Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.

Optoelectronic systems can exert precise control over targeted neurons and pathways throughout the brain in untethered animals, but similar technologies for the spinal cord are not well established. Here, we describe a novel system for ultrafast, wireless, closed-loop manipulation of targeted neurons and pathways across the entire dorsoventral spinal cord in untethered mice. We developed a soft stretchable carrier integrating micro-LEDs that conforms to the dura matter of the spinal cord. A coating of silicone-phosphor matrix over the micro-LEDs provides mechanical protection and light conversion for compatibility with the large library of opsins. A lightweight, head-mounted wireless platform powers the micro-LEDs and performs low-latency on-chip processing of sensed physiological signals to control photostimulation in a closed-loop. We use the device to reveal the role of various neuronal subtypes, sensory pathways and supraspinal projections in the control of locomotion in healthy and spinal-cord injured mice.

Activation of nociceptor sensory neurons by noxious stimuli both triggers pain and increases capillary permeability and blood flow to produce neurogenic inflammation 1 , 2 , but whether nociceptors also interact with the immune system remains poorly understood. Here we report a neurotechnology for selective epineural optogenetic neuromodulation of nociceptors and demonstrate that nociceptor activation drives both protective pain behavior and inflammation. The wireless optoelectronic system consists of sub-millimeter-scale light-emitting diodes embedded in a soft, circumneural sciatic nerve implant, powered and driven by a miniaturized head-mounted control unit. Photostimulation of axons in freely moving mice that express channelrhodopsin only in nociceptors resulted in behaviors characteristic of pain, reflecting orthodromic input to the spinal cord. It also led to immune reactions in the skin in the absence of inflammation and potentiation of established inflammation, a consequence of the antidromic activation of nociceptor peripheral terminals. These results reveal a link between nociceptors and immune cells, which may have implications for the treatment of inflammation.

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Conditioning and processing of biological signals represent interesting challenges for wearable electronics in health applications. Information gathering from these signals requires complex hardware circuitry and dedicated computation resources. The design of innovative analog front-end integrated circuits, combined with efficient signal processing algorithms, allows the development of platforms for monitoring, activity and gesture recognition based on embedded real-time systems. This paper describes an Electromyography pattern recognition system based on the combination of low cost passive sensors, an innovative analog front-end and a low power microcontroller. The performance of the proposed system matches state-of-the-art high-end active sensors, opening the way to the development of affordable and accurate wearable devices.