Student research projects

There are always topics for student research projects here at MMK (Bachelor's and Master's thesis, Research Internship, IDP).

When you have found a topic please contact the scientific assistant. If there is no suitable topic, please contact an assistant to get one.

Ingenieurpraxis: The aim of the Ingenieurpraxis is to have a look into the processes in the industry. For this reason we don't offer some Ingenieurpraxis here at MMK, but it is possible to supervise you if you find a position in a company.

Additionally, we do not offer any internships to students from outside TUM. Because of the volume of requests we receive, it is not possible for us to answer all emails with internship requests.

Current appointments of the MMK student research project talks

Topics for Student Projects

Area: Computer Vision

Development of a virtual reality environment for teaching medical skills
(cooperation project with the Klinikum rechts der Isar)

Thema Development of a virtual reality environment for teaching medical skills
(cooperation project with the Klinikum rechts der Isar)
Typ Research Internship (FP), Interdisciplinary Project (IDP), Bachelor's Thesis (BA), Master's Thesis (MA)
Betreuer Maximilian Rettinger, M.Sc.
Tel.: +49 (0)89 289-28547
E-Mail: maximilian.rettinger@tum.de

PD Dr. Christoph Schmaderer
E-Mail: christoph.schmaderer@mri.tum.de
Sachgebiet Virtual Reality
Beschreibung Motivation:
Medical Learning and Teaching is still rather old-fashioned and done either in lectures and seminars or even better bed-side. Bed-side Teaching has a huge advantage, that medical students, residents and nursing staff learn by seeing real cases (case-based learning) which are much easier to remember and more interactive than lectures. The downside is that bed-side teaching needs a considerable number of staff that is not available as needed and is highly dependent on the motivation of the teacher. Virtual reality teaching is a way to create an immersive environment where teaching can take place repeatedly without any cost after the VR teaching application has been developed. Furthermore, the teaching con-tent is standardized and can be continuously improved by the VR developers together with the teaching staff. The immersive environment might make learning more intense and memorable compared to text-books and lectures. Dialysis is needed by patients with renal failure. In these patients, the machine takes over the function of the native kidneys. The dialysis machine which cleans the blood has to be set up before each use (link). As there is a high fluctuation in residents and nursing staff it would be a great relief for caregivers if the teaching of the dialysis machine setup could be supported by a virtual reality application. Furthermore, the knowledge could be deepened by repeat training. Development for the standalone device Oculus Quest should be the main goal, as it is the most realistic device to be used in a hospital environment.

Task:
  • Research in the area of education in VR (deepen the learning expe-rience and make it more enjoyable)
  • Development a 3D Model of a dialysis machine (e.g. with blender or 3ds Max)
  • Implementing a 3D Application with Unity for the Oculus Quest
  • Evaluation of the system in the Klinikum rechts der Isar (with medical students and nursing staff)
References:
  1. Multimodal Learning in Health Sciences and Medicine: Merging Technologies to Enhance Student Learning and Communication. Moro C, Smith J, Stromberga. Adv Exp Med Biol. 2019;1205:71-78.
  2. Emerging Applications of Virtual Reality in Cardiovascular Medicine. Silva JNA, Southworth M, Raptis C, Silva J. JACC Basic Transl Sci. 2018 Jun 25;3(3):420-430.
  3. Effect of virtual reality training to decreases rates of needle stick/sharp injuries in new-coming medical and nursing interns in Taiwan. J Educ Eval Health Prof. 2020 Jan;17:1. Wu SH, Huang CC, Huang SS, Yang YY, Liu CW, Shulruf B, Chen CH.

Voraussetzung
  • Excellent coding skills in Unity C#
  • Experience with 3D modelling
  • High motivation and interest in biomedical applications as well as in translational medical research
Bewerbung If you are interested in the rapidly expanding field of biomedical VR applications and want to participate in translational research between engineering and medicine, please send your CV together with your grade report emphasizing your previous experience in this area and your desired starting date to maximilian.rettinger@tum.de.

