Development of Digital Diagnostic Devices 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.

Inclusion Criteria: * Diagnosis of Parkinson's disease or of another parkinsonian syndrome (atypical Parkinson's) * Refractive error between -6 and +4 diopters, on both eyes * Informed consent by participant 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 minutes and if over 69 used to carrying out this level of exercise Exclusion Criteria: * Other known neurological diseases * Current medication/drugs that could potentially influence performance in ocular motor tasks and/or compliance in the judgement of the investigator (e.g. benzodiazepines, alcohol, stimulants, or recreational drugs) - except Parkinson's medications * Incapacity to understand and comply with the examination (e.g. due to advanced cognitive decline, failure to comply with easy experimental instructions and tasks) * Any injury or disorder that may affect eye movement measurements or balance (other than Parkinson's or referring primary condition) * Any skin conditions or broken skin in the calf and behind the knee area * Lack of access or limited connectivity to WiFi in home setting