ARIAL@ECML 2023: 6th Workshop on AI for Aging, Rehabilitation, and Intelligent Assisted Living


Call For Papers
According to a United Nations’ report on World Population Aging (2020), the number of people in the world aged 60 or over is projected to grow to 1.5 billion by the year 2050. Aging can come with various complexities and challenges, such as a decline in the physical, cognitive and mental health of a person. These changes affect a person’s everyday life, resulting in decreased social participation, lack of physical activity, and vulnerability to injury and disability that can be exacerbated by the occurrence of various acute health events, such as strokes, or long-term illnesses.

The field of assistive technology amalgamates several multi-disciplinary areas including data mining, rehabilitation engineering, and clinical studies. The idea of assistive technology solutions is to promote independent, active and healthy aging with a specific focus on older adults, and those living with mild cognitive impairments.

The COVID-19 pandemic has highlighted the challenges encountered by vulnerable populations in terms of not getting adequate care, difficulties in access to healthcare services and lack of necessary support to stay independent and safe. Many clinical treatments and rehabilitation services have gone virtual due to strict social distancing guidelines that have added more complexity to supporting the older population.

Collecting and mining health data using assistive technology devices is a challenging task. Leveraging Artificial Intelligence (AI) techniques is essential to make advancements in the field of aging and rehabilitation. Building AI models on vast amounts of health data from older adults will facilitate independent assisted living, promote healthy living, and manage rehabilitation routines effectively.

** Call for Papers **

ARIAL@ECML23 will be held in Turin, Italy. In this workshop, we invite previously unpublished and novel submissions in the following areas (pertaining to aging and rehabilitation), but not limited to:

* Methods and protocols for multimodal data collection, data annotation, and data labeling with older adult populations.
* Data cleaning, curation, sharing, and harmonization.
* Data analytics and visualization techniques for healthcare data of older adults.
* Methodologies for big data and large-scale machine learning, including cloud computing.
* Challenges in machine learning such as handling missing data, and dealing with mixed, imbalanced, poorly labeled, and noisy data.
* Techniques for virtual rehabilitation, virtual coaches, and telemedicine.
* Development and deployment of long-term sensor-based remote monitoring systems.
* Audio/video, multimodal interaction for patient engagement, exercise monitoring, and successful delivery of rehabilitation.
* Addressing privacy concerns of patient data, e.g., privacy-protecting sensing modalities, federated learning, and differential privacy.
* Machine learning and deep learning algorithms to identify anomalous, harmful, life-threatening, and abnormal behaviors in older care settings.
* AI approaches for continuous streaming, monitoring, and analysis of health, activity, contextual, and online data for older adults.
* Techniques for handling data biases, and other biases related to sex, gender, ethnicity, and age (e.g., fair machine learning strategies).
* Machine learning methods for measuring health indicators, and progression of physical and cognitive health, e.g., frailty, dementia, mental health, and gait stability.
* AI approaches for data fusion from multi-modal sensor interaction and ensemble algorithm development (e.g., multi-view learning approaches).

** Important Dates **
Paper submissions: June 12, 2023 (anywhere in the world)
Paper notifications: June 17, 2023
Camera-ready deadline for the final version of accepted papers: To be announced later
Paper Registration Date: To be announced later
Workshop date: To be announced later

** Submission Guidelines **
Please use this link (https://cmt3.research.microsoft.com/ECMLPKDDworkshop2023/Track/38/Submission/Create) to submit your papers. All papers must be original and not simultaneously submitted to another journal or conference. We request you to submit Full papers - up to 8 pages (including references). The review process will be double blind; therefore, authors must not write their names, contact details or affiliations in their papers at the time of submission. The accepted papers will be published in Springer's Lecture Notes in Computer Science (LNCS) with a DOI.

Please use Springer's LNCS single-column format for paper submission (https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines).

**Committees**
*Workshop Co-chairs*
Shehroz Khan, KITE, University Health Network, Canada.
Luca Romeo, University of Macerata, Italy.
Ali Abedi, KITE, University Health Network, Canada.

*Organizing Committee*
Pratik K. Mishra, Institute of Biomedical Engineering, University of Toronto.

*Program Committee*
Daniele Riboni, University of Cagliari , Italy
Giacomo Turri, Istituto Italiano di Tecnologia, Italy
Ladislau Bölöni, University of Central Florida, USA
Mehdy Dousty, KITE, University Health Network, Canada.
Michele Bernardini, Marche Polytechnic University, Italy
Narges Armanfard, McGill University, Canada
Riccardo Rosati, Marche Polytechnic University, Italy
Ryan Koh, Toronto Rehabilitation Institute, Canada
Sazia Mahfuz, Queens University, Canada
Veselka Boeva, Blekinge Institute of Technology, Sweden

*Venue*
ARIAL@ECML2023 will be held in person at Officine Grandi Riparazioni, Turin, Italy.

*Contact*
All questions about submissions should be emailed to shehroz.khan@uhn.ca / ali.abedi@uhn.ca