity is to promote ‘wellbeing’ then ‘wellbeing’ should be a core design principle and an evaluation metric to assess AI technologies. We discuss how the potential adverse effects of social media can be a good case study for how we should assess the AI future – especially in the era of generative AI and AI companions. To illustrate a point, we explore a fictional scenario (akin to Laplace’s demon) that includes an AI agent that makes minute-by-minute ‘life recommendations’. We explore how such an AI could negatively affect our mental wellbeing, agency, autonomy and sense of free will – even if the ‘AI always knows best’. We explore conscious AI and the potential impact of AI from a positive psychology perspective.

Manual sleep stage scoring of polysomnography recordings is an expensive and time-consuming process, further complicated by inconsistent sleep stage agreement among sleep experts (clinicians and sleep technologists). Hence, development of automated sleep scoring algorithms are an emerging topic of interest. Automation typically mimics the clinical decision path by implementing a series of predefined rules, such as the American Academy of Sleep Medicine’s (AASM) scoring manual. Recently, data driven methods have emerged using machine or deep learning . Both manual and automated methods of scoring have known limitations; primarily, unacceptable variation in agreement between different scorers and algorithms. Within the literature, electroencephalogram (EEG) frequency is an important feature considered by both sleep experts and automated approaches for classifying sleep stages. This study presents a novel approach to sleep stage analysis, by developing a methodology to precisely determine the temporal location of sleep stage transitions. The current gold standard fails to identify such transitional changes, which leads to poor inter-scorer reliability. Therefore, development and implementation of such methodologies is a crucial, but overlooked, step in improving the consistency of scoring within sleep studies. In this work, EEG time–frequency ridge analysis was used to characterise the dominant frequency component of EEG signals in time, at the point of sleep stage transition. An in-depth analysis of N3 → N2 and N2 → N3 transitions in the 2018 PhysioNet challenge “You Snooze, You Win” and the Wisconsin Sleep Cohort (WSC) datasets (n = 994, n = 742; approximately 13,888 h of sleep data) showed consistent time–frequency patterns at the point of transition, from one sleep stage to another. This methodology allows simple and ‘interpretable’ features to be generated in future work, to precisely identify the temporal location of sleep stage transitions with the aim of improving inter-scorer reliability.

Employers now acknowledge the crucial role of employee wellbeing in promoting productivity, positive relationships, and engagement, as well as its impact on absenteeism and presenteeism. Consequently, there is an increasing need for affordable, evidence-supported, scalable innovative approaches to improve employee wellness. This paper presents usage analysis of a digital employee wellbeing platform, created by Inspire, a mental health social enterprise. The platform has several self-help components, including a chatbot that delivers mental health self-assessments, CBT -based e-learning modules, and a mood tracker. Analysis was conducted using the machine learning technique k-means clustering and descriptive analytics. Through the analysis of user tenure (i.e. the time interval between the first and last day of a user who engaged with the platform), total interactions, daily interactions, and number of unique days on the platform, K-means clustering successfully identified three user groups: short-term (95.5% of users), intermediate (3.4% of users), and long-term users (1.1 % of users). By using these analysis techniques, we can understand how employees utilize a digital employee wellbeing platform, helping to design more effective and personalized solutions.

Abstract This paper draws on diverse psychological, behavioural and numerical literature to understand some of the challenges we all face in making sense of large-scale phenomena and use this to create a road map for HCI responses. This body of knowledge offers tools and principles that can help HCI researchers deliver value now, but also highlights challenges for future HCI research. The paper is framed by looking at patterns and information that highlight some of the common misunderstandings that arise—not just for politicians and the general public but also for many in the academic community. This paper does not have all the answers to this, but we hope it provides some and, perhaps more importantly, raises questions that we need to address as scientific and technical communities.

The development and monitoring of Photovoltaic (PV) installations is of great interests for the Chinese energy management agency in recent years. The traditional land change detection of PV installations has issues pertaining to low efficiency and high missed detection rates. Therefore, this paper explores an efficient and high accurate detection method of PV installations land using changes from remote sensing images in order to help relevant stakeholders to better manage and monitor urban energy and environment. In this paper, Full Convolutional Network (FCN) and classical segmentation convolutional network (U-Net) based deep learning algorithms are used to build change detection models. To evaluate the model performance, we have built the change detection dataset from Northeast Petroleum University - Photovoltaic Remote Sensing Dataset (NEPU-PRSD) of PV installations in Western China. The experimental results show that both models can achieve good accuracy in change detection regarding PV installations.

