Journal Article

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

Küderle, Arne, Marxreiter, Franz, Roth, Nils, Eskofier, Bjoern M., Kluge, Felix, Ullrich, Martin, Gladow, Till, Klucken, Jochen

2021

Overview

Background 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. Method 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. Results 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. Conclusion 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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Automation / Biomedical and Life Sciences / Biomedical Engineering and Bioengineering / Biomedicine / Complications and side effects / Computer-aided medical diagnosis / Datasets / Diagnosis / Gait / Gait disorders / Hidden Markov models / HMM / IMU / Inertial sensing devices / Laboratories / Machine learning / Markov chains / Methods / Mobile gait analysis / Movement disorders / Neurodegenerative diseases / Neurology / Neurosciences / Parameter robustness / Parkinson's disease / Patients / Performance evaluation / Physiological aspects / Rehabilitation Medicine / Robustness / Segmentation / Sensors / Stride borders / Walking / Wearable sensors