Important Submission Information
Schedule:
- deadline for submissions: CLOSED
- first decision (accept/reject/revise, tentative): 1 December 2019
- submission of revised papers: 15 January 2020
- notification of final decision (tentative): 1 March 2020
- journal publication: TBD
Guest editors:
Raffaella Lanzarotti, Giuliano Grossi, University of Milan, Italy
Henry Medeiros, Marquette University, Milwaukee, USA
Francesca Odone, Nicoletta Noceti, University of Genova, Italy
Corresponding TETC Editor: Ramon Martin Rodriguez Dagnino, Tecnologico de Monterrey, Mexico
Authors are invited to submit a manuscript to the special section on Assistive Computing Technologies for Human Well-Being. Topics should fall in the domain of this special issue; relevant topics include (but are not limited to):
- Computer vision for assisted living and well-being assessment
- Machine learning methods for assisted living and well-being assessment
- Human-machine interactions in assisted living environments
- Multi-modal methods for well-being assessment
- Deployment-independent sensing techniques for assisted living environments
- Knowledge transfer and domain adaptation assisted living and well-being assessment
Applications to assisted living for elderly, people with disabilities, as well as computing technologies to support recovery or rehabilitation are welcome. Submitted papers must include new significant research-based technical contributions in the scope of the journal. Purely theoretical, technological or lacking methodological generality papers are not suitable for this special issue. Submissions must include clear evaluations of the proposed solutions (based on simulations and/or implementations results) and comparisons with state-of-the-art solutions.
For additional information please contact the Guest Editors by email: wellbeing_SI_TETC@di.unimi.it
Papers under review elsewhere are not acceptable for submission. Extended versions of published conference papers (to be included as part of the submission together with a summary of differences) are welcome but there must be at least 40% of new impacting technical/scientific material in the submitted journal version and there should be less than 50% verbatim similarity as reported by similarity detection tools (such as CrossRef).
Guidelines concerning the submission process, LaTeX and Word templates can be found at www.computer.org/publications/author-resources/peer-review/journals. While submitting through ScholarOne, at https://mc.manuscriptcentral.com/tetc-cs please select the option “Special Section on Assistive Technologies to Support the Well-being of an Aging Population.” As per TETC policies, only full-length papers (10-16 pages with technical material, double column – papers beyond 12 pages will be subject to MOPC, as per CS policies -) can be submitted to special sections. The bibliography should not exceed 45 items and each Author’s bio should not exceed 150 words.
Abstract
The notion of well-being is a complex multidimensional process that encompasses the physical, cognitive, psychological, economic, and social domains. Long-term or temporary disabilities, as well as the natural process of aging, have in general a negative impact in one or more of these domains, generally leading to a condition of reduced independence and increased vulnerability, which is commonly termed “frailty.”
It should be noticed that, as life expectancy continues to increase worldwide, so does the proportion of fragile citizens with respect to the entire population. Such a dramatic change in the population distribution has a number of long-term societal implications, which can be attenuated by devising more effective mechanisms to improve the quality of life and the independence of older individuals.
The changing needs of the population as well as progress in science and technology have encouraged significant research efforts carried out by different research communities: psychologists and clinicians directly involved with the patient’s health, engineers and scientists focusing on the design and development of enabling technologies. In recent years these scientific challenges have been formalized in different but related objectives, known by a variety of names — affective medicine, positive technology, or the more familiar Ambient Assisted Living (AAL).
Nowadays, while meaningful computing methodologies have reached maturity, and a full awareness of the problem dimension has been reached, we are facing the objective of designing ad hoc technologies with the real potential of improving the quality of life of fragile citizens. Indeed, despite significant advances in the AAL domain, much remains to be done towards the development of intelligent systems to improve the overall well-being of users in need for assistance. Several aspects of the existing technology deserve further research efforts to produce robust, reliable, and usable technologies. Moreover, closer interactions among healthcare and technology researchers are necessary to ensure that these new systems address the correct needs to ultimately benefit their target population.