Abstract Technological advances have facilitated new approaches to support different needs in healthcare environments. In particular, in the field of rehabilitation therapies, we can find software applications that have been developed to support the performance of specific exercises, often with different customization options. However, there are still many gaps that need to be addressed to provide more and better global solutions. In this article, we present a novel system aimed at physiotherapists, which allows them to create new rehabilitation exercises designed to meet the specific needs of their patients. This implies the creation of individualized therapies that contribute to a better and faster recovery of patients. The system consists of a virtual 3D environment with a 3D skeleton representing the patient. The physiotherapist can interact with the skeleton to design the desired postures that the patient should practice according to their personal limitations. The system allows physiotherapists to compose a complete and personalized set of exercises. Alternatively, the physiotherapist may also create the postures by means of a motion sensing device using motion- and voice-based interaction. Both options pose research challenges that the authors have addressed to provide the solution presented in this paper.

This study reports on the development and ‘in the wild’ trialling of a chatbot (ChatPal) which promotes good mental wellbeing. A stakeholder-centered approach for design was adopted where end users, mental health professionals and service users were involved in the design which was centered around positive psychology. In the wild usage of the chatbot was investigated from Jul-20-Mar-21. Exploratory analyses of usage metrics were carried out using the event log data. User tenure, unique usage days, total chatbot interactions and average daily interactions were used in K-means clustering to identify user archetypes. The chatbot was used by a variety of age groups (18-65+) and genders, mainly those living in Ireland. K-means clustering identified three clusters: sporadic users (n=4), frequent transient users (n=38) and abandoning users (n=169) each with distinct usage characteristics. This study highlights the importance of event log data analysis for making improvements to the mental health chatbot.

In this paper, we use the HappyDB (which is a corpus of more than 100,000 happy moments or happiness statements) to train machine learning classifiers to classify the type of happiness statements, i.e., whether they are related to different categories, for example Achievement or Affection. Having identified the best performing classifier, we then sought to assess if the classifier had variable performance when tested using happiness statements from different demographic groups, such as those written by a married or single person, female or male, young or old and whether they are a parent or non-parent. Three different classifiers were initially used in this classification task, to determine classification accuracy. Having determined the best performing model (the convolutional neural network - CNN, deep learning algorithm), this model was then used for further analysis of results per cross sectional demographic groups. The CNN achieved an F1 score of 0.897 but had variable performance when tested on different demographic groups. Generally, we found that accuracy of prediction within this dataset declines with age, where the results for certain sub-groups were declining with increased age or flatlining, except for the single parents' sub-group. This may be due to decreased numbers in these particular sub-groups, where the algorithm did not learn the patterns in the happiness statements for this cohort, due to a sparsity of training data for the sub-group. Results show that there is likely a change in word patterns in happiness statements for different demographics.

Good program layout and consistent application of style facilitates code readability and comprehension. Appropriate use of white space, in particular vertical space, is useful for organising code into logical groupings of text. Where this style is not followed then the code manifests crowding and can inhibit comprehension. When reading natural text, crowding has been recognised as disproportionately affecting the reading efficiency of dyslexic readers. We present an independent two-factorial study which examines the extent to which crowding in program code affects programmers with dyslexia. The study involved 30 participants (14 dyslexia, 16 control) reading and describing crowded and spaced versions of three Java programs. Comprehension time and accuracy were measured. An eye tracker was used to collect gaze metrics. Results are presented relating to the interaction between dyslexia and crowding. Noting the small sample size, the results show that, while there is an interaction effect on gaze metrics for some program features, the results do not suggest any significant effect whereby programmers with dyslexia are disproportionately affected by crowding in computer programs.

The objective of this study was to understand the attitudes of professionals who work in mental health regarding the use of conversational user interfaces, or chatbots, to support people’s mental health and wellbeing. This study involves an online survey to measure the awareness and attitudes of mental healthcare professionals and experts. The findings from this survey show that more than half of the participants in the survey agreed that there are benefits associated with mental healthcare chatbots (65%, p < 0.01). The perceived importance of chatbots was also relatively high (74%, p < 0.01), with more than three-quarters (79%, p < 0.01) of respondents agreeing that mental healthcare chatbots could help their clients better manage their own health, yet chatbots are overwhelmingly perceived as not adequately understanding or displaying human emotion (86%, p < 0.01). Even though the level of personal experience with chatbots among professionals and experts in mental health has been quite low, this study shows that where they have been used, the experience has been mostly satisfactory. This study has found that as years of experience increased, there was a corresponding increase in the belief that healthcare chatbots could help clients better manage their own mental health.

