ParCuR—A Novel AI-Enabled Gait Cueing Wearable for Patients with Parkinson’s Disease

Freezing of gait (FoG) is a common motor symptom in advanced Parkinson's disease, leading to falls, disability, and reduced quality of life. Although cueing systems using visual or auditory stimuli can help patients resume walking, existing solutions are often expensive, uncomfortable, and conspicuous. ParCuR (Parkinson Cueing and Rehabilitation) is a compact, ankle-worn wearable integrating an inertial sensor, haptic stimulator, and AI-based software. It was developed to detect FoG episodes in real time and provides automatic sensory cues to assist patients with Parkinson's Disease (PwP). A classifier was trained for FoG detection using the DAPHNet dataset, comparing patient-specific and patient-independent models. While a small-scale trial with PwP assessed usability and reliability. ParCuR is watch-sized (35 × 41 mm), discreet, and comfortable for daily use. The online detection algorithm triggers stimulation within 0.7 s of episode onset and achieves 94.9% sensitivity and 91.3% specificity using only 14 frequency-based features. Preliminary trials confirmed device feasibility and guided design refinements. This low-cost, wearable solution supports personalized, real-time FoG detection and responsive cueing, improving patient mobility while minimizing discomfort and continuous stimulation habituation.

Authors

Institutions

Publication Details

Journal
Sensors
Published
2025-11-20
DOI
https://doi.org/10.3390/s25227077
Citations
1
Primary Topic
Balance, Gait, and Falls Prevention
Type
article
Field-Weighted Citation Impact
4.76
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ParCuR—A Novel AI-Enabled Gait Cueing Wearable for Patients with Parkinson’s Disease

Ana Morgadinho, Diogo Reis Carneiro, Manuel Reis Carneiro, Telmo Lopes
1 citations
Sensors
Balance, Gait, and Falls Prevention
4.76
article

ParCuR—A Novel AI-Enabled Gait Cueing Wearable for Patients with Parkinson’s Disease

Ana Morgadinho, Diogo Reis Carneiro, Manuel Reis Carneiro, Telmo Lopes
article en
1 citations

Abstract

Freezing of gait (FoG) is a common motor symptom in advanced Parkinson's disease, leading to falls, disability, and reduced quality of life. Although cueing systems using visual or auditory stimuli can help patients resume walking, existing solutions are often expensive, uncomfortable, and conspicuous. ParCuR (Parkinson Cueing and Rehabilitation) is a compact, ankle-worn wearable integrating an inertial sensor, haptic stimulator, and AI-based software. It was developed to detect FoG episodes in real time and provides automatic sensory cues to assist patients with Parkinson's Disease (PwP). A classifier was trained for FoG detection using the DAPHNet dataset, comparing patient-specific and patient-independent models. While a small-scale trial with PwP assessed usability and reliability. ParCuR is watch-sized (35 × 41 mm), discreet, and comfortable for daily use. The online detection algorithm triggers stimulation within 0.7 s of episode onset and achieves 94.9% sensitivity and 91.3% specificity using only 14 frequency-based features. Preliminary trials confirmed device feasibility and guided design refinements. This low-cost, wearable solution supports personalized, real-time FoG detection and responsive cueing, improving patient mobility while minimizing discomfort and continuous stimulation habituation.

SensorsVol. 25(22)
Hospitais da Universidade de Coimbra (PT), ETH Zurich (CH), Institute for Systems Engineering and Computers (PT), University of Coimbra (PT)
Openalex Percentile: Top 5%
Balance, Gait, and Falls Prevention
4.76
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.