In this paper, we propose an original and biologically-inspired leg prosthesis control system. We demonstrate that human walk periodic patterns can be modeled by a Programmable Central Pattern Generator (PCPG) algorithm. Assuming that high-level commands reflecting the user’s intention (such as accelerate, decelerate or stop) — and optionally, with their associated confidence level — are available, we show that the PCPG can generate an output signal directly exploitable to control the prosthesis actuators at different desired walking speeds in a smooth way. Thanks to an adequate tuning of the PCPG parameters relying on realistic human walk kinematics, such a prosthesis would undoubtedly increase the comfort of the patient. In this study, we modeled the kinematics of foot angle of elevation of seven subjects walking on a treadmill at 10 different speeds. The method we used to modify at best the PCPG parameters is presented. We found that a low-level order polynomial interpolation of the PCPG parameters as a function of speed provides good similarity indices between real walk and generated patterns at different speeds. This proves the relevancy of our approach and paves the way for numerous applications of human walk rehabilitation. Additionally, results suggest that walk would be advantageously modeled by two PCPGs.

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Virtual reality simulators seek to immerse users in realistic interactive environments. However, at present, while several provide kinesthetic feedback, most lack tactile feedback. Current means of tactile feedback do not generate enough force to the digits, deliver a non-intuitive sense of feedback and are too large or heavy to be used in hand-worn configurations. The tactile feedback system developed herein uses electroactive polymers to create light touch and vibratory sensation to the fingertips, and DC motors to constrict the distal digit. In essence, when current is passed through the electroactive polymer in the shape of a cantilever (7 mm long by 19 mm wide), it bends on its long axis, providing a forces of 25 mN. Vibratory feedback is created by varying input voltage with a sinusoidal waveform. To generate fingertip constriction, two DC motors cinch a wire attached to a rubber thimble. These hardware components are controlled by a computer running X3D software, an ISO standard for representing 3D graphics, which affords a virtual environment for the tracking of one’s hand. Upon contact with a virtual object, the actuators generate prescribed forces or vibrations. With this setup, a series of human-subjects experiments will be conducted whereby the task is to contact and differentiate virtual spheres of differing stiffness. Experiment 1 will test the electroactive polymers to determine the threshold for recognizing light touch, Experiment 2 will test vibrational discrimination, and Experiment 3 will test the ability of the user to differentiate constriction forces.

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Exoskeleton systems have been largely developed in spite that quantitative performance estimation has not been reported so far. Consequently, we have been developing the wearable muscle suit for direct and physical motion supports with relevant reports on the performance. The McKibben artificial muscle has introduced “muscle suit” compact, lightweight, reliable, and wearable “assist-bots” enabling users to lift and carry heavy objects. Applying integral electromyo-graphy (IEMG), we show the results of quantitative suit performance and posture-preserving efficiency. However, for practical use, lifting seems to be one of the most important tasks for users. We improve the forearm so that the muscle suit assists the user in vertical lifting. Load carrying and lifting experi-ments show the muscle suit’s effectiveness.

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This chapter presents an experimental study of upper-limb surface EMG signals and two cases of applying surface EMG signals to control an upper-limb exoskeleton robot. The applied method to process surface EMG signals to use as input information to the control method is also explained. At first, surface EMG signal extraction method and processing method are presented in this chap-ter. Then, the upper-limb muscle activities during daily upper-limb motions have been studied to enable exoskeleton robots to estimate human upper-limb motions based on EMG signals of related muscles. The muscle combinations are identified to separate some motions of upper-limb. Minimum number of muscles to extract signals to control frequent daily upper-limb motions has been identified. In the next step, EMG signal of identified muscles are used to control two upper-limb exoskeleton robots. A three degree of freedom (DOF) exoskeleton robot (W-EXOS) for the forearm pronation/supination, wrist flexion/extension and ulnar/radial deviation are controlled by applying the surface EMG signals of six muscles. Surface EMG signals of upper-limb muscles are applied as input information to control a 6DOF exoskeleton robot (SUEFUL-6). In each case of applying EMG signals experiments have been carried out to evaluate the effectiveness of the EMG based control method.

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An instrumented prosthetic foot for use with an actuated leg prosthesis controlled by a controller, the instrumented prosthetic foot comprising a connector to connect the instrumented prosthetic foot to the leg prosthesis, an ankle structure connected to the connector, a ground engaging member connected to the ankle, at least one sensor for detecting changes in weight distribution along the foot, and an interface for transmitting signals from the sensor to the controller.

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