ArticleJournal of neuroengineering and rehabilitation2021
Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson's disease patients.
Article in Journal of neuroengineering and rehabilitation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- AI-Enabled Wearables for Motor Function Assessment and Rehabilitation in Parkinson Disease: Scoping Review.Journal of medical Internet research · 2026Review
- Parkinson's disease severity clustering based on gait activity from mobile device.Scientific reports · 2025Article
- Augmenting a ResNet + BiLSTM Deep Learning Model with Clinical Mobility Data Helps Outperform a Heuristic Frequency-Based Model for Walking Bout Segmentation.Sensors (Basel, Switzerland) · 2025Article
- Motion Smoothness Analysis of the Gait Cycle, Segmented by Stride and Associated with the Inertial Sensors' Locations.Sensors (Basel, Switzerland) · 2025Article
- Combining video telemetry and wearable MEG for naturalistic imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Deep learning-based detection of affected body parts in Parkinson's disease and freezing of gait using time-series imaging.Scientific reports · 2024Article
- Effects of physiotherapy and home-based training in parkinsonian syndromes: protocol for a randomised controlled trial (MobilityAPP).BMJ open · 2024Article
- Hidden Markov Model for Parkinson's Disease Patients Using Balance Control Data.Bioengineering (Basel, Switzerland) · 2024Article
- Gaitmap-An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking.IEEE open journal of engineering in medicine and biology · 2024Article
- A robust walking detection algorithm using a single foot-worn inertial sensor: validation in real-life settings.Medical & biological engineering & computing · 2023Article
- Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases.Frontiers in neurology · 2023Article
- A multi-sensor wearable system for the assessment of diseased gait in real-world conditions.Frontiers in bioengineering and biotechnology · 2023Article
- Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix: Focus on Automatic Segmentation.Biosensors · 2022Article
- The Diverse Gait Dataset: Gait Segmentation Using Inertial Sensors for Pedestrian Localization with Different Genders, Heights and Walking Speeds.Sensors (Basel, Switzerland) · 2022Article
- Review-Emerging Portable Technologies for Gait Analysis in Neurological Disorders.Frontiers in human neuroscience · 2022Review
- The placement of foot-mounted IMU sensors does affect the accuracy of spatial parameters during regular walking.PloS one · 2022Article
- An Inertial Sensor-Based Gait Analysis Pipeline for the Assessment of Real-World Stair Ambulation Parameters.Sensors (Basel, Switzerland) · 2021Article
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8 authors.
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Abstract
backgroundTo 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.
methodTo 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.
resultsThe 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 ([Formula: see text] 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 ([Formula: see text] 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.
conclusionThe 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.
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