ReviewNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2024
Detection and prediction of freezing of gait with wearable sensors in Parkinson's disease.
Review in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2024. 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 12 papers.
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
12 citing papers in PubMed, 26 citations in OpenAlex.
- Performance of a Personalized Smart Cueing Device to Detect Freezing of Gait in Parkinson's Disease.The European journal of neuroscience · 2026Article
- Deep Learning for Freezing of Gait Detection: Cross-Dataset Validation Reveals Critical Deployment Gaps Between Laboratory and Daily Living Wearable Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Beyond the Leaderboard: Evaluating the Robustness of Deep Learning Models for Detecting Freezing of Gait.The European journal of neuroscience · 2026Article
- Personalized prediction of gait freezing using dynamic mode decomposition.Scientific reports · 2025Article
- Digital Biomarkers for Parkinson Disease: Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait.Journal of medical Internet research · 2025Article
- Detecting Freezing of Gait in Parkinson Disease Using Multiple Wearable Sensors Sets During Various Walking Tasks Relative to Medication Conditions (DetectFoG): Protocol for a Prospective Cohort Study.JMIR research protocols · 2025Article
- Technology for measuring freezing of gait: Current state of the art and recommendations.Journal of Parkinson's disease · 2025Review
- Integration of wearable devices and artificial intelligence in Alzheimer's disease: A scoping review protocol.PloS one · 2025Article
- Wearable Online Freezing of Gait Detection and Cueing System.Bioengineering (Basel, Switzerland) · 2024Article
- A machine learning contest enhances automated freezing of gait detection and reveals time-of-day effects.Nature communications · 2024Article
- Unveiling the Unpredictable in Parkinson's Disease: Sensor-Based Monitoring of Dyskinesias and Freezing of Gait in Daily Life.Bioengineering (Basel, Switzerland) · 2024Article
- Knowledge mapping of freezing of gait in Parkinson's disease: a bibliometric analysis.Frontiers in neuroscience · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Freezing of gait (FoG) is one of the most distressing symptoms of Parkinson's Disease (PD), commonly occurring in patients at middle and late stages of the disease. Automatic and accurate FoG detection and prediction have emerged as a promising tool for long-term monitoring of PD and implementation of gait assistance systems. This paper reviews the recent development of FoG detection and prediction using wearable sensors, with attention on identifying knowledge gaps that need to be filled in future research. This review searched the PubMed and Web of Science databases to collect studies that detect or predict FoG with wearable sensors. After screening, 89 of 270 articles were included. The data description, extracted features, detection/prediction methods, and classification performance were extracted from the articles. As the number of papers of this area is increasing, the performance has been steadily improved. However, small datasets and inconsistent evaluation processes still hinder the application of FoG detection and prediction with wearable sensors in clinical practice.
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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.