First Workshop on
Computational & Affective Intelligence
in Healthcare Applications
(Vulnerable Populations)

In Conjunction with the International Conference on Pattern Recognition (ICPR), Milan, Italy, 10 January 2021

About The Workshop

Intelligent monitoring systems and affective computing applications have emerged in recent years to enhance healthcare. Examples of these applications include intelligent monitoring of physiological changes and sedation states as well as assessment of affective states such as pain and depression. Though the intelligent monitoring of affective states has been around for the past several years, integrating contextual, personalized, uncertainty, and multimodal information recorded from vulnerable populations has been less explored. This information paired with transparency and explainability can lead to the development of reliable applications for automated monitoring, detection, and prediction of affective states and health conditions in vulnerable populations. Such applications can deliver tailored and prompt interventions. CAIHA workshop provides an interdisciplinary forum for the exchange of ideas on novel applications, new datasets, current challenges, and future directions. It welcomes submissions on artificial and affective intelligence methods applied to healthcare applications of vulnerable populations.

Keynote Speakers

Rosalind Picard

Rosalind Picard is founder and director of the Affective Computing research group at the MIT Media Lab and faculty chair of MIT's MindHandHeart Initiative. She co-founded Affectiva, providing emotion AI technologies, and Empatica, providing wearable sensors and analytics to improve health. Picard is an active inventor with patents including algorithms, and systems for sensing, recognizing, and responding respectfully to human affective information. Her inventions have applications in autism, epilepsy, depression, sleep, stress, dementia, autonomic nervous system disorders, human and machine learning, health behavior change, and human-computer interaction. She has authored or co-authored > 300 scientific articles and chapters spanning computer vision, pattern recognition, machine learning, human-computer interaction, wearable sensors, neurology, and affective computing. Picard has been honored with dozens of distinguished and named lectureships and other international and prestigious awards. CNN named her one of seven "Tech Superheroes to Watch in 2015."

Jiebo Luo

Jiebo Luo is Professor of Computer Science at the University of Rochester and Distinguished Researcher with Goergen Institute for Data Science. His research spans computer vision, machine learning, data mining, healthcare applications, and biomedical informatics. He has authored more than 400 technical papers and more than 90 U.S. patents. He has served as the program chair of ACM Multimedia 2010, IEEE CVPR 2012, ACM ICMR 2016 and IEEE ICIP 2017, as well as on the editorial boards of several IEEE Transactions journals and publications. He is the Editor in Chief of the IEEE Transactions on Multimedia for a 3-year term (2020-2022). He is a Fellow of ACM, AAAI, IEEE, SPIE and IAPR.

Accepted Papers

Computational & Affective Intelligence for Pain Assessment

  • Pain Intensity Assessment in Sickle Cell Disease patients using Vital Signs during Hospital Visits
  • Neonatal pain scales and human visual perception: An exploratory analysis based on facial expression recognition and eye-tracking
  • Multi-stream Integrated Neural Networks for Facial Expression-based Pain Recognition

Computational & Affective Intelligence for Language, Speech, and Hearing Impairments

  • A New Facial Expression Processing System for an Affectively Aware Robot
  • Classification of Autism Spectrum Disorder Across Age using Questionnaire and Demographic Information

Computational & Affective Intelligence for Mental Health

  • Towards Robust Deep Neural Networks for Affect and Depression Recognition from Speech
  • COVID-19 and Mental Health/Substance Use Disorders on Reddit: A Longitudinal Study
  • Longitudinal Classification of Mental Effort Using Electrodermal Activity, Heart Rate, and Skin Temperature Data from a Wearable Sensor

Workshop Schedule