Welcome
Welcome to the MaD Lab!
The researchers in the Machine Learning and Data Analytics (MaD) lab conduct theoretical and applied research for wearable computing systems and machine learning algorithms for engineering applications at the intersection of sports and health care. Our motivation is generating a positive impact on human wellbeing, be it through increasing performance, maintaining health, improving rehabilitation, or monitoring disease.
Student theses and projects
The MaD Lab’s Animal Behavior Research Group Team VERA published their latest dataset 🎉🐻❄️
https://www.mdpi.com/2076-2615/13/5/801
Zuerl, M.; Dirauf, R.; Koeferl, F.; Steinlein, N.; Sueskind, J.; Zanca, D.; Brehm, I.; Fersen, L.v.; Eskofier, B. PolarBearVidID: A Video-Based Re-Identification ...
Exciting news! Our new paper has been published today in @PeerJ: https://peerj.com/articles/14852/. Our research explores the use of optimal control simulations in musculoskeletal models to reconstruct change of direction motions without prior knowledge of the motion path. Optimal control simulation...
The Tensor Tournament T3, held as part of the VHB courses "Machine Learning for Engineers I" and "Machine Learning for Engineers II", was a thrilling experience for all 27 teams of students who participated. The competition, which took place on January 21, 2023, challenged teams to solve thr...
We are happy to announce that our paper titled “Perspective on “in the wild” movement analysis using machine learning” is published: https://doi.org/10.1016/j.humov.2022.103042. Recent advances in wearable sensing and machine learning have created ample opportunities for “in the wild” movement analy...
Abstract: Inertial measurement units (IMU) are used diagnostically in the movement analysis of Parkinson’s disease (PD) patients, allowing an objective way to assess biomechanical motion and gait parameters. The Timed Up and Go (TUG) is a standardized clinical gait test widely used in the monitoring...
