Upper Limb Motion Simulation Algorithm for Prosthesis Prescription and Training A simulation algorithm to predict expected upper limb movements of prosthesis users performing activities of daily living (ADL) was developed. It is quite challenging to determine the right type and fit of a prosthesis and provide appropriate training to properly utilize it. The amputee care team typically uses prior experiences to provide prescription and training customized for each individual. It is also very difficult to anticipate expected and undesired compensatory motions due to reduced degrees of freedom of a prosthesis user. We have developed a tool to predict and visualize the expected upper limb movements resulting from using a prescribed prosthesis and its suitability to the needs of the amputee. It is expected to help clinicians make decisions such as the type of the prosthesis, and whether to include a wrist joint, based on the impact it will have on the rest of the joints. The main focus of this work is to use roboticsbased methods to simulate human use of prostheses and identify the expected posture of the limited joints on the upper limbs. Unlike other works, this paper does not discuss the control of the prosthesis but the posture of the body. A weighted leastnorm inverse kinematics algorithm was used to develop a roboticsbased model of the upper limbs and torso. Motion capture data from the subjects were used to determine the weighting matrix the algorithm required. Results show that this approach provides humanlike simulation of joint motions and matches the motion capture data. The algorithm uses the individualxe2x80x99s anthropometrics and level of amputation to create a personalized kinematic model of the upper body and the joint motions during ADLs. A graphic user interface (GUI) was created to allow the clinician to input the relevant data resulting in arm movements of the prospective prosthesis user. A custommade visualization software was developed to display an animation performing the simulated motion. It should be stressed that this work does not adjust and replay motion capture recordings, but solves the inverse kinematics of the human body.

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.