A Multitier Deep Learning Model for Arrhythmia Detection

Electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVD) in the hospital, which often helps in the early detection of such ailments. ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogues. It provides cardiologists with inferences regarding more serious cases. Notwithstanding its proven utility, deciphering large datasets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction. This is followed using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. The MIT-BIH Arrhythmia was employed in the validation to identify five arrhythmia categories based on the association for the advancement of medical instrumentation (AAMI) standard. The performance of the proposed technique alongside state-of-the-art in the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected in the acquired ECG data in a smart healthcare framework

Using Personality Traits and Chronotype for Personalized Feedback in a Sleep Web App

This project addresses the issue of sleep deprivation among college students by proposing and implementing an innovative approach to personalizing the web application, SleepHealth. Sleep deprivation has serious health repercussions and can be detrimental to academic success. SleepHealth uses individual personality and chronotype characteristics to support personalized feedback about users\u27 sleep patterns. Users personality and chronotype are assessed using questionnaires in the app, the results of which are used in personalizing the content, timing, and frequency of the apps notifications. These notifications are targeted at encouraging healthier sleep behaviors. This project accomplished full implementation of the questionnaires as well as the personalized feedback in SleepHealth

Behavioral biometrics and ambient intelligence: New opportunities for context-aware applications

Ambient Intelligence has always been associated with the promise of exciting new applications, aware of the users' needs and state, and proactive towards their goals. However, the acquisition of the necessary information for supporting such high-level learning and decision-making processes is not always straightforward. In this chapter we describe a multi-faceted smart environment for the acquisition of relevant contextual information about its users. This information, acquired transparently through the technological devices in the environment, supports the building of high-level knowledge about the users, including a quantification of aspects such as performance, attention, mental fatigue and stress. The environment described is particularly suited for milieus such as workplaces and classrooms, in which this kind of information may be very important for the effective management of human resources, with advantages for organizations and individuals alike.(UID/CEC/00319/2013)info:eu-repo/semantics/publishedVersio

Using mouse dynamics to assess stress during online exams

"Lecture notes in computer science series", ISSN 0302-9743, vol. 9121Stress is a highly complex, subjective and multidimensional phenomenon. Nonetheless, it is also one of our strongest driving forces, pushing us forward and preparing our body and mind to tackle the daily challenges, independently of their nature. The duality of the effects of stress, that can have positive or negative effects, calls for approaches that can take the best out of this biological mechanism, providing means for people to cope effectively with stress. In this paper we propose an approach, based on mouse dynamics, to assess the level of stress of students during online exams. Results show that mouse dynamics change in a consistent manner as stress settles in, allowing for its estimation from the analysis of the mouse usage. This approach will allow to understand how each individual student is affected by stress, providing additional valuable information for educational institutions to efficiently adapt and improve their teaching processes.This work is part-funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980 (PTDC/EEI-SII/1386/2012) and project PEst-OE/EEI/UI0752/2014

Ubi-RKE: A Rhythm Key Based Encryption Scheme for Ubiquitous Devices

As intelligent ubiquitous devices become more popular, security threats targeting them are increasing; security is seen as one of the major challenges of the ubiquitous computing. Now a days, applying ubiquitous computing in number of fields for human safety and convenience was immensely increased in recent years. The popularity of the technology is rising day by day, and hence the security is becoming the main focused point with the advent and rising popularity of the applications. In particular, the number of wireless networks based on ubiquitous devices has increased rapidly; these devices support transmission for many types of data traffic. The convenient portability of ubiquitous devices makes them vulnerable to security threats, such as loss, theft, data modification, and wiretapping. Developers and users should seriously consider employing data encryption to protect data from such vulnerabilities. In this paper, we propose a Rhythm Key based Encryption scheme for ubiquitous devices (Ubi-RKE). The concept of Rhythm Key based Encryption has been applied to numerous real world applications in different domains. It provides key memorability and secure encryption through user touching rhythm on ubiquitous devices. Our proposed scheme is more efficient for users than existing schemes, by providing a strong cipher

Pedagogical approaches for e-assessment with authentication and authorship verification in Higher Education

Checking the identity of students and authorship of their online submissions is a major concern in Higher Education due to the increasing amount of plagiarism and cheating using the Internet. The literature on the effects of e-authentication systems for teaching staff is very limited because it is a novel procedure for them. A considerable gap is to understand teaching staff’ views regarding the use of e-authentication instruments and how they impact trust in e-assessment. This mixed-method study examines the concerns and practices of 108 teaching staff who used the TeSLA - Adaptive Trust-based e-Assessment System in six countries: UK, Spain, Netherlands, Bulgaria, Finland and Turkey. The findings revealed some technological, organisational and pedagogical issues related to accessibility, security, privacy and e-assessment design and feedback. Recommendations are to provide: a FAQ and an audit report with results, to raise awareness about data security and privacy, to develop policies and guidelines about fraud detection and prevention, e-assessment best practices and course team support

Wireless body sensor networks for health-monitoring applications

This is an author-created, un-copyedited version of an article accepted for publication in Physiological Measurement. The publisher is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01

Eyewear Computing \u2013 Augmenting the Human with Head-Mounted Wearable Assistants

The seminar was composed of workshops and tutorials on head-mounted eye tracking, egocentric vision, optics, and head-mounted displays. The seminar welcomed 30 academic and industry researchers from Europe, the US, and Asia with a diverse background, including wearable and ubiquitous computing, computer vision, developmental psychology, optics, and human-computer interaction. In contrast to several previous Dagstuhl seminars, we used an ignite talk format to reduce the time of talks to one half-day and to leave the rest of the week for hands-on sessions, group work, general discussions, and socialising. The key results of this seminar are 1) the identification of key research challenges and summaries of breakout groups on multimodal eyewear computing, egocentric vision, security and privacy issues, skill augmentation and task guidance, eyewear computing for gaming, as well as prototyping of VR applications, 2) a list of datasets and research tools for eyewear computing, 3) three small-scale datasets recorded during the seminar, 4) an article in ACM Interactions entitled \u201cEyewear Computers for Human-Computer Interaction\u201d, as well as 5) two follow-up workshops on \u201cEgocentric Perception, Interaction, and Computing\u201d at the European Conference on Computer Vision (ECCV) as well as \u201cEyewear Computing\u201d at the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)

Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This research aims to identify a feasible model to predict a learner’s stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner’s emotions. The few signals produced by mouse and keyboard could enable such solution to measure real world individual’s affective states. It is also important to ensure that the measurement can be applied regardless the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes of duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification