Ullrich M, Hannink J, Gaßner H, Klucken J, Eskofier B, Kluge F (2019)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2019
Event location: Chicago
Sensor-based gait analysis is a valuable tool in
diagnosis and assessment of Parkinson’s disease. Especially for
large data sets, efficient analysis pipelines are required. Presegmentation
of long time series into chunks of interest is a possible
approach to increase efficiency. Therefore, we developed an
unsupervised algorithm for the detection of gait sequences from
continuous sensor signals. In the proposed method, gyroscope
signals representing the angular rate of the feet are analyzed in
the frequency domain using moving windows. A gait sequence
was detected, if the frequency spectrum of a given window
contained harmonic frequencies. The approach was tested on
two data sets that differed in the ratio of clinical gait and cyclic
movement tests. Sensitivity in both data sets was higher than
99% in a stride-to-stride comparison with ground truth. The
specificity was measured with 76.1% (data set 1) and 94.5% (data
set 2) for tests against sequences of other cyclic movements. In
conclusion, the algorithm offers a reliable and efficient approach
for the detection of gait sequences in time series data and is
also promising for the application in long-term home-monitoring
scenarios.
APA:
Ullrich, M., Hannink, J., Gaßner, H., Klucken, J., Eskofier, B., & Kluge, F. (2019). Unsupervised harmonic frequency-based gait sequence detection for Parkinson’s disease. In Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). Chicago.
MLA:
Ullrich, Martin, et al. "Unsupervised harmonic frequency-based gait sequence detection for Parkinson’s disease." Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Chicago 2019.
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