Prof. Dr. Björn Eskofier

Machine Learning and Data Analytics Lab Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)

Combined Accelerometer and EMG Analysis to Differentiate Essential Tremor from Parkinson’s Disease (2016) Haji Ghassemi N, Marxreiter F, Pasluosta CF, Schlachetzki J, Schramm A, Eskofier B, Klucken J Conference contribution, Original article Automatic clustering of code changes (2016) Kreutzer P, Dotzler G, Ring M, Eskofier B, Philippsen M Conference contribution, Original article Unobtrusive and Energy-Efficient Swimming Exercise Tracking Using On-Node Processing (2016) Jensen U, Blank P, Kugler P, Eskofier B Journal article An approximation of the Gaussian RBF kernel for efficient classification with SVMs (2016) Ring M, Eskofier B Journal article Virtual and augmented reality in sports-an overview and acceptance study (2016) Gradl S, Eskofier B, Eskofier D, Mutschler C, Otto S Conference contribution, Original article miPod 2: a new hardware platform for embedded real-time processing in sports and fitness applications (2016) Blank P, Hofmann S, Kulessa M, Eskofier B Conference contribution, Conference Contribution Ball impact localization on table tennis rackets using piezo-electric sensors (2016) Blank P, Kautz T, Eskofier B Conference contribution, Conference Contribution Wearable Trick Classification in Freestyle Snowboarding (2016) Groh B, Fleckenstein M, Eskofier B, Groh B, Fleckenstein M, Eskofier B Conference contribution, Conference Contribution Wearable sensors provide objective gait parameters correlating to clinical scores in Parkinson’s disease (2016) Gaßner H, Schlachetzki J, Marxreiter F, Kohl Z, Barth J, Goßler J, Reinfelder S, et al. Conference contribution, Abstract of lecture Predicting the Effects of Midsole Materials on Running Using Optimal Control Simulation (2016) Dorschky E, Kurfess N, Krüger D, Schlarb H, Eskofier B, van den Bogert AJ Conference contribution, Conference Contribution