Exoskeleton robots are capable of safe torque-controlled interactions with a wearer while moving their limbs through predefined trajectories. However, affecting and assisting the wearer's movements while incorporating their inputs (effort and movements) effectively during an interaction re-mains an open problem due to the complex and variable nature of human motion. In this paper, we present a control algorithm that leverages task-specific movement behaviors to control robot torques during unstructured interactions by implementing a force field that imposes a desired joint angle coordination behavior. This control law, built by using principal component analysis (PCA), is implemented and tested with the Harmony exoskeleton. We show that the proposed control law is versatile enough to allow for the imposition of different coordination behaviors with varying levels of impedance stiffness. We also test the feasibility of our method for unstructured human-robot interaction. Specifically, we demonstrate that participants in a human-subject experiment are able to effectively perform reaching tasks while the exoskeleton imposes the desired joint coordination under different movement speeds and interaction modes. Survey results further suggest that the proposed control law may offer a reduction in cognitive or motor effort. This control law opens up the possibility of using the exoskeleton for training the participating in accomplishing complex multi-joint motor tasks while maintaining postural coordination.

While human-robot interaction studies are becoming more common, quantification of the effects of repeated interaction with an exoskeleton remains unexplored. We draw upon existing literature in human skill assessment and present extrinsic and intrinsic performance metrics that quantify how the human-exoskeleton system's behavior changes over time. Specifically, in this paper, we present a new performance metric that provides insight into the system's kinematics associated with ‘successful’ movements resulting in a richer characterization of changes in the system's behavior. A human subject study is carried out wherein participants learn to play a challenging and dynamic reaching game over multiple attempts, while donning an upper-body exoskeleton. The results demonstrate that repeated practice results in learning over time as identified through the improvement of extrinsic performance. Changes in the newly developed kinematics-based measure further illumi-nate how the participant's intrinsic behavior is altered over the training period. Thus, we are able to quantify the changes in the human-exoskeleton system's behavior observed in relation with learning.

How best to attach exoskeletons to human limbs is an open and understudied problem. In the case of upperbody exoskeletons, cylindrical handles are commonly used attachments due to ease of use and cost effectiveness. However, handles require active grip strength from the user and may result in undesirable flexion synergy stimulation, thus limiting the robot’s effectiveness. This paper presents a new design, the Eminence Grip, for attaching an exoskeleton to the hand while avoiding the undesirable consequences of using a handle. The ergonomic design uses inverse impedance matching and does not require active effort from the user to remain interfaced with the exoskeleton. We compare the performance of the Eminence Grip to the handle design in a healthy subject target reaching experiment. The results show that the Eminence Grip achieves similar performance to a handle in terms of relative motion between the user and the exoskeleton while eliminating the requirement of grip force to transfer loads to/from the exoskeleton and avoiding stimulation of the flexion synergy. Taken together, the kinematic equivalence and improvement in ergonomics suggest that the Eminence Grip is a promising exoskeleton-hand attachment interface supporting further experiments with impaired populations.

Humans learn complex motor skills with practice and training. Though the learning process is not fully understood, several theories from motor learning, neuroscience, education, and game design suggest that curriculum-based training may be the key to efficient skill acquisition. However, designing such a curriculum and understanding its effects on learning are challenging problems. In this paper, we define the Human-skill Curriculum Markov Decision Process (H-CMDP) to systematize the design of training protocols. We also identify a vocabulary of performance features to enable the approximation for a human’s skill level across a variety of cognitive and motor tasks. A novel task domain is introduced as a testbed to evaluate the effectiveness of our approach. Human subject experiments show that (1) participants can learn to improve their performance in tasks within this domain, (2) the learning is quantifiable via our performance features, and (3) the domain is flexible enough to create distinct levels of difficulty. The long-term goal of this work is to systematize the process of curriculum-based training toward the design of protocols for robot-mediated rehabilitation.

In this study, we address the inverse kinematics problem for an upper-limb exoskeleton by presenting a novel method that guarantees the satisfaction of joint-space constraints, and solves closed-chain mechanisms in a serial robot configuration. Starting from the conventional differential kinematics method based on the inversion of the Jacobian matrix, we describe and test two improved algorithms based on the Projected-Gradient method, that take into account joint-space equality constraints. We use the Harmony exoskeleton as a platform to demonstrate the method. Specifically, we address the joint constraints that the robot maintains in order to match anatomical shoulder movement and the closed-chain mechanisms used for the robot's joint control. Results show good performances of the proposed algorithms, which are confirmed by the ability of the robot to follow the desired task-space trajectory while ensuring the fulfilment of joint-space constraints, with a maximum error of about 0.05 degrees.

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 flexion-extension 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 subject's 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.