SCH: Spine-Hip Exoskeletons with Learning-Based Optimal Control for Low Back Pain Alleviation

This collaborative project will leverage the spine-hip exoskeleton, an interdisciplinary and integrative platform uniting rapidly advancing areas of science and engineering to advance knowledge and understanding within its field and across different fields. The state-of-the-art exoskeletons lack human adaptability and task versatility for injury mitigation of workers. Moreover, the anatomy of the human back presents unique challenges for the design and control of wearable robots. Thus spine exoskeletons are required to reduce at least one of three forces (also not increase other forces), including erector spinae muscle force and lumbar vertebral compressive and shear forces. This necessitates new solutions for robot design, modeling, and control to achieve all objectives. This project will 1) develop mechanics-guided spine-hip soft exoskeletons, 2) understand the high-fidelity musculoskeletal model of the human spine and its response to exoskeletons, and 3) investigate learning-based optimal control to reduce musculoskeletal joint loadings thus ultimately mitigate low back injuries to workers. Our multidisciplinary team consisting of experts in robotics (Dr. Hao Su), computational biomechanics (Dr. Katherine Saul), and learning-based optimal control (Dr. Zhong-Ping Jiang) will take a convergent approach to assist multiple joints using a bio-inspired powered soft exoskeleton composed of the spine and hip modules for low back pain prevention of workers who conduct lifting tasks (including squat and stoop postures).