Normal aging results in subtle changes, including increased tremors and slowing of the movement of the hands that impair daily activities and quality of life. Using wearable sensor technology and innovative machine-learning techniques, a pioneering study entitled, “A machine-learning method isolating changes in wrist kinematics that identify age-related changes in arm movement,” from the laboratory of Anne Sereno, professor of Psychological Sciences and Biomedical Engineering, has recently been published in Nature: Scientific Reports.

The research team, led by Biomedical Engineering PhD candidate Aditya Shanghavi, analyzed the wrist kinematics of young and older adults performing standard clinic-based tasks and identified kinematic variables that accurately and reliably distinguished healthy older adults from their younger counterparts. Accurately identifying normal age-related tremors is critical so that they don’t interfere with the diagnosis of tremor disorders in older adults.

The sensitivity and accuracy demonstrated by the novel data-driven methodology paves the way for a range of applications: isolating changes in motion across various body parts and conditions and facilitating early detection of tremors in neurological diseases like Parkinson’s disease. Shanghavi noted that “tremors can be exacerbated by food, medications, and even sleep, making the development of objective, repeatable, and portable measures of tremors key for more reliable assessments.”

“These findings suggest many possible exciting future directions, such as enhancing current subjective evaluation approaches in the clinic or making possible telehealth and treatment monitoring outside the clinic,” said Sereno.