Limited research has been done on exoskeletons to enable faster movements of the lower extremities. An exoskeleton’s mechanism can actually hinder agility by adding weight, inertia and friction to the legs; compensating inertia through control is particularly difficult due to instability issues. The added inertia will reduce the natural frequency of the legs, probably leading to lower step frequency during walking. We present a control method that produces an approximate compensation of an exoskeleton’s inertia. The aim is making the natural frequency of the exoskeleton-assisted leg larger than that of the unaided leg. The method uses admittance control to compensate for the weight and friction of the exoskeleton. Inertia compensation is emulated by adding a feedback loop consisting of low-pass filtered acceleration multiplied by a negative gain. This gain simulates negative inertia in the low-frequency range. We tested the controller on a statically supported, single-degree-of-freedom exoskeleton that assists swing movements of the leg. Subjects performed movement sequences, first unassisted and then using the exoskeleton, in the context of a computer-based task resembling a race. With zero inertia compensation, the steady-state frequency of the leg swing was consistently reduced. Adding inertia compensation enabled subjects to recover their normal frequency of swing.

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(EN)Systems, devices and methods for capturing motion from a body as disclosed. The systems, devices and methods allow for accurate placement of sensors relative to a body in order capture and analyze motion inlormation. Systems and methods described herein include associated computer systems configured to capture and analyze such data.
(FR)La présente invention concerne des systèmes, des dispositifs et des procédés de capture d’un mouvement d’un corps. Les systèmes, dispositifs et procédés permettent le positionnement précis de capteurs par rapport à un corps en vue de capturer et d’analyser des informations de mouvement. Les systèmes et les procédés décrits dans la présente invention comprennent des systèmes informatiques associés configurés pour capturer et analyser ces données.

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Limited research has been done on exoskeletons to enable faster movements of the lower extremities. An exoskeleton’s mechanism can actually hinder agility by adding weight, inertia and friction to the legs; compensating inertia through control is particularly difficult due to instability issues. The added inertia will reduce the natural frequency of the legs, probably leading to lower step frequency during walking. We present a control method that produces an approximate compensation of an exoskeleton’s inertia. The aim is making the natural frequency of the exoskeleton-assisted leg larger than that of the unaided leg. The method uses admittance control to compen-sate the weight and friction of the exoskeleton. Inertia compensation is emulated by adding a feedback loop consisting of low-pass filtered acceleration multiplied by a negative gain. This gain simulates nega-tive inertia in the low-frequency range. We tested the controller on a statically supported, single-DOF exoskeleton that assists swing movements of the leg. Subjects performed movement sequences, first unas-sisted and then using the exoskeleton, in the context of a computer-based task resembling a race. With zero inertia compensation, the steady-state frequency of leg swing was consistently reduced. Adding inertia compensation enabled subjects to recover their normal frequency of swing.

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A wearable action-assist device which assists or executes an action of a wearer by substituting for the wearer is provided with an action-assist tool 2 having an actuator 201 which gives power to the wearer 1, a biosignal sensor 221 which detects a wearer’s biosignal, a biosignal processing unit 3 which acquires from a biosignal “a” detected by the biosignal sensor a nerve transfer signal “b” for operating a wearer’s muscular line skeletal system, and a myoelectricity signal “c” accompanied with a wearer’s muscular line activity, an optional control unit 4 which generates a command signal “d” for causing the actuator 201 to generate power according to the wearer’s intention using the nerve transfer signal “b” and the myoelectricity signal “c” acquired by the biosignal processing unit 3, and a driving current generating unit 5 which generates a current according to the nerve transfer signal b and a current according to the myoelectricity signal “c”, respectively, based on the command signal “d” generated by the optional control unit 4, and supplies the currents to the actuator 201.

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In monkeys, activity of multiple single neurons related to arm movement can be employed to control an external actuator. Based on this work, there is an increasing interest in designing implantable brain-machine interfaces (BMI), enabling real-time control of neuroprosthetic devices. Such movement inference has been demonstrated in humans, but little is yet known about the possibility to decode information for the control of reaching and grasping from different sensorimotor areas activated by hand movements. Evidently, specificity and longterm stability of the recorded signals is essential for successful brain-machine interface applications. Thus, a promising novel approach for robust neurointerfacing is based on neuronal population activity, instead of multiple single neuron activity. Earlier, we demonstrated that local field potentials from monkey primary motor cortex can be as efficient as single neuron activity for decoding arm movements. I will give an overview of these results and present more recent findings, aimed at studying the feasibility of inferring hand movements in humans from population activity measured with electrodes on the cortical surface or even from the scalp.

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