A neuromuscular model-based controller for a robotic limb having at least one joint includes a neuromuscular model having a muscle model, muscle geometry and reflex feedback loop to determine at least one torque or impedance command to be sent to the robotic limb. One or more parameters that determine relation between feedback data and activation of the muscle model are adjusted consequent to sensory data from at least one of an intrinsic sensor and an extrinsic sensor. A controller in communication with the neuromuscular model is configured to receive the at least one torque or impedance command and controls at least one of position, torque and impedance of the robotic limb joint.

A biomimetics based robot and process for simulation is disclosed. The robot may include filament driven and fluid pumped elastomer based artificial muscles coordinated for slow twitch/fast twitch contraction and movement of the robot by one or more microcontrollers. A process may provide physics based simulation for movement of a robot in a virtual setting. Successfully tested movement data may be stored and embedded into a robot at build and/or before a new movement in programmed into the robot.

A biomimetics based robot and process for simulation is disclosed. The robot may include filament driven and fluid pumped elastomer based artificial muscles coordinated for slow twitch/fast twitch contraction and movement of the robot by one or more microcontrollers. A process may provide physics based simulation for movement of a robot in a virtual setting. Successfully tested movement data may be stored and embedded into a robot at build and/or before a new movement in programmed into the robot.

This disclosure relates generally to a method and system that provides personalized neuro motor rehabilitation therapy using a musculoskeletal arm model. The arm model is personalized using anthropometric measures and further adapted to operate using an optimized set of muscle actuators considering associated redundancy. The method generates trajectories associated with reach motion profiles for each motion task utilizing joint kinematics and inverse dynamics by integrating forward dynamics and muscle synergy concepts to select the optimized set of muscle actuators. The generated trajectories are further ranked based on muscle synergy, minimum energy consumption and optimized trajectory to select rehabilitation therapy best suited for effective recovery. Conventional methods that work with neural dynamics in deriving muscle synergy are dependent on single tasks, leaving synergy variation with task variability unexplored. The present disclosure provides understanding of work space, task variability and synergy paradigm to derive conclusive control actions for aiding rehabilitation effectively.