The design of a wearable robotic exoskeleton needs to consider the interaction, either physical or cognitive, between the human user and the robotic device. This paper presents a method to analyse the interaction between the human user and a unilateral, wearable lower-limb exoskeleton. The lower-limb exoskeleton function was to compénsate for muscle weakness around the kneejoint. It isshown that the cognitive interaction is bidirectional; on theone hand, the robot gathered information from the sensors in order to detect human actions, such as the gait phases, but the subjects also modified their gait patterns to obtain the desired responses from the exoskeleton. The results of the two-phase evaluation of learning with healthy subjects and experiments with a patient case are presented, regarding the analysis of the interaction, assessed in terms of kinematics, kinetics and/or muscle recruitment. Human-driven response of the exoskeleton after training revealed the improvements in the use of the device, while particular modifications of motion patterns were observed in healthy subjects. Also, endurance (mechanical) tests provided criteria to perform experiments with one post-polio patient. The results with the post-polio patient demónstrate the feasibility of providing gait compensation by means of the presented wearable exoskeleton, designed with a testing procedure that involves the human users to assess the human-robot interaction.

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In recent years, the rate of aged people is continuously increasing in Japan: in ten years, around 30% of the Japanese population will be aged more than 65 years old. These people often need some help from caretakers, and this causes a problem for the caretakers: it takes a very big burden for caretakers, for example, to lift people from their bed, when they need to go to toilets. So, a system to help caretakers would be needed: This is a master-slave unit exoskeleton, which helps the caretaker while loading the patient. It is moved by a Hydraulic Bilateral Servo system, whose master cylinders are moved by servo-motors. Presently, the conceptions of the body and hardware are finished. We are developing the servo-loop system and the driving software for the system, and more specially the pulse generation software for the motor of the master part of the exoskeleton. We will be able to discuss about the driving of the entire robotic-type exoskeleton system with a computer, through a PCMCIA interface system.

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The work presented in this paper is our first step toward the develop-ment of an exoskeleton for human gait support. The device we foresee should be suitable for assisting walking in paralyzed subjects and should be based on myoelectrical muscular signals (EMGs) as a communi-cation channel between the human and the machine. This paper concentrates on the design of a biomechanical model of the human lower extremity. The system predicts subject’s intentions from the analysis of his/her electromyographical activity. Our model takes into account three main factors. Firstly, the main muscles spanning the knee articulation. Secondly, the gravity affecting the leg during its movement. Finally, it considers the limits within which the leg swings. Furthermore, it is capable of estimating several knee parameters such as joint moment, angular acceleration, angular velocity, and angular position. In order to have a visual feedback of the predicted movements we have implemented a three-dimensional graphical simulation of a human leg which moves in response to the commands computed by the model.

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The metabolic cost of carrying an additional mass at different locations on the body relates to many areas, such as ergonomics, the military, obesity, and the design of prosthetic and powered exoskeletons devices. It was found that the most important factors that affect change in energy expenditure are the speed of locomotion , the magnitude of the additional mass, and the location of the additional mass on the body . It was suggested that metabolic cost increases linearly as the speed increases, and that it also in-creases linearly as the mass carried is increased. However, other researchers depicted these relations as nonlinear. Further, to the best of our knowledge the combined effect of the weight speed has not been studied. Yet for practical applications it is important to be able to predict the metabolic cost for any given combination of speed and mass, which has not been possible from previous studies. Therefore, in this study we aim to model the change in metabolic cost as a function of walking speed and the additional mass, at three body locations: the back, the knee, and the ankle. This has been done by a meta-analysis of previous published studies.

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Performing closed-loop modifications of a brainmachine interface (BMI) decoder is a technique that shows great promise for improving performance. We compare two algorithms for implementing adaptations that update decoder parameters on different time-scales (discrete batches vs. online), and present experimental results of a non-human primate performing a standard center-out BMI task. To ensure that our experimental training models are representative of a broad range of paralyzed patients, our decoders were initially trained using neural activity recorded during subject observation of cursor movement. We find that both closed-loop adaptation algorithms can be used to boost BMI performance from 20-30% to 80%, yielding movement kinematics similar to natural arm movements. Based on insights derived from the performance of each algorithm, we propose that a hybrid of batch and online decoder adaptation may be the best approach.

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