ArticleSensors (Basel, Switzerland)2025
Ankle Sensor-Based Detection of Freezing of Gait in Parkinson's Disease in Semi-Free Living Environments.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Usefulness of Wearable Sensors for Detecting Freezing of Gait in Parkinson's Disease: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- Deep learning for freezing of gait assessment using inertial measurement units: a multicentre validation study.NPJ Parkinson's disease · 2026Article
- AI-Enabled Wearables for Motor Function Assessment and Rehabilitation in Parkinson Disease: Scoping Review.Journal of medical Internet research · 2026Review
- Real-time detection and subtyping of "On-meds" freezing of gait in Parkinson's disease using lower-limb acceleration data.Journal of neurology · 2026Observational
- A multi-level annotated sensor dataset of gait freezing manifestations and severity in Parkinson's disease.Scientific data · 2026Article
- Enhanced EfficientNet-Extended Multimodal Parkinson's disease classification with Hybrid Particle Swarm and Grey Wolf Optimizer.Scientific reports · 2025Article
- A Conceptual Framework for User Trust in AI Biosensors: Integrating Cognition, Context, and Contrast.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Freezing of gait (FOG) is a motor symptom experienced by people with Parkinson's Disease (PD) where they feel like they are glued to the floor. Accurate and continuous detection is needed for effective cueing to prevent or shorten FOG episodes. A convolutional neural network (CNN) was developed to detect FOG episodes in data recorded from an inertial measurement unit (IMU) on a PD patient's ankle under semi-free living conditions. Data were split into two sets: one with all movements and another with walking and turning activities relevant to FOG detection. The CNN model was evaluated using five-fold cross-validation (5Fold-CV), leave-one-subject-out cross-validation (LOSO-CV), and performance metrics such as accuracy, sensitivity, precision, F1-score, and AUROC; Data from 24 PD participants were collected, excluding three with no FOG episodes. For walking and turning activities, the CNN model achieved AUROC = 0.9596 for 5Fold-CV and AUROC = 0.9275 for LOSO-CV. When all activities were included, AUROC dropped to 0.8888 for 5Fold-CV and 0.9017 for LOSO-CV; the model effectively detected FOG in relevant movement scenarios but struggled with distinguishing FOG from other inactive states like sitting and standing in semi-free-living environments.
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Registered trials
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