Compartment-based infectious disease models that consider the transmission rate (or contact rate) as a constant during the course of an epidemic can be limiting regarding effective capture of the dynamics of infectious disease. This study proposed a novel approach based on a dynamic time-varying transmission rate with a control rate governing the speed of disease spread, which may be associated with the information related to infectious disease intervention. Integration of multiple sources of data with disease modelling has the potential to improve modelling performance. Taking the global mobility trend of vehicle driving available via Apple Maps as an example, this study explored different ways of processing the mobility trend data and investigated their relationship with the control rate. The proposed method was evaluated based on COVID-19 data from six European countries. The results suggest that the proposed model with dynamic transmission rate improved the performance of model fitting and forecasting during the early stage of the pandemic. Positive correlation has been found between the average daily change of mobility trend and control rate. The results encourage further development for incorporation of multiple resources into infectious disease modelling in the future.

Abstract The discovery of new medications in a cost-effective manner has become the top priority for many pharmaceutical companies. Despite decades of innovation, many of their processes arguably remain relatively inefficient. One such process is the prediction of biological activity. This paper describes a new deep learning model, capable of conducting a preliminary screening of chemical compounds in-silico. The model has been constructed using a variation autoencoder to generate chemical compound fingerprints, which have been used to create a regression model to predict their LogD property and a classification model to predict binding in selected assays from the ChEMBL dataset. The conducted experiments demonstrate accurate prediction of the properties of chemical compounds only using structural definitions and also provide several opportunities to improve upon this model in the future.

This study uses classifications of Metropolitan and Non-Metropolitan behavioural archetypes of General Practitioner practice in Northern Ireland. Of the 333 practices operating in Northern Ireland at the start of the study period (March 2018), 90 were classified as Metropolitan and 243 as Non-Metropolitan. This paper seeks to examine any associations between deprivation and each archetype and to investigate what their prescription behaviours would be when controlled for deprivation. It was found that for each archetype, as the deprivation level of the area in which a practice was located increased, so too did the prescribing levels associated with that practice. A large proportion of Metropolitan practices (52.2%) were located in high deprivation areas and these practices had prescribing levels that were 40.3% higher in the number of items prescribed per registered patient than practices in low deprivation areas within the same archetype. Only 13.9% of Non-Metropolitan practices were located in high deprivation areas and had levels of prescribing 11.2% greater than practices in areas of low deprivation within the same archetype. A comparison of only practices in low deprivation areas in both archetypes found that higher prescribing levels were seen in Non-Metropolitan practices, the complete opposite of the trend observed when comparing all practices where Metropolitan practices show higher prescribing levels.

This study uses classifications of Metropolitan and Non-Metropolitan behavioural archetypes of General Practitioner practice in Northern Ireland and seeks to examine any associations between practice size and each archetype. It was found that the highest prescribing levels were in Small practices with two registered doctors for both archetypes. The lowest levels of prescribing were found in Single-Handed practices with only one registered doctor in Non-Metropolitan areas whilst Large practices with five or more registered doctors had the lowest prescribing levels in Metropolitan areas. One possible reason for Large practices having the lowest prescribing rates in Metropolitan areas may be the availability of more alternatives to medication. The highest prescribing levels in Non-Metropolitan areas were almost 4% higher than the lowest. This difference rose to 32% in Metropolitan areas. Further research into this difference is needed with deprivation levels found in Metropolitan areas being a possible factor. Examining each practice size individually showed that the same archetypes (i.e. Metropolitan and Non-Metropolitan) is also observed at this level with the number of registered patients increasing in line with the size of the practice although practices in Non-Metropolitan areas generally had more patients than those in Metropolitan areas.

