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Day 1 18/12/2020
Room #1

Welcome message by the General Chair 09:00 - 09:05

Greenwich Mean Time (GMT), UTC +0

Welcome message by the EAI Conference manager 09:05 - 09:10

Welcome message by the EAI Community Manager 09:10 - 09:15

Keynote speech on "A Look at Smartphone Data for Sensing Health Conditions" from Prof. Pan Hui 09:15 - 10:35

Coffee Break - Morning 10:35 - 10:45

Session 1: Wearable Technologies 10:45 - 11:35

10:45 - 11:10
Evaluating memory and cognition via a wearable EEG system: a preliminary study

Human memory comprises one of the most complex brain functions, attracting researchers to unveil the neural mechanisms governing its effective operation. In this respect, the current study examines the application of a wearable single-channel EEG to the interpretation of cognitive operations reflecting memory processes. For this purpose, we implemented a set of tasks for evaluating the participants’ processing skills and memory efficiency, in order to examine potential outcomes derived from a specialized cognitive training routine. The employed training method targeted the distinction of automatic and controlled processing and its effects on memory, while we also investigated transfer effects to untrained tasks. Based on the electrophysiological data recorded during the cognitive tasks, we computed measures of induced EEG activity for each frequency band to examine the influence of cognitive training on both task performance and brain activity, as well as whether the EEG metrics could provide insight into the underlying brain processes and augment the interpretation of behavioral outcomes. Ultimately, statistical analysis showed an apparent contribution of EEG in understanding the observed behavioral differences, while our training program had a clear impact on the participants’ performance and brain activity. Moreover, we observed the reported distinction between automatic and controlled memory processes which play an integral part in both ageing and cognitive impairments.
Authors: Stavros-Theofanis Miloulis (School of Electrical and Computer Engineering, National Technical University of Athens, Greece), Ioannis Kakkos (School of Electrical and Computer Engineering, National Technical University of Athens, Greece), Georgios Dimitrakopoulos (Department of Medicine, University of Patras, Greece), Yu Sun (Department of Biomedical Engineering, Zhejiang University, China), Irene Karanasiou (Department of Mathematics and Engineering Sciences, Hellenic Military Academy, Greece), Panteleimon Asvestas (Department of Biomedical Engineering, University of West Attica, Greece), Errikos-Chaim Ventouras (Department of Biomedical Engineering, University of West Attica, Greece), George Matsopoulos (School of Electrical and Computer Engineering, National Technical University of Athens, Greece),
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11:10 - 11:35
Towards Mobile-based Preprocessing Pipeline for Electroencephalography (EEG) Analyses: The Case of Tinnitus

Recent developments in Brain-Computer Interfaces (BCI)-technologies to collect brain imaging data allow to record EEG data outside of a laboratory setting by means of mobile EEG systems. Brain imaging has been pivotal in understanding the neurobiological correlates of human behavior in many disorders. Such is the case with tinnitus, a disorder that causes phantom noise sensations in the ears. As studies have shown that tinnitus is also influenced by complexities in non-auditory brain areas, mobile EEG can be a viable solution in better understanding the influencing factors causing tinnitus. Mobile EEG will become even more useful, if real-time analysis in mobile environments is enabled, e.g., as an immediate feedback to physicians and patients or in undeveloped areas where a laboratory setup is unfeasible. The volume and complexity of brain imaging data has made preprocessing a pertinent step in the process of analysis. We introduce the first smartphone-based preprocessing pipeline for real-time EEG analysis. More specifically, we present a mobile app with a rudimentary EEG preprocessing pipeline and evaluate the app and its resource consumption underpinning the feasibility of smartphones for EEG preprocessing. Our proposed approach will allow researchers to collect brain imaging data of tinnitus and other patients in real-world environments and everyday situations, thereby collect evidence for previously unknown facts about tinnitus and other conditions.
Authors: Muntazir Mehdi (Institute of Distributed Systems, Ulm University), Lukas Hennig (Institute of Distributed Systems, Ulm University), Florian Diemer (Institute of Distributed Systems, Ulm University), Albi Dode (Institute of Databases and Information Systems, Ulm University), Ruediger Pryss (Institute of Clinical Epidemiology and Biometry, University of Wuerzburg), Winfried Schlee (Clinic and Policlinic for Psychiatry and Psychotherapy, Regensburg, Germany), Manfred Reichert (Institute of Databases and Information Systems, Ulm University), Franz Hauck (Institute of Distributed Systems, Ulm University),
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Session 2: Health Telemetry 11:35 - 12:50

