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 2.92mA of current absorption during active functioning and 1.34mA prediction latency, this prototype confirmed our expectations and can be an appealing solution for long-term (up to 60 h) medical and consumer applications

Guest Editorial Circuits and Systems for Smart Agriculture and Healthy Foods

This Special Issue of the IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS (JETCAS) is dedicated to Circuits and Systems applied to innovative products for the Agriculture and Food value chain

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 3.8µM, 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

A Periodic Transmission Line Model for Body Channel Communication

Body channel communication (BCC) is a technique for data transmission exploiting the human body as communication channel. Even though it was pioneered about 25 years ago, the identification of a good electrical model behind its functioning is still an open research question. The proposed distributed model can then serve as a supporting tool for the design, allowing to enhance the performances of any BCC system. A novel finite periodic transmission line model was developed to describe the human body as transmission medium. According to this model, for the first time, the parasitic capacitance between the transmitter and the receiver is assumed to depend on their distance. The parameters related to the body and electrodes are acquired experimentally by fitting the bio-impedentiometric measurements, in the range of frequencies from 1 kHz to 1 MHz, obtaining a mean absolute error lower than 4° and 30Ω for the phase angle and impedance modulus, respectively. The proposed mathematical framework has been successfully validated by describing a ground-referred and low-complexity system called Live Wire, suitable as supporting tool for visually impaired people, and finding good agreement between the measured and the calculated data, marking a ±3% error for communication distances ranging from 20 to 150 cm. In this work we introduced a new circuital approach, for capacitive-coupling systems, based on finite periodic transmission line, capable to describe and model BCC systems allowing to optimize the performances of similar systems

A Periodic Transmission Line Model for Body Channel Communication

Body channel communication (BCC) is a technique for data transmission exploiting the human body as communication channel. Even though it was pioneered about 25 years ago, the identification of a good electrical model behind its functioning is still an open research question. The proposed distributed model can then serve as a supporting tool for the design, allowing to enhance the performances of any BCC system. A novel finite periodic transmission line model was developed to describe the human body as transmission medium. According to this model, for the first time, the parasitic capacitance between the transmitter and the receiver is assumed to depend on their distance. The parameters related to the body and electrodes are acquired experimentally by fitting the bio-impedentiometric measurements, in the range of frequencies from 1 kHz to 1 MHz, obtaining a mean absolute error lower than 4° and 30 $Omega$ for the phase angle and impedance modulus, respectively. The proposed mathematical framework has been successfully validated by describing a ground-referred and low-complexity system called Live Wire, suitable as supporting tool for visually impaired people, and finding good agreement between the measured and the calculated data, marking a ±3% error for communication distances ranging from 20 to 150 cm. In this work we introduced a new circuital approach, for capacitive-coupling systems, based on finite periodic transmission line, capable to describe and model BCC systems allowing to optimize the performances of similar systems

Rakeness-based Compressed Sensing of Surface ElectroMyoGraphy for Improved Hand Movement Recognition in the Compressed Domain

Surface electromyography (sEMG) waveforms are widely used to generate control signals in several application areas, ranging from prosthetic to consumer electronics. Classically, such waveforms are acquired at Nyquist rate and digitally transmitted trough a wireless channel to a decision/actuation node. This causes large energy consumption and is incompatible with the implementation of ultra-low power acquisition nodes. We already proposed Compressed Sensing (CS) as a low-complexity method to achieve substantial energy saving by reducing the size of data to be transmitted while preserving the information content. We here make a significant leap forward by showing that hand movements recognition task can be performed directly in the compressed domain with a success rate greater than 98 % and with a reduction of the number of transmitted bits by two order of magnitude with respect to row data

Tutorial: A Versatile Bio-Inspired System for Processing and Transmission of Muscular Information

