Ambient Intelligence and Assisted Living

Course Description

Holistic approach to the design, engineering and implementation of supported living systems that rely on ambient intelligence is based on integration of knowledge acquired during the bachelor's education as well as some topics from master's curriculum. Attention is paid to ambient intelligence, sensors and actuators, user interfaces, industrial communication interfaces, communication protocols, system software and software tools. In addition students acquire skills to analyze users' need through "design thinking", project work and project documentation as well as popular an technical reporting. All this enables students to build systems assisting users in their living and working environment and daily activities from components of highest level of integration like personal and industrial computers, specialized measurement and control equipment as well as communication equipment of general purpose. The whole course is embedded in the concept of service learning.

Learning Outcomes

  1. Design a device or a system assiting user in his daily work and life
  2. Compose systems as a netowrk of dedidated devices
  3. Use standardized protocols, interfaces and components
  4. Assess and appropriatly leverage properties of industrial devices, subsystems and methods
  5. Define project requirements
  6. Plan and organize a simple project from idea to realisation
  7. Criticize and argument decisions and ideas in communication with other teams whose projects comprise a bigger whole with students' project
  8. Choose appropriate sensors.
  9. Analyze users' needs.
  10. Publicly present their results.

Forms of Teaching

Lectures

Lectures are held weekly.

Seminars and workshops

Within the course students prepare practical projects in groups of up to 4 students on a topic related to ambient intelligence and assisted living chosen by them or assigned by course lecturer.

Laboratory

Laboratory exercises are held at the University with the available equipment.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 50 % 30 % 0 % 0 %
Seminar/Project 50 % 40 % 50 % 40 %
Mid Term Exam: Written 0 % 1 % 0 %
Final Exam: Written 50 % 4 %
Final Exam: Oral 15 %
Exam: Written 50 % 60 %

Week by Week Schedule

  1. Course introduction
  2. Ambiental intelligence and supported living
  3. Project management
  4. Design thinking
  5. Modem
  6. LAN
  7. Wireless networks
  8. Midterm exam
  9. Seriall communication
  10. User interfaces and user experience
  11. User's needs analysis
  12. Sensors and actuators
  13. Data exchange
  14. Project presentations
  15. Final exam

Study Programmes

University graduate
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Elective Courses (2. semester)
[FER3-HR] Communication and Space Technologies - profile
Elective Courses (2. semester)
[FER3-HR] Computational Modelling in Engineering - profile
Elective Courses (2. semester)
[FER3-HR] Computer Engineering - profile
Elective Courses (2. semester)
[FER3-HR] Computer Science - profile
Elective Courses (2. semester)
[FER3-HR] Control Systems and Robotics - profile
Elective Courses (2. semester)
[FER3-HR] Data Science - profile
Elective Courses (2. semester)
[FER3-HR] Electrical Power Engineering - profile
Elective Courses (2. semester)
[FER3-HR] Electric Machines, Drives and Automation - profile
Elective Courses (2. semester)
[FER3-HR] Electronic and Computer Engineering - profile
Elective Courses (2. semester)
Elective Courses of the Profile (2. semester)
[FER3-HR] Electronics - profile
Elective Courses (2. semester)
[FER3-HR] Information and Communication Engineering - profile
Elective Courses (2. semester)
[FER3-HR] Network Science - profile
Elective Courses (2. semester)
[FER3-HR] Software Engineering and Information Systems - profile
Elective Course of the profile (2. semester)
Elective Courses (2. semester)

Literature

Andree Woodcock, Louise Moody, Deana McDonagh, Ajita Jain, Lakhmi C. Jain (2019.), Design of Assistive Technology for Ageing Populations, Springer Nature
Zaigham Mahmood (2019.), Guide to Ambient Intelligence in the IoT Environment, Springer
Albert M. Cook, Pedro Encarnação, Janice Miller Polgar (2019.), Assistive Technologies, Mosby
Hideyuki Nakashima, Hamid Aghajan, Juan Carlos Augusto (2009.), Handbook of Ambient Intelligence and Smart Environments, Springer Science & Business Media

Associate Lecturers

Laboratory exercises

For students

General

ID 222442
  Summer semester
5 ECTS
L1 English Level
L2 e-Learning
30 Lectures
0 Seminar
0 Exercises
15 Laboratory exercises
0 Project laboratory

Grading System

90 Excellent
80 Very Good
70 Good
60 Sufficient