The software engineering industry is increasingly aware of the role and value of neurodiverse engineers within the workforce. One motivation is the alignment between skills needed for software development and the processing strengths of individuals with autistic spectrum conditions. One aspect of neurodiversity is dyslexia, typically presenting in individuals through a range of reading deficiencies. In this paper we build on recent work which has sought to investigate if programmers with dyslexia read program code in a way which is different from programmers without dyslexia. The particular focus of this analysis is the nature of saccadic movement and patterns of linearity when reading code. A study is presented in which the eye gaze of 28 programmers (14 with dyslexia and 14 without) was recorded using an eye tracking device while reading and understanding three on-screen Java programs. Using insights from the wider dyslexia literature, hypotheses are formulated to reflect the expected saccadic gaze behaviour of programmers with dyslexia. A range of existing metrics for linearity of program reading are adapted and used for statistical analysis of the data. Results are consistent with recent work elsewhere and indicate that programmers with dyslexia do not exhibit patterns of linearity significantly different from the control group. Non-linear gaze is shown to be approximately 40% of all saccadic movement. Some preliminary insights are offered based on the data available, suggesting that the extent of non-linear reading when comprehending program code might complement the processing and problem solving style of the programmer with dyslexia.

Obesity and overweight are significant health risks. Effective communication can improve weight loss maintenance. There are technologies to support communication in weight management, such as text messaging that has been shown to benefit self-reporting and motivation but has its limitations. Having a conversation is an emotional experience, and chatbots are conversation-driven intelligent systems. Chatbots have been reported to increase compliance with health interventions. The aim of this study was to identify the needs of individuals who are maintaining weight loss in order to design and develop WeightMentor, a weight loss maintenance chatbot. This was a needs assessment and technology (chatbot) development study. The needs assessment identified the needs adults aged 18+ who were maintaining weight loss using semi-structure interviews. These needs were used to inform the design and development of a chatbot. Data were analyzed using thematic analysis. Findings identified five key themes: (1) Weight loss maintenance is challenging; (2) Social contact is beneficial but may also reinforce unhealthy habits; (3) Apps should be convenient and support progress tracking; (4) Personal messages should be specific and relevant; (5) Chatbots have potential for weight loss maintenance. Chatbot, WeightMentor, was designed and developed based on findings from the needs assessment and a review of the most popular nutrition apps. WeightMentor operates within Facebook messenger, which is currently one of the most popular social media platform. WeightMentor's purpose is to positively influence the user's emotions while maintaining weight loss. Its functions include self-reporting of diet and physical activity, viewing previous self-reporting trends, and motivational messaging. Chatbots such as WeightMentor has the potential to aid weight loss maintenance. Further research is required to test the usability and effectiveness of this WeightMentor.

Primary percutaneous coronary intervention (PPCI) is a minimally invasive procedure to unblock the arteries which carry blood to the heart. This procedure is carried out once patients are accepted based on the STEMI criteria upon the assessment of 12-lead ECG. This paper reports the analyses of a dataset compiled from patients accepted for PPCI. The primary objective was to explore the features which may predict 30days mortality. The 30 day mortality was?? The main features identified were a patient's age, sex, door to balloon time, call time, pain time, and activation status. Together these features appear to be a predictor of 30day mortality in patients referred for PPCI (76% accuracy, 70% sensitivity and 85% specificity).

A primary percutaneous coronary intervention (PPCI) re-establishes blood flow in an obstructed coronary artery. PPCI referrals vary in admission criteria partly on the basis of ECG findings, hence, not all the referrals are accepted. The aim of the paper is to discover archetypes of accepted patients referred to the PPCI center. Cluster analysis was performed on a PPCI referral dataset to identify patient archetypes and identify any key patterns of patients who were accepted for PPCI. A k-means clustering algorithm was used with the elbow method for determining the optimum number of clusters (groups of patients). A silhouette plot was generated for within cluster validation. Among the accepted PPCI referrals, there were four different groups of patients. The patients within each group have similar characteristics. The largest cluster of patients include male patients being referred out of hours and with excessive door to balloon times (DTBTs) as compared to those referred in hours. Another cluster includes older female patients who are referred out of hour. Also, it was discovered that the false activation rate and DTBTs are higher in females as compared to male clusters. The smallest cluster include the most elderly patients in the whole referral dataset and mainly includes more males than female's who are referred out of hour and have the highest false activation rate, DTBT, and 30 days mortality rate. The cluster analysis of PPCI dataset revealed different patient archetypes. Each group of patients have a different mean age, out of hours referral rate, DTBT, false activation and 30 days mortality rate compared to other group. The identified clusters could be helpful for the clinicians to better understand their patients and utilize this information to aid the clinical decision making.