This Special Issue aims to encourage this conversation by providing an opportunity for researchers from both fields to submit their contributions to the design and evaluation of new technologies for monitoring and supporting human well-being under several aspects: physical, cognitive, emotional, affective and social. Applications to assisted living for elderly, people with disabilities, as well as computing technologies to support recovery or rehabilitation are of interest. Within this domain, we welcome original research manuscripts including new significant technical contributions to machine learning, computer vision, human-machine interaction methods for assisted living, as well as multi-modal systems, sensor fusion, knowledge transfer, and domain adaptation to assisted living scenarios. Given the lack of publicly available data specific to this topic, contributions also reporting realistic benchmarks related to elderly are very welcome.
Guest Editor Bios:
Francesca Odone is an Associate Professor in Computer Science at the University of Genova, where she leads the Computational Vision group. She received a Laurea degree in Information Sciences and a PhD in Computer Science both from the University of Genova. In 1999-2000, she was a visiting student at Heriot-Watt University (Edinburgh UK) with a EU Marie Curie research grant. Her research interests are in the fields of computer vision and machine learning, including multi-resolution signal processing, feature extraction, feature selection and data-driven representations for visual data. She authored over 100 papers on international conferences and journals. She has been involved in various research projects and acted as a scientific coordinator of technology transfer contracts with SMEs, large companies and hospitals. https://www.dibris.unige.it/odone-francesca
Nicoletta Noceti received the Laurea cum laude (2006) and the PhD in Computer Science (2010) from the University of Genova. She is assistant professor in Computer Science at the University of Genova. Her research covers theoretical foundations of computer vision and image processing and applications to human-machine interaction and natural interfaces, robotics and ambient assisted living. She authored more than 50 publications and organized international workshops on motion understanding themes. She has been guest editor for the IEEE Transactions on Cognitive and Developmental Systems, and she is currently editing the Springer book “Modelling human motion: from human perception to robot design.” https://www.dibris.unige.it/noceti-nicoletta
Raffaella Lanzarotti is assistant professor in Computer Science at the University on Milan where she was constituent of the “Perceptual Computing and Human Sensing Laboratory” (PHuSE Lab). She received the Laurea cum laude (1999) and the PhD in Computer Science (2004) from the University of Milan. Current research concerns modelling and understanding human face identities and affective expressions, cognitive/emotional states and, more generally, non-verbal behaviors. She authored over fourty papers on international conferences and journals, and she participated to several projects on these topics collaborating with other universities, hospitals, and companies. She is now principal investigator of a national project concerning elderly people well-being. More information can be found at http://lanzarotti.di.unimi.it
Giuliano Grossi is an Assistant Professor in Computer Science at the University of Milan (UNIMI), where he received his Ph.D. in Computer Science (1999). His research interests include sparse recovery in signal processing and applications of dictionary learning. As a member of the PHuSe Lab (UNIMI) focused on affective and perceptive computing, his recent activities aim to apply both computer vision and machine learning techniques to human behaviour understanding particularly referred to social interaction, emotional state and gaze analysis. He authored 60 papers on international conferences and journals, and has been involved in several national and international projects concerning elderly wellbeing and other topics. More information can be found at grossi.di.unimi.it
Henry Medeiros is an Assistant Professor of Electrical and Computer Engineering at Marquette University. His research interests include computer vision and robotics, and his work focuses on the application of machine learning and signal processing techniques to solve problems of practical relevance in areas ranging from manufacturing to agricultural automation and assisted living environments. He has published over thirty journal and peer-reviewed conference papers and holds several US and international patents. Before joining Marquette, he was a Research Scientist at Purdue University and the Chief Technology Officer of Spensa Technologies, a technology start-up company located at the Purdue Research Park. He received his Ph.D. from the School of Electrical and Computer Engineering at Purdue University. He is a senior member of the IEEE. More information can be found at http://coviss.org/medeiros
