Researchers tested machine learning techniques to estimate parameters such as step length, width and velocity. The method makes clinical diagnosis more precise and can determine the stage reached in progression of the disease
Scientists affiliated with the Department of Physical Education's Human Movement Laboratory (Movi-Lab) at São Paulo State University (UNESP) in Bauru, Brazil, are using artificial intelligence to help diagnose Parkinson's disease and estimate its progression.
An article published in the journal Gait & Posture reports the findings of a study in which machine learning algorithms identified cases of the disease by analyzing spatial and temporal gait parameters.
The researchers found four gait features to be most significant for the purposes of diagnosing Parkinson's: step length, velocity, width and consistency (or width variability). To gauge the severity of the disease, the most significant factors were step width variability and double support time (during which both feet are in contact with the ground).
"Our study innovated in comparison with the scientific literature by using a larger database than usual for diagnostic purposes. We chose gait parameters as the key criteria because gait impairments appear early in Parkinson's and get worse over time, and also because they don't correlate with physiological parameters like age, height and weight," Fabio Augusto Barbieri, a co-author of the article, told Agência FAPESP. Barbieri is a professor in the Department of Physical Education at UNESP's School of Sciences (FC).
The study was supported by FAPESP via three projects (14/20549-0, 17/19516-8 and 20/01250-4).
The study sample comprised 63 participants in Ativa Parkinson, a multidisciplinary program of systematized physical activity for Parkinson's patients conducted at FC-UNESP, and 63 healthy controls. All volunteers were over 50 years old. Data was collected and fed into the repository used in the machine learning processes for seven…
www.eurekalert.org