NDLI: Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson’s disease patients

Content Provider	Springer Nature : BioMed Central
Author	Roth, Nils Küderle, Arne Ullrich, Martin Gladow, Till Marxreiter, Franz Klucken, Jochen Eskofier, Bjoern M. Kluge, Felix
Abstract	To objectively assess a patient’s gait, a robust identification of stride borders is one of the first steps in inertial sensor-based mobile gait analysis pipelines. While many different methods for stride segmentation have been presented in the literature, an out-of-lab evaluation of respective algorithms on free-living gait is still missing. To address this issue, we present a comprehensive free-living evaluation dataset, including 146.574 semi-automatic labeled strides of 28 Parkinson’s Disease patients. This dataset was used to evaluate the segmentation performance of a new Hidden Markov Model (HMM) based stride segmentation approach compared to an available dynamic time warping (DTW) based method. The proposed HMM achieved a mean F1-score of 92.1% and outperformed the DTW approach significantly. Further analysis revealed a dependency of segmentation performance to the number of strides within respective walking bouts. Shorter bouts ( $< 30$ strides) resulted in worse performance, which could be related to more heterogeneous gait and an increased diversity of different stride types in short free-living walking bouts. In contrast, the HMM reached F1-scores of more than 96.2% for longer bouts ( $> 50$ strides). Furthermore, we showed that an HMM, which was trained on at-lab data only, could be transferred to a free-living context with a negligible decrease in performance. The generalizability of the proposed HMM is a promising feature, as fully labeled free-living training data might not be available for many applications. To the best of our knowledge, this is the first evaluation of stride segmentation performance on a large scale free-living dataset. Our proposed HMM-based approach was able to address the increased complexity of free-living gait data, and thus will help to enable a robust assessment of stride parameters in future free-living gait analysis applications.
Related Links	https://jneuroengrehab.biomedcentral.com/counter/pdf/10.1186/s12984-021-00883-7.pdf
Ending Page	15
Page Count	15
Starting Page	1
File Format	HTM / HTML
ISSN	17430003
DOI	10.1186/s12984-021-00883-7
Journal	Journal of NeuroEngineering and Rehabilitation
Issue Number	1
Volume Number	18
Language	English
Publisher	BioMed Central
Publisher Date	2021-06-03
Access Restriction	Open
Subject Keyword	Neurosciences Neurology Rehabilitation Medicine Biomedical Engineering and Bioengineering HMM IMU Machine learning Mobile gait analysis Stride borders Wearable sensors
Content Type	Text
Resource Type	Article
Subject	Health Informatics Rehabilitation
Journal Impact Factor	5.2/2023
5-Year Journal Impact Factor	5.6/2023

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