Checking date: 25/05/2023

Course: 2023/2024

Ambient Intelligence
Master in Applied Artificial Intelligence (Plan: 475 - Estudio: 378)

Coordinating teacher: BELLUCCI , ANDREA

Department assigned to the subject: Computer Science and Engineering Department

Type: Electives
ECTS Credits: 3.0 ECTS


The subject introduces the fundamental principles, methods and technologies for the development of Ambient Intelligence (AmI) systems, physical spaces that are sensitive and respond to the presence of people. The AmI paradigm requires the application of artificial intelligence to process data from sensors embedded in the environment (cameras, microphones or touch screens, accelerometers, etc.) and thus assist people in a multitude of scenarios through of a natural user interface. The applications of the AmI paradigm cover the domestic, industrial, hospital or vehicle environment, among others. In this subject, following a responsible and Human-Centered Artificial Intelligence approach, the concepts and main characteristics of an AmI system will be examined, as well as methodologies for developing AmI applications. The subject will have a practical nature, facilitating experimentation with the main technologies for the implementation of prototypes of AmI systems. In addition, the main challenges for the development of AmI systems will be analyzed. The objective of the course is to train students to: - Analyze the application of the Ambient Intelligence paradigm - Develop prototypes of Ambient Intelligence systems
Skills and learning outcomes
Description of contents: programme
1. Concept and approaches of the Ambient Intelligence (AmI) paradigm - Ubiquitous Computing - Internet of Things - Context Awareness - Human-centric Artificial Intelligence 2. Main characteristics of an AmI system - Sensitive - Responsive - Adaptive - Transparent - Intelligent 3. Design methodologies for AmI - End-user development and artificial intelligence 4. Interaction in AmI - User interaction requirements - Presence and proxemic interaction - Voice interaction - Gestures and body movements 5. Practical programming of AmI systems - Machine Learning with sensors data (microcontrollers, mobile devices, cameras, etc.) - Voice processing as a means of interaction - Technologies for the development of AmI prototypes + Tensorflow.js + Web programming for AmI
Learning activities and methodology
Learning activities AF1 - Theoretical class AF2 - Practical classes AF3 - Theoretical-practical classes AF5 - Tutorials AF6 - Group work AF7 - Individual student work Teaching methodology MD1 - Lectures with the support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the students' learning. MD2 - Critical reading of texts recommended by the professor of the subject: press articles, reports, manuals and / or academic articles, either for later discussion in class, or to expand and consolidate the knowledge of the subject. MD3 - Resolution of practical cases, problems, etc. individually or in groups. MD5 - Preparation of work and reports individually or in groups.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
  • Ben Shneiderman. Human-centered AI. Oxford University Press. 2022
  • Hamid K. Aghajan, Juan Carlos Augusto & Ramón López-Cózar Delgado. Human-centric interfaces for ambient intelligence. Academic Press. 2010
  • Shanqing Cai, Stanley Bileschi, Eric D. Nielsen & Francóis Chollet. Deep learning with JavaScript : neural networks in TensorFlow.js. Manning. 2020
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • John Krumm. Ubiquitous computing fundamentals. Chapman & Hall/CRC Press. 2010
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

The course syllabus may change due academic events or other reasons.