ArticleJournal of neuroengineering and rehabilitation2019
Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson's disease.
Article in Journal of neuroengineering and rehabilitation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 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
- Construct validity of instrumented gait assessments in hospital and daily life mobility in patients with Parkinson's disease and atypical Parkinson's syndromes: an exploratory study.Journal of neurology · 2026Trial
- The inertial-based gait normalcy index of dual task cost during turning quantifies gait automaticity improvement in early-stage Parkinson's rehabilitation.Journal of neuroengineering and rehabilitation · 2024Trial
- Flexible and Self-Powered Wearable Sensors for Tremor Monitoring in Parkinson'S Disease: Recent Advances in Materials and Device Architectures.Advanced healthcare materials · 2026Review
- Quantifying Asymmetric Gait Pattern Changes Using a Hidden Markov Model Similarity Measure (HMM-SM) on Inertial Sensor Signals.Sensors (Basel, Switzerland) · 2024Article
- Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease.Sensors (Basel, Switzerland) · 2024Article
- Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson's Disease.Journal of big data · 2024Article
- Inertial Sensor Technologies-Their Role in Equine Gait Analysis, a Review.Sensors (Basel, Switzerland) · 2023Review
- Personalised Gait Recognition for People with Neurological Conditions.Sensors (Basel, Switzerland) · 2022Article
- Review-Emerging Portable Technologies for Gait Analysis in Neurological Disorders.Frontiers in human neuroscience · 2022Review
- Validation of a Sensor-Based Gait Analysis System with a Gold-Standard Motion Capture System in Patients with Parkinson's Disease.Sensors (Basel, Switzerland) · 2021Article
- Atypical Gait Cycles in Parkinson's Disease.Sensors (Basel, Switzerland) · 2021Article
- Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson's disease patients.Journal of neuroengineering and rehabilitation · 2021Article
- From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning.Sensors (Basel, Switzerland) · 2021Article
- Correlation between serum renin-angiotensin system (RAS) level and depression and anxiety symptoms in patients with Parkinson's disease.Saudi journal of biological sciences · 2021Article
- Concurrent Validity, Test-Retest Reliability, and Sensitivity to Change of a Single Body-Fixed Sensor for Gait Analysis during Rollator-Assisted Walking in Acute Geriatric Patients.Sensors (Basel, Switzerland) · 2020Article
- Different response to instrumental tests in relation to cognitive demand after dopaminergic stimulation in previously treated patients with Parkinson's disease.Journal of neural transmission (Vienna, Austria : 1996) · 2020Article
- Deep Phenotyping of Parkinson's Disease.Journal of Parkinson's disease · 2020Review
- JNER at 15 years: analysis of the state of neuroengineering and rehabilitation.Journal of neuroengineering and rehabilitation · 2019Article
- Correction to: Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson's disease.Journal of neuroengineering and rehabilitation · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGait symptoms and balance impairment are characteristic indicators for the progression in Parkinson's disease (PD). Current gait assessments mostly focus on straight strides with assumed constant velocity, while acceleration/deceleration and turning strides are often ignored. This is either due to the set up of typical clinical assessments or technical limitations in capture volume. Wearable inertial measurement units are a promising and unobtrusive technology to overcome these limitations. Other gait phases such as initiation, termination, transitioning (between straight walking and turning) and turning might be relevant as well for the evaluation of gait and balance impairments in PD.
methodIn a cohort of 119 PD patients, we applied unsupervised algorithms to find different gait clusters which potentially include the clinically relevant information from distinct gait phases in the standardized 4x10 m gait test. To clinically validate our approach, we determined the discriminative power in each gait cluster to classify between impaired and unimpaired PD patients and compared it to baseline (analyzing all straight strides).
resultsAs a main result, analyzing only one of the gait clusters constant, non-constant or turning led in each case to a better classification performance in comparison to the baseline (increase of area under the curve (AUC) up to 19% relative to baseline). Furthermore, gait parameters (for turning, constant and non-constant gait) that best predict motor impairment in PD were identified.
conclusionsWe conclude that a more detailed analysis in terms of different gait clusters of standardized gait tests such as the 4x10 m walk may give more insights about the clinically relevant motor impairment in PD patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.