AIDL_B_CS03: Wearable and Affective Computing
Wearable and Affective Computing


Modern multifunctional fabrics and current electronic systems have made possible theirsynergyin order to create wearable electronic and interactive clothing. Topics of interest on this course are activity recognition, personalized data processing, and user modeling, while physical and mental health monitoring and various types of personal assistance systems constitute important applications. Regarding research topics, these include non-intrusive communication channels for immediate real-time feedback, such as tactile feedback via body-activated actuators or audio feedback. Based on the above, in this course, a wide range of modern wearable equipment technology will be presented, the basic electronic principles and the interaction with software platforms for the provision of innovative technologies will be also analyzed,in order the students to be able to develop their own projects, as well as special emphasis will be placed on usability, interaction design and environmental interfaces, focusing on multimodal interfaces.

Emotion AI & Affective Computing arise from the need that Artificial Intelligence and Machine Learning must be feedbacked by knowledge which already exists and/or it is derived from the socio-emotional and psychological sciences in order to become more “Human”. In this context, this course will dive into the study and analysis of models and methods of EmotionComputing (a branch of Artificial Intelligence) and their application in various fields (e.g. education). As a result, students will become familiar with models and theories of emotions from psychology, neuroscience and pedagogy, as well as cutting-edge applications and technologies for the collection, analysis and visualization of emotional information, derived from Emotional Computing and Artificial Intelligence.


  • Affective Computing
    • Theoretical background (emotion & affect theories, models, ontologies, neurological background, measurement & emotion dimensions),
    • Emotion/Affect Detection & Recognition (tools of reference-self-report, detection of physiological signals EEG, EMG, EDA/SC, EKG/ECG, pupillometry, etc., motor/behavioral activity (facial expressions, voice tone, posture), text analysis – sentiment analysis, classification algorithms and computational pattern recognition models – pattern recognition,
    • Affective Feedback & Visualization (virtual agents, chatbots, recommendation systems,visualization ofinformation)
  • Human Activity Recognition:Analysis of body data for real-time interactive feedback (physical exercises, ergonomics, work assistance), high-precision body movement algorithms and tracking applications, positioning systems for position estimation, online and offline visualization of profiled data
  • Development of smart integrated fabrics and development of hardware and software for optimized energy consumption

This course is using project-based assessment. Students are asked to complete an individual project on the following (indicative) topics, and their grade (100%) is based on the level of completeness, algorithm performance and documentation of the solution:

  • Case study assignment in the design of a solution making use of wearable technology
  • Development of a Deep Learning algorithm for human activity recognition via motion signal analysis
  • Development of a Deep Learning algorithm for sentiment recognition via image processing
  • Comprehend the core concepts related to wearable electronic and interactive clothing, including activity recognition, personalized data processing, and user modeling.
  • Explain the fundamental principles behind the synergy of modern multifunctional fabrics and electronic systems in wearable technology.
  • Apply knowledge of non-intrusive communication channels for immediate real-time feedback, such as tactile feedback and audio feedback, in the context of wearable technology.
  • Evaluate the various applications of wearable technology, including physical and mental health monitoring and personal assistance systems, by considering their impact on users.
  • Design and develop innovative wearable technology projects, utilizing basic electronic principles and software platforms, while considering usability, interaction design, and environmental interfaces.
  • Assess the role of Emotion AI and Affective Computing in enhancing Artificial Intelligence and Machine Learning, and critically analyze the models, methods, and applications of Emotion Computing in various fields, particularly in education.



Course Features

Course type: Minor

Semester: 2nd


Duration: 13 weeks  

Courses: Instructor-led + online

Language: English

Assessment: Project based



Professor Savvas Vassiliadis

Department of Electrical and Electronic Engineering, School of Engineering, UNI.W.A.

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