A Wearable Real-Time System for Simultaneous Wireless Power and Data Transmission to Cortical Visual Prosthesis.

Wireless, miniaturised and distributed neural interfaces are emerging neurotechnologies. Although extensive research efforts contribute to their technological advancement, the need for real-time systems enabling simultaneous wireless information and power transfer toward distributed neural implants remains crucial. Here we present a complete wearable system including a software for real-time image capturing, processing and digital data transfer; an hardware for high radiofrequency generation and modulation via amplitude shift keying; and a 3-coil inductive link adapt to operate with multiple miniaturised receivers. The system operates in real-time with a maximum frame rate of 20 Hz, reconstructing each frame with a matrix of 32 × 32 pixels. The device generates a carrier frequency of 433.92 MHz. It transmits the highest power of 32 dBm with a data rate of 6 Mbps and a variable modulation index as low as 8 %, thus potentially enabling wireless communication with 1024 miniaturised and distributed intracortical microstimulators. The system is primarily conceived as an external wearable device for distributed cortical visual prosthesis covering a visual field of 20 °. At the same time, it is modular and versatile, being suitable for multiple applications requiring simultaneous wireless information and power transfer to large-scale neural interfaces.

Hand Gestures Recognition for Human-Machine Interfaces: A Low-Power Bio-Inspired Armband.

Hand gesture recognition has recently increased its popularity as Human-Machine Interface (HMI) in the biomedical field. Indeed, it can be performed involving many different non-invasive techniques, e.g., surface ElectroMyoGraphy (sEMG) or PhotoPlethysmoGraphy (PPG). In the last few years, the interest demonstrated by both academia and industry brought to a continuous spawning of commercial and custom wearable devices, which tried to address different challenges in many application fields, from tele-rehabilitation to sign language recognition. In this work, we propose a novel 7-channel sEMG armband, which can be employed as HMI for both serious gaming control and rehabilitation support. In particular, we designed the prototype focusing on the capability of our device to compute the Average Threshold Crossing (ATC) parameter, which is evaluated by counting how many times the sEMG signal crosses a threshold during a fixed time duration (i.e., 130 ms), directly on the wearable device. Exploiting the event-driven characteristic of the ATC, our armband is able to accomplish the on-board prediction of common hand gestures requiring less power w.r.t. state of the art devices. At the end of an acquisition campaign that involved the participation of 26 people, we obtained an average classifier accuracy of 91.9% when aiming to recognize in real time 8 active hand gestures plus the idle state. Furthermore, with 2.92 mA of current absorption during active functioning and 1.34 ms prediction latency, this prototype confirmed our expectations and can be an appealing solution for long-term (up to 60 h) medical and consumer applications.

Raspberry Pi based Modular System for Multichannel Event-Driven Functional Electrical Stimulation Control.

This paper describes the implementation and testing of a modular software for multichannel control of Functional Electrical Stimulation (FES). Moving towards an embedded scenario, the core of the system is a Raspberry Pi, whose different models (with different computing powers) best suit two different system use-cases: user-supervised and stand-alone. Given the need for real-time and reliable FES applications, software processing timings were analyzed for multiple configurations, along with hardware resources utilization. Among the results, the simultaneous use of eight channels has been functionally achieved (0% lost packets) while minimizing system timing failures (excessive processing latency). Further investigations included stressing the system using more constraining acquisition parameters, eventually limiting the usable channels (only for the stand-alone use-case).

Motion Analysis for Experimental Evaluation of an Event-Driven FES System.

In this work, a system for controlling Functional Electrical Stimulation (FES) has been experimentally evaluated. The peculiarity of the system is to use an event-driven approach to modulate stimulation intensity, instead of the typical feature extraction of surface ElectroMyoGraphic (sEMG) signal. To validate our methodology, the system capability to control FES was tested on a population of 17 subjects, reproducing 6 different movements. Limbs trajectories were acquired using a gold standard motion tracking tool. The implemented segmentation algorithm has been detailed, together with the designed experimental protocol. A motion analysis was performed through a multi-parametric evaluation, including the extraction of features such as the trajectory area and the movement velocity. The obtained results show a median cross-correlation coefficient of 0.910 and a median delay of 800 ms, between each couple of voluntary and stimulated exercise, making our system comparable w.r.t. state-of-the-art works. Furthermore, a 97.39% successful rate on movement replication demonstrates the feasibility of the system for rehabilitation purposes.

Smart Portable Pen for Continuous Monitoring of Anaesthetics in Human Serum With Machine Learning.

Continuous monitoring of anaesthetics infusion is demanded by anaesthesiologists to help in defining personalized dose, hence reducing risks and side effects. We propose the first piece of technology tailored explicitly to close the loop between anaesthesiologist and patient with continuous drug monitoring. Direct detection of drugs is achieved with electrochemical techniques, and several options are present in literature to measure propofol (widely used anaesthetics). Still, the sensors proposed do not enable in-situ detection, they do not provide this information continuously, and they are based on bulky and costly lab equipment. In this paper, we present a novel smart pen-shaped electronic system for continuous monitoring of propofol in human serum. The system consists of a needle-shaped sensor, a quasi digital front-end, a smart machine learning data processing, in a single wireless battery-operated embedded device featuring Bluetooth Low Energy (BLE) communication. The system has been tested and characterized in real, undiluted human serum, at 37  °C. The device features a limit of detection of 3.8  µM, meeting the requirement of the target application, with an electronics system 59% smaller and 81% less power consuming w.r.t. the state-of-the-art, using a smart machine learning classification for data processing, which guarantees up to twenty continuous measure.

Miniaturised Wireless Power Transfer Systems for Neurostimulation: A Review.

In neurostimulation, wireless power transfer is an efficient technology to overcome several limitations affecting medical devices currently used in clinical practice. Several methods were developed over the years for wireless power transfer. In this review article, we report and discuss the three most relevant methodologies for extremely miniaturised implantable neurostimulators: ultrasound coupling, inductive coupling and capacitive coupling. For each powering method, the discussion starts describing the physical working principle. In particular, we focus on the challenges given by the miniaturisation of the implanted integrated circuits and the related ad-hoc solutions for wireless power transfer. Then, we present recent developments and progresses in wireless power transfer for biomedical applications. Last, we compare each technique based on key performance indicators to highlight the most relevant and innovative solutions suitable for neurostimulation, with the gaze turned towards miniaturisation.