TAU Model Measures Neurological Disease Via Step Length

Researchers at Tel Aviv University (TAU) and the city’s Sourasky Medical Center have developed a model to assess step length as an indicator of neurological diseases and of aging. 

The model uses an algorithm to gather data from a wearable device attached to the lower back, which continuously monitors a patient’s steps as they go about their daily life. According to the researchers, their model is nearly four times more accurate than the currently accepted biomechanical model.  

The algorithm was developed by examining step length data from a previous study of 472 people with a range of conditions, including neurodegenerative disease Parkinson’s; people with mild cognitive impairment; healthy elderly people; healthy younger people; and people with autoimmune disease multiple sclerosis. 

In all, the researchers gathered a diverse database of 83,569 steps.  

“Step length is a sensitive measure of a wide range of problems and diseases, from cognitive decline and aging to Parkinson’s,” the researchers said.  

“The conventional measuring devices that exist today are stationary and cumbersome, and are only found in specialized clinics and laboratories. The model we developed enables accurate measurement in a patient’s natural environment throughout the day, using a wearable sensor.” 

The study was led by Assaf Zadka, a graduate student in TAU’s Department of Biomedical Engineering; Prof. Jeffrey Hausdorff from the Faculty of Medical and Health Sciences and Sagol School of Neuroscience at TAU and the Department of Neurology at Sourasky; and Prof. Neta Rabin from TAU’s Fleischman Faculty of Engineering. 

“Step length is a very sensitive and non-invasive measure for evaluating a wide variety of conditions and diseases, including aging, deterioration as a result of neurological and neurodegenerative diseases, cognitive decline, Alzheimer’s, Parkinson’s, multiple sclerosis and more,” said Hausdorff. 

“Today it is common to measure step length using devices found in specialized laboratories and clinics, which are based on cameras and measuring devices like force-sensitive gait mats. While these tests are accurate, they provide only a snapshot view of a person’s walking that likely does not fully reflect real-world, actual functioning,” he said. 

“Daily living walking may be influenced by a patient’s level of fatigue, mood, and medications, for example. Continuous, 24/7 monitoring like that enabled by this new model of step length can capture this real-world walking behavior.”