Experimental Study on Human Arm Reaching with and without a Reduced Mobility for Applications in Medical Human-Interactive Robotics

Along with increasing advances in robotic technologies, there are now significant efforts under way to improve the quality of life especially those with physical disabilities or impairments. Control of such medical human-interactive robotics (HIR) involves complications in its design and control due to uncertain human factors. This dissertation makes its efforts to resolve three main challenges of an advanced HIR controller development: 1) detecting the operator’s motion intent, 2) understanding human motor behavior from the robotic perspective, and 3) generating reference motion for the HIR. Our interests in such challenges are limited to the point-to-point reaching of the human arm for applications of their solutions in the control of rehabilitation exoskeletons, therapeutic haptic devices, and prosthetic arms. In the context of human motion intent detection, a mobile motion capture system (MCS) enhanced with myoprocessors is developed to capture kinematics and dynamics of human arm in reaching movements. The developed MCS adopts wireless IMU (inertial measurement unit) sensors to capture ADL (activities of daily life) motions in the real-life environment. In addition, measured muscle activation patterns from selected muscle groups are converted into muscular force values by myoprocessors. This allows a reliable motion intent detection by quantify one of the most frequently used driving signal of the HIR, EMG (electromyography), in a standardized way. In order to understand the human motor behavior from the robotic viewpoint, a computational model on reaching is required. Since such model can be constituted by experimental observations, this dissertation look into invariant motion features of reaching with and without elbow constraint condition to establish a foundation of the computational model. The HIR should generate its reference motions by reflecting motor behavior of the natural human reaching. Though the accurate approximation of such behavior is critical, we also need to take into account the computational cost, especially for real-time applications such as the HIR control. In this manner, a higher order kinematic synthesis of mechanical linkage systems is adopted to approximate natural human hand profiles. Finally, a novel control concept of a myo-prosthetic arm is proposed as an application of all findings and efforts made in this dissertation

Modeling the neural correlates of imitation from a neuropsychological perspective

Imitation is a fundamental mechanism by which humans learn and understand the actions of others. This thesis addresses the low-level neural mechanisms underlying the imitation of meaningless gestures, using tools from computational neuroscience. We investigate how the human brain perceives these gestures and translates them into appropriate motor commands. In addition, we take a relatively unexplored neuropsychological perspective, which looks at imitation following a brain lesion. The analysis of how imitation breaks down in apraxia, a complex disorder of voluntary movement, enables us to reverse engineer brain function through the identification of those building blocks that are preserved. To better understand the phenomenon of apraxia, we develop a neurocomputational model of imitation that proposes potential neuroanatomical correlates, such as the flow of information across the two brain hemispheres. The model accounts for the pattern of errors observed in apraxic patients with disconnected brain hemispheres. To validate the predictions of our model, we further analyze the experimental errors and uncover a goal-dissociation, where a goal is defined as the spatial relation between two body parts. The experimental observations suggest that the imitation deficit in apraxia arises from an incorrect coordination between the reproductions of multiple goals. A prediction of this hypothesis was validated on three apraxic patients. The collected body of kinematic and neuropsychological data allowed us to refine our neurocomputational model of imitation, and to propose a biologically plausible mathematical model for the execution stage of the imitation. The model controls movement by following nonlinear dynamics, and precisely reproduces both the spatial and temporal aspects of unconstrained and natural three-dimensional reaching movements. Importantly, the model is stable and robust against external perturbations. Overall, our computational models and neuropsychological experiments contribute to a better understanding of how the brain performs the imitation of meaningless gestures; that is, by first decomposing the gesture into imitation goals, and then reproducing these goals through the association of different sensory modalities