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Day 1 21/10/2020
Room #1

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

Conference starts at 9:00am (Tallin Time Zone)

Welcome Message by the EAI Conference Manager 09:05 - 09:10

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

Keynote Speaker 1: Prof. Mohsen Guizani 09:15 - 10:05

IoT Security Schemes for Healthcare Systems

Keynote Speaker 2: Helena Gapeyeva MD, PhD 10:05 - 11:00

Movement analysis in Physical and Rehabilitation Medicine: Data monitoring

Coffee Break 11:00 - 11:10

10-minute Coffee Break

Session 1 - Connectivity and Radio Propagation 11:10 - 13:05

After each paper presentation, please join us for a short Q&A session at Slack platform.
11:10 - 11:30
Providing Connectivity to Implanted Electronics Devices: Experimental Results on Optical Communications over Biological Tissues with Comparisons against UWB

Radio and acoustic waves have been conventionally used for transmitting information through biological tissues. However, radio-based communications often suffer from several drawbacks like security, safety, privacy, and interference. In this paper, we demonstrate that optical wireless communications can be practically used for communications through biological tissues, particularly to transmit in-formation to and from implanted devices. In the experiment, ex vivo samples of pork meat were used as the optical channel. Initial results show that information can be optically transmitted through biological tissues to distances of several centimeters, a range of practical interest as many implants today are placed within this extent. Optical links are inherently secure, and interference to and from other equipment is not an issue. With numerous potential benefits, optical wireless communication can be considered as a complementary approach to the existing radio frequency (RF) communications. In this paper, a comparison between the measurement results of ultra-wideband (UWB) and optical communications through the biological tissues is presented. Both experiments have been taken place in a similar environment, with the same meat samples. We have also explored the effect of tissue temperature on successful communications through biological tissues. These initial results are very promising and indicate various potential benefits for in-body communication in the future.
Authors: Senjuti Halder (University of Oulu), Mariella Särestöniemi (University of Oulu), Iqrar Ahmed (University of Oulu), Marcos Katz (University of Oulu),
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11:30 - 11:50
On the UWB in-body propagation measurements using pork meat

This paper presents a study on the ultra wideband (UWB) in-body propagation measurements using pork meat. The first objective of this paper is to investigate by simulations the propagation differences between human and pork tissue layer models. The simulations results show clear differences between the channel characteristics obtained using a human tissues and pork tissues: within the frequency range of interest at 3.75 – 4.25 GHz, the path loss with pork meat can be up to 5 dB less than with the human meat. The second objective of this paper is to study, by measurements, the in-body channel characteristics using different types of pork meat piece having different fat and muscle compositions. It was found that path loss is clearly higher with the pork meat having separate skin, fat, and muscle layers compared to the pork meat having interlaced fat and muscle layers. Furthermore, the third objective of this paper is to study the impact of the meat temperature on the measured channel characteristics by comparing the channels obtained with the meat at the temperatures of 12°C and at 37°C. Also, in this case clear differences were observed in path loss: within the frequency range of interest, the path loss was maximum 5 dB lower with meat at 37°C than with a colder meat. The results presented in this paper provide useful information and relevant aspects for the in-body propagation studies conducted with pork meat.
Authors: Mariella Särestöniemi (University of Oulu), Carlos Pomalaza-Raez (Purdue University), Chaimaa Kissi (Electronics and Telecommunication Systems Research Group, National School of Applied Sciences (ENSA), Ibn Tofail University, Marocco), Jari Iinatti (University of Oulu),
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11:50 - 12:15
Detection of brain hemorrhage in white matter using analysis of radio channel characteristics

