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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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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Many of the current implementations of exoskeletons for the lower extremities are conceived to either augment the user’s load-carrying capabilities or reduce muscle activa-tion during walking. Comparatively little research has been conducted on enabling an exoskeleton to increase the agility of lower-limb movements. One obstacle in this regard is the inertia of the exoskele-ton’s mechanism, which tends to reduce the natural frequency of the human limbs. A control method is presented that produces an approximate compensation of the inertia of an exoskeleton’s mechanism. The con-troller was tested on a statically mounted, single-degree-offreedom (DOF) exoskeleton that assists knee flexion and extension. Test subjects performed multiple series of leg-swing movements in the context of a computer-based, sprint-like task. A large initial acceleration of the leg was needed for the subjects to track a virtual target on a computer screen. The uncompensated inertia of the exoskeleton mechanism slowed down the transient response of the subjects’ limb, in comparison with trials performed without the exoske-leton. The subsequent use of emulated inertia compensation on the exoskeleton allowed the subjects to improve their transient response for the same task.

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Many of the current implementations of exoskeletons for the lower extremi-ties are conceived to either augment the user’s load-carrying capabilities or reduce muscle activation during walking. Comparatively little research has been conducted on enabling an exoskeleton to increase the agility of lower-limb movements. One obstacle in this regard is the inertia of the exoskeleton’s mechanism, which tends to reduce the natural frequency of the human limbs. A control method is presented that produces an approximate compensation of the inertia of an exoskeleton’s mechanism. The controller was tested on a statically mounted, single-degree-offreedom (DOF) exoskeleton that assists knee flexion and extension. Test subjects performed multiple series of leg-swing movements in the context of a computer-based, sprint-like task. A large initial acceleration of the leg was needed for the subjects to track a virtual target on a computer screen. The uncompensated inertia of the exoskeleton mechanism slowed down the transient response of the subjects’ limb, in comparison with trials performed without the exoskeleton. The subsequent use of emulated inertia compensation on the exoskeleton allowed the subjects to improve their transient response for the same task.

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Exoskeletons are a new class of articulated mechanical systems whose performance is realized while in intimate contact with the human user. The overall performance depends on many factors including selection of architecture, device, parameters and the nature of the coupling to the human, offering numerous challenges to design-evaluation and refinement. In this paper, we discuss merger of techniques from the musculoskeletal analysis and simulation-based design to study and analyze the performance of such exoskeletons. A representative example of a simplified exoskeleton interacting with and assisting the human arm is used to illustrate principal ideas. Overall, four different case-scenarios are developed and examined with quantitative performance measures to evaluate the effectiveness of the design and allow for design refinement. The results show that augmentation by way of the exoskeleton can lead to a significant reduction in muscle loading.

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