Web-based study tracks motor and memory tasks from home devices

Written by Marisa Horak, MS | Sept. 2, 2026

• Parkinson's disease is a neurological disorder causing motor and nonmotor symptoms.
• Traditional in-person clinical evaluations present challenges, prompting the need for remote assessment solutions.
• Scientists have compiled a large data set to train and test AI for objective, remote Parkinson's diagnosis.

A team of U.S. scientists has compiled a large data set of remotely collected data from people with and without Parkinson’s disease, which the scientists hope will serve as an important tool for developing strategies to diagnose the disorder remotely using artificial intelligence (AI).

“By pairing task-derived behavioral features with device type and handedness metadata, this dataset provides a benchmark resource for evaluating AI robustness, subgroup performance, and generalizability in remote [Parkinson’s disease] assessment,” the scientists wrote in “RobustPDx: A Structured Web-Based Parkinson’s Assessment Dataset and Benchmark for AI Robustness to Device Type and Handedness,” which was published in Scientific Data.

In-person assessment poses logistical hurdles

Parkinson’s is a neurological disorder that causes motor symptoms such as slowness and tremor, as well as nonmotor symptoms that can range from memory problems to digestive complaints.

Assessments conducted by a clinical expert are currently the gold standard for diagnosing Parkinson’s and tracking the disease’s severity. But conducting an in-person assessment poses notable logistical hurdles, especially for people who don’t live near specialty centers, and the reliance on a human evaluator makes the assessments inherently subjective.

As it’s become more common for people to have computers and smart devices at home, scientists are increasingly exploring whether these devices could be used to monitor Parkinson’s remotely, offering greater convenience and objectivity.

AI is a broad field of computational analysis that uses large amounts of data and advanced algorithms to identify patterns. For AI tools to be effective, they need to be trained and tested on large, well-defined data sets.