Automatic clinical gait test detection from inertial sensor data
Fischer S, Ullrich M, Küderle A, Gaßner H, Klucken J, Eskofier B, Kluge F (2020)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2020
Pages Range: 789 - 792
Event location: Montreal
DOI: 10.1109/EMBC44109.2020.9176440
Abstract
The analysis of gait data is one approach to support clinicians with the diagnosis and therapy of diseases, for example Parkinson’s disease (PD). Traditionally, gait data of standardized tests in the clinic is analyzed, ensuring a predefined setting. In recent years, long-term home-based gait analysis has been used to acquire a more representative picture of the patient’s disease status. Data is recorded in a less artifical setting and therefore allows a more realistic perception of the disease progression. However, fully unsupervised gait data without additional context information impedes interpretation. As an intermediate solution, performance of gait tests at home was introduced. Integration of instrumented gait test requires annotations of those tests for their identification and further processing. To overcome these limitations, we developed an algorithm for automatic detection of standardized gait tests from continuous sensor data with the goal of making manual annotations obsolete. The method is based on dynamic time warping, which compares an input signal with a predefined template and quantifies similarity between both. Different templates were compared and an optimized template was created. The classification scored a F1-measure of 86.7% for evaluation on a data set acquired in a clinical setting. We believe that this approach can be transferred to home-monitoring systems and will facilitate a more efficient and automated gait analysis.
Authors with CRIS profile
Martin Ullrich
Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)
Arne Küderle
Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)
Heiko Gaßner
Department of Molecular Neurology
Jochen Klucken
Department of Molecular Neurology
Björn Eskofier
Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)
Felix Kluge
Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)
Additional Organisation(s)
Related research project(s)
How to cite
APA:
Fischer, S., Ullrich, M., Küderle, A., Gaßner, H., Klucken, J., Eskofier, B., & Kluge, F. (2020). Automatic clinical gait test detection from inertial sensor data. In Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 789 - 792). Montreal, CA.
MLA:
Fischer, Stefan, et al. "Automatic clinical gait test detection from inertial sensor data." Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal 2020. 789 - 792.
BibTeX: Download