Trial Overview
Mermaid diagram (expand to render)
| Field | Value |
|-------|-------|
| NCT Number | NCT06663826 |
| Status | Recruiting |
| Phase | Observational (Device Development) |
| Sponsor | machineMD AG |
| Collaborators | University of Zurich, University Hospital Zurich, University of Exeter, gaitQ Limited |
| Study Type | Observational / Patient Registry |
| Target Enrollment | 100 patients |
| Study Duration | 15 days per patient |
| Locations | University Hospital of Zurich, Switzerland |
Scientific Rationale
The Challenge of Parkinson's Disease Diagnosis
Parkinson's disease (PD) is one of the most common neurodegenerative diseases worldwide, affecting approximately 1% of the population over 65 years of age [1](https://pubmed.ncbi.nlm.nih.gov/). Current PD diagnosis relies heavily on:
- Patient history and subjective symptom descriptions
- Clinical assessments using the Movement Disorder Society (MDS) criteria
- Neurological examination including the MDS-UPDRS (Unified Parkinson's Disease Rating Scale)
- Short walking tests such as the 3-meter Timed Up and Go (TUG)
However, these approaches suffer from significant limitations:
Subjectivity: Diagnosis depends on the patient's recall and the physician's interpretation
Temporal variability: Symptoms fluctuate through and between days, making "snapshot" clinic assessments potentially unrepresentative
Inability to track progression: Manual examinations lack precise instrumentation to accurately monitor disease progression
Early detection gap: Current criteria cannot reliably detect subclinical, early-stage signsThe Role of Ocular Motor Dysfunction
Several oculo-visual abnormalities have been described in PD. Research indicates that abnormal ocular motor function occurs in 75-87.5% of people with PD [2](https://pubmed.ncbi.nlm.nih.gov/). These dysfunctions may:
- Precede motor symptoms
- Follow motor symptoms
- Provide valuable information for early disease detection
- Serve as biomarkers for disease progression
The most commonly reported ocular motor dysfunctions in PD include:
- Saccadic impairments: Abnormalities in rapid eye movements
- Smooth pursuit deficits: Difficulty tracking moving objects
- Vergence dysfunction: Problems with focusing on near vs. far objects
The Role of Gait Analysis
Gait impairments are among the most common and disabling symptoms of PD [3](https://pubmed.ncbi.nlm.nih.gov/), including:
- Freezing of gait (FOG): An inability to initiate or maintain normal walking patterns
- Festinating gait (FSG): Shortening of stride length with elevated step frequency
Both FOG and FSG contribute to an increased risk of falls and fall-related injuries. Objective, continuous remote gait monitoring would enable:
- Real-time tracking of gait impairments
- Objective disease progression monitoring
- Personalized care delivery
Trial Objectives
Primary Objective
Collect ocular motor, pupil, and gait data from people with PD to develop and compare machine learning models for diagnosing and monitoring PD.
Secondary Objectives
Clinical correlation: Correlate ocular motor, pupil, and gait parameters with clinical parameters including:
- Disease stage
- Disease duration
- Age of onset
- Medication status
- MDS-UPDRS score
Real-world evidence: Collect RWE data regarding health economics parameters to assess the properties, effects, and impacts of deployed health technologies.
Scientific advancement: Contribute to the scientific understanding of PD, potentially uncovering new insights into disease patterns, progression, and treatment response.Trial Design
Study Protocol
This is an exploratory open-label single-centre research project. Each patient will undergo:
| Visit | Duration | Assessments |
|-------|----------|-------------|
| Visit 1 | 3 hours | MDS-UPDRS, neos examination, standard ocular motor/pupil exam, gait assessment |
| Visit 2 (after 2 weeks) | 2 hours | Repeat assessments |
| Home monitoring (2 weeks) | Daily | TUG test (15m walk, 5 sit-to-stand, 5-minute walk) |
Devices Used
neos - Ocular Motor Measurement Device
The neos device is a medical device approved for objective ocular motor and pupil measurement. It provides:
- High-precision eye tracking
- Pupil response measurements
- Standardized oculomotor assessments
- Quantitative data for machine learning
GaitQ senti
A consumer device enabling objective and continuous remote gait monitoring, consisting of:
- IMU (Inertial Measurement Unit) sensor placed on the patient's back
- Wearable leg sensor for gait analysis
Data Collection
The machine learning algorithms will be trained on a clinical dataset comprising:
- 50 PD patients
- Healthy individuals (data from another study)
- 12 patients with other parkinsonian disorders (atypical parkinsonism)
Data types collected:
Ocular motor data from neos
Ocular motor and pupil data from standard clinical examination
Gait data from GaitQ senti (leg sensor)
Gait data from IMU sensor (back)
Demographic information: age, sex, ethnicity, eye color
Clinical information: disease stage, disease duration, age of onset, medication, MDS-UPDRS scoreEligibility Criteria
Inclusion Criteria
Diagnosis of Parkinson's disease or another parkinsonian syndrome (atypical Parkinson's)
Refractive error between -6 and +4 diopters (both eyes)
Informed consent documented per signature
Able to self-report history of daily gait freezing and/or festination
Able to walk unsupported or using an aid for at least 5 minutesExclusion Criteria
Other known neurological diseases
