Emory University uses Oracle Cloud to help fight Parkinson’s disease

According to a Parkinson's Foundation-supported investigation conducted in 2022, nearly 90,000 Americans are diagnosed with Parkinson's disease (PD) each year, with the total number of individuals with PD predicted to rise to 1.2 million by 2030. With the world's population aging at a rapid rate, it’s becoming increasingly urgent to treat neurodegenerative conditions that affect movement.

The way scientists measure PD symptoms is essentially unchanged since the late 1980s. Neurologists ask patients with PD to walk, tap fingers, move hands, speak, and balance. Then the neurologist rates how well the patient does each task from 0 to 4. This approach is subjective and so involved that many patients receive the test only once a year, making accurate observation and treatment difficult.

Researchers at Emory University set out to design a machine learning system using videos and wearable devices that would precisely measure PD symptoms every day. The project involved designing deep learning models for 3D motion capture data or video datasets, which demand significant GPU computing and memory usage. However, the researchers’ on-premises computer lacked the computation time needed for the project, and the computer was continuously in use by other research teams.

Because the university’s project involved designing deep learning models for 3D motion capture data or video datasets, it demanded significant GPU computing and memory usage. Getting access to Oracle Cloud Infrastructure Compute and GPU instances would significantly accelerate the team’s research by testing variations of deep learning models in a parallel manner.

For example, training a neural network model on-premises with an in-lab motion capture dataset used 13GB of GPU memory and took four days for a single experiment. Using Oracle Cloud Infrastructure (OCI), the team realized it could run 4 to 8 GPUs in parallel and vastly accelerate research progress—taking only a few hours to complete an experiment. 

Assistant Professors J Lucas McKay and Hyeokhyen Kwon were relieved to discover that Oracle’s cloud compute instance was HIPAA-compliant from the beginning. Also the team’s ability to spin up bare metal GPUs was critical, enabling the team to achieve faster compute speeds without a Virtual Machine (VM) layer. Oracle for Research’s cloud solutions architects were also very responsive, making machine learning and cloud technologies more understandable and helping the team with training.

The neural network’s results matched with expert ratings for freezing of gait (FOG) in Parkinson’s disease (PD) at a high precision rate of 96.2%. The team designed a ML model that is also interpretable automatically demonstrating, which exact patterns of movement are most important when determining PD and FOG severity scores. This feature will be particularly useful for physicians, who will have a direct insight into how the ML reaches its conclusions.

Lucas, Hyeokhyen, and the team expect that this project will benefit the Human Activity Recognition research community in at least two ways. First, the researchers plan to release the motion capture and video dataset with dense PD behavior statuses labeled by motion disorder specialists. Because the scale of this dataset is expected to be larger than any currently available public dataset detailing PD and “freezing of gait” or FOG, it should encourage more ML/AI experts to advance PD analysis with their own novel deep learning models.

Second, the team plans to release the ML models with the large-scale dataset used in this project. A model pretrained with large-scale data for a domain typically helps any other downstream tasks in advanced analysis.