Chair: Michael Riegler, firstname.lastname@example.org
The development of intelligent medical data analysis systems has experienced a significant boost in recent years thanks to the emergence of artificial intelligence (AI). AI in medicine has become an important part in several areas such as computer aided diagnosis, medical image analysis and medical sensor data analysis, etc. AI in medical care is an important contributor to improved health care systems that provide better support to medical experts and better care to the patients. This special session is meant to provide a discussion platform for researchers in the field and to share novel research on the topic. For this special session, we seek papers in the area of medical AI systems, including, but not limited to, AI for: assisted diagnosis, electronic health records analysis, medical data analysis, medical visualization, medical data generation and preprocessing methods, multimodal analysis and fusion of medical data, and different fields of medicine such as psychology.
Chair: Gill R. Tsouri, email@example.com
Photoplethysmography (PPG) is a standard tool used in the assessment of cardiovascular conditions. The PPG sensor attaches to the skin, shines a light at on the skin and captures the intensity of light being reflected back or transmitted through it. Since light absorption varies with the quantity of blood flowing through the vessels, the PPG sensor provides a pulsatile signal that provides important insights into the cardiovascular function. While the gold standard for evaluating cardiac activity is the Electrocardiogram (ECG), PPG technology is cheaper, less invasive, simpler and more convenient for everyday use. In many applications, such as Heart Rate (HR) and Heart Rate Variability (HRV) measurements, PPG provides comparable performance to that of ECG. Consequently, the FDA has recently approved the use of PPG sensors for identifying irregular heartbeats in products such as the Apple Watch. Despite its popularity, PPG technology has its limitations and shortcomings. Long-term monitoring with sensors attached to the finger or earlobe is associated with discomfort and pose a risk of infection. Furthermore, the monitoring process depends on compliance of the subjects being monitored. Over the past decade, various groups experienced with the feasibility of performing a PPG measurement from ambient light reflected off the skin using a camera as the PPG sensor. This approach is often called remote-PPG or Video-plethysmography (VPG). VPG is an attractive approach to acquiring a pulsatile signal, since it requires no physical contact with the subject being monitored. It can therefore operate without burdening the subject with wearable devices and the need to participate in the monitoring process. An additional consequence is that compliance with long term monitoring is achieved. The increasing proliferation of smart devices with embedded front cameras, such as tablets, smartphones and laptops enable VPG based applications even further. Potential applications range from monitoring various health conditions of subjects to inferring their stress levels based on inferring vagal tone activity. VPG-based applications are poised to play an important role in upcoming digital health services. Extracting a reliable pulsatile signal using VPG is challenging. The light reaching the camera is much weaker than the light captured by sensors attached to the skin, thus forcing signal extraction to operate under low signal to ratio conditions. In addition, subject motion and changes in ambient light intensity corrupt the extracted pulsatile signals. There are also challenges associated with operating across different hardware platforms making use of various types of cameras and video capture. More work is needed to make VPG a reliable alternative to sensors attached to the skin. We invite papers to this special session addressing all aspects of VPG, including but not limited to: methods of extracting reliable pulsatile signals, techniques for motion compensation and mitigation, applications of VPG technology, validation of VPG methods and applications and assessment of performance using different types of cameras.
Wout Joseph, firstname.lastname@example.org
Emmeric Tanghe, Emmeric.email@example.com
Wireless Body Area Networks (WBANs) have a multitude of applications in the areas of medicine, agriculture, sports, and multimedia. WBANs for human health monitoring are becoming increasingly important: they allow for the continuous monitoring and recording of the patient’s physiological parameters such as pulse, blood pressure, electrocardiogram, and body temperature. Physiological data over long periods of time increases the accuracy of the diagnosis and improves the treatment. Moreover, WBANs have promising applications in the health monitoring of animals.
Modeling of the on-, off-, and in-to-out-body radio channel is needed for the design WBANs with robust wireless links and to optimize their energy efficiency, body-centric radio channel modeling is done either through measurements or numerical electromagnetic simulations. Measurements can be done on electromagnetic phantoms or on real human bodies.
Other research areas tangential to WBAN design are the exposure of humans to the electromagnetic fields emitted by the WBAN nodes, and the design of Wireless Power Transfer (WPT) solutions to charge the battery-powered WBAN nodes.
This special session will create a discussion platform for researchers in the field of body communication, antennas, exposure, and phantoms. For this special session, papers in the following topics are considered, but not limited to:
- WBAN communication
- Experiments and simulations for body communication
- On-, off-, in-to-out-body propagation for humans and animals
- On-, off-, in-to-out-body antennas for medical applications
- Electromagnetic exposure
- Phantoms for WBAN communication
- Health aspects of wireless power transfer
- Energy efficiency in WBANs
Dr. Habtamu Abie, firstname.lastname@example.org
Dr. Savola Reijo, Reijo.Savola@vtt.fi
Healthcare services and infrastructures are more critical, sophisticated and interconnected than ever before, placing healthcare among the top sectors of major security risks. The situation is exacerbated by the Cyber-Physical Systems (CPS)/Internet of Things (IoT) enabled healthcare services and infrastructures, which are vulnerable to a variety of emerging cyber-attacks. Since the CPS/IoT systems are classified as safety and security critical systems there is a need for safety and security assurance and certification.
Carrying out certification, and integrating safety and security requirements represent a major challenge due to CPS/IoT systems’ characteristics of fragmentation, interconnectedness, heterogeneity, cross-organizational nature and high interference between safety and security requirements, combined with the assurance of compliance to multiple standards. As technology continues to evolve, cybersecurity threats do as well. CPS/IoT will therefore present expanded attack surface making the public safety risks higher for healthcare services and critical infrastructure through their interfaces and more flexible access to services and information. Such attacks can potentially lead to a violation of users’ privacy, physical damages, financial loses and threats to human life and preventing them is critical.
The rise of cyber-physical attacks shows us that the current, security solutions are unable to tackle the dynamicity, complexity, uncertainty, and high connectivity of CPS/IoT enabled healthcare services and critical infrastructures. These threats present us with a growing need for research and development in intelligent methods and techniques for cybersecurity, safety, forensic, adaptive privacy in healthcare, and need for cybersecurity to become an integral part of patient safety.
The special session on Cybersecurity in Healthcare at IEEE ISMICT will provide a discussion platform for researchers in the field and to share novel research on the topic. For this special session, we seek paper contributions in the following cybersecurity topics, but not limited to:
- Security, trust and privacy metrics
- Methods for addressing new, dynamic, and uncertain situations
- Correlating and balancing timely in-situ data and behavioural analysis
- Cybersecurity vulnerabilities in CPS/IoT-enabled healthcare services and infrastructures
- Measures of Patient Safety
- Assurance and certification for cybersecurity in healthcare
- AI/machine learning for cybersecurity
- Evidence-driven AI for cybersecurity
- Uncertainty quantification and risk management
- New security design and analysis methods
- Computational tools for real-time forensic and data analytics
Security for bio-nanosensors, on-body and in-body sensors