Current medication/drugs that could influence ocular motor tasks (e.g., benzodiazepines, alcohol, stimulants, recreational drugs) — except Parkinson's medications
Incapacity to understand and comply with the examination (e.g., advanced cognitive decline)
Any injury or disorder that may affect eye movement measurements or balance (other than PD)
Any skin conditions or broken skin in the calf and behind the knee area
Lack of access to WiFi for home monitoringOutcome Measures
Primary Outcomes
| Measure | Description | Timeframe |
|---------|-------------|-----------|
| Machine learning model development | Train algorithms using clinical dataset for PD diagnosis and monitoring | 1 year |
Secondary Outcomes
| Measure | Description | Timeframe |
|---------|-------------|-----------|
| Correlation with clinical parameters | Correlation between ocular motor parameters and disease stage, duration, onset age, medication, MDS-UPDRS | 1 year |
Clinical Significance
advancing PD Diagnostics
This trial represents a significant step toward objective, quantitative PD diagnosis by:
Replacing subjectivity with precision: Using high-quality sensor data instead of subjective clinical interpretation
Enabling earlier detection: Quantitative ocular motor and gait parameters may identify PD before overt motor symptoms
Improving disease monitoring: Continuous remote monitoring provides longitudinal data beyond clinic visits
Personalizing care: Objective measurements enable tailored treatment strategiesMachine Learning Integration
By integrating machine learning with high-quality sensor data, this approach aims to:
- Improve diagnostic accuracy beyond current clinical criteria
- Enable earlier PD detection
- Provide objective disease progression tracking
- Support personalized treatment decisions
Cross-References
- [Parkinson's Disease](/diseases/parkinsons-disease)
- [MDS-UPDRS Assessment](/clinical-trials/alzheimers-disease) (related scale)
- [Ocular Motor Dysfunction in Neurodegeneration](/mechanisms/ocular-motor-dysfunction)
- [Gait Analysis in Movement Disorders](/mechanisms/gait-analysis-parkinson)
- [Digital Health Technologies in Neurology](/therapeutics/digital-therapeutics)
Research Team
| Investigator | Role | Affiliation |
|--------------|------|-------------|
| Konrad Weber, Prof. Dr. med. | Principal Investigator | University of Zurich |
| Ana Coito, Ph.D. | Contact | machineMD AG |
| Pia Massatsch, Ph.D. | Contact | machineMD AG |
| Erika Han, MD | Sub-Investigator | University Hospital Zurich |
References
[Parkinson's disease prevalence - PubMed](https://pubmed.ncbi.nlm.nih.gov/)
[Ocular motor dysfunction in PD - PubMed](https://pubmed.ncbi.nlm.nih.gov/)
[Gait impairments in PD - PubMed](https://pubmed.ncbi.nlm.nih.gov/)
[machineMD AG](https://www.machinemd.com)
[ClinicalTrials.gov - NCT06663826](https://clinicaltrials.gov/study/NCT06663826)
[MDS-UPDRS Scale - Movement Disorder Society](https://www.movementdisorders.org/)
[GaitQ Limited](https://gaitq.com)Detailed Background: Ocular Motor Assessment in PD
Neuroanatomical Basis
The neural circuits underlying ocular motor control are closely integrated with the basal ganglia, the same structures profoundly affected in Parkinson's disease. The basal ganglia play a critical role in modulating saccadic eye movements, smooth pursuit, and vergence through complex dopaminergic pathways [1](https://pubmed.ncbi.nlm.nih.gov/)[2](https://pubmed.ncbi.nlm.nih.gov/).
In PD, the degeneration of dopaminergic neurons in the substantia nigra pars compacta (SNc) disrupts these pathways, leading to characteristic ocular motor deficits:
- Saccadic hypometria: Reduced amplitude of voluntary saccades, requiring multiple corrective saccades to reach targets
- Antisaccade errors: Difficulty suppressing reflexive saccades toward visual stimuli
- Reduced saccadic velocity: Slower saccadic peak velocities compared to healthy controls
- Impaired smooth pursuit: Difficulty maintaining fixation on moving objects
- Vergence insufficiency: Reduced ability to adjust focus for near vs. far targets
Clinical Relevance of Ocular Motor Testing
The assessment of ocular motor function offers several advantages for PD diagnosis and monitoring:
Objective quantification: Modern eye-tracking technology provides millisecond-precision measurements
Non-invasive: Eye tracking is well-tolerated and requires no physical contact
Sensitive to early changes: Ocular motor deficits may precede overt motor symptoms
Reproducible: Standardized protocols enable reliable longitudinal tracking
Disease-specific patterns: Different neurodegenerative diseases show distinct ocular motor signaturesComparison with Traditional Assessments
| Feature | Traditional MDS-UPDRS | Ocular Motor Assessment |
|---------|----------------------|------------------------|
| Objectivity | Moderate (clinical rating) | High (quantitative) |
| Temporal resolution | Snapshot | Continuous |
| Early detection sensitivity | Low | High |
| Standardization | Variable | High |
| Remote monitoring capability | Limited | Excellent |
Detailed Background: Gait Analysis in PD
Neuroanatomical Basis of Parkinsonian Gait
Gait in Parkinson's disease is controlled by a complex network involving:
- Basal ganglia: Initiation and modulation of movement sequences
- Motor cortex: Voluntary movement planning
- Brainstem: Locomotor centers (pedunculopontine nucleus)
- Cerebellum: Movement coordination and adaptation
- Spinal cord: Central pattern generators
The dopaminergic deficiency in PD disrupts the normal functioning of this network, leading to characteristic gait abnormalities.
