ArticleSensors (Basel, Switzerland)2018
A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer.
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 30 papers, 5 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
30 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- The evolution of trends and technology in wearable sensors used to detect falls in people with neurodegenerative diseases: a systematic review.Frontiers in neurorobotics · 2026Pooled it
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Insights into Parkinson's Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis.Sensors (Basel, Switzerland) · 2024Pooled it
- Recent trends in wearable device used to detect freezing of gait and falls in people with Parkinson's disease: A systematic review.Frontiers in aging neuroscience · 2023Pooled it
- The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Wearable Online Freezing of Gait Detection and Cueing System.Bioengineering (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
- Assessing the impact of body location on the accuracy of detecting daily activities with accelerometer data.iScience · 2024Article
- Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review.Sensors (Basel, Switzerland) · 2023Review
- IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring.Sensors (Basel, Switzerland) · 2023Review
- Design and validation of a multi-task, multi-context protocol for real-world gait simulation.Journal of neuroengineering and rehabilitation · 2022Article
- Machine Learning and Wearable Sensors for the Early Detection of Balance Disorders in Parkinson's Disease.Sensors (Basel, Switzerland) · 2022Article
- Enhanced Classification of Dog Activities with Quaternion-Based Fusion Approach on High-Dimensional Raw Data from Wearable Sensors.Sensors (Basel, Switzerland) · 2022Article
- Monitoring gait at home with radio waves in Parkinson's disease: A marker of severity, progression, and medication response.Science translational medicine · 2022Article
- Detection and assessment of Parkinson's disease based on gait analysis: A survey.Frontiers in aging neuroscience · 2022Review
- Objectifying clinical gait assessment: using a single-point wearable sensor to quantify the spatiotemporal gait metrics of people with lumbar spinal stenosis.Journal of spine surgery (Hong Kong) · 2021Article
- Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2021Article
- Review
- A Wearable Sensor System to Measure Step-Based Gait Parameters for Parkinson's Disease Rehabilitation.Sensors (Basel, Switzerland) · 2020Article
- Advantages of timing the duration of a freezing of gait-provoking test in individuals with Parkinson's disease.Journal of neurology · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
One of the most common symptoms observed among most of the Parkinson's disease patients that affects movement pattern and is also related to the risk of fall, is usually termed as "freezing of gait (FoG)". To allow systematic assessment of FoG, objective quantification of gait parameters and automatic detection of FoG are needed. This will help in personalizing the treatment. In this paper, the objectives of the study are (1) quantification of gait parameters in an objective manner by using the data collected from wearable accelerometers; (2) comparison of five estimated gait parameters from the proposed algorithm with their counterparts obtained from the 3D motion capture system in terms of mean error rate and Pearson's correlation coefficient (PCC); (3) automatic discrimination of FoG patients from no FoG patients using machine learning techniques. It was found that the five gait parameters have a high level of agreement with PCC ranging from 0.961 to 0.984. The mean error rate between the estimated gait parameters from accelerometer-based approach and 3D motion capture system was found to be less than 10%. The performances of the classifiers are compared on the basis of accuracy. The best result was accomplished with the SVM classifier with an accuracy of approximately 88%. The proposed approach shows enough evidence that makes it applicable in a real-life scenario where the wearable accelerometer-based system would be recommended to assess and monitor the FoG.
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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.