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).
