Degree | Type | Year | Semester |
---|---|---|---|
4316624 Internet of Things for e-Health | OB | 0 | 1 |
None
This module introduces the essential concepts, metrics, technologies and platforms of the value chain of the Internet of Things ranging from the enormous amount of connected devices that operate autonomously (mostly independent of the users) collecting information (and acting when needed) in an energy-efficient way until its cloud storage and processing passing through embedded and/or mobile platforms connected via interfaces and communications wireless or wired protocols. These skills are integrated into IoT projects that are introduced as use cases based on real problems. These use cases will be used in other modules.
Labs: Implementation of a Fall Detection Algorithm in Different Platforms
L0. Fundamentals on C programming. 2h
L1. Introduction to programming on a MCU. 2h
L2. Fall Detection Algorithm on Accelerometre + MCU + Bluetooth. 2h
L3. Android Programming I: Bluetooth Low energy Data Acquisition. 2h
L4. Android Programming II: Compute and JSON application to a server. 2h
L5. Cloud application: Acquisition & Computation. 2h
The learing methodology will combine: master classes, activities in tutored session. problem based-learning and use cases; debates and other collaborative activities and laboratory sessions.
Attendance will be mandatory for all face-to-face activities.
This course will employ UAB's virtual campus at https://cv.uab.cat.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Lessons and Seminars | 30 | 1.2 | 1, 5, 6, 8, 4, 10 |
Type: Supervised | |||
Laboratories & Exercices | 28 | 1.12 | 5, 9, 6, 7, 8, 3, 4, 2, 10 |
Type: Autonomous | |||
Study & Homework | 90 | 3.6 | 5, 9, 6, 7, 8, 4, 2, 10 |
The final mark for the course, is calculated in the following way:
A - 10% from the mark obtained by the student for class attendance and active participation in class discussions.
B - 45% from the mark obtained by the student for a practical project developed through problem-based learning (TFM).
C - 45% from the mark obtained by the student for an oral defense
A final weighted average mark not lower than 50% is sufficient to pass the course, provided that a score over one third of the range is attained in everyone of the 3 marks.
Plagiarism will not be tolerated. All students involved in a plagiarism activity will be failed automatically. A final mark no higher than 30% will be assigned.
An student not having achieved a sufficient final weighted average mark, may opt to apply for remedial activities the subject under the following conditions:
- the student must have participated in the problem-based learning activities, and
- the student must have participated in the oral defense, and
- the student must have a final weighted average higher than 35%, and
- the student must not have failed any activity due to plagiarism.
Students not having participated in any evaluation activity will receive a final mark of "No evaluable".
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Activities & Reports from supervised sessions (labs) | 30% | 0 | 0 | 5, 6, 8, 3, 2, 10 |
Attendance and active participation in class | 10% | 0 | 0 | 5, 9, 6, 3, 4 |
Report(s) evalution | 30% | 0 | 0 | 1, 5, 9, 6, 7, 8, 3, 4, 2, 10 |
Synthesis examination | 30% | 2 | 0.08 | 1, 5, 9, 6, 7, 3, 2, 10 |
C. Pfister. Getting Started with the Internet of Things: Connecting Sensors and Microcontrollers to the Cloud (Make: Projects) . O’Really. 2011.
A. McEwen, H. Cassimally. Designing the Internet of Things.2014. Willey.
A. Bahga, V. Madisetti. Internet of Things: A Hands-on Approach. VTP. 2015.
S. Greengard, The Internet of Things. The MIT Press Essential Knowledge series.
V. Zimmer. Development Best Practices for the Internet of Things.
A. Bassi, M. Bauer, M. Fiedler, T. Kramp, R. van Kranenburg, S. Lange, S. Meissner. (Eds) Enabling Things to Talk - Designing IoT solutions with the IoT Architectural Reference Model. Springer.
J. Olenewa, Guide to Wireless Communications, 3rd Edition, Course Technology, 2014.
P. Raj and A. C. Raman, The Internet of Things: Enabling Technologies, Platforms and Use Cases, CRC Press 2017.
H. Geng (Ed.), Internet of the Things and Data Analytics Handbook, Wiley 2017.
Y. Noergaard, "Embedded Systems Architecture" 2nd Edition, 2012, Elsevier
K. Benzekki, Software‐defined networking (SDN): a survey, 2017, https://doi.org/10.1002/sec.1737
https://blogs.cisco.com/innovation/barcelona-fog-computing-poc
https://aws.amazon.com/
A.K. Bourke et al. Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities, Journal of Biomechanics, Volume 43, Issue 15, 2010, pp. 3051-3057, ISSN 0021-9290, http://www.sciencedirect.com/science/article/pii/S0021929010003866
N. Jia. Detecting Human Falls with a 3-Axis Digital Accelerometer. Analog Devices. http://www.analog.com/en/analog-dialogue/articles/detecting-falls-3-axis-digital-accelerometer.html