BSN 2021 Special Sessions
Title of the special session: “Body Sensor Networks in the era of pandemic”
Organizing Chairs: Dr. Raffaele Gravina, University of Calabria, Italy, email@example.com, Dr. Massaroni, Università Campus Bio-Medico di Roma, Italy, Dr. Valeria Loscri, Inria Lille, France, Prof. Edmund Seto, University of Washington, USA, Dr. Rich Fletcher, Massachusetts Institute of Technology, USA, Prof. Ye Li, Shenzhen Institutes of Advanced Technology, China
Abstract: The COVID-19 pandemic clearly highlighted, in most countries worldwide, weakness of healthcare systems and lack of appropriate measures to contain the pandemic diffusion of the virus which is also the result of slow diagnosis and incapability of fine-grain outbreak focus recognition. As a matter of facts, where the pandemic has been well kept under control, a pivotal role was played by quick and accurate diagnosis methods coupled with efficient and precise contact tracing technologies.
The advances of body sensor networks, the massive diffusion of smart wearable sensing and computing, and the availability of machine learning and Big Data analytics platforms represent fundamental ingredients of infected patients assistance in terms of real-time remote monitoring, early symptoms screening, contact tracing, quarantine/self-isolation monitoring, and clinical management.
The main objective of this special session is to provide a medium for researchers and practitioners to present their research findings related to the synergy among bio-compatible sensor development, BSN, Edge/Cloud computing infrastructure, and human factors to leverage new and more effective technologies to promptly identify, track at fine-grain and possibly contain pandemics.
Click here to download the CFP
Link to EDAS: https://edas.info/newPaper.php?c=27988&track=107005
Title of the special session: “Advancements in Sensors, Algorithms and Clinical Trials for Non-invasive and Cuffless Blood Pressure Monitoring”
Organizing Chairs:Yali Zheng, Shenzhen Technology University, China, firstname.lastname@example.org, Qing Liu, Xi’an Jiaotong-Liverpool University, China,
Abstract: The importance of cuffless blood pressure (BP) monitoring has been well recognized by the academic, industrial and clinical communities as it brings great convenience to measure BP more frequently in daily life, and this has great clinical significance in terms of diagnosing different types of hypertension, providing important risk factors of CV events, and providing effective treatment targets, and etc. In the past five years, there have been exciting advancements in this area including the development of new flexible sensors, novel sensing systems, machine/deep learning algorithms modeling multi-sensory data as well as clinical studies that validated the accuracy and feasibility of the state-of-the-art cuffless BP measurement techniques, etc. This special session aims to provide an overview of the advancements of these new methods for cuffless BP monitoring in the past five years. It is expected this special session will help identifying the major challenges faced by this area and provide key insights for future research.
Click here to download the CFP
Link to EDAS: https://edas.info/newPaper.php?c=27988&track=107006
Title of the special session: “Wearable solutions for continuous, efficient and accurate lung monitoring for innovative diagnostics in lung related chronic diseases”
Organizing Chairs: Rita Paradiso, Smartex S.r.l., Italy, email@example.com
Abstract: The diagnosis, monitoring and treatment of lung diseases depend on the availability of a variety of medical products. These include devices such as stethoscopes, blood gas analysis systems, spirometers, whole body plethysmographs as well as radiological (chest X-ray, computed tomography (CT), magnetic resonance imaging (MRI) and functional imaging methods (electrical impedance tomography (EIT)) that are discontinuously used both in the diagnosis and monitoring of the lung disease progression and treatment. Patients with lung diseases require regular visits to healthcare professionals (general practitioners and pulmonologists) who rely on these methods to properly assess the patients’ status and make therapy decisions. In the case of disease exacerbations, the patients often need to be hospitalized. The common drawbacks of all the stated examination methods are that they are not portable (with the exception of stethoscopes and simple spirometers) and mainly that they only allow a momentary patient assessment at the time point of out-clinic visit or hospitalization. Short-term trends in disease development, either deterioration or improvement, are not accessible. Up to now, continuous, wearable and real-time monitoring, especially in remote settings (e.g. patients’ home) is not available. The purpose of this Special Session is to update the audience on the latest research in lung monitoring with new wearable systems built with cooperative sensors. Cooperative sensors are a novel sensor architecture which allows connecting a high number of sensors with only two wires (and not shielded cables), without any loss in quality when compared to medical reference systems, even for delicate signals such as ECG, lung and heart sounds and EIT. The potential for the development of new annotated databases from COPD/COVID-19 patients through the use of the new wearable technologies especially in high stress clinical environments such as ICU will be also addressed.
