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.

Parkinson's disease (PD) is one of the most common neurodegenerative diseases worldwide, affecting 1% of the population older than 65.

Currently, PD diagnosis is based on history, clinical assessments, and neurological examination.

The most widely used criteria for diagnosis are the Movement Disorder Society (MDS) criteria and instrument (i.e. The MDS-UPDRS). Further information may be gained from people's subjective description of their symptoms and/or via some short walking tests, such as 3-meter Timed Up and Go (TUG) performed as a snapshot in the clinic. However, people's symptoms vary through and between days and subjective descriptions rely on their memory and observations at home. These recollections can be unreliable or lack enough detail (particularly when the person has cognitive impairment). Therefore, current PD diagnosis criteria are highly dependent on the person and on the diagnosing physician. This subjectivity may lead to a variability in the diagnosis. Furthermore, these clinical assessments are unable to accurately track disease progression over time, making it difficult to provide personalized care. Additionally, manual examinations lack precise measurement instruments, resulting in a low precision of observed measurements and the inability to detect early-stage, subclinical signs. An objective diagnosis based on quantitative data rather than subjective interpretation of clinical findings is important2. Therefore, an early and accurate diagnosis of PD, as well as accurate disease progression monitoring, are still important challenges in PD.

Several oculo-visual abnormalities have been described in PD. Studies report an abnormal ocular motor function in 75-87.5% of people with PD (3,4). These dysfunctions may precede or follow motor symptoms and thus, the evaluation of ocular motor function may provide valuable information regarding early disease detection or disease progression (5). The most commonly reported ocular motor dysfunctions are impairments in saccades, smooth pursuit, and vergence (3,4,6).

Gait impairments are among the most common and disabling symptoms of PD (29). Gait impairments include freezing of gait (FOG), an inability to initiate or maintain normal walking patterns, often resulting in a stochastic stop/start gait, and festinating gait (FSG), which is a shortening of stride length with elevated step frequency, resulting in fast, shuffling steps. Both FOG and FSG contribute to an increased risk of falls (and fall-related injuries) in people with PD relative to the wider elderly population. Objective, and continuous remote gait monitoring would be highly important in people with PD, to objectively track gait impairments in real-time, and potentially contribute to objectively track disease progression, which may lead to personalized care for individuals with PD.

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.

The primary objective of this project is to collect ocular motor, pupil and gait data from people with PD in order to develop and compare machine learning models for diagnosing and monitoring PD.

Secondary objectives are:

• Correlate ocular motor, pupil and gait parameters with several clinical parameters, including the MDS-UPDRS.
• Collect real-world evidence (RWE) data regarding health economics parameters to address the individual and combined properties, effects, and/or impacts of the deployed health technologies.
• By analysing the data collected, we also aim to contribute to the scientific understanding of PD, potentially uncovering new insights into disease patterns, progression, and response to treatments.

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

At least 100 patients with typical PD and at least 12 patients with atypical Parkinson's.