Sensor-based analysis of human kinematics is valuable for providing personalized and detailed feedback when learning complex psychomotor skills. Kinematics examines the motion of a system of objects without directly considering the underlying forces. A visualization approach for a dynamic system such as complex human motion needs to account for changes over time.
In a learning scenario, highlighting differences between the performed move and a pedagogical framework is important for conveying necessary corrections to be undertaken. The goal of this thesis is to build a visualization system to show the movements and highlight potential errors on specific limbs/joints as meaningful feedback.
Thesis Type |
|
Status |
Running |
Supervisor(s) |
Stefan Decker |
Advisor(s) |
Michal Slupczynski |
Contact |
slupczynski@dbis.rwth-aachen.de |
In our currently running project MILKI-PSY, we focus on multimodal immersive mentoring to facilitate Self Regulated Learning (SRL). To support mentors with the tools they need to provide adaptive and personalized tutoring, a set of learning services was developed and is currently under development. These services utilize our flagship feedback framework Immersive Multimodal Psychomotor Environments for Competence Training (IMPECT).
Recent advances in data analysis and visualization techniques allow information systems to react to the movements performed by learners and respond by providing context-specific personalized corrective feedback to maximize learning outcomes. However, it may not always be possible to see or notice all aspects of a movement for the teachers or learners, so a system to highlight the temporal motion changes can illustrate detailed information.
The goal of this thesis is to work with artists to build a Unity- and Web-based to analyze and visualize learner movements and highlight potential errors in their body motions to provide corrective and meaningful feedback.
If you are interested in this thesis, a related topic or have additional questions, please do not hesitate to send a message to slupczynski@dbis.rwth-aachen.de and ks@colognegamelab.de
Please apply with a meaningful CV and a recent transcript of your academic performance.
Potentially relevant literature:
- Dance Central 3
- Fast Local and Global Similarity Searches in Large Motion Capture Databases (2010)
http://dx.doi.org/10.2312/SCA/SCA10/001-010 - Trend-Centric Motion Visualization: Designing and Applying a New Strategy for Analyzing Scientific Motion Collections (2014)
https://doi.org/10.1109/TVCG.2014.2346451 - Mova: Interactive Movement Analytics Platform (2014)
https://doi.org/10.1145/2617995.2618002 - Visual-Interactive Semi-Supervised Labeling of Human Motion Capture Data (2017)
http://dx.doi.org/10.2352/ISSN.2470-1173.2017.1.VDA-387 - Visualization of Human Movements Kinematics for Personalized Mobile Information-Measuring System (2019)
https://doi.org/10.1109/ITQMIS.2019.8928431 - Motion Volume: Visualization of Human Motion Manifolds (2019)
https://doi.org/10.1145/3359997.3365684
- Data Visualization
- Interest and/or experience in:
- Unity
- Python
- Web Technologies (HTML, JS, …)
MILKI-PSY - Multimodales Immersives Lernen mit KI für Psychomotorische Fähigkeiten