11:35 - 12:00
A home-based self-administered assessment of neck proprioception

Proprioception is fundamental for maintaining balance and moving - hence for daily living. As proprioception deficits may occur with aging, neurologi-cal and musculoskeletal (especially cervical) conditions, assessment of pro-prioception can be relevant for a very large cohort of individuals. We designed a web page that allows measuring the neck joint position sense while sitting in front of a standard webcam. The web page tracks the sub-jects’ head movement and instructs them on how to perform a head reposi-tioning accuracy protocol. We performed a test re-test analysis of this tool in order to assess its feasibility and reliability. Eleven healthy subjects partici-pated in two sessions over consecutive days, at their homes. We calculated average errors across four directions Bland-Altman level of agreement be-tween the measurements on the two sessions. All participants could complete the test in approximately six minutes. The aver-age absolute error did not differ between the two sessions, showing close to zero bias and a 95% limit of agreement of 1.676° . These values changed significantly across directions, suggesting that the performance of the head tracking software for neck flexion movements may be limited. By comparing our results with normative values, we suggest that the narrow limit of agreement we observed makes the web page potentially capable of distinguishing healthy subjects from subjects with proprioceptive deficit in the neck joint.
Authors: Angelo Basteris (University of Southern Denmark), Charlotte Tornbjerg (University of Southern Denmark), Frederikke Leth (University of Southern Denmark), Uffe Wiil (University of Southern Denmark),
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12:00 - 12:25
Health Telescope: system design for longitudinal data collection using mobile applications

This paper describes the process of developing the technical infrastructure of the Health Telescope: an interventional panel study designed to measure the long term effects of eHealth usage. We describe the design and implementation of both the Health Telescope application --- an Android application that allows us to interact with participants and obtain measurements --- and the researcher authoring client --- a web-based application that allows us to flexibly submit experience sampling tasks to participants. This paper serves as a blueprint for those wanting to study long-term behavioral change in the wild. The paper furthermore describes a pilot study that was conducted to evaluate the research software. We conclude with design guidelines aimed at those aiming to undertake a similar endeavor that are vital when developing similar software; this paper aims to highlight both the importance and challenges of measuring the effects of eHealth applications longitudinally.
Authors: Bas Willemse (Jheronimus Academy of Data Science), Maurits Kaptein (Jheronimus Academy of Data Science), Nikolaos Batalas (Technische Universiteit Eindhoven), Fleur Hasaart (CZ Zorgverzekeraars),
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12:25 - 12:50
Design of a Mobile-Based Neurological Assessment Tool for Aging Populations

Mobile devices are becoming more pervasive in the monitoring of individuals' health as device functionalities increase as does overall device prevalence in daily life. Therefore, it is necessary that these devices and their interactions are usable by individuals with diverse abilities and conditions. This paper assesses the usability of a neurocognitive assessment application by individuals with Parkinson's Disease (PD) and proposes a design that focuses on the user interface, specifically on testing instructions, layouts, and subsequent user interactions. Further, we investigate potential benefits of cognitive interference (e.g., the addition of outside stimuli that intrude on task-related activity) on a user's task performance. Understanding the population's usability requirements and their performance on configured tasks allows for the formation of usable and objective neurocognitive assessments.
Authors: John Templeton (University of Notre Dame), Christian Poellabauer (University of Notre Dame), Sandra Schneider (University of Notre Dame),
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Lunch break 12:50 - 13:20

Session 3: Mobile sensing and assessment 13:20 - 14:25

13:20 - 13:45
Experiences in Designing a Mobile Speech-Based Assessment Tool for Neurological Diseases

Mobile devices contain an increasing number of sensors, many of which can be used for disease diagnosis and monitoring. Thus along with the ease of access and use of mobile devices there is a trend towards developing neurological tests onto mobile devices. Speech-based approaches have shown particular promise in detection of neurological conditions. However, designing such tools carries a number of challenges, such as how to manage noise, delivering the instructions for the speech based tasks, handling user error, and how to adapt the design to be accessible to specific populations with Parkinson's Disease and Amyotrophic Lateral Sclerosis. This report discusses our experiences in the design of a mobile-based application that assesses and monitors disease progression using speech changes as a biomarker.
Authors: Louis Daudet (University of Notre Dame), Christian Poellabauer (University of Notre Dame), Sandra Schneider (Saint Mary's College),
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13:45 - 14:10
Patient-independent Schizophrenia Relapse Prediction Using Mobile Sensor based Daily Behavioral Rhythm Changes