Device wearability and operating time are trending topics in recent state-of-art works on surface ElectroMyoGraphic (sEMG) muscle monitoring. No optimal trade-off, able to concurrently address several problems of the acquisition system like robustness, miniaturization, versatility, and power efficiency, has yet been found. In this tutorial we present a solution to most of these issues, embedding in a single device both an sEMG acquisition channel, with our custom event-driven hardware feature extraction technique (named Average Threshold Crossing), and a digital part, which includes a microcontroller unit, for (optionally) sEMG sampling and processing, and a Bluetooth communication, for wireless data transmission. The knowledge acquired by the research group brought to an accurate selection of each single component, resulting in a very efficient prototype, with a comfortable final size (57.8mm x 25.2mm x 22.1mm) and a consistent signal-to-noise ratio of the acquired sEMG (higher than 15 dB). Furthermore, a precise design of the firmware has been performed, handling both signal acquisition and Bluetooth transmission concurrently, thanks to a FreeRTOS custom implementation. In particular, the system adapts to both sEMG and ATC transmission, with an application throughput up to 2 kB s-1 and an average operating time of 80 h (for high resolution sEMG sampling), relaxable to 8Bs-1 throughput and about 230 h operating time (considering a 110mAh battery), in case of ATC acquisition only. Here we share our experience over the years in designing wearable systems for the sEMG detection, specifying in detail how our event-driven approach could benefit the device development phases. Some previous basic knowledge about biosignal acquisition, electronic circuits and programming would certainly ease the repeatability of this tutorial

Live Demonstration: 3D Wound Detection & Tracking System Based on Artificial Intelligence Algorithm

Every year over 2% of the worldwide population (estimated 20 million people between US and Europe) will develop a cutaneous wound during their lifetime experiencing chronic pain, reduced mobility and a high amputation and mortality risk (75% within 5 years). Aging of the population and a sharp rise in the incidence of obesity and chronic diseases such as diabetes are the main drivers of this epidemic that is forecast to grow with a 5-8% rate over the following 5 years. Clinical studies proved that is possible to reduce healing time and the advent of adverse consequences of 50% carrying out a monitoring of the variation of key parameters. Nowadays physicians don’t have access to a precise decision-supporting tool to valuate and monitor healing process and the effectiveness of the delivered treatment. Our solution is called Wound Viewer, a Class 1 medical device able to acquire and automatically process wound images through an artificial intelligence (AI) algorithm providing to the physician fundamental parameters such as area, depth, recognize the tissue composing the wound (example: granular, necrotic) and wound exudate. Wound Viewer has been tested on over 400 patients reaching a measurement accuracy of over 94%

A Low-Complexity 6DOF Magnetic Tracking System Based on Pre-Computed Data Sets for Wearable Applications

4We present a 6DOF magnetic tracking system based on a low-complexity algorithm, operating with an Inertial Measurement Unit (IMU) orientation estimation and regression functions formed with simulated data sets, capable of running using only a single microcontroller unit (MCU), for use in low-complexity wearable and wireless systems. A prototype based on a commercial magnetometer and IMU, a Cortex-M4 MCU was implemented and tested in both static and dynamic conditions, using a VICON motion tracking system as reference. Static and dynamic spatial accuracy performance is 2.6,mm and 5.4,mm respectively, after applying a calibration procedure based on a two layers Neural Network (NN) and a measured data set. Comparison with the state-of-the-art, supported by a defined Figure-of-Merit (FoM) show excellent performance compared to commercial and research systems in a low-complexity and portable solutionpartially_openopenDavid A. Fernandez Guzman; Paolo Motto Ros; Danilo Demarchi; Marco CrepaldiFernandez Guzman, David A.; MOTTO ROS, Paolo; Demarchi, Danilo; Crepaldi, Marc

Quasi-Digital Biosensor-Interface for a Portable Pen to Monitor Anaesthetics Delivery

Monitoring of patient response to the anaesthetic drugs is an attractive improvement for achieving a correct balance of sedation level, increasing the chance of success in the right procedure of anaesthesia. Nowadays, there are no commercial tools able to offer real-time monitoring of anaesthetics, indeed, there is still a lack in sensing technologies able to maintain high performances in long term monitoring within a portable miniaturised hardware system. To overcome these limitations, we are here presenting the innovative concept of a portable pen-device able to sense anaesthetic compounds over time. This study is based on an electrochemical sensor to be fully integrated into a complete pen-shaped point-of-care for the monitoringof anaesthesia delivery. The design of the system is based on a bio-inspired event-based approach that is guaranteeing low complexity, low power consumption and is therefore suitable to be scaled to fit the barrel of a pen. An exhaustive comparison between the proposed system and a lab instrument proves that the presented approach obtains comparable performances in terms of sensitivity and resolution with the ones obtained by expensive commercial instrumentation, meanwhile, the results show a 95 % power consumption reduction and a 92 % area decrease w.r.t. previously presented implementation