79 research outputs found

    Fatigue detection system to aid in remote work

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    During the Covid-19 pandemic there was a noticeable surge in the amount of remote workers. In the aftermath of the pandemic working from home still remains a reality for many workers with noticeable impacts on the mental health of people. With the increased stress caused by current situation and the harder time establishing boundaries there was an increase in the overall stress and fatigue in workers, leading to burnouts. Fatigue detection systems are used in several areas, mainly in the automotive industry as a mean to decrease the number of accidents. This research started by approaching the Artificial Intelligence (AI) area and its domains, followed by a study of the current techniques used in order to predict fatigue. With the main ones utilising eye state, facial landmarks, electrocardiogram or heart rate. After a research into existing Fatigue detection systems was done in order to identify the strengths of solutions currently in the market, whether in the automotive industry or other applications. This thesis proposes the creation of a system able to detect fatigue in a user as well as warn him when fatigue levels increase. This system incorporates a webcam analysing the users face and performing eye state detection in order to calculate the percentage of the time the eyes are closed (PERCLOS). Heart rate data was also analysed and a model was developed in order to incorporate this data, the percentage of time the eyes are closed, the program the user has open and time of day in order to predict the level of fatigue. By combining these two different techniques this system can be more effective and more accurate in giving predictions of the level of fatigue. The review of literature showed that the conjunction of these two techniques in predicting fatigue is novelty. The developed system also contains integration with smartwatch technology in order to both harness heart rate data as well as communicate with the user via pop up notifications to inform him when fatigue levels get too high. The conclusion of this work is that eye state detection using Artificial Intelligence can achieve a high accuracy and be a reliable tool in identifying fatigue in an user. The combination of Heart Rate and PERCLOS allows the system to have a higher accuracy as well as not being completely reliant on one sensor. The creation of a fatigue prediction model was hindered by the lack of existent data in order to train a model, a problem that could be fixed with the adoption of the system in a broader scope.Durante a pandemia de Covid-19, houve um aumento notável na quantidade de trabalhadores remotos. No rescaldo da pandemia, trabalhar a partir de casa continua a ser uma realidade para muitos trabalhadores, com impactos visíveis na saúde mental das pessoas. Com o aumento do stresse causado pela situação atual e a dificuldade de estabelecer limites, houve um aumento do stresse geral e da fadiga dos trabalhadores, levando ao esgotamento. Os sistemas de detecção de fadiga são utilizados em diversas áreas, principalmente na indústria automobilística como forma de diminuir o número de acidentes. Este estudo começou por abordar a área de Inteligência Artificial (IA) e os seus domínios, seguida de um estudo das técnicas atuais utilizadas para prever a fadiga. Com os principais utilizando o estado dos olhos, pontos de referência faciais, eletrocardiograma ou frequência cardíaca. Depois foi feita uma pesquisa sobre os sistemas de detecção de fadiga existentes de forma a identificar os pontos fortes das soluções actualmente no mercado, quer seja na indústria automóvel ou outras aplicações. Esta dissertação propõe a criação de um sistema capaz de detectar fadiga num utilizador, bem como alertar quando os níveis de fadiga aumentam. Este sistema incorpora uma webcam que analisa a face do utilizador e realiza a detecção do estado dos olhos para calcular a percentagem de tempo em que os olhos estão fechados (PERCLOS). Os dados de frequência cardíaca também foram analisados e um modelo foi desenvolvido para incorporar estes dados, a percentagem de tempo que os olhos ficam fechados, o programa que o utilizador tem aberto e a hora do dia para prever o nível de fadiga. Ao combinar essas duas técnicas diferentes, este sistema pode ser mais eficaz e mais preciso em fornecer previsões do nível de fadiga. A revisão da literatura mostrou que a conjunção dessas duas técnicas na previsão da fadiga é novidade. O sistema desenvolvido também contém integração com a tecnologia smartwatch para aproveitar os dados da frequência cardíaca e comunicar com o utilizador por meio de notificações pop-up para informá-lo quando os níveis de fadiga se encontrarem altos. A conclusão deste trabalho é que a detecção do estado ocular usando Inteligência Artificial pode alcançar uma alta precisão e ser uma ferramenta confiável na identificação de fadiga num utilizador. A combinação da frequência cardíaca e PERCLOS permite que o sistema tenha maior precisão, além de não depender completamente de um unico sensor. A criação de um modelo de previsão de fadiga foi dificultada pela falta de dados existentes para treinar um modelo, problema que poderia ser colmatado com a adoção do sistema numa população maior

