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

IJCNN 2020, Glagow, 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.

All accepted papers and presented will be published in the proceedings of IJCNN 2020 in IEEE Xplore Library.

This special session is part of the program at IJCNN (WCCI 2020).

Important Dates

15 January 2020 Extended to 30 January 2020 Paper submission deadline

15 March 2020 – Paper acceptance notification date

15 April 2020 – Final paper submission & early registration deadline

19-24 July 2020 – Conference WCCI 2020 (Glasgow, Scotland UK)

All dates are subjected to the official timeline of WCCI 2020.

Submission

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