Automatic Recognition of Freezing of Gait Episodes
- Conditions
- Parkinson's Disease
- Registration Number
- NCT00738075
- Lead Sponsor
- Tel-Aviv Sourasky Medical Center
- Brief Summary
The investigators hypothesize that mathematical algorithm can identify freezing episodes based on data obtained from accelerometer.
- Detailed Description
The subjects will walk with an accelerometer. The acceleration traces will be online analyzed, and if freezing episode is recognized, an auditory cue will be heard.
Recruitment & Eligibility
- Status
- UNKNOWN
- Sex
- All
- Target Recruitment
- 30
Inclusion Criteria
- Patients with Parkinsons disease prone to freezing of gait
Exclusion Criteria
- Patients who are not able to ambulate independently
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method
- Secondary Outcome Measures
Name Time Method
Related Research Topics
Explore scientific publications, clinical data analysis, treatment approaches, and expert-compiled information related to the mechanisms and outcomes of this trial. Click any topic for comprehensive research insights.
What mathematical algorithms are used for freezing of gait detection in Parkinson's Disease using accelerometers?
How does accelerometer-based freezing of gait recognition compare to traditional clinical assessment methods in Parkinson's patients?
Are there specific biomarkers associated with freezing of gait episodes in Parkinson's Disease that correlate with accelerometer data?
What are the potential adverse events of auditory cueing interventions for freezing of gait in Parkinson's patients?
How do machine learning approaches in NCT00738075 contribute to the broader field of Parkinson's Disease monitoring technologies?
Trial Locations
- Locations (1)
Laboratory for Gait and Neurodynamics, Tel Aviv Sourasky Medical Center
🇮🇱Tel Aviv, Israel
Laboratory for Gait and Neurodynamics, Tel Aviv Sourasky Medical Center🇮🇱Tel Aviv, IsraelMeir Plotnik, PhDSub Investigator