MS-ADAPT

Research

Chronic recurrent low back pain (LBP) is a significant public health problem that results in functional limitations and disability, as well as financial burden for the individual and society. Up to 80% of people will experience LBP at some point in their lifetime, and the total costs of LBP in the U.S. exceed 100 billion dollars per year, including lost wages resulting from an inability to work. Physical therapy is effective for managing chronic LBP and improving patient outcomes. However, patient adherence to physical therapist (PT) recommendations is low, and research suggests that real-time personalized feedback on posture and movement throughout the day can improve outcomes.

We are working on a sensor system that supports remote monitoring of posture and movement in patients with LBP, patient adherence to PT recommendations, and the impact of adherence on outcomes. The MS-ADAPT system is proposed as a human-in-the-loop cyber-physical system that integrates data from novel fabric sensors with data from wrist accelerometers and app-based patient-reported outcomes, and uses machine-learning analytics to enable predictions in support of personalized physical therapy.

MS-ADAPT will both advance the science through development of non-invasive, low-profile sensors to measure low back posture and movement in a real-life setting, and connect this novel sensor information with existing devices and clinical measures that are used to monitor activity and the impact of pain in patients with LBP. Further, new diagnostic and treatment information gathered from these novel sensors could be used to advance treatments and improve outcomes for people with LBP.

We strive for innovations in wearable technologies, deep learning, system integration, and human-computer interfaces. Notably, we will validate the fabric sensors for distributed motion monitoring and develop algorithms to capture not how much strain the wearer is experiencing but also the direction of these strains. For predictive modeling, we will use mathematics and a statistical approach that maps the received data to the category of strain being experienced.