Background: Novel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients’ voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort. Methods: Five hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants’ arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression. Results: From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced. Conclusions: The concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this.

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In this paper we explore the mechanical behavior of the ankle in the progression stage of stance during normal walking. We show that the torque/angle behavior of the ankle during this stage can be approximated by an augmented linear torsional spring. The mechanical parameters completely specifying this spring are identified, including stiffness, amount of rotation, and angle of zero moment. The effect of load weight, gait speed and ground slope on those parameters and the propulsive work of the ankle are also discussed. The findings of this paper can be applied to the design of leg orthoses, prostheses and exoskeletons, and bipedal robots in general, allowing the implementation of human-like leg compliance during stance with a relatively simple latchedspring mechanism.

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Brain–machine interfaces1,2 use neuronal activity recorded fromthe brain to establish direct communication with external actuators, such as prosthetic arms. It is hoped that brain–machine interfaces can be used to restore the normal sensorimotor functions of the limbs, but so far they have lacked tactile sensation. Here we report the operation of a brain–machine–brain interface (BMBI) that both controls the exploratory reaching movements of an actuator and allows signalling of artificial tactile feedback through intracortical microstimulation (ICMS) of the primary somatosensory cortex. Monkeys performed an active exploration task in which an actuator (a computer cursor or a virtual-reality arm) was moved using a BMBI that derived motor commands from neuronal ensemble activity recorded in the primary motor cortex. ICMS feedback occurred whenever the actuator touched virtual objects. Temporal patterns of ICMS encoded the artificial tactile properties of each object. Neuronal recordings and ICMS epochs were temporally multiplexed to avoid interference. Two monkeys operated this BMBI to search for and distinguish one of three visually identical objects, using the virtual-reality arm to identify the unique artificial texture associated with each. These results suggest that clinical motor neuroprostheses might benefit from the addition of ICMS feedback to generate artificial somatic perceptions associated with mechanical, robotic or even virtual prostheses.

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The dynamic simulation of incomplete spinal cord in-jured individuals equipped with active orthoses is a challenging problem due to the redundancy of the simu-ltaneous human-orthosis actuation. The objective of this work is two-fold. Firstly, a physiological static optimization approach to solve the muscle-orthosis actuation sharing problem is presented. For this pur-pose, a biomechanical model based on multibody dynamics techniques is used. The muscles are modeled as Hill-type actuators and the atrophy of denervated muscles is considered by adding stiff and dissipative elements. Secondly, the mechanical design of a new active stance-control knee-ankle-foot orthosis (A- CKAFO) is addressed. The proposed device consists of a passive joint that constrains ankle plantar fle-xion, along with a powered knee unit that prevents flexion during stance and controls flexion-extension du-ring swing. The knee actuation is selected based on the results obtained through the optimization ap-proach.

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Background: Interpreting gait data is challenging due to intersubject variability observed in the gait pattern of both normal and pathological populations. The objective of this study was to investigate the impact of using principal component analysis for grouping knee osteo-arthritis (OA) patients’ gait data in more homogeneous groups when studying the effect of a physiotherapy treatment. Methods: Three-dimensional (3D) knee kinematic and kinetic data were recorded during the gait of 29 participants diagnosed with knee OA before and after they received 12 weeks of physiotherapy treat-ment. Principal component analysis was applied to extract groups of knee flexion/extension, adduction/abduction and internal/external rotation angle and moment data. The treatment’s effect on parameters of interest was assessed using paired t-tests performed before and after grouping the knee kinematic data. Findings: Increased quadriceps and hamstring strength was observed following treatment (Pb0.05). Except for the knee flexion/extension angle, two different groups (G1 and G2) were extracted from the angle and moment data. When pre- and post-treatment analyses were performed considering the groups, participants exhibiting a G2 knee moment pattern demonstrated a greater first peak flexion moment, lower adduction moment impulse and smaller rotation angle range post-treatment (Pb0.05). When pre- and post-treatment comparisons were performed without grouping, the data showed no treatment effect. Interpretation: The results of the present study suggest that the effect of physiotherapy on gait mechanics of knee osteoarthritis patients may be masked or underestimated if kinematic data are not separated into more homogeneous groups when performing pre- and post-treatment comparisons.

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