Obesity is a global challenge that affects health and wellbeing worldwide. In this position paper, we review the digital technology used in prevention of obesity and present the proposed STOP project that integrates state-of-the-art wearable technology, chatbot, gamification data fusion, and machine learning with the aim to provide personalised supportive feedback for preventing obesity and maintaining healthy weight. Implication of sensitive data with General Data Protection Regulation (GDPR) is discussed. We conclude that machine learning plays an important role in data fusion, analytics, and providing optimal messaging tailored design to support healthy weight.

The MIDAS consortium is a partnership involving health authorities, and technical big data experts from universities, research institutions, MNCs and SMEs across six EU countries (UK (NI and England), Ireland, Belgium, Finland, Spain and Slovenia), and the USA. The management of big data for 'health in all' poses a significant challenge for health policy makers. This challenge is addressed by the MIDAS project through the development of an integrated solution enabling knowledge liberation from data silos and unification of heterogeneous big data sources that can provide evidence-based actionable information and transform the way care is provided.

Smartphone-based digital phenotyping can provide insight into mood, cognition and behaviour. In this study, data analytics was carried out with data generated from a maternal mental health app to address the following question: what is the temporal behaviour of users when completing ecological momentary assessments (EMAs) with EMAs in the form of mental health scales versus EMAs in the form of mood logs? The methodology involved using the Health Interaction Log Data Analytics (HILDA) pipeline to analyse 1461 app users. Clustering was used to characterise archetypical user engagement with the two forms of EMA. Users preferred mood log EMAs, with 6993 mood log completions compared to 2129 scale completions. Users are more willing to log moods at 9am and 12pm and complete mental health scales between 8pm and 10pm. The fewest number of mood logs and scale completions take place on Saturday followed by a Sunday. Whilst ‘happiness’ is the dominant mood during day times, ‘anxiety’ and ‘sadness’ peak during night times. The overall findings are that users prefer completing mood log EMAs and that the temporal behaviour of users engaging with EMAs in the form of mental health scales are distinctly different from how they engage with mood logs.

Welcome to this special issue ‘Highlights from the European Conference on Cognitive Ergonomics (ECCE-2019)’ of Behaviour & Information Technology, presenting six papers selected from the programme ...

This paper investigates the impact of dyslexia on the reading and comprehension of computer program code. Drawing upon work from the fields of program comprehension, eye tracking, dyslexia, models of reading and dyslexia gaze behaviour, a set of hypotheses is developed with which to investigate potential differences in the gaze behaviour of programmers with dyslexia compared to typical programmers. The hypotheses posit that, in general terms, programmers with dyslexia will show gaze behaviour of longer duration and a greater number of fixations on program features than typical programmers. An experiment is described in which 28 programmers (14 with dyslexia, 14 without dyslexia) were asked to read and explain three simple computer programs. Eye tracking technology is used to capture the gaze behaviour of the programmers. Data analysis suggests that the code reading behaviour of programmers with dyslexia is not what would be expected based on the dyslexia literature relating to natural text. In conjunction with further exploratory analysis, observations are made in relation to spatial differences in how programmers with dyslexia read and scan code. The results show that the gaze behaviour of programmers with dyslexia requires further study to understand effects such as code layout, identifier naming and line length. A possible impact on dyslexia gaze behaviour is from the visual crowding of features in program code which might cause certain program features to receive less attention during a program comprehension task.

The 32nd International BCS Human Computer Interaction Conference was held in Belfast, Northern Ireland in July 2018. The conference proceedings included over 230 papers with various topics and applications in Human Computer Interaction (HCI). There were over 60 full papers, more than 90 work-in-progress papers, almost 30 position papers, 17 industry talks (2 submitted papers), around 25 workshop papers, 17 doctoral consortium papers and 4 interactions gallery papers. Pre-conference activities included a doctoral consortium, a masterclass in chatbot design and five workshops. This introduction presents an analysis of the topics presented at the 2018 conference providing a reflection of the popular themes that exist in HCI as a research discipline. Interestingly, the doctoral consortium papers included topics such as affective computing, tangible user interfaces, information quality, behaviour change interventions in digital healthcare, playful interactions, storytelling, trust, human–data interaction, reading behaviour, interactive public displays, haptic devices, visualizing personal health data and decision-making. The five workshops included the topics: affective computing; using data to design user interfaces; human-centred design in intelligent environments; digital technology for older people; and digital health. Perhaps as expected, the two application areas include interactive technologies for older people and digital health which is well aligned to the current public need and growing demands.

