According to a United Nations’ report on World Population Aging (2020), currently, there are more than 700 million persons aged 65 years or older over in the world. This number is projected to more than double, reaching over 1.5 billion by 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, which 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 computer science, rehabilitation engineering, data mining, clinical studies, health care, and psychology. Assistive technology solutions (ATS) can be used 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 and building novel machine learning (ML) models is essential to make advancements in the field of aging and technology. Building AI models on health data will facilitate independent assisted living, promote a healthy and active lifestyle, and manage rehabilitation routines effectively. To reason about the collected data, to classify it, and to detect abnormalities, new AI tools and methods are required.

With this workshop, we will bring together interdisciplinary researchers from different sub-fields of AI, in general, machine learning and deep learning to identify and approach the ARIAL-related problems. We will also facilitate discussion, interaction, and comparison of approaches, methods, and ideas related to the domain of aging and technology.


*Call for papers*

In this workshop, we invite previously unpublished and novel submissions in the following areas, but not limited to:

* Methods and protocols for data collection, data annotation, and data labeling with older adult populations.
* Development and deployment of long-term sensor-based monitoring systems.
* Techniques for telerehabilitation, telemedicine, and remote monitoring.
* Video Analysis to explore patient engagement, therapy compliance, exercise monitoring, and body pose estimations for the successful delivery of rehab.
* Addressing privacy concerns of patient data, such as by using privacy-protecting sensing modalities, federated learning, and differential privacy.
* Techniques 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.
* Developing interpretable, explainable, and ethical AI models for the aging population.
* Methodologies for big data, large-scale data mining, including cloud computing.
* Data analytics and visualization techniques for healthcare data.
* ML techniques to identify harmful, life-threatening, abnormal behaviors and coping with rare events in health care.
* ML methods for measuring health indicators, progression of physical and cognitive health, e.g. frailty, dementia, mental health, gait stability.
* Data mining challenges such as handling missing information, dealing with mixed, imbalanced, poorly labeled, and noisy data.
* ML approaches for data fusion from multi-modal sensor interaction and ensemble algorithm development.
* Multi-agent models to capture the interaction between patients, caregivers, and family to provide assistance.
* Probabilistic and Case-based reasoning to provide assistance.
* Using Deep Learning solutions for supporting assistive technology devices and facilitating transfer learning.
* Developing smart agents to understand the sentiments of the client for providing focused care.
* Developing smart-home solutions and interventions for connecting and engaging older adults with the environment.
* Models that use Smart Technology, Speech recognition, and dialog-based interaction with older adults to handle social isolation.


*Important Dates*

Paper submissions: May 11, 2021 (anywhere in the world)
Paper notifications: May 25, 2021
Camera-ready deadline for the final version of accepted papers: To be announced later
Paper Registration Date: To be announced later
Workshop date: August to be announced later


*Submission Guidelines*

All papers must be original and not simultaneously submitted to another journal or conference. We request you to submit Full papers - 5 pages (including 1 page for 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.

Please use the IJCAI paper Template [1] and the EasyChair portal [2] for paper submission.
[1] https://www.ijcai.org/authors_kit
[2] https://easychair.org/conferences/?conf=arial2021


*Committees*

*Workshop Co-Chairs*

Shehroz Khan, KITE, Toronto Rehabilitation Institute, Canada
Alex Mihailidis, University of Toronto, Canada
Amir Ahmad, United Arab Emirate University, UAE

*Organizing committee*

Ali Abedi, KITE, Toronto Rehabilitation Institute, Canada
Sayeh Bayet, University of Toronto, Canada

*Program Committee*

Muhammad Raisul Alam, University of Toronto, Canada
Narges Armanfard, McGill University, Canada
Sebastian Bader, MMIS, Computer Science, Rostock University, Germany
Jennifer Boger, University of Waterloo, Canada
Ladislau Bölöni, University of Central Florida, USA
Ian Cleland, University of Ulster, Northern Ireland
Dinesh Babu Jayagopi, IIIT Bangalore, India
Ryan Koh, Toronto Rehabilitation Institute, Canada
Vicki Komisar, The University of British Columbia, Canada
Alexandra König, Stars Team, INIRIA, Valbonne , France
Sina Mehdizadeh, KITE, Toronto Rehabilitation Institute, Canada
Riona McArdle, Newcastle University, UK
Christopher Nugent, University of Ulster, Northern Ireland
José Zariffa, University of Toronto, Canada

*Venue*

ARIAL@IJCAI21 will be held virtually in Montreal, Canada


*Contact*

All questions about submissions should be emailed to sayeh.bayat@mail.utoronto.ca / ali.abedi@uhn.ca