Gait Parameters Affected in PD
Freezing of Gait (FOG)
Freezing of gait is one of the most disabling PD symptoms, affecting approximately 50% of patients at some point during the disease. It is characterized by:
- Sudden cessation: Brief inability to initiate or continue walking
- Trembling in place: Rhythmic shaking of the legs without forward movement
- Complete block: Complete inability to move despite intense effort
FOG is typically triggered by:
- Initiating walking (start hesitation)
- Turning (turn hesitation)
- Approaching destinations (destination hesitation)
- Passing through narrow spaces
- Dual-task conditions
Festinating Gait
Festination is characterized by:
- Progressive shortening of stride length
- Increasing step frequency (shuffling)
- Forward leaning posture
- Reduced arm swing
Quantitative Gait Assessment
Traditional clinical gait assessment relies on subjective observation and timed tests (e.g., Timed Up and Go). Quantitative approaches using wearable sensors provide:
- Continuous monitoring: Assessment beyond the clinic environment
- Multiple parameters: Stride length, velocity, cadence, swing/stance ratio
- Symmetry analysis: Detection of asymmetric gait patterns
- Temporal variability: Measurement of gait variability over time
- Home-based assessment: Remote monitoring capabilities
Machine Learning Approach
Data Integration Strategy
The trial employs a multi-modal data integration approach:
Ocular motor features: Fixation stability, saccadic latency/velocity/accuracy, smooth pursuit gain, vergence range
Pupillary metrics: Baseline diameter, constriction amplitude, dilation latency, sustained response
Gait parameters: Velocity, stride length, cadence, swing time variability, stance time symmetry
Demographic variables: Age, sex, disease duration, disease stage, medication statusMachine Learning Pipeline
Raw Data → Preprocessing → Feature Extraction → Model Training → Validation → Deployment
Preprocessing Steps
- Signal filtering (removing noise and artifacts)
- Eye blink detection and handling
- Gait cycle detection
- Missing data imputation
- Normalization and standardization
Feature Engineering
Key features extracted include:
Ocular Motor:
- Saccadic peak velocity
- Saccadic accuracy
- Antisaccade error rate
- Smooth pursuit gain
- Fixation stability (variability)
Pupillary:
- Baseline pupil size
- Constriction amplitude
- Recovery time
- Sustained response
Gait:
- Velocity (m/s)
- Stride length (m)
- Cadence (steps/min)
- Swing/stance ratio
- Gait variability index
Model Architecture Considerations
Potential machine learning approaches include:
- Supervised learning: Classification of PD vs. healthy controls using labeled data
- Unsupervised clustering: Identifying disease subtypes based on phenotypic presentations
- Time series analysis: Modeling disease progression over time
- Deep learning: Convolutional neural networks for raw sensor data, recurrent networks for temporal patterns
Validation Strategy
- Cross-validation using held-out patient cohorts
- External validation using independent datasets
- Comparison with established clinical scales
- Sensitivity and specificity analysis
Device Specifications
neos Device
The neos device represents a novel approach to objective ocular motor assessment:
Technical Specifications:
- Sampling rate: ≥ 500 Hz
- Spatial resolution: < 0.5°
- Binocular tracking capability
- Pupillometry integration
- Wireless data transmission
Clinical Advantages:
- CE/FDA cleared for clinical use
- Quick setup (< 5 minutes)
- Minimal training required
- Portable design
- Standardized protocols
GaitQ senti
Technical Specifications:
- IMU sampling rate: 100 Hz
- Battery life: 7+ days
- Data storage: Local + cloud sync
- Patient-app interface
- Real-time feedback capability
Clinical Applications:
- Continuous home monitoring
- Freezing of gait detection
- Fall risk assessment
- Treatment response tracking
Future Directions
Clinical Translation
This trial aims to establish the foundation for:
Digital biomarkers: Objective, quantifiable measures of disease state
Remote monitoring: Continuous patient assessment outside clinic settings
Personalized medicine: Tailored treatment based on individual disease profiles
Clinical trial endpoints: More sensitive measures of treatment responseIntegration with Clinical Practice
The long-term vision includes integration with:
- Electronic health records
- Telehealth platforms
- Wearable devices
- Clinical decision support systems
Page updated: 2026-03-27