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Activity of an innervated tissue can be modulated based on an acquired biomarker through feedback loops. How to convert this biomarker into a meaningful stimulation pattern is still a topic of intensive research. In this article, we present a simple closed-loop mechanism to control the mean angle of a locust’s leg in real time by modulating the frequency of the stimulation on its extensor motor nerve. The nerve is interfaced with a custom-designed cuff electrode and the feedback loop is implemented online with a proportional control algorithm, which runs solely on a microcontroller without the need of an external computer. The results show that the system can be controlled with a single-input, single-output feedback loop. The model described in this article can serve as a primer for young researchers to learn about neural control in biological systems before applying these concepts in advanced systems. We expect that the approach can be advanced to achieve control over more complex movements by increasing the number of recorded biomarkers and selective stimulation units.

Peripheral nerve interfacing (PNI) has a high clinical potential for treating various diseases, like obesity or diabetes. However, currently existing electrodes present challenges to the interfacing procedure, which limits their clinical application, in particular, when targeting small peripheral nerves (200 μm). To improve the electrode handling and implantation, we fabricate a nerve interface that can fold itself to a cuff around a small nerve, triggered by the body moisture during insertion. We achieve this folding by printing a bilayer of a flexible polyurethane printing resin and a highly swelling sodium acrylate hydrogel using photopolymerization. When immersed in an aqueous liquid, the hydrogel swells and folds the electrode softly around the nerve. Furthermore, the electrodes are robust, can be stretched (20%), and bend to facilitate the implantation due to the use of soft and stretchable printing resins as substrates and a microcracked gold film as conductive layer. We demonstrate the straightforward implantation and extraction of the electrode as well as stimulation and recording capabilities on a small peripheral nerve in vivo. We believe that such simple and robust to use self-folding electrodes will pave the way for bringing PNI to a broader clinical application. This article is protected by copyright. All rights reserved.

The capacity of the biomechanics of human limbs to absorb energy during physical human-robot interaction (pHRI) can play an imperative role in controlling the performance of human-centered robotics systems. Using the concept of “excess of passivity,” we have recently designed passivity signature maps for elbow and wrist joints. We have also shown that this knowledge can be exploited and extrapolated during the interaction with a robotic system by transparency-maximized algorithms. A major application is in robotic rehabilitation systems and assistive technologies. Here, for the first time, the nonlinear energy capacitance of the hip joint and the affecting factors are decoded. This can be critical for maximizing the performance of wearable exoskeletons. Knowledge regarding energy absorption behavior can significantly help to reduce the conservatism of control algorithms. In this work, the energetic behavior is studied for three different hip angles, while perturbations were provided at three different interaction speeds. The results show that the increase in agonist and antagonist muscle contractions can consistently expand the margins of the passivity map. Additionally, by separating the effects of agonist and antagonist contractions, it was identified that the passivity margins have a correlation with the subject's posture during interaction with the robot and the correlation depends on the type of muscle contraction. A preliminary design of a stabilizer is also formulated that takes into account variable passivity behavior of the joint, in the energy domain, to enhance the performance while guaranteeing pHRI stability.