This paper presents a study on detecting brain hemorrhage with radio channel characteristics analysis based on electromagnetic simulations with anatomical voxel model. The idea is to utilize the fact that blood has different dielectric properties than brain white and grey matters and, thus, additional blood areas inside the brain change radio channel characteristics between the transmitter and receiver antennas located on the opposite sides of the head. The antennas should be strongly directive and designed to work attached to the body surface. The study is conducted using the electromagnetic simulation software CST and two different simulation models: a spherical tissue layer model and an anatomical voxel model. The antennas used in this study are bio-matched mini-horn antennas designed for implant communications. Different sizes of the blood areas are evaluated. This initial study shows how even small sizes of hemorrhage can change radio channel even when the hemorrhage is located in the middle of the brain, in the white matter. A practical solution of this hemorrhage detection technique could be a portable helmet type of structure having several small sized antennas around the internal part of the helmet. Such a helmet would be easy to use e.g. in ambulance, which would enable early detection of hemorrhage in its early phase and, hence, improve prospects of the cure significantly.
Authors: Mariella Särestöniemi (University of Oulu), Carlos Pomalaza-Raez (Purdue University), Jaakko Hakala (Optoelectronics and Measurement Techniques Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland), Sami Myllymäki (Microelectronics Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland), Joni Kilpijärvi (Microelectronics Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland), Jari Iinatti (University of Oulu), Matti Hämäläinen (University of Oulu), Teemu Myllylä (Optoelectronics and Measurement Techniques Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland),
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12:15 - 12:40
UWB Microwave Imaging for Inclusions Detection: Methodology for Comparing Artefact Removal Algorithms

An investigation is presented on Artefact Removal Methods for Ultra-Wideband (UWB) Microwave Imaging. Simulations have been done representing UWB signals transmitted onto a cylindrical head-mimicking phantom containing an inclusion having dielectric properties imitating an haemorragic stroke. The ideal image is constructed by applying a Huygens’ Principle based imaging algorithm to the difference between the electric field outside the cylinder with an inclusion and the electric field outside the same cylinder with no inclusion. Eight different artefact removal methods are then applied, with the inclusion positioned at π and -π/4 radians, respectively. The ideal image is then used as a reference image to compare the artefact removal methods employing a novel Image Quality Index, calculated using a weighted combination of image quality metrics. The Summed Symmetric Differential method performed very well in our simulations.
Authors: James Puttock (London South Bank University), Behnaz Sohani (London South Bank University), Banafsheh Khalesi (London South Bank University), Gianluigi Tiberi (London South Bank University), Sandra Dudley-McEvoy (London South Bank University), Mohammad Ghavami (London South Bank University),
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12:40 - 13:05
BSNCloud: Cloud-centered Wireless Body Sensor Data Collection, Streaming, and Analytics System

Cloud-assisted body area networks have been the focus of researchers in past years as a response to the development of robust wireless body area networks (WBANs). While software such as Signal Processing in Node Environment (SPINE) provide Application Programming In-terfaces (APIs) to manage heterogeneous biomedical sensor networks, others have focused on developing tools that address the issue of sensor connection/control, data receiving, and visuali-zation. However, existing software tools lack sufficient flexibility, scalability, and support for complicated biomedical systems. In this paper, BSNCloud, a cloud-centered heterogeneous and comprehensive wireless body sensor data collection, streaming, and analytics framework is pro-posed. The system combines the sensor control and data aggregator event detection, real-time data analysis, visualization, and streaming into one Android App and incorporated four key com-ponents in the cloud server, data repository, algorithm repository, machine learning engine, and web portal. A prototype has been implemented with preliminary performance evaluation. Results show that the system is promising in its full utilization of the high performance computing power as well as the large volume storage capacity.
Authors: Ming Li (CSU Fresno), Ai Enkoji (CSU Fresno), Matthew Key (CSU Fresno), Aaron Marroquin (CSU Fresno), B. Prabhakaran (The University of Texas at Dallas),
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Lunch Break 13:05 - 13:35

30-minute lunch break

Session 2 - Secure Communication Networks for SmartHealth 13:35 - 15:25

After each paper presentation, please join us for a short Q&A session at Slack platform.
13:35 - 14:00
Model-Based Analysis of Secure and Patient-Dependent Pacemaker Remote Monitoring System

