ArticleSensors (Basel, Switzerland)2019
Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson's Disease: Addressing the Class Imbalance Problem.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04274478 (Biofeedback to Reduce Freezing of Gait in Parkinson Patients - Ceriter/Insole Parkinson Disease), which is not on this map. Cited by 16 papers, 4 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.
Biofeedback to Reduce Freezing of Gait in Parkinson Patients - Ceriter/Insole Parkinson Disease (IPD)-1
Who cites it
16 citing papers in PubMed, 4 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 Usefulness of Wearable Sensors for Detecting Freezing of Gait in Parkinson's Disease: A Systematic Review.Sensors (Basel, Switzerland) · 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
- Personalized prediction of gait freezing using dynamic mode decomposition.Scientific reports · 2025Article
- Analysis of gait characteristics and related factors in patients with Parkinson's disease based on wearable devices.Brain and behavior · 2024Article
- Freezing of gait assessment with inertial measurement units and deep learning: effect of tasks, medication states, and stops.Journal of neuroengineering and rehabilitation · 2024Article
- Detection and prediction of freezing of gait with wearable sensors in Parkinson's disease.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Review
- Machine Learning and Wearable Sensors for the Early Detection of Balance Disorders in Parkinson's Disease.Sensors (Basel, Switzerland) · 2022Article
- Prediction of Freezing of Gait in Parkinson's Disease Using Unilateral and Bilateral Plantar-Pressure Data.Frontiers in neurology · 2022Article
- Modelling and identification of characteristic kinematic features preceding freezing of gait with convolutional neural networks and layer-wise relevance propagation.BMC medical informatics and decision making · 2021Article
- Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data.Sensors (Basel, Switzerland) · 2021Article
- Prediction of Freezing of Gait in Parkinson's Disease Using Wearables and Machine Learning.Sensors (Basel, Switzerland) · 2021Article
- Real-Time Detection of Freezing Motions in Parkinson's Patients for Adaptive Gait Phase Synchronous Cueing.Frontiers in neurology · 2021Article
- A Supervised Machine Learning Approach to Detect the On/Off State in Parkinson's Disease Using Wearable Based Gait Signals.Diagnostics (Basel, Switzerland) · 2020Article
- Lower Limb Kinematics Trajectory Prediction Using Long Short-Term Memory Neural Networks.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Freezing of gait (FoG) is a common motor symptom in patients with Parkinson's disease (PD). FoG impairs gait initiation and walking and increases fall risk. Intelligent external cueing systems implementing FoG detection algorithms have been developed to help patients recover gait after freezing. However, predicting FoG before its occurrence enables preemptive cueing and may prevent FoG. Such prediction remains challenging given the relative infrequency of freezing compared to non-freezing events. In this study, we investigated the ability of individual and ensemble classifiers to predict FoG. We also studied the effect of the ADAptive SYNthetic (ADASYN) sampling algorithm and classification cost on classifier performance. Eighteen PD patients performed a series of daily walking tasks wearing accelerometers on their ankles, with nine experiencing FoG. The ensemble classifier formed by Support Vector Machines, K-Nearest Neighbors, and Multi-Layer Perceptron using bagging techniques demonstrated highest performance (F1 = 90.7) when synthetic FoG samples were added to the training set and class cost was set as twice that of normal gait. The model identified 97.4% of the events, with 66.7% being predicted. This study demonstrates our algorithm's potential for accurate prediction of gait events and the provision of preventive cueing in spite of limited event frequency.
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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.