Modeling human walk by PCPG for lower limb neuroprosthesis control

摘要: In this paper, we propose an original and biologically-inspired leg prosthesis control system. We demonstrate that human walk periodic patterns can be modeled by a Programmable Central Pattern Generator (PCPG) algorithm. Assuming high-level commands reflecting the user's intention (such as accelerate, decelerate or stop) - optionally, with their associated confidence level are available, show PCPG generate output signal directly exploitable to actuators at different desired walking speeds in smooth way. Thanks adequate tuning of parameters relying on realistic kinematics, such would undoubtedly increase comfort patient. study, kinematics foot angle elevation seven subjects treadmill 10 speeds. The method used modify best is presented. found low-level order polynomial interpolation function speed provides good similarity indices between real generated This proves relevancy our approach paves way for numerous applications rehabilitation. Additionally, results suggest advantageously two PCPGs.