Pacemakers' safety, security and reliability are of utmost importance for patient's life quality in various daily situations. An integral characteristic of the pacemaker that depends on all of these attributes is its lifetime. In current medical practice the pacemaker's expected lifetime is estimated relying on manufacturer's data sheet and expert knowledge that may result in quite rough approximations if patient's specifics are not taken into account. In this paper we perform a model-based quantitative analysis of pacemaker lifetime that takes into account patient specific factors, including general health condition, acting environment, remote reporting and others. We demonstrate that including these factors in analysis can provide drastically different results compared to that of average approximating estimates.
Authors: Leonidas Tsiopoulos (Tallinn University of Technology), Alar Kuusik (Tallinn University of Technology), Jüri Vain (Tallinn University of Technology), Hayretdin Bahsi (Tallinn University of Technology),
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14:00 - 14:20
Amplitude Modulation in a Molecular Communication Testbed with Superparamagnetic Iron Oxide Nanoparticles and a Micropump

Molecular communication uses molecules or other nanoscale particles to transmit data in scenarios where conventional communication techniques are not feasible. In previous work a testbed using superparamagnetic iron oxide nanoparticles (SPIONs) as information carriers in a fluid transmission channel with constant background flow was proposed. The SPIONs are detected at a receiver as change of a coils inductance. We now improve the testbed by using a piezoelectric micropump as transmitter, making amplitude modulation (AM) with different injection volumes possible. Machine learning is employed at the receiver to differentiate between six different amplitude levels and grey code is used to reduce bit errors. With AM and the designed coding scheme, the achievable effective data rate was doubled to 4.45 bit s−1.
Authors: Max Bartunik (Institute for Electronics Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany), Thomas Thalhofer (Fraunhofer EMFT Research Institution for Microsystems and Solid State Technologies, Munich, Germany), Christian Wald (Fraunhofer EMFT Research Institution for Microsystems and Solid State Technologies, Munich, Germany), Martin Richter (Fraunhofer EMFT Research Institution for Microsystems and Solid State Technologies, Munich, Germany), Georg Fischer (Institute for Electronics Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany), Jens Kirchner (Institute for Electronics Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany),
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14:20 - 14:40
An Enhanced DNA Sequence Table for Improved Security and Reduced Computational Complexity of DNA Cryptography

Recently, DNA cryptography rejuvenates the art of secret writing by combining biological information and cryptography. DNA’s double-helical structure serves as a template for encoding decoding information, vast storage and randomness. The structure includes DNA encryption that uses a DNA sequence table to substitute plaintext into the DNA sequence. However, this encoding table can result in leakage of information about the plaintext, character frequency, and key, by carefully examining the ciphertext through frequency analysis attack. Therefore, this paper proposes an enhanced DNA table for all 96 printable ASCII characters which are created to improve the entropy so that the probability of each encoding base (A, T, C, G) is equally likely and to reduce the computational complexity of DNA cryptography. An algorithm has been selected to implement both tables for performance measurement. The results show that encoding and encryption time is reduced, high entropy ciphertext, better frequency distribution ciphertext is obtained. Information leakage in terms of conditional entropy is also reduced by the proposed table. In conclusion, the pro-posed table can be used as a DNA sequence table in DNA cryptography to improve overall system security.
Authors: Maria Imdad (Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia), Sofia Najwa Ramli (Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia), Hairulnizam Mahdin (Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia), Boppana Udaya Mouni (Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia), Shakira Sahar (Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia),
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14:40 - 15:05
Solving Generic Decision Problems by in-Message Computation in DNA-Based Molecular Nanonetworks

