A Neuromuscular Human-Machine Interface for Applications in Rehabilitation Robotics

This research work presents a novel neuromusculoskeletal (NMS) model of the human lower limb that is physiologically accurate and computationally fast. The NMS model uses electromyography (EMG) signals recorded from 16 muscles to predict the force developed by 34 musculotendon actuators (MTAs). The operation of each MTA is constrained to simultaneously satisfy the joint moments generated with respect to 4 degrees of freedom (DOF) including: hip adduction-abduction, hip flexion-extension, knee flexion-extesion and ankle dorsi-plantar flexion. Advanced methods are developed to capture the human movement and produce realistic motion simulations. These are used to provide dynamic consistency to the NMS model operation. Pattern recognition and machine learning technology is used to predict the human motor intention from the analysis of EMG signals and integrate context knowledge into the EMG-driven NMS model. This research develops the technology needed to establish an EMG-driven human-machine interface (HMI) for the simultaneous actuation of multiple joints in a lower limb powered orthosis. This work, indeed, shows for the first time it is possible to use EMG signals to estimate the joint moments simultaneously produced about multiple DOFs and this is crucial to provide better estimates of muscle force with respect to the state of the art. This thesis also suggests the NMS model can be exploited to address the challenge of autonomous locomotion in musculoskeletal humanoids. The objective of this work therefore, is to provide effective solutions and readily available software tools to improve the human interaction with robotic assistive devices. This is achieved by advancing research in neuromusculoskeletal modeling to better understand the mechanisms of actuation provided by human muscles. Understanding these mechanisms is the key to realize human interaction with wearable assistive devices. This work designs and develops the technology for achieving this.

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