392162 Human Behaviour Understanding Using Machine Learning and Psychological Methods (Pj) (SoSe 2020)

Contents, comment

In this project, students will develop computational models to detect human behaviours (e.g. engagement, interest, boredom, etc.) during human-robot or human-computer interaction. To develop these models, the students will combine psychological knowledge about human behaviours with computational methods for behavioural signal processing. The models will be developed on available datasets either by using raw multimodal sensor data or by using annotated behavioural cues such as facial expressions and body pose. The developed models will be later tested using new data collected through self-designed user studies in human-robot or human-computer interaction. The user studies will be designed based on psychological principles and practices.

NOTE: Only a maximum of 10 participants is possible. A first-come-first-serve procedure will be followed. The first 10 students who attend the first session(s) will be able to participate. If more than 10 students participate in the first session(s), then a selection process will be conducted, in which the students will be asked to prepare and submit project proposals in groups. The three best project proposals will be selected, and these project groups will be able to continue their participation in future sessions (exceptions may be possible on a case-by-case basis).

All sessions will take place live via videoconferencing using the software Zoom. The links to the Zoom videoconference will be emailed to participants at least one day in advance. The first session will take place on 22.04.2020 at 10:15 am.

Teaching staff

Dates ( Calendar view )

Frequency Weekday Time Format / Place Period  

Show passed dates >>

Subject assignments

Module Course Requirements  
39-M-Inf-GP Grundlagenprojekt Intelligente Systeme Gruppenprojekt Ungraded examination
Student information
39-M-Inf-VHC_a Virtual Humans and Conversational Agents Virtual Humans/Verhaltenssimulation Study requirement
Ungraded examination
Graded examination
Student information

The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.


No more requirements
E-Learning Space
E-Learning Space
Registered number: 10
This is the number of students having stored the course in their timetable. In brackets, you see the number of users registered via guest accounts.
eKVV participant management:
eKVV participant management is used for this course.
Show details
Address:
SS2020_392162@ekvv.uni-bielefeld.de
This address can be used by teaching staff, their secretary's offices as well as the individuals in charge of course data maintenance to send emails to the course participants. IMPORTANT: All sent emails must be activated. Wait for the activation email and follow the instructions given there.
If the reference number is used for several courses in the course of the semester, use the following alternative address to reach the participants of exactly this: VST_201730138@ekvv.uni-bielefeld.de
Coverage:
1 Students to be reached directly via email
Notes:
Additional notes on the electronic mailing lists
Last update basic details/teaching staff:
Monday, August 31, 2020 
Last update times:
Wednesday, February 19, 2020 
Last update rooms:
Wednesday, February 19, 2020 
Type(s) / SWS (hours per week per semester)
project (Pj) / 2
Language
This lecture is taught in english
Department
Faculty of Technology
Questions or corrections?
Questions or correction requests for this course?
Planning support
Clashing dates for this course
Links to this course
If you want to set links to this course page, please use one of the following links. Do not use the link shown in your browser!
The following link includes the course ID and is always unique:
https://ekvv.uni-bielefeld.de/kvv_publ/publ/vd?id=201730138
Send page to mobile
Click to open QR code
Scan QR code: Enlarge QR code
ID
201730138