One of the biggest unsolved problems in nanonetwork research is the actual construction of the components required for building such networks. Most existing ideas are limited to partial solutions of construction of nanodevices, computation within them, and communication between them. While many ideas are promising, the problem remains how to combine those various building blocks into operational and efficient nanonetworks. In this paper we use DNA as \emph{the} basic building block for all components of nanonetworks. The inherent properties of this molecule are used to assemble complex nanostructures. DNA can be utilized to create both nanodevices and a communication mechanism. Properly designed DNA-molecules can even be utilized for computational purposes. In summary, DNA forms the base for an exhaustive nanonetwork concept. This work specifically presents an approach how to solve arbitrary mathematical problems that can be modeled as boolean formulas using DNA-based nanonetworks by in-message computation. The computation itself is encoded in the assembly process of a message. This avoids often-stated space constraints for computations at the nanoscale, as the medium of transportation is commonly less constrained than the size of nanodevices dictates. This method thereby presents a constructive approach on how to actually create message molecules, rather than only proving the general possibility.
Authors: Florian-Lennert Lau (University of Lübeck), Regine Wendt (University of Lübeck), Stefan Fischer (University of Lübeck),
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15:05 - 15:25
A model for electro-chemical neural communication

The neuro-spike communication is conducted using electro- chemical nervous signal transmissions between neurons and synapses. The nervous signal is composed of a sequence of electrically charged ions exchange in the neurons. It passes to other from one neuron to another one through the process of release and a combination of chemical substances in synapses. The neuro-spike communication is subject to disruptions due to different biological factors that impact the permanence of neural communications. In this paper, we investigate the performance of a neuro-spike communication between two neighboring neurons. We first present a mathematical model to capture the inherent biological characteristics of the nervous system. Next, the error probability of signal detection as a function of biological parameters has been characterized. Finally, we study the impacts of some specific medicines on the parameters of neuro-spike communication in the diseases of Multiple Sclerosis and Alzheimer's.
Authors: Maryam Hosseini (Birjand University), Reza Ghazizadeh (Birjand University), Hamed Farhadi (KTH Royal Institute of Technology),
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Coffee Break 15:25 - 15:35

10-minute coffee break

Session 3 - Connected Wearables Sensors for Healthcare Applications 15:35 - 17:25

After each paper presentation, please join us for a short Q&A session at Slack platform.
15:35 - 15:55
Activity Monitoring Using Smart Glasses: Exploring the Feasibility of Pedometry on Head Mounted Displays

Fitness tracking, fall detection, indoor navigation, and visual aid applications for smart glasses are rapidly emerging. The performance of these applications heavily relies on the accuracy of step detection, which has rarely been studied for smart glasses. In this paper, we develop an accelerometer-based algorithm for step calculation on smart glasses. Designed based on a salience-analysis approach, the algorithm provides a highly accurate step calculation. An activity monitoring application for Android-based smart glasses (Vuzix M100) is designed and realized for algorithm evaluation. Experimental results from 10 participants wearing commercial smart glasses running our application achieved average step detection error of 2.6% demonstrating the feasibility of our salience-based algorithm for smart glasses.
Authors: Zhiquan You (California State University, Los Angeles), Farnaz Mohammadi (University of California, Los Angeles), Emily Pascua (California State University, Los Angeles), Daniel Kale (California State University, Los Angeles), Abraham Vega (California State University, Los Angeles), Gian Tolentino (California State University, Los Angeles), Pedro Angeles (California State University, Los Angeles), Navid Amini (California State University, Los Angeles),
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15:55 - 16:20
Real-time Human Activity Recognition Using Textile-based Sensors

