Development of Digital Diagnostics and Intervention Services for Parkinson's Disease
In this project, ocular motor, pupil and gait data in people with Parkinson's disease (PD) will be collected in order to develop machine learning models for the diagnosis and monitoring of PD. With this, the investigators aim to advance the state of the art in PD diagnosis and monitoring. By integrating the principles of machine learning with high-quality sensor data, more accurate and earlier diagnosis could potentially be achieved. Ocular motor and pupil data will be collected with the standard clinical examination and with neos, a medical device approved for objective ocular motor and pupil measurement. Gait will be collected using an IMU sensor and GaitQ senti, a consumer device that allows for an objective and continuous remote gait monitoring.
• Diagnosis of idiopathic Parkinson's disease (UK Brain Bank Criteria) or other appropriate condition specific scale \[stroke, multiple sclerosis, arthritis or osteoporosis\]
• Able to self-report history of daily gait freezing and/or festination for people with PD or gait and/or transfers affected by condition
• Able to walk unsupported or using an aid for at least 5 minutes and satisfactory completion of the Canadian PARQ and if over 69 used to carrying out this level of exercise
• Adult (+18 years old)
• Normal or corrected-to-normal vision (Snellen Visual Acuity \> 12/18) or safe to mobilise with support
• Montreal Cognitive assessment score \>21 or ability to follow 2 stage commands
⁃ Healthy participants \[Phase 1,2,3\]
• With no long-term conditions affecting movement
• Able to walk unsupported or using an aid for at least 3 minutes and satisfactory completion of the Canadian PARQ and if over 69 used to carrying out this level of exercise
• Adult (+18 years old)
• Normal or corrected-to-normal vision (Snellen Visual Acuity \> 12/18) or safe to mobilise with support
• Montreal Cognitive assessment score \>21 or ability to follow 2 stage commands