ArticleSensors (Basel, Switzerland)2021
Data-Driven Investigation of Gait Patterns in Individuals Affected by Normal Pressure Hydrocephalus.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
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Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Serial Assessment of Gait Changes After Interventions Using Smart Insole in a Patient With iNPH: A Proof-of-Concept Case Report.Brain & NeuroRehabilitation · 2025Article
- Technological Advances for Gait and Balance in Normal Pressure Hydrocephalus: A Systematic Review.Bioengineering (Basel, Switzerland) · 2025Review
- 'Watkins' & 'Watkins2.0': Smart phone applications (Apps) for gait-assessment in normal pressure hydrocephalus and decompensated long-standing overt ventriculomegaly.Acta neurochirurgica · 2024Article
- Kinematic movement and balance parameter analysis in neurological gait disorders.Journal of biological engineering · 2024Article
- Gait pattern analysis in the home environment as a key factor for the reliable assessment of shunt responsiveness in patients with idiopathic normal pressure hydrocephalus.Frontiers in neurology · 2023Article
- TRPV4 mRNA is elevated in the caudate nucleus with NPH but not in Alzheimer's disease.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Normal pressure hydrocephalus (NPH) is a chronic and progressive disease that affects predominantly elderly subjects. The most prevalent symptoms are gait disorders, generally determined by visual observation or measurements taken in complex laboratory environments. However, controlled testing environments can have a significant influence on the way subjects walk and hinder the identification of natural walking characteristics. The study aimed to investigate the differences in walking patterns between a controlled environment (10 m walking test) and real-world environment (72 h recording) based on measurements taken via a wearable gait assessment device. We tested whether real-world environment measurements can be beneficial for the identification of gait disorders by performing a comparison of patients' gait parameters with an aged-matched control group in both environments. Subsequently, we implemented four machine learning classifiers to inspect the individual strides' profiles. Our results on twenty young subjects, twenty elderly subjects and twelve NPH patients indicate that patients exhibited a considerable difference between the two environments, in particular gait speed (
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Registered trials
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