Abstract The System Usability Scale (SuS) survey is a widely respected tool for measuring usability. Generally, a SuS score is administered directly after a usability test to assess the usability and user experience of digital products. However, some researchers have used SuS as a survey as part of longitudinal ‘in the wild’ trials where SuS is often completed some period after the trial. The aim of this research was to determine if a participant’s memory of a product’s usability would change if a SuS survey was administered at different times after a test. Hence, we sought to understand if recalling the usability of a digital technology was affected by temporal bias or memory decay. This paper includes results and findings from two studies, study 1 involved evaluating a web application and study 2 involved evaluating a virtual learning environment. Collectively the two studies had 212 participants (n = 212). The findings conclude that there is no significant change of the user’s recollection of the usability of digital product as evidenced by an analysis of users who completed multiple SuS surveys over a short term period of 3 weeks or over an extended period of time of 6 months. RESEARCH HIGHLIGHTS The system usability scale is used to test for memory decay and temporal bias in judging the user experience of technologies at different time points. 212 participants took part in two studies ranging from 3 weeks to 6 months. There is no evidence that there is a temporal bias or memory decay when users complete a SuS survey at the two different time points of 3 weeks and 6 months.

Nurses welcome innovative training and assessment methods to effectively interpret physiological vital signs. The objective is to determine if eye-tracking technology can be used to develop biometrics for automatically predict the performance of nurses whilst they interact with computer-based simulations. A total of 47 nurses were recruited, 36 nursing students (training group) and 11 coronary care nurses (qualified group). Each nurse interpreted five simulated vital signs scenarios whilst “thinking-aloud.” The participant's visual attention (eye-tracking metrics), verbalisation, heart rate, confidence level (1-10, 10 = most confident), and cognitive load (NASA-TLX) were recorded during performance. Scenario performances were scored out of ten. Analysis was used to find patterns between the eye-tracking metrics and performance score. Multiple linear regression was used to predict performance score using eye tracking metrics. The qualified group scored higher than the training group (6.85 ± 1.5 versus 4.59 ± 1.61, p =2= 0.80, p = <;0.0001). This shows that eye tracking alone could predict a nurse's performance and can provide insight to the performance of a nurse when interpreting bedside monitors.

Obesity is increasing globally and is a risk factor for many chronic conditions such as such as heart disease, sleep apnea, type-2 diabetes, and some cancers. Research shows that food logging is beneficial in promoting weight loss. Crowdsourcing has also been used in promoting dietary feedback for food logging. This work investigates the feasibility of crowdsourcing to provide support in accurately determining calories in meal images. Two groups, 1. experts and 2. non-experts, completed a calorie estimation survey consisting of 15 meal images. Descriptive statistics were used to analyse the performance of each group. Collectively, non-experts could determine which meals had larger amounts of calories and analysis showed that meals with greater calories resulted in greater standard deviations of non-expert estimates. Secondary experiments were completed that used crowdsourcing to adjust user calorie estimations using non-expert calorie estimations. Five-fold cross validation was used and results from the calorie adjustment process show a reduced overall mean calorie difference in each fold and the mean error percentage decreased from 40.85% to 25.52% in comparing original mean estimations against adjusted mean estimations. As such, there is credibility in adjusting calorie estimates from a crowd as opposed to simply taking a central measure such as the mean.

We are living in an ageing population with an escalation in chronic illnesses including dementia and other age related diseases. People living with dementia often continue to live at home and are supported by caregivers and next of kin. It is often important to monitor the wellbeing of people living with dementia in order to measure their level of independence and to provide proper support at the time of need as well as supporting their quality of life. Some researchers have focused on monitoring physical wellbeing and activities of daily living (ADL). However, there has been a paucity of research focussed on monitoring mood, affect and the emotional wellbeing of people living with dementia, despite these people experiencing frustration, agitation, depression and social isolation to name but a few known effects. As a result, the SenseCare project aims to build an affective computing platform that uses sensors placed in the home environment to monitor moods, affect and the emotional wellbeing of people living with dementia. This platform is being iteratively designed and will likely use plug-n-play sensors such as passive infrared, wearables and camera technologies to infer emotions from facial expressions, voice intonations and physical behaviour and other modalities. However, it is important to interact iteratively with people living with dementia and their caregivers in order to understand their profound needs. In this study, we report on two focus groups that were conducted to elicit user stories and eventual requirements for the SenseCare platform. Since participatory design involving people living with dementia could bring about unique challenges, we adopted a dyad approach where a caregiver and the person living with dementia participate together in the focus group. This ensures that their needs are fully represented and that consent is fully transparent. In this paper, we report the personal stories elicited during these discussions which will ultimately inform the implementation of the SenseCare platform.