We will look at the topic from different perspectives, including the clinical requirements, the technological infrastructure, the use of ML/DL algorithms for digital diagnostics of lung diseases such as COPD and COVID-19 especially focusing on the analysis and correlation of EIT and chest sound signals along with information from ICU parameters as well as the stakeholders’ vision.
Click here to download the CFP
Link to EDAS: https://edas.info/newPaper.php?c=27988&track=107007
Title of the special session: “Digital Biomarkers in movement disorders”
Organizing Chairs:Federico Parisi, Dept of Physical Medicine & Rehabilitation Harvard Medical School, USA, firstname.lastname@example.org
Abstract: Movement disorders, such as Parkinson’s Disease, Dystonia, and essential tremor, affect more than 40 million people in the US alone. Typical manifestations of these diseases are motor symptoms (e.g., tremor, rigidity, slowness of movements, dyskinesias, etc.) which considerably impact patients’ quality of life. Current clinical measures of disease progression are highly subjective, based on episodic visits and rely heavily on patients’ diaries for at-home symptoms monitoring. Recent advances in Mobile Health Technologies and wearable devices have enabled the identification of new digital biomarkers that can be translated in objective and accurate insights for the management and the monitoring of movement disorders, in both the clinic and the patients’ home and community. The Special Session “Digital Biomarkers in Movement Disorders” aims at bringing together researchers doing outstanding research on this topic, stimulating a discussion on the achievements and future challenges in the field.
Link to EDAS: https://edas.info/newPaper.php?c=27988&track=107008
Title of the special session: “Challenges and opportunities in assessing biomarkers of mental states during cognitive demanding tasks”
Organizing Chairs: Marco Simões, University of Coimbra, Portugal, email@example.com, Ricardo Couceiro, University of Coimbra, Portugal
Abstract: The assessment of biomarkers capable of identifying mental states during cognitive demanding tasks is still a major challenge in the current research agenda. This topic gains even more importance when focusing on non-invasive and non-intrusive sensors and as gained an increasing audience in several application fields. The main concept behind these systems is that the mental states are not restricted to the confines of the mind but are manifested in the body, through changes at multiple levels, such as, neurobiological, physiological (automatic nervous system (ANS)), body expressions, overt actions and subjective/metacognitive (i.e. feeling, reflection).
The automatic detection of a mental state can provide actionable information that can be used in both clinical applications, such as depression and the autism spectrum disorder, or non-clinical applications, such as error detection and prevention systems in highly demanding tasks. This session covers the recent advances in the fields of biomedical sciences to assess cognitive states, with the goal of developing biofeedback systems based on wearable and non-intrusive sensors.
Link to EDAS: https://edas.info/newPaper.php?c=27988&track=107184
Title of the special session: “Toward Artificial General Intelligence for Wearable Systems”
Organizing Chairs: Dr. Hassan Ghasemzadeh, Washington State University, USA
Abstract: Machine learning is becoming an integral part of wearable systems by providing data-driven support for monitoring, assessment, intervention, and decision making. While significant progress has been made in utilizing machine learning in various application domains that use wearable technologies, these computational models perform poorly when deployed in the real-world settings. This has limited the potential of the wearable systems for deployment outside laboratory settings and across various domains where the distribution of the data changes from the original setting in which the models are trained. The importance of designing reliable machine learning models that address the challenges associated with distribution shift in the data has been recognized by the BSN community in recent years. However, various computational approaches that can be used to address the performance degradation problem has not been fully explored in the community. The main objective of this special session is to provide a forum for researchers and practitioners to present their research findings related to reliable machine learning applied to wearable technologies, body sensor networks, and their applications, and to discuss future directions in this area.
