Microelectromechanical systems (MEMS) are revolutionizing a multitude of industries world wide, from consumer products to the scientific community. Rehabilitation robotics is a robotic field specially interested in using the advantages of inertial sensors. The essential aspect in this area is the intrinsic interaction between human and robot, which imposes several restrictions in the design of this sort of robots. This paper addresses the analysis of the application of inertial sensors as sensing technologies in controlled orthotic devices with a detailed analysis with two biomechatronic robotics rehabilitation exoskeletons, one for the upper and other for the lower limb. Eventually, the results and conclusion of the experiments are given.

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It is proposed that copepods grow between one moult and the next in much the same fashion as established by Klaus Anger and others for decapod crustacean larvae. The analogy is justified by commonality of approximately isochronal development patterns, potential for continuously exponential growth at stage-to-stage resolution, and demonstrated points of reserve saturation. Thus, as for crab zoeae, the copepod pattern should be very fast initial growth, then slowing as activity shifts to preparation of the new exoskeleton prior to moult. As much as 80% of growth may occur in the first half of the moult cycle, with no growth at all in the last third. Establishing the exact patterns for copepods faces difficulties not presented by decapod larvae, and some solutions to these problems are suggested. Obtaining precise data will help to predict and interpret (model correctly) the effects of food limitation in the field.

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We propose a novel control method for lower-limb assist that produces a virtual modification of the mechanical impedance of the human limbs. This effect is accomplished by making the exoskeleton display active impedance properties. Active impedance control emphasizes control of the exoskeleton’s dynamics and regulation of the transfer of energy between the exoskeleton and the user. Its goal is improving the dynamic response of the human limbs without sacrificing the user’s control authority. The proposed method is an alternative to myoelectrical exoskeleton control, which is based on estimating muscle torques from electromyographical (EMG) activity. Implementation of an EMG-based controller is a complex task that involves modeling the user’s musculoskeletal system and requires recalibration. In contrast, active impedance control is less dependent on estimation of the user’s attempted motion, thereby avoiding conflicts resulting from inaccurate estimation. In this paper we also introduce a new form of human assist based on improving the kinematic response of the limbs. Reduction of average muscle torques is a common goal of research in human assist. However, less emphasis has been placed so far on improving the user’s agility of motion. We aim to use active impedance control to attain such effects as increasing the user’s average speed of motion, and improving their acceleration capabilities in order to compensate for perturbations from the environment.

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This document discloses, among other things, a wearable structure having links and joints corresponding to those of a human upper body. Transducers are located on the wearable structure and are coupled to a processor. The transducers exchange energy and information between the user and the wearable structure and enable control of the movement of the structure.

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Robotic gait training is an emerging technique for retraining walking ability following spinal cord injury (SCI). A key challenge in this training is determining an appropriate stepping trajectory and level of assistance for each patient, since patients have a wide range of sizes and impairment levels. Here, we demonstrate how a lightweight yet powerful robot can record subject- specific, trainer-induced leg trajectories during manually assisted stepping, then immediately replay those trajectories. Replay of the subject-specific trajectories reduced the effort required by the trainer during manual assistance, yet still generated similar patterns of muscle activation for six subjects with a chronic SCI. We also demonstrate how the impedance of the robot can be adjusted on a step-by-step basis with an error-based, learning law. This impedance-shaping algorithm adapted the robot’s impedance so that the robot assisted only in the regions of the step trajectory where the subject consistently exhibited errors. The result was that the subjects stepped with greater variability, while still maintaining a physiologic gait pattern. These results are further steps toward tailoring robotic gait training to the needs of individual patients.

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