Pupillary behaviour and dilation have been considered in the literature as an effective input for the measurement of cognitive workload and stress. In this work, we explore the correlation between pupil dilation and features extracted from low quality video frames that have been captured using a normal webcam during a set of computer-based tasks. The methodology presented herein attempts to develop an alternative, cost effective technique for the representation of pupil dilation in order to track pupillary behaviour from images instead of employing specialised, high-cost eye-tracking devices, which typically require specialist expertise during setup and calibration. A description of the data collection protocol and subsequent data analysis is presented. The results obtained indicate that there is a moderate correlation achieved through the use of a linear regression model, which employs fiducial point features as independent variables, and pupil size measured by an infrared-based eye-tracker as the dependent variable. Furthermore, an example of the pupil size variation within a game-based task context is shown, whereby one can easily relate the engagement and the amount of mental processing during gameplay.

Abstract Current approaches to patient education provide generic standardized materials to all patients regardless of their demographics such as age and cognitive abilities. Thus, the effectiveness of this approach may suffer from a patient's motivation to fully engage with the material. To alleviate these concerns, this study proposes a personalized approach to patient education that is tailored to the individual characteristics and health objectives of the patient. Personalized features will enhance the comprehensibility and usability of the process of medical education. Taking this into consideration, this paper introduces a conceptual architecture to create a web‐based personalized patient education experience. A key component of this architecture comprises ontological models of the patient themselves, their medical conditions, physical activities and their educational attainments. Furthermore, rule‐based reasoning is also proposed to achieve this personalization. A use case scenario is provided to highlight the effectiveness of personalized education provision.

One of the challenges in virtual environments is the difficulty users have in interacting with these increasingly complex systems. Ultimately, endowing machines with the ability to perceive users emotions will enable a more intuitive and reliable interaction. Consequently, using the electroencephalogram as a bio-signal sensor, the affective state of a user can be modelled and subsequently utilised in order to achieve a system that can recognise and react to the user’s emotions. This paper investigates features extracted from electroencephalogram signals for the purpose of affective state modelling based on Russell’s Circumplex Model. Investigations are presented that aim to provide the foundation for future work in modelling user affect to enhance interaction experience in virtual environments. The DEAP dataset was used within this work, along with a Support Vector Machine and Random Forest, which yielded reasonable classification accuracies for Valence and Arousal using feature vectors based on statistical measurements and band power from the α , β , δ , and 𝜃 waves and High Order Crossing of the EEG signal.

Objective: Many public access defibrillators (PADs) incorporate computer programs to provide audiovisual feedback to assist the user to deliver cardiopulmonary resuscitation (CPR) according to current international guidelines. This usability study assessed if a PAD integrated with a real-time audiovisual CPR feedback system can guide lay-users to optimum chest compression rates, and if it is detrimental to chest compression depth. Methods: Randomly selected volunteers (15+ years) were recruited for two experiments. Experiment 1 (n = 156) assessed the time taken to achieve the “good speed” audio prompt (i.e., perform compressions at a rate of 100-120 compressions/min) and chest compression fraction (CCF). Experiment 2 (n = 140) assessed the effect of rate-only CPR feedback on chest compression depth. Two devices of the same model were used: one with CPR rate feedback and the other with CPR feedback disabled. The difference in compression depths and CCF was assessed. Results: Experiment 1: A total of 136 (87.2%) participants achieved “good speed” within 45s with a mean CCF of 90.3% recorded. Experiment 2: The device with feedback lead to a mean (standard error-SE) depth of 24.61mm (0.99) compared with 20.08mm (0.96) for the feedback disabled device. Analysis of covariance provided a mean significant difference (SE) of 4.52mm (1.38mm; p-value = 0.001) favoring the device with CPR rate feedback. Conclusions: CPR rate-only feedback was not detrimental to chest compression depth and suggests rate-only feedback may improve compression depth. Significance: The incorporation of clear, intuitive, audiovisual CPR feedback systems can assist lay-users to optimize compression rates and maintain a high CCF.

