ERI: Engineering a Hybrid Wearable System to Study High-Risk Postures and Low Back Pain-Related Disability

Low back pain is the most common cause of disability worldwide and leads to missed workdays and high healthcare costs. It is often treated after pain begins, using methods like medication and surgery. These treatments do not relieve pain in all patients, and surgery can reduce mobility of the spine. Low back pain is especially common in jobs that require repeated bending, twisting, and lifting. Wearable devices that monitor posture can help researchers measure real-world movement patterns and identify risk factors linked to back pain and disability. However, current wearable devices can miss important high-risk postures, such as bending and twisting at the same time, and they often cannot tell whether someone is bending at the back versus using a safer strategy such as squatting. This Engineering Research Initiation (ERI) project will develop and test a lightweight, tape-based wearable device designed to capture these differences during real-world activities. The long-term goal is to reduce back pain-related disability by supporting safer work practices and better prevention strategies. The project could lead to new, easy-to-wear healthcare technologies for studying pain. The project will also train undergraduate and graduate students through hands-on “learn by doing” experiences in wearable device design, biomechanics testing, and research in local communities. Overall, this project will lead to new healthcare technologies and biomechanics knowledge that could help mitigate back pain. Measuring the impacts of multi-plane postures on spinal loading during everyday activities is challenging. Current sensors cannot distinguish between low-body postures. Laboratory motion capture is too complex for studying spinal motion in real world settings. The goal of this project is to explore new spine-mounted wearable technologies to study the biomechanical drivers of low back pain during common tasks. The project will fuse inertial measurements units (IMUs) and resistive flex sensors into a comfortable system modeled after athletic tape. IMUs will estimate upper body orientation and thin flex sensors will measure changes in back curvature during movement. The device will combine the signals from both sensors to estimate forward bending, lateral bending, and twisting angles. Gold-standard motion capture data will be used to train a supervised machine learning model to map sensor outputs to ground truth angles and classify postures. The system will be validated in 30 adults performing a range of postures. A field pilot study will be conducted to evaluate all-day wear, usability, and device performance during real-world tasks. The pilot study will also explore the link between time spent in risky postures and self-reported back pain. This project will generate important datasets needed to connect real-world posture exposures to biomechanical loading and back pain. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2553421 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Britta Berg-Johansen | Institution: California Polytechnic State University Foundation, SAN LUIS OBISPO, CA | Award Amount: $200,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2553421 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2553421.html