Special Session on Machine Learning and Deep Learning Approaches for Ambient Assisted Living

As part of the IEEE IJCNN (WCCI 2020), Glasgow, Scotland UK, July 19-24, 2020

Aims and Scope:
Due to the increasing of the world aging phenomenon, solutions that allow assisted living enhancing the quality of life and independent living of elderly people are more relevant nowadays. Sensors, computers, wireless networks, Internet-of-Things technologies and the availability of mobile devices contribute to develop an ideal ecosystem for ambient assisted living (AAL) systems that address this important social problem. In fact, multimodal amounts of data can be generated with these technologies. Therefore, data analysis techniques and machine learning are needed to detect activities of daily living, gestures and context for determining behaviors and anomalies in order to perform customized (health) monitoring systems for elder.

In the field of AAL, it has been detected the necessity to focus the research on experimental analysis and data-driven solutions that involve continuous monitoring. However, there are several challenges involved, i.e. data heterogeneity, reliability of sensors, the lack of realistic assumptions, real-time performance, privacy issues, among others. Thus, the aim of this special session is to promote current research progress, in both academia and industry, on machine learning (ML) and deep learning (DL) approaches for ambient assisted living. This special session invites researchers, scientists, students and practitioners to submit their contributions on topics related, but not limited, to the following:

– Applied ML and DL for health monitoring, rehabilitation and sports.
– Data-driven models for healthcare.
– Technologies for ageing well, active ageing and healthy living.
– ML and DL for promoting autonomy and self-care in elderly people.
– ML and DL for integrating ambient, active and assisted living (A3L).
– Data models, security and privacy in A3L environments.
– ML and DL in behavioral analysis in A3L scenarios, e.g. human activity recognition.
– ML and DL in abnormal behavior detection, e.g. human fall detection.
– ML and DL approaches in data fusion and data heterogeneity.
– ML and DL for reliability of sensors, explainability and fairness.

Important Dates:
EXTENDED! New Deadline 30 January, 2020 – Paper submission deadline
15 March, 2020 – Paper acceptance notification

Submission Guidelines: https://wcci2020.org/submissions/
IJCNN-42 Use this code when submitting a paper to this special session

Session Chairs:
Hiram Ponce (hponce@up.edu.mx), Universidad Panamericana, Mexico
Lourdes Martínez-Villaseñor, Universidad Panamericana, Mexico
Marley Vellasco, Pontifical Catholic University of Rio de Janeiro, Brazil

More information, conference website: https://wcci2020.org
More information, special session website: https://sites.google.com/up.edu.mx/aal-ijcnn-2020