ReviewSensors (Basel, Switzerland)2019
Wearable-Sensor-based Detection and Prediction of Freezing of Gait in Parkinson's Disease: A Review.
Review 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 NCT05718739 (Assessing and Understanding Freezing of Gait At-home Environment), which is not on this map. Cited by 79 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Assessing and Understanding Freezing of Gait At-home Environment: FOG@Home
Who cites it
79 citing papers in PubMed, 3 syntheses or guidelines pooled it, 195 citations in OpenAlex.
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- The experience and perception of wearable devices in Parkinson's disease patients: a systematic review and meta-synthesis of qualitative studies.Journal of neurology · 2025Pooled it
- Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Multi-Target Repetitive Transcranial Magnetic Stimulation Improves Freezing of Gait in Parkinson's Disease: A Randomized Controlled Trial.CNS neuroscience & therapeutics · 2025Trial
- Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study.Journal of medical Internet research · 2023Trial
- Intelligent Real-Time Healthcare and Biomedical Monitoring Systems: A Narrative Review of AI, IoT, and Emerging Technologies.Bioengineering (Basel, Switzerland) · 2026Review
- Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026Review
- 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
- A multi-level annotated sensor dataset of gait freezing manifestations and severity in Parkinson's disease.Scientific data · 2026Article
- Personalized prediction of gait freezing using dynamic mode decomposition.Scientific reports · 2025Article
- Predicting Freezing of Gait in Parkinson's Disease: A Machine-Learning-Based Approach in ON and OFF Medication States.Journal of clinical medicine · 2025Article
- Ankle Sensor-Based Detection of Freezing of Gait in Parkinson's Disease in Semi-Free Living Environments.Sensors (Basel, Switzerland) · 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
- Freezing of gait detection: The effect of sensor type, position, activities, datasets, and machine learning model.Journal of Parkinson's disease · 2025Article
- Technology for measuring freezing of gait: Current state of the art and recommendations.Journal of Parkinson's disease · 2025Review
- Gait-based Parkinson's disease diagnosis and severity classification using force sensors and machine learning.Scientific reports · 2025Article
- Effects of augmented reality cueing strategies on freezing of gait: The ELIMINATE FoG trial.Clinical parkinsonism & related disorders · 2025Article
- The Application of Machine Learning to Predict Clinical Outcomes of Deep Brain Stimulation in Parkinson's Disease: A Systematic Review.Medical journal of the Islamic Republic of Iran · 2025Review
- Deep learning techniques for detecting freezing of gait episodes in Parkinson's disease using wearable sensors.Frontiers in physiology · 2025Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Freezing of gait (FOG) is a serious gait disturbance, common in mid- and late-stage Parkinson's disease, that affects mobility and increases fall risk. Wearable sensors have been used to detect and predict FOG with the ultimate aim of preventing freezes or reducing their effect using gait monitoring and assistive devices. This review presents and assesses the state of the art of FOG detection and prediction using wearable sensors, with the intention of providing guidance on current knowledge, and identifying knowledge gaps that need to be filled and challenges to be considered in future studies. This review searched the Scopus, PubMed, and Web of Science databases to identify studies that used wearable sensors to detect or predict FOG episodes in Parkinson's disease. Following screening, 74 publications were included, comprising 68 publications detecting FOG, seven predicting FOG, and one in both categories. Details were extracted regarding participants, walking task, sensor type and body location, detection or prediction approach, feature extraction and selection, classification method, and detection and prediction performance. The results showed that increasingly complex machine-learning algorithms combined with diverse feature sets improved FOG detection. The lack of large FOG datasets and highly person-specific FOG manifestation were common challenges. Transfer learning and semi-supervised learning were promising for FOG detection and prediction since they provided person-specific tuning while preserving model generalization.
Indexed as
Identifiers
What OpenQuestion holds
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.