Mobile and Wearable Computing
People
Description
The widespread use of mobile and wearable devices enables the implementation of novel services in applications areas like, e.g., mobile health, sustainability, smart working, and more. This course introduces the building blocks of such services and discusses the challenges that arise on the path towards their realization. Specific topics covered include: hardware platforms; programming environments and tools; the collection and processing of sensor data; the design of mobile user interfaces; local and remote data storage; privacy and security issues. In addition to theoretical concepts, the course also includes Android programming tutorials as well as tutorials showing how specific sensor data (e.g., heart rate data) can be collected, processed, and used to enable services and applications. A programming project accompanies the course and allows students to put in practice both the theoretical and programming concepts learnt in the classes and tutorials.
Objectives
The course aims to equip students with the knowledge and skills required to design, develop, and deploy applications and systems for mobile and wearable devices. The course will cover key concepts including mobile operating systems, sensor integration, user interface design, connectivity, and energy efficiency. Students will be able to design and prototype services and applications for mobile and wearable devices – such as smartphones, smartwatches, smartglasses, earbuds, digital rings, and more. Students will gain hands-on experience through projects and assignments, preparing them to innovate in the rapidly evolving landscape of mobile technology and wearable devices, addressing real-world challenges and enhancing user experiences.
Teaching mode
In presence
Learning methods
The course includes instructor-led lectures, interactive discussion sessions, Android programming tutorials, and in-class programming sessions. A good knowledge of the Java or Kotlin programming languages is strongly recommended, although not a formal prerequisite.
Examination information
The final grade will result from a combination of the grade obtained through the final exam and the grades of the assignments and the programming project carried out during the semester.
Education
- Master of Science in Artificial Intelligence, Lecture, Elective, 1st year
- Master of Science in Artificial Intelligence, Lecture, Elective, 2nd year
- Master of Science in Economics in Finance, Lecture, Corsi a scelta o Field Project o Semestre all'estero (Digital Finance), Elective, 2nd year
- Master of Science in Financial Technology and Computing, Lecture, Elective, 2nd year
- Master of Science in Informatics, Lecture, Computer Systems, Elective, 1st year
- Master of Science in Informatics, Lecture, Computer Systems, Elective, 2nd year
- Master of Science in Informatics, Lecture, Information Systems, Elective, 1st year
- Master of Science in Informatics, Lecture, Information Systems, Elective, 2nd year
- Master of Science in Informatics, Lecture, Software Development, Elective, 1st year
- Master of Science in Informatics, Lecture, Software Development, Elective, 2nd year
- Master of Science in Management and Informatics, Lecture, Elective, 2nd year
- Master of Science in Software & Data Engineering, Lecture, Elective, 1st year
- Master of Science in Software & Data Engineering, Lecture, Elective, 2nd year
- PhD programme of the Faculty of Informatics, Lecture, Elective, 1st year (4.0 ECTS)