Operational space analysis of human muscular effort in robot assisted reaching tasks Abstract   Human motor performance is a key area of investigation in both biomechanics and robotics. In robotics, understanding human muscular control is important to synthesize prosthetic motions and ensure safe humanxe2x80x93robot interaction. Building controllable biomechanical models can help in quantifying the characteristics of a subjectxe2x80x99s motion and in designing effective treatments, like motion training. This paper presents the taskbased motion analysis of muscular effort using an upperbody musculoskeletal model, validated through motion capture experiments and dynamic simulations. To study the contribution of robotic assistance in improving human motor skills, the muscular effort of the task of reaching with and without robotic assistance was investigated for 10 subjects. Reduction of 21.4% in the arm muscular effort was observed for the tasks with robotic assistance.

Estimating the Effect of Robotic Intervention on Elbow Joint Motion Much effort has been placed into the development of robotic devices to support, rehabilitate, and interact with humans. Despite these advances, reliably modeling the neuromuscular changes in human motion resulting from a robotic intervention remains difficult. This paper proposes a method to uncover the relationship between robotic intervention and human response by combining surface electromyography (sEMG), the musculoskeletal modeling platform OpenSim, and artificial neural networks (ANNs). To demonstrate the method, a one degree of freedom (DOF) elbow flexionextension motion is performed and analyzed. Preliminary results show that while the robot provides assistance to the subject, it also appears to produce other unexpected responses in the movement. Further investigation using the new method reveals the neuromuscular effect of an unintended resistance to the subjectxe2x80x99s motion applied by the robot as it enforces a speed slower than the subject selects. The characterization of the differences in expected and actual interaction is enabled by the method presented in this paper. Thus, the method uncovers previously obscured aspects of human robot interaction, and creates possibilities for new training modalities.