    Analysis of Parkinson's Disease Gait using Computational Intelligence

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    Millions of individuals throughout the world are living with Parkinson’s disease (PD), a neurodegenerative condition whose symptoms are difficult to differentiate from those of other disorders. Freezing of gait (FOG) is one of the signs of Parkinson’s disease that have been utilized as the main diagnostic factor. Bradykinesia, tremors, depression, hallucinations, cognitive impairment, and falls are all common symptoms of Parkinson’s disease (PD). This research uses a dataset that captures data on individuals with PD who suffer from freezing of gait. This dataset includes data for medication in both the “On” and “Off” stages (denoting whether patients have taken their medicines or not). The dataset is comprised of four separate experiments, which are referred to as Voluntary Stop, Timed Up and Go (TUG), Simple Motor Task, and Dual Motor and Cognitive Task. Each of these tests has been carried out over a total of three separate attempts (trials) to verify that they are both reliable and accurate. The dataset was used for four significant challenges. The first challenge is to differentiate between people with Parkinson’s disease and healthy volunteers, and the second task is to evaluate effectiveness of medicines on the patients. The third task is to detect episodes of FOG in each individual, and the last task is to predict the FOG episode at the time of occurrence. For the last task, the author proposed. a new framework to make real-time predictions for detecting FOG, in which the results demonstrated the effectiveness of the approach. It is worth mentioning that techniques from many classifiers have been combined in order to reduce the likelihood of being biased toward a single approach. Multilayer Perceptron, K-Nearest Neighbors, random Forest, and Decision Tree Classifier all produced the best results when applied to the first three tasks with an accuracy of more than 90% amongst the classifiers that were investigated

    Deep Learning Methods for Fingerprint-Based Indoor and Outdoor Positioning

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    Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. The contribution of this dissertation is fourfold: First, a Convolutional Neural Network (CNN)-based method for localizing a smartwatch indoors using geomagnetic field measurements is presented. The proposed method was tested on real world data in an indoor environment composed of three corridors of different lengths and three rooms of different sizes. Experimental results show a promising location classification accuracy of 97.77% with a mean localization error of 0.14 meter (m). Second, a method that makes use of cellular signals emitting from a serving eNodeB to provide symbolic indoor positioning is presented. The proposed method utilizes Denoising Autoencoders (DAEs) to mitigate the effects of cellular signal loss. The proposed method was evaluated using real-world data collected from two different smartphones inside a representative apartment of eight symbolic spaces. Experimental results verify that the proposed method outperforms conventional symbolic indoor positioning techniques in various performance metrics. Third, an investigation is conducted to determine whether Variational Autoencoders (VAEs) and Conditional Variational Autoencoders (CVAEs) are able to learn the distribution of the minority symbolic spaces, for a highly imbalanced fingerprinting dataset, so as to generate synthetic fingerprints that promote enhancements in a classifier\u27s performance. Experimental results show that this is indeed the case. By using various performance evaluation metrics, the achieved results are compared to those obtained by two state-of-the-art oversampling methods known as Synthetic Minority Oversampling TEchnique (SMOTE) and ADAptive SYNthetic (ADASYN) sampling. Fourth, a novel dataset of outdoor location fingerprints is presented. The proposed dataset, named OutFin, addresses the lack of publicly available datasets that researchers can use to develop, evaluate, and compare fingerprint-based positioning solutions which can constitute a high entry barrier for studies. OutFin is comprised of diverse data types such as WiFi, Bluetooth, and cellular signal strengths, in addition to measurements from various sensors including the magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor. The collection area spanned four dispersed sites with a total of 122 Reference Points (RPs). Before OutFin was made available to the public, several experiments were conducted to validate its technical quality