Distracted Driver Dataset

Thema Distracted Driver Dataset
Typ Master
Betreuer Okan Köpüklü, M.Sc.
Tel.: +49 (0)89 289-28554
E-Mail: okan.kopuklu@tum.de
Sachgebiet Computer Vision
Beschreibung Motivation: According to the last National Highway Traffic Safety Administration (NHTSA) report, one in ten fatal crashes and two in ten injury crashes were reported as distracted driver crashes in the United State in 2014. Therefore detecting the drivers distraction state is utmost important to reduce driver-related accidents. For this task, properly annotated dataset for drivers actions observation is necessary. With such a dataset, state-of-the art Deep Learning Architectures can be used to recognize the distraction state of the drivers.

Task: The main task is to collect a “Distracted Driver Dataset”, and use a light-weight Convolutional Neural Networks (CNN) architecture in order to detect driver’s distractive actions. The dataset should contain the following annotations:
1. Predefined distractive actions that the drivers do
2. Drivers hand states (whether they are on the wheel or not)

During the thesis, the following steps will be followed in general:
1. State-of-the-art research
2. Dataset collection and preparation (i.e. labeling and formating)
3. Light-weight CNN Architecture design
4. Evaluation of the CNN Architecture on the prepared dataset
5. Demonstration of the working system

References:
[1] Baheti, B., Gajre, S., & Talbar, S. (2018). Detection of Distracted Driver using Convolutional Neural Network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 1032-1038).
[2] Hssayeni, M. D., Saxena, S., Ptucha, R., & Savakis, A. (2017). Distracted driver detection: Deep learning vs handcrafted features. Electronic Imaging, 2017(10), 20-26.
[3] G. Borghi, E. Frigieri, R. Vezzani and R. Cucchiara, "Hands on the wheel: A Dataset for Driver Hand Detection and Tracking," 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi'an, 2018, pp. 564-570.
Voraussetzung 1. Excellent coding skills, preferable in Python
2. Experience in deep learning frameworks, preferably in Torch/PyTorch
3. Motivation to work on deep learning.
Bewerbung If you are interested in this topic, we welcome the applications via the email address above. Please set the email subject to " application for topic 'XYZ'", ex. "Master’s thesis application for topic 'XYZ'", while clearly specifying why are you interested in the topic in the text of the message. Also make sure to attach your most recent CV (if you have one) and grade report.

Real-time Detection and classification of Dynamic Hand Gestures

Thema Real-time Detection and classification of Dynamic Hand Gestures
Typ Forschungspraxis, Masterarbeit
Betreuer Okan Köpüklü, M.Sc.
Tel.: +49 (0)89 289-28554
E-Mail: okan.kupuklu@tum.de
Sachgebiet Computer Vision
Beschreibung Motivation :Detection and classification of dynamic hand gestures is a challenging task since there is no indication when an action starts in a video stream. However, most of the deep learning architectures which are working offline can also function online with proper adjustments. The topic of this thesis is convert an offline-working architecture to an online-working one.
Task : The main task is to bring an already working deep architecture, which can be seen below, to online functionality. Details of the architecture can be found in [1].
As a further reading, [2] also provides a detailed online detection architecture.


References :
[1] O. Köpüklü, N. Köse, and G. Rigoll. Motion fused frames: Data level fusion strategy for hand gesture recognition. arXiv preprint, arXiv:1804.07187, 2018.
[2] P. Molchanov, X. Yang, S. Gupta, K. Kim, S. Tyree, and J. Kautz. Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4207–4215, 2016.
Voraussetzung 1. Excellent coding skills in Python,
2. Experience in deep learning frameworks, preferably in Torch/PyTorch.
3. Motivation to work on deep learning.
Bewerbung If you are interested in this topic, we welcome the applications via the email address above. Please set the email subject to “ application for topic 'XYZ'”, ex. “Master’s thesis application for topic 'XYZ'”, while clearly specifying why are you interested in the topic in the text of the message. Also make sure to attach your most recent CV (if you have one) and grade report.