Stimulation of peripheral nerves has transiently restored lost sensation and has the potential to alleviate motor deficits. However, incomplete characterization of the long-term usability and bio-integration of intra-neural implants has restricted their use for clinical applications. Here, we conducted a longitudinal assessment of the selectivity, stability, functionality, and biocompatibility of polyimide-based intra-neural implants that were inserted in the sciatic nerve of twenty-three healthy adult rats for up to six months. We found that the stimulation threshold and impedance of the electrodes increased moderately during the first four weeks after implantation, and then remained stable over the following five months. The time course of these adaptations correlated with the progressive development of a fibrotic capsule around the implants. The selectivity of the electrodes enabled the preferential recruitment of extensor and flexor muscles of the ankle. Despite the foreign body reaction, this selectivity remained stable over time. These functional properties supported the development of control algorithms that modulated the forces produced by ankle extensor and flexor muscles with high precision. The comprehensive characterization of the implant encapsulation revealed hyper-cellularity, increased microvascular density, Wallerian degeneration, and infiltration of macrophages within the endoneurial space early after implantation. Over time, the amount of macrophages markedly decreased, and a layer of multinucleated giant cells surrounded by a capsule of fibrotic tissue developed around the implant, causing an enlargement of the diameter of the nerve. However, the density of nerve fibers above and below the inserted implant remained unaffected. Upon removal of the implant, we did not detect alteration of skilled leg movements and only observed mild tissue reaction. Our study characterized the interplay between the development of foreign body responses and changes in the electrical properties of actively used intra-neural electrodes, highlighting functional stability of polyimide-based implants over more than six months. These results are essential for refining and validating these implants and open a realistic pathway for long-term clinical applications in humans.

Hand gestures are a form of non-verbal communication used by individuals in conjunction with speech to communicate. Nowadays, with the increasing use of technology, hand-gesture recognition is considered to be an important aspect of Human-Machine Interaction (HMI), allowing the machine to capture and interpret the user's intent and to respond accordingly. The ability to discriminate between human gestures can help in several applications, such as assisted living, healthcare, neuro-rehabilitation, and sports. Recently, multi-sensor data fusion mechanisms have been investigated to improve discrimination accuracy. In this paper, we present a sensor fusion framework that integrates complementary systems: the electromyography (EMG) signal from muscles and visual information. This multi-sensor approach, while improving accuracy and robustness, introduces the disadvantage of high computational cost, which grows exponentially with the number of sensors and the number of measurements. Furthermore, this huge amount of data to process can affect the classification latency which can be crucial in real-case scenarios, such as prosthetic control. Neuromorphic technologies can be deployed to overcome these limitations since they allow real-time processing in parallel at low power consumption. In this paper, we present a fully neuromorphic sensor fusion approach for hand-gesture recognition comprised of an event-based vision sensor and three different neuromorphic processors. In particular, we used the event-based camera, called DVS, and two neuromorphic platforms, Loihi and ODIN + MorphIC. The EMG signals were recorded using traditional electrodes and then converted into spikes to be fed into the chips. We collected a dataset of five gestures from sign language where visual and electromyography signals are synchronized. We compared a fully neuromorphic approach to a baseline implemented using traditional machine learning approaches on a portable GPU system. According to the chip's constraints, we designed specific spiking neural networks (SNNs) for sensor fusion that showed classification accuracy comparable to the software baseline. These neuromorphic alternatives have increased inference time, between 20 and 40%, Ceolini et al. Sensor Fusion Neuromorphic Benchmark with respect to the GPU system but have a significantly smaller energy-delay product (EDP) which makes them between 30× and 600× more efficient. The proposed work represents a new benchmark that moves neuromorphic computing toward a real-world scenario.

Human pose estimation has dramatically improved thanks to the continuous developments in deep learning. However, marker-free human pose estimation based on standard frame-based cameras is still slow and power hungry for real-time feedback interaction because of the huge number of operations necessary for large Convolutional Neural Network (CNN) inference. Event-based cameras such as the Dynamic Vision Sensor (DVS) quickly output sparse moving-edge information. Their sparse and rapid output is ideal for driving low-latency CNNs, thus potentially allowing real-time interaction for human pose estima-tors. Although the application of CNNs to standard frame-based cameras for human pose estimation is well established , their application to event-based cameras is still under study. This paper proposes a novel benchmark dataset of human body movements, the Dynamic Vision Sensor Human Pose dataset (DHP19). It consists of recordings from 4 synchronized 346x260 pixel DVS cameras, for a set of 33 movements with 17 subjects. DHP19 also includes a 3D pose estimation model that achieves an average 3D pose estimation error of about 8 cm, despite the sparse and reduced input data from the DVS.