Machine learning leads to novel way to track tremor severity in Parkinson's patients

To effectively manage and treat tremors in PD patients, there is an urgent need for an approach that can continuously measure tremors accurately without the need for patients to perform specific tasks as they go about their daily activities. Researchers from Florida Atlantic University's College of Engineering and Computer Science in collaboration with the Icahn School of Medicine at Mount Sinai and the University of Rochester Medical Center, are teaching machines to accomplish this job. They have developed algorithms that, combined with wearable sensors, can continuously monitor patients and estimate total Parkinsonian tremor as they perform a variety of free body movements in their natural environments. Results of the study, published in the journal Sensors, indicate that this new approach holds great potential for providing a full spectrum of patients' tremors throughout the course of the day. "A single, clinical examination in a doctor's office often fails to capture a patient's complete continuum of tremors in his or her routine daily life," said Behnaz Ghoraani, Ph.D., senior author, an assistant professor in FAU's Department of Computer and Electrical Engineering and Computer Science, and a fellow of FAU's Institute for Sensing and Embedded Network Systems (I-SENSE) and FAU's Brain Institute (I-BRAIN).