A multidisciplinary team led by researchers at UC San Diego has received $1.2 million from the National Science Foundation (NSF) to create a novel system to study and inform the treatment of chronic low back pain. The work will feature a suite of tools including wearable sensors and machine learning to augment physical therapy evaluation and treatment.
This research will support remote monitoring of the patient's posture and movement throughout the day, with the ultimate goal of enabling personalized physical therapy treatments and improving health outcomes,"
Emilia Farcas, grant's principal investigator and assistant research scientist, Qualcomm Institute (QI), UC San Diego
New technology for an old problem
Low back pain affects up to 80% of people during their lifetime, and treatment costs and wages lost due to disability exceed $100 billion in the U.S. annually.
The NSF award funds four years of work to develop the Multi-Sensor Adaptive Data Analytics for Physical Therapy (MS-ADAPT) system, which will use wearable technology and smartphone-based applications to remotely monitor low back posture and movement, and upkeep with physical therapy and patient-reported pain. Study participants wear a Fitbit as well as a network of smart sensors crafted by integrating nanotechnology with over-the-counter kinesiology tape. These "Motion Tape" sensors can measure skin strains, which is the stretch or change in skin texture during physical activity, as well as spine movement and the degree of muscle engagement or activity.
Researchers are also developing novel machine learning analytics to predict the impact of physical therapy on low back pain to achieve faster recovery, reduced healthcare costs and more personalized medicine.
The project exists at the intersection of multiple fields of study, including software engineering, wearable sensors, machine learning, precision medicine, spine biomechanics and physical therapy.
"The highly collaborative environment and close partnership between all researchers have enabled us to pursue this highly multidisciplinary and important topic," said Ken Loh, a co-principal investigator of MS-ADAPT and a structural engineering professor with the UC San Diego Jacobs School of Engineering.
Other co-principal investigators include Sara Gombatto, professor in the Doctor of Physical Therapy Program at San Diego State University, and Arun Kumar and Qi (Rose) Yu, associate professor and assistant professor, respectively, with the Computer Science and Engineering Department at the Jacobs School and Halicioğlu Data Science Institute. Senior personnel collaborating on the project include Kevin Patrick, professor in the School of Public Health and researcher at QI, and Job Godino, associate research scientist in the School of Public Health and director of the Exercise and Physical Activity Resource Center (EPARC) at QI.
An adaptive platform
As part of the team's long-term goals, researchers may someday leverage MS-ADAPT as a universal platform to study other health conditions, such as limb loss, spinal cord injury and stroke. Within the context of examining and treating chronic low back pain, the MS-ADAPT team hopes the new technology will lend itself to predicting a person's progress during treatment and assessing risk of reinjury.
In keeping with the project's multidisciplinary nature, Farcas and colleagues plan to use this collaboration to train a new generation of researchers working across academic lines.
Boundary-breaking research is a hallmark of the work at QI. For more on endeavors led by QI researchers, visit https://qi.ucsd.edu/.
University of California – San Diego
Posted in: Medical Research News | Medical Condition News
Tags: Back Pain, Chronic, Disability, Doctor, Exercise, Healthcare, Kinesiology, Machine Learning, Medicine, Menopause, Muscle, Nanotechnology, Orthopedic, Osteoporosis, Pain, Physical Activity, Physical Therapy, Posture, Precision Medicine, Public Health, Research, Skin, Spinal Cord Injury, Spine, Stroke, Syndrome
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