Philipp Achenbach, M.Sc.

Wissenschaftlicher Mitarbeiter


Serious Games, Gestenerkennung, Machine Learning


work +49 (0) 6151 16 29468

Work S3|20 208
Rundeturmstraße 10
64283 Darmstadt

Philipp completed his master's degree in mechatronics here at KOM in 2018 in the Serious Games research group. The title of his thesis was "Implementation of an IK approach for tracking body movements considering different aspects of perception". The thesis is about full-body reconstruction using Inverse Kinematics in the context of Virtual Reality.

He joined KOM as a research assistant in March 2019 and is doing research in the area of gesture recognition in the Serious Games group (more on this below). In addition, he is active in teaching (Communication Networks II and Serious Games).

My research topic is Gesture Detection for Sign Language Interpretation using Wearables and deals with the question of how to reliably recognize gestures while avoiding external observers, such as cameras, for privacy reasons. The approach to gesture recognition should be mobile and able to return qualitative feedback to the user. For this purpose, different parameters of the gesture have to be distinguished, e.g. hand shape, movement, place of execution of the gesture and orientation of the hand.

As a result of my research, I must address the following questions:

  • How do individual sensors impact recognition of hand shape?
  • How to determine the relative position of the performed gesture?
  • How to reliably determine a gesture’s start and end in continuous data?

In the context of my research, I am concerned with the following topics:

Related Topics

  • Machine Learning
  • Sensors & Sensor Fusion
  • (Serious Games)

Through my affiliation with the serious games group, I always try to see my research in this context as well.

In addition to my research, I also supervise the following courses

  • Serious Games Lecture
  • Serious Games Seminar
  • Serious Games Lab/Project
  • Earlier: Communication Networks II (also known as KN2)

I am always looking for motivated students interested in writing a thesis in the field of gesture recognition and/or serious games. If you are interested, have a look at open theses or contact me with your own idea.

Student Topic Submission Type
Theo Kastner-Guhl Generierung einer dynamischen Baustellenumgebung anhand regelbasierten Restriktionen 2020/03 Bachelor
Martin Wende Entwicklung einer App zur Ermittlung des ökologischen Fußabdrucks im Kontext einer Mitfahrgelegenheit mit Hilfe von ML 2020/08 Master
Tobias Alexander Wach Konzeption und Implementierung eines Fingergesten-gesteuerten Spiels 2020/09 Bachelor
Lea Schott Erfassung einfacher Handformen mittels Elektromyografie unter Berücksichtigung verschiedener Konfigurationen 2021/03 Bachelor
Dennis Purdack Entwicklung und Evaluation zweier Ansätze zur Erkennung primitiver Handgesten anhand eines Leap Motion Controllers 2021/04 Bachelor
Marius Kempf Konzeption und Entwicklung eines Mechanomyografie-Controllers zur Erkennung von einfachen Fingergesten 2021/04 Master
Sebastian Wolf Comparing the suitability of Decision Tree classifier and Support Vector Machines for hand gesture recognition 2021/07 Bachelor
Felix Klose Recognition of gesture movement, orientation and location using IMUs and Inverse Kinematics 2021/08 Master
Leon Petri Augmenting Finger Motion Data for Hand Gesture Recognition using Video-based Zero-Shot Domain Adaption 2022/06 Bachelor
Nico Kunz Recognition and classification of handshapes of American finger alphabet 2022/07 Bachelor
Benedikt Hock Towards sign parametrization by using MediaPipe 2022/09 Bachelor
Qilin Tan Towards arm posture detection using sensor fusion and extended Kalman filter 2022/09 Master
Sebastian Laux Augmenting wearable sensor data for sign language recognition 2022/10 Bachelor
Dennis Purdack Towards metaclassification of complex handshape recognition 2022/12 Master
Elena Bock Classification of hand gestures using MediaPipe with qualitative feedback (wird in neuem Tab geöffnet) 2023/04 Bachelor
Alexandra Skogseide Automatic generation of a parametric sign lexicon with MediaPipe (wird in neuem Tab geöffnet) 2023/08 Master
Sarah Kuhlmann IMU data segmentation for pose recognition in sign language (wird in neuem Tab geöffnet) 2023/08 Master
Thanh Huan Hoang Dancing with the gloves on: Using data gloves and arm controllers to teach the Macarena (wird in neuem Tab geöffnet) 2023/08 Bachelor
Philipp Kaul Extraction and Augmentation of Sensor Data from Video Material using MediaPipe (wird in neuem Tab geöffnet) 2024/02 Master
Rebekka Schiller Live Classification of Battison’s Sign Types (wird in neuem Tab geöffnet) 2024/04 Master