Real-time human activity recognition is a popular and challenging topic in the research of sensor systems. Inertial measurement units, vision-based systems and wearable sensor systems are mostly used for gathering motion data, however, each system has some drawback such as drift error, illumination, occlusion, etc. Therefore, in some cases they are not efficient alone in activity estimation. To overcome this, different systems were used together as an alternative approach in the last decade. In this study, a human activity recognition system is proposed using textile-based capacitive sensors. The aim of the system is to recognize the basic human actions such as walking, running, squatting, and standing in real-time. The proposed sensors are utilized to collect motion data from human participants with different anthropometrics. Classifiers are trained using activity data and machine learning models are created. The performance of the models is tested in real-time on unseen activity data. We showed the effectiveness of our approach by achieving high accuracy up to 99.4\% in the prediction of performed actions in real-time.
Authors: Ugur Ayvaz (Istanbul Technical University), Hend Elmoughni (Istanbul Technical University), Asli Atalay (Marmara University), Ozgur Atalay (Istanbul Technical University), Gökhan Ince (Istanbul Technical University),
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16:20 - 16:45
Extraction of respiratory signals and respiratory rates from the photoplethysmogram

Respiration rate (RR) is an important indicator of human health assessment which can be estimated by extracting respiratory signals from the photoplethysmogram (PPG). The goal of this study is to propose an alternative method, for obtaining accurate estimation of respiratory rate (RR) from the PPG signal. The proposed algorithm is based on the multiple autoregressive models and autocorrelation analysis (AC-AR). In AC-AR, the autoregressive model (AR) is applied to de-termining the dominant respiratory rate from the PPG, and autocorrelation is ap-plied to reduce the effect of clutter in the three respiratory-induced variations. Meanwhile, this paper introduced signal quality indices (SQI) to improve reliabil-ity of results. This algorithm is tested using an open source database: The Cap-noBase benchmark dataset, which comprising 42 eight-minute PPG recording and respiratory signal acquired form both children and adults in different clinical setting. Compared with that of existing method in the literature, the average abso-lute error percentage (AAEP) of the proposed algorithm is less than 3.72%, which demonstrated that our presented AC-AR bring a significant improvement in accuracy.
Authors: Shenglang Xiao (Xidian University), Pengfei Yang (Xidian University), Luyao Liu (University of Science and Technology Beijing), Zhiqiang Zhang (University of Leeds), Jiankang Wu (University of Chinese Academy of Sciences),
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16:45 - 17:05
An Ultra-Low-Power Integrated Heartbeat Detector for Wearable Sensors

To optimize energy consumption in wearable sensor networks, an efficient scheme is to set the sensors in sleep mode and wake them up to engage communication. However, synchronicity between the sensors needs to be assured by always-on local oscillators. This work proposes a different topology that takes advantage of the heart beat to wake-up wearable sensors. The electrocardiogram (ECG) is detected by two probes and then converted into a pulse signal. Using 28nm FD-SOI CMOS technology, this solution is implemented on a circuit consuming 19nW at a 900mV supply voltage, hence suitable for long term and wearable applications.
Authors: Antoine Gautier (Yncréa Hauts-de-France, Lille, France), Marine Dael (Yncréa Hauts-de-France, Lille, France), Robin Benarrouch (STMicroelectronics), Benoit Larras (University of Lille), Antoine Frappé (University of Lille),
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17:05 - 17:25
Anxiety Detection Leveraging Mobile Passive Sensing

Anxiety disorders are the most common class of psychiatric problems affecting both children and adults. However, tools to effectively monitor and manage anxiety are lacking, and comparatively limited research has been applied to addressing the unique challenges around anxiety. Leveraging passive and unobtrusive data collection from smartphones could be a viable alternative to classical methods, allowing for real-time mental health surveillance and disease management. This paper presents eWellness, an experimental mobile application designed to track a full-suite of sensor and user-log data off an individual’s device in a continuous and passive manner. We report on an initial pilot study tracking ten people over the course of a month that showed a nearly 76% success rate at predicting daily anxiety and depression levels based solely on the passively monitored features.
Authors: Lionel Levine (UCLA), Migyeong Gwak (UCLA), Kimmo Karkkainen (UCLA), Shayan Fazeli (UCLA), Bita Zadeh (Chapman University), Tara Peris (UCLA), Alexander Young (UCLA), Majid Sarrafzadeh (UCLA),
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Closing Message by the General Chair and Best Paper Announcement 17:25 - 17:30

Day 2 22/10/2020