In recent years, substantial progress has been made in the AI community on designing reliable machine learning solutions that address the problem of distribution shift in the data. Examples of such relevant topics include transfer learning, active learning, continual learning, semi-supervised learning, and self- supervised learning. These various learning paradigms can be viewed as essential computational approaches that bring us closer to bridging the gap between machine and human-level intelligence, thus realizing the vision of artificial general intelligence. In this special session, we aim to discuss the various learning paradigms that are essential in moving toward creating wearable systems that achieve human level intelligence.
Link to EDAS: https://edas.info/newPaper.php?c=27988&track=107369
Title of the special session: “Non-Contact Technologies for Pervasive Healthcare”
Organizing Chairs: Dr. Sarah Sun, Michigan Technological University, USA, Dr. Mehdi Boukhechba, University of Virginia, USA
Abstract: Chronic diseases cause high percentage of all deaths globally, among which seven of the top ten causes are chronic diseases according to statistical data. The high cost of prolonged in-hospital healthcare on these chronic conditions facilitates the transformation from hospital-centered to preventive proactive and pervasive healthcare. Wearable and mobile devices provide good solutions for long-term monitoring for both in-hospital and out-of-hospital settings; however, long-term contact with human skins may cause comfort issues.
The advances of non-contact sensing such as wearable and mobile sensing and computing and other types of remote monitoring technologies enable the comfort, biocompatibility, and operability in the long run. The goal of this special session is to provide a forum for researchers and stakeholders to present and communicate their research progress related to the synergy of wearable and mobile sensing and computing, signal processing, machine learning, and data analytics for pervasive healthcare.
Link to EDAS: https://edas.info/newPaper.php?c=27988&track=107388
Title of the special session: “Body-interfaced Flexible Sensors and Actuators: recent developments, challenges and opportunities”
Organizing Chairs: Hongliang Ren, CUHK (HK) & NUS (SG), Giancarlo Fortino, University of Calabria, Italy, John Ho, NUS (SG), Guohua Hu, CUHK (HK)
Abstract: Flexible transducers, including sensors and actuators that are nonplanar and dynamically morphing, are emerging to enable unobtrusive, noninvasive perceptions and interactions with the unstructured surrounding environments. Flexible transducers have a wide range of applications, especially in body sensor networks, wearable electronics, soft robotics, human-machine interaction, and sensorized biomedical devices. These sensors to be flexible and stretchable, conforming to the arbitrary surfaces of their relatively stiff counterparts. The challenges in maintaining the fundamental features of these sensors, such as flexibility, sensitivity, repeatability, linearity, and durability, are tackled by the progress in the fabrication techniques and customization of the material properties. This special session aims to provide a forum for the recent progress of flexible sensors/actuators, intelligent perceptions, promising applications and highlights challenges and opportunities in this research paradigm.
Link to EDAS: https://edas.info/newPaper.php?c=27988&track=107393
Title of the special session: “Body Sensor Networks for Tele-Health Monitoring and Coaching ”
Organizing Chairs: Bobak J. Mortazavi, Texas A&M University, USA, Sunghoon Ivan Lee, UMass Amherst, USA
Abstract: The advances of body sensor networks have enabled real-world, natural environment, remote health monitoring. These tele-health opportunities can improve clinical care, particularly for remote and underserved communities. These opportunities allow for improved adherence through personalized sensing, analytics, and shared decision making between participants and their clinicians.
The main objective of this special session is to enable discussions around challenges and barriers to enabling behavior change through sensing, analytics, and behavior modification. Through the presentation of research findings, researchers will teach the community about the latest in sensing technologies, time-varying analytics, and personalized monitoring, coaching, and behavior change. These techniques will help accelerate remote and personalized care, which the COVID-19 pandemic highlighted a significant need for.
Link to EDAS: https://edas.info/newPaper.php?c=27988&track=107401
Title of the special session: “Wearable Sensing for Detecting and Monitoring Shock”
Organizing Chairs: Brian A. Telfer, MIT Lincoln Laboratory, USA, Victor A. Convertino, U.S. Army Institute of Surgical Research, USA
Abstract: Vital signs have traditionally been used to monitor trauma patients for impending shock, but a more sensitive approach has been developed that exploits changes in arterial waveform features. This approach, termed the compensatory reserve measurement (CRM), can operate on noninvasive measurements, most commonly photoplethysmography (PPG), making it well suited to wearable sensing.
