Research Group for Applied Software Engineering
Forschungsgruppe für Angewandte Softwaretechnik

Maximilian Kapsecker

Office: 01.07.037
E-Mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Phone: +49 (89) 289 - 18239

Technische Universität München
Institut für Informatik I1
Boltzmannstraße 3
85748 Garching b. München, Germany
Office hours by appointment—please contact me via mail.

 

Hello, it's Max! I am affiliated with the Digital Health Group (TUM) and the Institute of Digital Medicine (UKB).

Research Fields

My research area is at the intersection of mobile software development, machine learning, and medicine. Specifically, I am interested in using mobile sensor data to detect anomalies in an unsupervised manner to infer medical conditions. The nature of this sensitive data requires consideration of security aspects, such as secure aggregation, differential privacy, and federated learning.

#unsupervised-federated-learning #mobile-sensor-data #medical-applications

Projects

  • Unsupervised federated learning for the detection of anomalies in electrocardiogram signals
  • Deep embedding of biosignals for the detection of anomalies
  • Software Campus and Zeiss: Detection of vision impairment with mobile devices
  • MRI - Telemonitoring of athletes and patients (iPraktikum SS21 - WS21/22 - SS22)
  • Solgenium - Verifying workflows in a clinical environment (iPraktikum SS21)
  • footpower - Mobile measurement of the human leg (iPraktikum WS 20/21)

Publications

2022

  • Kapsecker, M., Osterlehner, S., & Jonas, S. M. (2022, July). Analysis of Mobile Typing Characteristics in the Light of Cognition. In 2022 IEEE International Conference on Digital Health (ICDH) (pp. 87-95). IEEE.
  • Kapsecker, M., Strobel, B., & Jonas, S. (2022). SAFMA: Secure Aggregation Framework for mHealth Applications. 67. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)
  • Reimer, L. M., Kapsecker, M., Fukushima, T., & Jonas, S. M. (2022). Evaluating 3D Human Motion Capture on Mobile Devices. Applied Sciences12(10), 4806.

Theses

2022

  • Florian Kristof - Assessment of Electrocardiogram Beat Detectors Using Synthetic and Real-World Data
  • Daniel Nugraha - A Prototype Implementation of a Mobile Federated Learning Framework
  • Ramona Eckert - Development of a Mobile Application for Automated Functional Movement Analysis
  • Cornelius Born - Predicting Dysglycemia for Individuals with Diabetes Mellitus from Wearable Data
  • Simon Osterlehner - Development of a privacy-preserving keyboard application for the detection of dementia
  • Benedikt Strobel - Implementation of a Scalable Secure Aggregation Framework for Mobile Applications
  • Sebastien Letzelter - Implementing a Mobile Application for Detecting Vision Impairments

 

2021

  • Simone Schmidpeter - Federated Learning for Anomaly Detection in Electrocardiograms using Clustering
  • Stephan Schmiedmayer - Implementation of a Secure Aggregation Protocol for Mobile Devices to Model Health Related Parameters
  • Victor Dzhagatspanyan - Comparison of generative models for the synthetic generation of electrocardiogram data
  • Ernst Pappenheim - Conception and Implementation of a Software Tool to Optimize and Standardize Data Management in Personnel Planning in the Clinical Environment