High quality CPR in conjunction with early defibrillation can enhance survival outcomes following cardiac arrest. This usability study aimed to evaluate a novel prototype user interface of a public access defibrillator for the improvement of CPR during a simulated resuscitation attempt. Test candidates were asked to use the device in the absence of audible instructions, relying solely on the device membrane, to perform CPR chest compressions. The rate of the rescuer chest compression were then assessed and evaluated according to current resuscitation guidelines. All participants improved their rate of CPR within-test. All but one achieved the correct compression rate within 20 seconds. This study suggests that the device interface could potentially enhance real-time CPR quality during resuscitation attempts.

To address an increasing global health problem of obesity, further innovative initiatives are required. One such initiative is personalized messaging using mobile applications as a potential tool contributing to obesity prevention and management. In order to achieve this, there are challenges that need to be considered first including the accurate estimation of calories of meals and individuals' calorific intakes using a smartphones. There is also a lack of evidence indicating whether novices, peers and family members can provide accurate tailored feedback on calorie intake and nutrition. The two study objectives were i. To determine the feasibility of experts in nutrition and non-experts accurately identifying calories of meals from photographs as taken on a smartphone; and ii. To inform the development a personalized messaging system for obesity prevention and management using a mobile application. This study was an experimental design using a quantitative online survey with 24 participants, consisting of 12 experts in nutrition and/or dietetics, and 12 non-experts. The non-expert group attended a training session and both groups completed an online survey. The survey consisted of 15 meals, the participants were required to view the photographs and then answer the following question for each photograph: “From viewing the above photograph, enter the number of calories you consider is in this meal? ___Kcal OR ___KJ”. Crowdsourcing was used. The results revealed that the percentage difference between the estimated calories count in the meals against the actual number of calories was on average +55% (SD 79.9) for the non-expert group and +8% (SD 15.1) for the expert group (t-test, P<;0.001). When using crowdsourcing, aggregating opinions from experts and also non-experts improves accuracy. The mode estimate from a crowd of experts is more accurate than 79% of individual experts. The crowd of non-experts' average median difference out performed 63% of individual non-experts. Thus the crowd of non-experts is more accurate in estimating calories from photographs taken on a smartphone than most individuals. When designing a personalized messaging system for obesity prevention and management using a mobile application, a crowd of experts in nutrition and also a crowd of non-experts should be included to estimate calories in foods from photographs taken on a smartphone. This may have potential in contributing to obesity prevention and management, which warrant further research.

Obesity is prevalent worldwide including UK and Ireland, affecting all demographics. Obesity can have a detrimental affect on an individual's health, which can lead to chronic conditions. Different digital interventions have enabled users to photograph food items to be identified using different feature extraction methods. In this research, we proposed a system that allows users to draw a polygon around a food item for segmentation. After segmented, the region is then classified using an automated voting system. Different features will then be extracted from the specified area. Support Vector Machines will be issued for each feature type. This system is a proof-of-concept and is designed to research the effectiveness of employing multiple feature detection algorithms to classify food images. To classify food regions a Bag-of-features (BoFs) approach will be used for each. Speeded Up Robust Features point detection and descriptors was used along with colour spatial features, and also MSER region detection with SURF. Each of these methods will have their own BoF to train an SVM. The aim of this research was to create a voting classification system that utilises each feature detection algorithm to ultimately identify the segmented food region through plurality (or majority) vote. Testing showed that the system achieved 75% accuracy when combining each feature SVM to create a voting system. The system outperforms two of the feature classifiers (SURF and MSER with SURF). LAB colour classifier slightly outperformed the voting mechanism within the developed system. In regards to future work, further development and testing would be completed through increasing the variety of food items used in the training phase and a larger test dataset would also be used.

Electrocardiograms can be used for diagnosing various cardiac conditions. They are traditionally printed as a hard copy on thermal graph paper, which a clinician can then use to support the overall diagnosis. Nevertheless, as technology evolves aspects of healthcare are embracing these technological advancements. One such technology that is being accepted is the Smartphone. Lightweight, portable and with impressive processing speed, smartphones are becoming a familiar sight within healthcare, and used not only for the purposes of making phone calls. There are many healthcare applications available which are already proving popular amongst clinicians and patients, such as WebMD, Trusted Health and Wellness Information as well as First Aid - Emergency Handbook. Additionally, with a range of five to twelve mega pixel camera as standard, images can be taken and details can be presented in ways previously not thought possible. This paper reports upon the motivations of using a smartphone within healthcare and describes the findings of having fifteen electrocardiograms diagnoses using an Apple iPhone by a clinician.