The objective of this special session is to explore the use of wearable sensing to support CRM and similar approaches for detecting and monitoring shock. Presentations will be provided to introduce the concept, describe the state of algorithm development, and the state of integrating wearable sensing and CRM. Real-world considerations will be discussed, such as the value of individual baselining, motion artifact mitigation, and sensor wear locations. The aim is to help accelerate translational R&D that integrates wearable sensing and shock detection and monitoring algorithms.
Link to EDAS: https://edas.info/newPaper.php?c=27988&track=107443
Title of the special session: “New Technologies for the Future of Prenatal Health”
Organizing Chairs: Julien Penders, Co-Founder & COO, Bloomlife
Abstract: Maternity and newborn care is one of the largest expenditures in our healthcare systems with $100b+ spent per year in the United States, and EUR 30b+ in Europe. Yet, the rate of high-risk pregnancies is steadily raising due to more women having babies later in life, higher rates of chronic disease, and poorly addressed environmental and socioeconomic factors.
Despite increasing rates of maternal death and preterm births globally, innovation is lagging, and pregnancy and maternal health remain the land that science and technology forgot. There is an urgent need for new solutions to increase access to care, empower mom to manage her care more actively, and provide better solutions to clinicians to more efficiently and cost effectively screen and manage high risk pregnancies where a disproportionate volume of care and cost is realized.
The objective of this special session is to bring together a panel of experts at the cutting edge of prenatal health technologies to highlight recent innovations in wearable sensors, new sensor technologies, sensor informatics, machine learning, and solutions designed for and around expecting moms, that contribute to bringing prenatal health in the 21st century.
Link to EDAS: https://edas.info/newPaper.php?c=27988&track=107444
Title of the special session: “Digital Biomarkers for Monitoring and Predicting Upper Limb Recovery After Stroke”
Organizing Chairs: Christoph M. Kanzler, Postdoctoral Researcher, Singapore-ETH Centre and ETH Zurich; Olivier Lambercy, Deputy Director & Senior Researcher, ETH Zurich and Singapore-ETH Centre
Abstract: Stroke is a common neurological injury with around 800’000 persons experiencing a stroke every year in the United States alone. Approximately 77% of stroke survivors are left with upper limb sensorimotor impairments, which negatively affect independence and quality of life and are therefore often a primary target during neurorehabilitation. In order to describe those impairments, track their temporal evaluation during recovery, and to predict rehabilitation outcomes, tools to accurately characterize upper limb sensorimotor impairments are necessary. Over the last decades, a strong focus of the research community has been on sensor-based technologies that provide objective, precise, and sensitive ‘digital biomarkers’ of upper limb sensorimotor impairments in clinical and daily life environments. However, the validation and clinical application of those biomarkers as well as their integration into computational models predicting neurorehabilitation outcomes and influencing clinical decision making still remain challenging.
Within this Special Session on “Digital Biomarkers for Monitoring and Predicting Upper Limb Recovery After Stroke”, we aim to revisit recent achievements in this promising field, identify and discuss remaining grand challenges, and lay out a way forward to address them.
Link to EDAS: https://edas.info/newPaper.php?c=27988&track=107442
Guidelines for the submission of Special Session papers
Authors must submit a 1–page Special Session paper to participate in Special Sessions. Optionally, authors can submit a 4-page paper (for the same 1-page paper) to be reviewed and included in the conference proceedings (this is covered as 1 registration).
The benefits for each category are the following:
–4-pages SS papers: These papers will undergo the normal review process of regular-full length papers. The authors of accepted 4-pages SS papers will have the privilege to publish their work in IEEE Xplore and in the Conference Proceedings. In case of a potential rejection of a 4-page SS paper, it can be submitted as a 1-page extended abstract.
–1-page SS papers: These papers will undergo a sanity check. The authors of 1-page SS papers will have the privilege to present their work orally. However, they will not have the right to publish their work in IEEE Xplore and in the Conference Proceedings.