    Latent deep sequential learning of behavioural sequences

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    The growing use of asynchronous online education (MOOCs and e-courses) in recent years has resulted in increased economic and scientific productivity, which has worsened during the coronavirus epidemic. The widespread usage of OLEs has increased enrolment, including previously excluded students, resulting in a far higher dropout rate than in conventional classrooms. Dropouts are a significant problem, especially considering the rising proliferation of online courses, from individual MOOCs to whole academic programmes due to the pandemic. Increased efficiency in dropout prevention techniques is vital for institutions, students, and faculty members and must be prioritised. In response to the resurgence of interest in the student dropout prediction (SDP) issue, there has been a significant rise in contributions to the literature on this topic. An in-depth review of the current state of the art literature on SDP is provided, with a special emphasis on Machine Learning prediction approaches; however, this is not the only focus of the thesis. We propose a complete hierarchical categorisation of the current literature that correlates to the process of design decisions in the SDP, and we demonstrate how it may be implemented. In order to enable comparative analysis, we develop a formal notation for universally defining the multiple dropout models examined by scholars in the area, including online degrees and their attributes. We look at several other important factors that have received less attention in the literature, such as evaluation metrics, acquired data, and privacy concerns. We emphasise deep sequential machine learning approaches and are considered to be one of the most successful solutions available in this field of study. Most importantly, we present a novel technique - namely GRU-AE - for tackling the SDP problem using hidden spatial information and time-related data from student trajectories. Our method is capable of dealing with data imbalances and time-series sparsity challenges. The proposed technique outperforms current methods in various situations, including the complex scenario of full-length courses (such as online degrees). This situation was thought to be less common before the outbreak, but it is now deemed important. Finally, we extend our findings to different contexts with a similar characterisation (temporal sequences of behavioural labels). Specifically, we show that our technique can be used in real-world circumstances where the unbalanced nature of the data can be mitigated by using class balancement technique (i.e. ADASYN), e.g., survival prediction in critical care telehealth systems where balancement technique alleviates the problem of inter-activity reliance and sparsity, resulting in an overall improvement in performance

    Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

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    Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

    Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

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    Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

    Prediction of drivers’ performance in highly automated vehicles

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    Purpose: The aim of this research was to assess the predictability of driver’s response to critical hazards during the transition from automated to manual driving in highly automated vehicles using their physiological data.Method: A driving simulator experiment was conducted to collect drivers’ physiological data before, during and after the transition from automated to manual driving. A total of 33 participants between 20 and 30 years old were recruited. Participants went through a driving scenario under the influence of different non-driving related tasks. The repeated measures approach was used to assess the effect of repeatability on the driver’s physiological data. Statistical and machine learning methods were used to assess the predictability of drivers’ response quality based on their physiological data collected before responding to a critical hazard. Findings: - The results showed that the observed physiological data that was gathered before the transition formed strong indicators of the drivers’ ability to respond successfully to a potential hazard after the transition. In addition, physiological behaviour was influenced by driver’s secondary tasks engagement and correlated with the driver’s subjective measures to the difficulty of the task. The study proposes new quality measures to assess the driver’s response to critical hazards in highly automated driving. Machine learning results showed that response time is predictable using regression methods. In addition, the classification methods were able to classify drivers into low, medium and high-risk groups based on their quality measures values. Research Implications: Proposed models help increase the safety of automated driving systems by providing insights into the drivers’ ability to respond to future critical hazards. More research is required to find the influence of age, drivers’ experience of the automated vehicles and traffic density on the stability of the proposed models. Originality: The main contribution to knowledge of this study is the feasibility of predicting drivers’ ability to respond to critical hazards using the physiological behavioural data collected before the transition from automated to manual driving. With the findings, automation systems could change the transition time based on the driver’s physiological state to allow for the safest transition possible. In addition, it provides an insight into driver’s readiness and therefore, allows the automated system to adopt the correct driving strategy and plan to enhance drivers experience and make the transition phase safer for everyone.</div

    pHealth 2021. Proc. of the 18th Internat. Conf. on Wearable Micro and Nano Technologies for Personalised Health, 8-10 November 2021, Genoa, Italy

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    Smart mobile systems – microsystems, smart textiles, smart implants, sensor-controlled medical devices – together with related body, local and wide-area networks up to cloud services, have become important enablers for telemedicine and the next generation of healthcare services. The multilateral benefits of pHealth technologies offer enormous potential for all stakeholder communities, not only in terms of improvements in medical quality and industrial competitiveness, but also for the management of healthcare costs and, last but not least, the improvement of patient experience. This book presents the proceedings of pHealth 2021, the 18th in a series of conferences on wearable micro and nano technologies for personalized health with personal health management systems, hosted by the University of Genoa, Italy, and held as an online event from 8 – 10 November 2021. The conference focused on digital health ecosystems in the transformation of healthcare towards personalized, participative, preventive, predictive precision medicine (5P medicine). The book contains 46 peer-reviewed papers (1 keynote, 5 invited papers, 33 full papers, and 7 poster papers). Subjects covered include the deployment of mobile technologies, micro-nano-bio smart systems, bio-data management and analytics, autonomous and intelligent systems, the Health Internet of Things (HIoT), as well as potential risks for security and privacy, and the motivation and empowerment of patients in care processes. Providing an overview of current advances in personalized health and health management, the book will be of interest to all those working in the field of healthcare today
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