Physical exams only provide a snapshot of a Parkinson’s patient’s daily tremor experience. Scientists have developed algorithms that, combined with wearable sensors, can continuously monitor patients at home or elsewhere to estimate the severity rating of their tremors based on the way that it manifests itself in movement patterns. This approach has the potential to provide clinicians with a full spectrum of their patients’ tremors and medication response to effectively manage and treat this disorder.

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). «Wearable sensors, combined with machine-learning algorithms, can be used at home or elsewhere to estimate a patient’s severity rating of tremors based on the way that it manifests itself in movement patterns.»

The majority of existing approaches used today are task-dependent, requiring patients to perform standardized tasks like those used in rating scales. Furthermore, these approaches only provide moderate to good performance because of limitations in underlying algorithms to characterize tremor patterns from patients’ free body movements.

Story Source:
Materials provided by Florida Atlantic University. Original written by Gisele Galoustian. Note: Content may be edited for style and length.