In recent years, interest in affective computing (AC) have led to advances in speech recognition, natural language processing, facial expression detection, and applying machine learning using wearables. The workshop will focus on the convergence of methodologies that contribute to detecting emotional and psychometric patterns based on machine learning algorithms, wearables, Internet of Things (IoT), and databases to capture important aspects of affective computing.

Active research areas that are relevant to affective computing include:

• Health centric applications using affective computing to enhance healthcare
• Multimodal sensor fusion to comprehensively detect and classify affect in users
• User environments for the design of systems to better detect and classify affect
• Applications using wearables to detect/classify affect, stress, fatigue, and medical emergencies
• Recognition/prediction of affect and emotion using artificial neural networks and/or deep learning
• Social informatics applications: group behavioral effects and feedback, location-awareness
• Predicting/classifying real-time annotated data using spatiotemporal learning and inference
• Machine learning using biometric data to classify biosignals
• Wearable computing applications, especially based on experience sampling methods
• Facial recognitions in predicting bonding in conversations
• Electrothermal methodologies in affective computing
• Understanding Emotions in Context: Home vs. work, friends vs. strangers, online vs. in person, conversations, while driving, and etc

The emphasis of this workshop shall be approaches based on the extraction of emotional and physiometric patterns from heterogeneous sources including but not limited to wearables, spatiotemporal methods, artificial neural networks, deep learning, and other machine learning and inference algorithms.

Application areas that exhibit extant needs for affective computing include:

• Biomedical Research: medical informatics, behavioral and cognitive neuroscience
• Environments: ubiquitous computing, mobile computing, user experience design
• Data Science for Social Good: computational sustainability, disaster management
• Wearable Computing: health applications, sensor analytics, mobile applications
• Internet of Things (IoT) and Cyber-Physical Systems: spatiotemporal, hybrid systems
• Human Computer Interaction (HCI): augmented reality/mixed reality systems, usability
• Other Application Areas: mobile computing, virtual reality

This workshop shall help to bring together people from these different areas and present an opportunity for researchers and practitioners to share new techniques for identifying and analyzing applications in affective computing that integrate multiple fields and disciplines. We also propose to coordinate with the wearables community to find opportunities for cross-fertilization and interdisciplinary collaboration.

The intended audience shall consist of artificial intelligence researchers from core areas such as statistical methodologies, machine learning, pattern recognition, probabilistic reasoning, ontologies and learning representation, as well as transdisciplinary and multidisciplinary domains such as data science, spatiotemporal analytics of affect, data modeling and mining, cyber-physical systems (CPS) and hybrid systems including wearable computing and IoT analytics, and virtual reality (VR) / augmented reality (AR) / mixed reality systems. Benefits will thus accrue to the data science of affective computing and to advances in CPS/IoT, VR/AR, and smart environments. The workshop will also be of interest to researchers and practitioners of application areas, such as: Smart environments including homes, offices, and schools; assistive technologies, especially for children, the elderly, and the disabled; and medical and social uses of affect recognition.