A schizophrenia relapse has severe consequences for a patient’s health, work, and sometimes even life safety. If an oncoming relapse can be predicted on time, for example by detecting early behavioral changes in patients, then interventions could be provided to prevent the relapse. In this work, we investigated a machine learning based schizophrenia relapse prediction model using mobile sensing data to characterize behavioral features. A patient-independent model providing sequential predictions, closely representing the clinical deployment scenario for relapse prediction, was evaluated. The model uses the mobile sensing data from the recent four weeks to predict an oncoming relapse in the next week. We used the features extracted from the daily templates of mobile sensing data, EMA (Ecological Momentary Assessment), and demographics to compare different classifiers for the relapse prediction. Naive Bayes based model gave the best results with an F2 score of 0.083 when evaluated in a dataset consisting of 63 schizophrenia patients, each monitored for up to a year. The obtained F2 score, though low, is better than the baseline performance of random classification (F2 score of 0.02 ± 0.024). Thus, mobile sensing has predictive value for detecting an oncoming relapse and needs further investigation to improve the current performance. Towards that end, further feature engineering and model personalization based on the behavioral idiosyncrasies of a patient could be helpful.
Authors: Bishal Lamichhane (Rice University, USA), Dror Ben-Zeev (University of Washington, USA), Andrew Campbell (Dartmouth College, USA), Tanzeem Choudhury (Cornell University, USA), Marta Hauser (Northwell Health, USA), John Kane (Northwell Health, USA), Mikio Obuchi (Dartmouth College, USA), Emily Scherer (Dartmouth College, USA), Megan Walsh (Northwell Health, USA), Rui Wang (Dartmouth College), Weichen Wang (Dartmouth College), Akane Sano (Rice University),
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14:10 - 14:25
Understanding E-Mental Health for People with Depression: An Evaluation Study

Depression is widespread and, despite a wide range of treatment options, causes considerable suffering and disease burden. Digital health interventions, including self-monitoring and self-management, are becoming increasingly important to offer e-mental health treatment and to support the recovery of people affected. SELFPASS is such an application designed for the individual therapy of patients suffering from depression. To gain more insights, this study aims to examine e-mental health treatment using the example of SELFPASS with two groups: healthy people and patients suffering from depression. The analysis includes the measurement of the constructs Usability, Trust, Task-Technology Fit, Attitude and Intention-to-use, the causal relationships between them and the differences between healthy and depressive participants as well as differences between participants’ evaluations at the beginning and at the end of the usage period. The results show that the Usability has the biggest influence on the Attitude and the Intention-to-use. Moreover, the study reveals clear differences between healthy and depressive participants and indicates the need for more efforts to improve compliance.
Authors: Kim Janine Blankenhagel (Technische Universität Berlin), Johannes Werner (Technische Universität Berlin), Gwendolyn Mayer (Universitätsklinikum Heidelberg), Jobst-Hendrik Schultz (Universitätsklinikum Heidelberg), Rüdiger Zarnekow (Technische Universität Berlin),
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Session 4: Health platform 14:25 - 15:25

14:25 - 14:55
Improving Patient Throughput By Streamlining The Surgical Care-Pathway Process

The delivery of a patient, to the operating theatre, in every hospital, consists of several heterogeneous departments working synchronously via communicating and sharing information, in relation to the current state of a patient’s care, as they travel through the surgical care-path way. The surgical care-pathway typically starts at admissions and finishes as the patient is leaving recovery. The problem being, as a patient navigates the care-pathway, there are numerous risk factors in the forms of technical, environmental and human that can influence a delay in the delivery of care. This paper will discuss these risk factors and highlight different approaches taken by several authors to address such issues. Additionally, a software application will be discussed that has being developed by the author that uses portable mobile devices, to address similar issues, for a private health care provider in the south of Ireland. The results of implementing the new solution show a potential decrease in patient throughput time and an overall increase of task visibility, across the surgical care-pathway.
Authors: David Mc Mahon (Tralee Institute Of Technology Co.Kerry Ireland), Prof Joseph Walsh (Head Of School (STEM) Tralee Institute Of Technology Co.Kerry Ireland), Dr. Eilish Broderick (Head Of Department Tralee Institute Of Technology Co.Kerry Ireland), Dr. Juncal Nogales (Project Coordinator Tralee Institute Of Technology Co.Kerry Ireland),
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14:55 - 15:25
Connect - Blockchain and Self-Sovereign Identity Empowered Contact Tracing Platform

