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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To overcome some of the difficulties of robotic teleoperation using hand controllers, a new approach is necessary, namely gesture-based control. A review of sensors currently in use for human joint angle measurement is presented. Based on this review, a method was chosen that uses a variable-length fiber optic sensor. Several different types of optical fibers, along with a variety of test configurations, were initially evaluated, and the most promising of these were selected for further testing. This thesis describes these methods of evaluation and the final system design and testing of a wearable system for gesture-based control of a robotic manipulator, including a discussion of sensor placement to obtain improved results. The final system presented requires improvements and continued research to become usable for robotic control. However, the basic concept and design are shown to provide reliable information regarding relative human joint motion.

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Exoskeletons for human performance augmentation (EHPA) are controlled and wearable devices and machines that can increase the speed, strength, and endurance of the operator. EHPA is expected to do that by increasing the physical performance of the solider wearing it, including:
• Increased payload: more fire power, more supplies, and thicker and heavier armor
increasing the soldier chance of surviving a direct hit or even an explosion.
• Increased speed and extended range: enhanced ground reconnaissance and battle space
dominance.
• Increased strength: larger caliber weapons, obstacle clearance, repairing heavy ma
chinery such as tack.

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We used a lower limb robotic exoskeleton controlled by the wearer’s muscle activity to study human locomotor adaptation to disrupted muscular coordination. Ten healthy sub-jects walked while wearing a pneumatically powered ankle exoskeleton on one limb that effectively in-creased plantar flexor strength of the soleus muscle. Soleus electromyography amplitude controlled plantar flexion assistance from the exoskeleton in real time. We hypothesized that subjects’ gait kinematics would be initially distorted by the added exoskeleton power, but that subjects would reduce soleus muscle recruitment with practice to return to gait kinematics more similar to normal. We also examined the abi-lity of subjects to recall their adapted motor pattern for exoskeleton walking by testing subjects on two separate sessions, 3 days apart. The mechanical power added by the exoskeleton greatly perturbed ankle joint movements at first, causing subjects to walk with significantly increased plantar flexion during stance. With practice, subjects reduced soleus recruitment by 35% and learned to use the exoskeleton to perform almost exclusively positive work about the ankle. Subjects demonstrated the ability to retain the adapted locomotor pattern between testing sessions as evidenced by similar muscle activity, kinematic and kinetic patterns between the end of the first test day and the beginning of the second. These results demonstrate that robotic exoskeletons controlled by muscle activity could be useful tools for testing neural mechanisms of human locomotor adaptation.

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