The Asymmetric Back Exosuit: Design, Realization, and Biomechanical Evaluation

Musculoskeletal disorders of the back are an extremely prevalent health issue across the workforce in the United States. This is especially a concern in industries involving manual materials handling tasks that cause low back pain. While these injuries are generated by both symmetric and asymmetric lifting, asymmetric movements are often more damaging. Exoskeleton technology has become an increasingly popular preventative measure to low back pain, but many devices do not assist in asymmetry. Thus, I present a new system called the Asymmetric Back Exosuit (ABX). The ABX addresses this important gap in the field through unique design geometry and active cable-driven actuation. The suit allows the user to move in a wide range of lumbar trajectories while the “X” pattern cable routing allows for variable assistance application for these trajectories, enabling assistance during asymmetric movements. As indicated by a biomechanical model of the system made in OpenSim, the cable forces can be mapped to effective lumbar torque assistance for a given lumbar trajectory, allowing for intuitive controller design over the complex kinematic chain for varying lifting techniques. An early human subject study indicated that the ABX was able to reduce low back muscle activation during symmetric and asymmetric lifting by an average of 37.8% and 16.0%, respectively, compared to lifting without the exosuit. This was expanded to a larger biomechanics study of the ABX for which preliminary results of three subjects are examined and discussed. These evaluations indicate the potential for the ABX to reduce lumbar injury risk during symmetric and asymmetric manual materials handling tasks.M.S

Classification of Lifting Techniques for Application of A Robotic Hip Exoskeleton

The number of exoskeletons providing load-lifting assistance has significantly increased over the last decade. In this field, to take full advantage of active exoskeletons and provide appropriate assistance to users, it is essential to develop control systems that are able to reliably recognize and classify the users' movement when performing various lifting tasks. To this end, the movement-decoding algorithm should work robustly with different users and recognize different lifting techniques. Currently, there are no studies presenting methods to classify different lifting techniques in real time for applications with lumbar exoskeletons. We designed a real-time two-step algorithm for a portable hip exoskeleton that can detect the onset of the lifting movement and classify the technique used to accomplish the lift, using only the exoskeleton-embedded sensors. To evaluate the performance of the proposed algorithm, 15 healthy male subjects participated in two experimental sessions in which they were asked to perform lifting tasks using four different techniques (namely, squat lifting, stoop lifting, left-asymmetric lifting, and right-asymmetric lifting) while wearing an active hip exoskeleton. Five classes (the four lifting techniques plus the class "no lift") were defined for the classification model, which is based on a set of rules (first step) and a pattern recognition algorithm (second step). Leave-one-subject-out cross-validation showed a recognition accuracy of 99.34 ± 0.85%, and the onset of the lift movement was detected within the first 121 to 166 ms of movement