The controllers were test-walked by healthy users with the focal point to collect the motion and torque patterns and to obtain a subjective perception of the generated assistance.
(1) The “flow-field” (“velocity-field”) controller (Figure 2) works based on the velocity field and the difference between actual and reference joint velocities, instead of a fixed reference trajectory and limited stiffness. It allows the walking subject to voluntarily determine the length of each step, without being hindered when performing a longer step.
The results of the walk-test showed that the reference trajectory was not followed by the subject very accurately. After additional tests, it was visible that the issue was not related to the changed control parameters, but the swing and stance state detection within the controller. The problem appeared as too complex to proceed with the controller implementation during the TERRINet Trans-national access at UT.
The successful implementation of both approaches into commercial devices could result in facilitating more extensive clinical studies into the clinical effectiveness of AAN training for gait recovery of neurological patients.
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Martinez, A., Lawson, B., Durrough, C., & Goldfarb, M. (2018). A Velocity-Field-Based Controller for Assisting Leg Movement During Walking with a Bilateral Hip and Knee Lower Limb Exoskeleton. IEEE Transactions on Robotics.
Wu, A. R., Dzeladini, F., Brug, T. J., Tamburella, F., Tagliamonte, N. L., Van Asseldonk, E. H., … & Ijspeert, A. J. (2017). An adaptive neuromuscular controller for assistive lower-limb exoskeletons: A preliminary study on subjects with spinal cord injury. Frontiers in neurorobotics, 11, 30.
Dzeladini, F., Wu, A., Renjewski, D., Arami, A., Burdet, E., van Asseldonk, E., Kooij, H., & Ijspeert, A. J. (2016). Effects of a Neuromuscular Controller on a Powered Ankle Exoskeleton During Human Walking. 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob).
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