Enabling Accessible Screening of
Parkinsonian Disorders
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The Challenge
Parkinson’s disease is the fastest growing neurological disease, but access to neurological care is limited, and many individuals do not receive proper treatment or diagnosis.
Early Diagnosis
While there is no cure for Parkinson’s disease, early diagnosis and treatment can help individuals manage symptoms and improve their quality of life.
Accessibility
However, access to care is much scarce in developing and underdeveloped regions, where there may be only one neurologist per millions of people.
Remote care
Even for those with access to care, arranging clinical visits can be challenging, especially for older individuals living in rural areas with cognitive and driving impairments.
Our solution
That is why our team created PARK.
A web framework that allows accessible and remote screening of parkinsonian disorders using a laptop.
Enabling anyone, anywhere to receive an assessment at the comfort of their home, powered by AI.
Easy to use
Our framework is easy to follow for anyone to use
Step 1
Watch Tutorial
Watch a short tutorial video
Step 2
Complete Tasks
Complete the tasks in the video
Step 3
Contribute data
Help us improve our model
Our team
Publications
PARK - Parkinson’s Analysis with remote Kinetics tasks
Affective Computing and Intelligent Interaction Demo Track (ACII 2023)
We present a web-based framework to screen for Parkinson’s disease (PD) by allowing users to perform neurological tests in their homes. Our web framework guides the users to complete three tasks involving speech, facial expression, and finger movements. The task videos are analyzed to classify whether the users show signs of PD. We present the results in an easy-to-understand manner, along with personalized resources to further access to treatment and care. Our framework is accessible by any major web browser, improving global access to neurological care.
Using AI to measure Parkinson’s disease severity at home
npj Digital Medicine 2023
We present an artificial intelligence (AI) system to remotely assess the motor performance of individuals with Parkinson’s disease (PD). In our study, 250 global participants performed a standardized motor task involving finger-tapping in front of a webcam. To establish the severity of Parkinsonian symptoms based on the finger-tapping task, three expert neurologists independently rated the recorded videos on a scale of 0–4, following the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). The inter-rater reliability was excellent, with an intra-class correlation coefficient (ICC) of 0.88. We developed computer algorithms to obtain objective measurements that align with the MDS-UPDRS guideline and are strongly correlated with the neurologists’ ratings. Our machine learning model trained on these measures outperformed two MDS-UPDRS certified raters, with a mean absolute error (MAE) of 0.58 points compared to the raters’ average MAE of 0.83 points. However, the model performed slightly worse than the expert neurologists (0.53 MAE). The methodology can be replicated for similar motor tasks, providing the possibility of evaluating individuals with PD and other movement disorders remotely, objectively, and in areas with limited access to neurological care.
Detecting Parkinson Disease Using a Web-Based Speech Task
Journal of Medical Internet Research (JMIR 2021)
Access to neurological care for Parkinson disease (PD) is a rare privilege for millions of people worldwide, especially in resource-limited countries. In 2013, there were just 1200 neurologists in India for a population of 1.3 billion people; in Africa, the average population per neurologist exceeds 3.3 million people. In contrast, 60,000 people receive a diagnosis of PD every year in the United States alone, and similar patterns of rising PD cases can be seen worldwide. In this paper, we propose a web-based framework that can help anyone anywhere around the world record a short speech task and analyze the recorded data to screen for PD.
The PARK Framework for Automated Analysis of Parkinson's Disease Characteristics
In Proceedings of ACM on Interactive, Mobile, Wearable, and Ubiquitous Computing (IMWUT 2019)
There are about 900,000 people with Parkinson's disease (PD) in the United States. Even though there are benefits of early treatment, unfortunately, over 40% of individuals with PD over 65 years old do not see a neurologist. It is often very difficult for these individuals to get to a physician's office for diagnosis and subsequent monitoring. To address this problem, we present PARK, Parkinson's Analysis with Remote Kinetic-tasks. PARK instructs and guides users through six motor tasks and one audio task selected from the standardized MDS-UPDRS rating scale and records their performance via webcam. An initial experiment was conducted with 127 participants with PD and 127 age-matched controls, in which a total of 1,778 video recordings were collected. 90.6% of the PD participants agreed that PARK was easy to use, and 93.7% mentioned that they would use the system in the future. We explored objective differences between those with and without PD. A novel motion feature based on the Fast Fourier Transform (FFT) of optical flow in a region of interest was designed to quantify these differences in the collected video recordings. Additionally, we found that facial action unit AU4 (brow lowerer) was expressed significantly more often, while AU12 (lip corner puller) was expressed less often in various tasks for participants with PD.