ArticleSensors (Basel, Switzerland)2018
Estimation of Temporal Gait Parameters Using a Human Body Electrostatic Sensing-Based Method.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Lightweight FMCW radar framework for human activity recognition under limited data conditions.Scientific reports · 2026Article
- Beyond timing and step counting in 360° turning-in-place assessment: a scoping review.Biomedical engineering online · 2024Article
- Walking Step Monitoring with a Millimeter-Wave Radar in Real-Life Environment for Disease and Fall Prevention for the Elderly.Sensors (Basel, Switzerland) · 2022Article
- Implementation of Unobtrusive Sensing Systems for Older Adult Care: Scoping Review.JMIR aging · 2021Article
- Levodopa improves handwriting and instrumental tasks in previously treated patients with Parkinson's disease.Journal of neural transmission (Vienna, Austria : 1996) · 2020Article
- Application of an Improved Correlation Method in Electrostatic Gait Recognition of Hemiparetic Patients.Sensors (Basel, Switzerland) · 2019Article
- Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate estimation of gait parameters is essential for obtaining quantitative information on motor deficits in Parkinson's disease and other neurodegenerative diseases, which helps determine disease progression and therapeutic interventions. Due to the demand for high accuracy, unobtrusive measurement methods such as optical motion capture systems, foot pressure plates, and other systems have been commonly used in clinical environments. However, the high cost of existing lab-based methods greatly hinders their wider usage, especially in developing countries. In this study, we present a low-cost, noncontact, and an accurate temporal gait parameters estimation method by sensing and analyzing the electrostatic field generated from human foot stepping. The proposed method achieved an average 97% accuracy on gait phase detection and was further validated by comparison to the foot pressure system in 10 healthy subjects. Two results were compared using the Pearson coefficient
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Registered trials
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.