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SleepMon Workshop 2016 : (SleepMon) 2nd Workshop on Unobtrusive Sleep Monitoring Technologies for Sleep Disorder Assessment


When Jun 29, 2016 - Jul 1, 2016
Where Greece, Corfu Island
Submission Deadline Mar 28, 2016
Notification Due Apr 10, 2016
Final Version Due May 5, 2016
Categories    computer science   pattern recognition   medicine   ubiquitous

Call For Papers

(SleepMon) 2nd Workshop on Unobtrusive Sleep Monitoring Technologies for Sleep Disorder Assessment
Submission Guidelines & Important Dates (
• Paper Submission: Mar 28, 2016
• Paper Notification: Apr 10, 2016
• Camera-Ready Paper: May 5, 2016
• Workshop:
during PETRAE conference (29.06 - 01.07.2016), Greece, Corfu Island
Paper submissions should following PETRA'16 general guidelines found at
Topics & Motivation
A sleep disorder is a medical disorder of the sleep patterns of a person. Sleep disorders can interfere with normal physical, mental, social and emotional functioning. Furthermore, sleep disorders are associated with comorbidities such as stroke, heart disease, osteoporosis or arthritis impact, which are directly related to an individual’s quality of sleep. In addition, sleep is an indicator of the state of well-being. Furthermore, the analysis of biodata during sleep enables background knowledge about stress, mental load or lack of physical activities during daytime.
The current mainstream sleep disorder detection and assessment methods (e.g. Polysomnography) are too extensive or inconvenient for patients due to the cumbersome wiring that is required for the biosignal acquisition. In addition, there is a high cost associated with a sleep test at a specialized lab, which includes the use of facilities and equipment, as well as technical staff and physicians involved in data collection and the interpretation process. As a result, many patients with sleep-related problems go undiagnosed, and even the ones who are tested in a sleep lab cannot be monitored of over longer periods of time to assess longitudinal variations.
Topics of interest include, but are not limited to:
• Non-invasive sensing devices for biosignal data acquisition
• Methods and tools for sleep disorder detection and biosignal data analysis
• Audio and video-based methods for sleep pattern monitoring
• Devices for in-home sleep monitoring
• Multi-source data fusion methods for sleep-related decision making
• Sensor data stream fusion, feature extraction and pattern analysis
• Usability issues of sleep monitoring devices
• Data privacy concerns in sleep monitoring
• Methods and tools for sleep disorder treatment
• Use of mobile devices for sleep monitoring
• Automatic detection of events of interest during sleep
• Machine learning and data mining tools for sleep disorder-related decision making
• Methods for evaluating the effect of sleep disorders on daily activities
• Circadian rhythm
• Social and cultural aspects of sleep
• Methods for visualizing sleep activity and mental states
• Relaxation and anti-burn-out support
• Studies and new applications
• Project reports and lessons learned from international research projects
The points mentioned above, emphasize the need for automatic, unobtrusive methods for sleep disorder assessment, which patients can use in their homes. This would not only help decrease health care costs but also increase the number of diagnosed patients due to increased accessibility of sleep disorder testing. The potential of home sleep testing has lately attracted attention due to its many benefits:
1. The patient self-administers the home sleep test (HST), and is able to spend the night in the patient’s own bed in familiar surroundings (reducing first night effect).
2. HST can be especially advantageous and accessible to the home-bound, elderly in assisted living care, or those with chronic illness, who require specialized care. It is also beneficial for those with difficulty arranging time out of their schedules to spend the night in-lab by removing this barrier to diagnosis.
3. HST diagnostics would reduce cost compared to the cost of an in-lab sleep study.
4. HST enables personalized assessment of additional, sleep related data to improve the analyses of the patient environment, lifestyle and not only symptoms but causes for sleep disorders.
This workshop invites contributions that seek to advance the current standards in sleep monitoring by taking advantage of new sensing devices that can effectively and non-invasively collect biosignals and behavioral sleep data, as well as methods and tools for more efficient and automated data analysis. Even the workshop is technology orientated, it invites researchers and experts to present and discuss their experiences in the context of Sleep Disorder Assessment.
Workshop Goals
This workshop aims to attract an interdisciplinary group of researchers who are involved in research related to sleep disorder detection, assessment, monitoring and treatment, focusing on novel technologies that aim to make sleep pattern analysis tools more meaningful, more easily accessible, less invasive and less expensive. The researchers will present their work and receive feedback from their peers. As part of the larger theme of the PETRA conference, the participants will also have the opportunity to interact with top scientists working with pervasive assistive technologies to exchange valuable ideas that could advance the state-of-the-art in the field.
• Vangelis Metsis
Dept. of Computer Science, Texas State Univ., USA
• Gerald Bieber Fraunhofer Institute for Computer Graphics Research IGD, Germany

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