The COVID-19 pandemic in 2020 has resulted in increased fatality rates across the world and has stretched the resources in healthcare facilities. There have been several proposed efforts to contain the spread of the virus among humans. In order for us to get back to the "new normal", there is a need for automated and efficient human contact tracing that would be non-intrusive and effective in containing the spread of the virus. In this paper, we have developed "Connect", a Blockchain and Self-Sovereign Identity (SSI) based digital contact tracing platform. "Connect" will provide an automated mechanism to notify people in their immediate proximity of an occurrence of a positive case and would reduce the rate at which the infection could spread. The platform's self-sovereign identity capability will ensure no attribution to a user and the user will be empowered to share information. The ability to notify in a privacy-preserving fashion would provide businesses to put in place dynamic and localized data-driven mitigation response. "Connect's" SSI based identity wallet platform encodes user's digital identities and activity trace data on a permissioned blockchain platform and verified using SSI proofs. The activity trace records can be leveraged to identify suspected patients and notify the local community in real-time. Simulation results demonstrate transaction scalability and demonstrate the effectiveness of "Connect" in realizing data immutability and traceability.
Authors: Eranga Bandara (Old Dominion University, Virginia, USA), Xueping Liang (University of North Carolina at Greensboro, North Carolina, USA), Peter Foytik (Old Dominion University, Virginia, USA), Sachin Shetty (Old Dominion University, Virginia, USA), Crissie Hall (Sentara Healthcare, Norfolk, VA, USA), Daniel Bowden (Sentara Healthcare, Norfolk, VA, USA), Nalin Ranasinghe (University of Colombo School of Computing, Sri Lanka), Kasun De Zoysa (University of Colombo School of Computing, Sri Lanka), Wee Keong Ng (School of Computer Science and Engineering Nanyang Technological University, Singapore),
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Coffee Break - Afternoon 15:25 - 15:35

Keynote speech on "Ethical Assurance of Technology through Standardization & Certification" by Prof. Ali Hessami 15:35 - 16:05

Session 5: Machine Learning in eHealth Applications 16:05 - 17:25

16:05 - 16:30
Forecasting Health and Wellbeing for Shift Workers Using Job-role Based Deep Neural Network

Shift workers who are essential contributors to our society, face high risks of poor health and wellbeing. To help with their problems, we collected and analyzed physiological and behavioral wearable sensor data from shift working nurses and doctors, as well as their behavioral questionnaire data and their self-reported daily health and wellbeing labels, including alertness, happiness, energy, health, and stress. We found the similarities and differences between the responses of nurses and doctors. According to the differences in self-reported health and wellbeing labels between nurses and doctors, and the correlations among their labels, we proposed a job-role based multitask and multilabel deep learning model, where we modeled physiological and behavioral data for nurses and doctors simultaneously to predict participants’ next day’s multidimensional self-reported health and wellbeing status. Our model showed significantly better performances than baseline models and previous state-of-the-art models in the evaluations of binary/3-class classification and regression prediction tasks. We also found features related to heart rate, sleep, and work shift contributed to shift workers’ health and wellbeing.
Authors: Han Yu (Rice University), Asami Itoh (Mie Univeristy), Ryota Sakamoto (Mie Univeristy), Motomu Shimaoka (Mie University), Akane Sano (Rice University),
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16:30 - 16:55
A Deep Learning Model for Exercise-Based Rehabilitation using Multi-channel Time-Series Data from a Single Wearable Sensor

The ability to accurately and automatically recognize and count the repetitions of exercises using a single sensor is essential for technology-assisted exercise-based rehabilitation. In this paper, we present a single deep learning architecture to undertake both of these tasks based on multi-channel time-series data. The models are constructed and tested using the INSIGHT-LME ~\cite{prabhu2020recognition} exercise dataset which consists of ten local muscular endurance (LME) exercises. For exercise recognition, we achieved an overall F1-score measure of 96\% and for repetition counting we were correct within an error of ±1 repetitions in 88\% of the observed exercise sets. To the best of our knowledge, our approach of using the same deep learning model for both tasks using raw time-series sensor data information is novel.
Authors: Ghanashyama Prabhu (Insight Centre for Data Analytics, Dublin City University, Dublin, Ireland), Noel O'Connor (Insight Centre for Data Analytics, Dublin City University, Dublin, Ireland), Kieran Moran (Insight Centre for Data Analytics, Dublin City University, Dublin, Ireland),
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16:55 - 17:25
Bayesian Inference Federated Learning for Heart Rate Prediction

The advances of sensing and computing technologies pave the way to develop novel applications and services for wearable devices. For example, wearable devices measure heart rate, which accurately reflects the intensity of physical exercise. Therefore, heart rate prediction from wearable devices benefits users with optimization of the training process. Conventionally, Cloud collects user data from wearable devices and conducts inference. However, this paradigm introduces significant privacy concerns. Federated learning is an emerging paradigm that enhances user privacy by remaining the majority of personal data on users' devices. In this paper, we propose a statistically sound, Bayesian inference federated learning for heart rate prediction with autoregression with exogenous variable (ARX) model. The proposed privacy-preserving method achieves accurate and robust heart rate prediction. To validate our method, we conduct extensive experiments with real-world outdoor running exercise data collected from wearable devices.
Authors: Lei Fang (University of St Andrews), Xiaoli Liu (University of Helsinki), Xiang Su (University of Helsinki), Juan Ye (University of Saint Andrews), Simon Dobson (University of St. Andrews UK), Pan Hui (The Hong Kong University of Science and Technology), Sasu Tarkoma (University of Helsinki),
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Closing message by the General Chair and Best Paper Announcement 17:25 - 17:30