Noise Efficient Integrated Amplifier Designs for Biomedical Applications

The recording of neural signals with small monolithically integrated amplifiers is of high interest in research as well as in commercial applications, where it is common to acquire 100 or more channels in parallel. This paper reviews the recent developments in low-noise biomedical amplifier design based on CMOS technology, including lateral bipolar devices. Seven major circuit topology categories are identified and analyzed on a per-channel basis in terms of their noise-efficiency factor (NEF), input-referred absolute noise, current consumption, and area. A historical trend towards lower NEF is observed whilst absolute noise power and current consumption exhibit a widespread over more than five orders of magnitude. The performance of lateral bipolar transistors as amplifier input devices is examined by transistor-level simulations and measurements from five different prototype designs fabricated in 180 nm and 350 nm CMOS technology. The lowest measured noise floor is 9.9 nV/√Hz with a 10 µA bias current, which results in a NEF of 1.2

A Human-Machine Interface Using Electrical Impedance Tomography for Hand Prosthesis Control

This paper presents a human-machine interface that establishes a link between the user and a hand prosthesis. It successfully uses electrical impedance tomography, a conventional bio-impedance imaging technique, using an array of electrodes contained in a wristband on the user's forearm. Using a high-performance analog front-end application specific integrated circuit (ASIC) the user's forearm inner bio-impedance redistribution is accurately assessed. These bio-signatures are strongly related to hand motions and using artificial neural networks, they can be learned so as to recognize the user's intention in real-time for prosthesis operation. In this work, eleven hand motions are designed for prosthesis operation with a gesture switching enabled sub-grouping method. Experiments with five subjects show that the system can achieve 98.5% accuracy with a grouping of three gestures and an accuracy of 94.4% with two sets of five gestures. The ASIC comprises a current driver with common-mode reduction capability and a current feedback instrumentation amplifier. The ASIC operates from $\pm$1.65 V power supplies, occupies an area of 0.07 mm2, and has a minimum bio-impedance sensitivity of 12.7 mΩp-p

Конструкція автоматизованої системи протезу руки

В дипломному проекті розглянуто проблему відновлення втраченої або ампутованої кінцівки, шляхом проектування та розроблення протезу кінцівки, а саме руки з відновленням її косметичних та функціональних можливостей. Даний проект поділено на два розділи, один з них це конструкторський розділ, та технологічний розділ. Де конструкторський розділ це огляд літератури та патентів для вирішення заданих завдань та розробки даного проекту, конструкторський розділ, де відбувається побудова схем протезу, його систем руху, роботи і так далі. Проект розміщений на 84 сторінках, і містить 33 рисунків, 3 таблиці, 18 формул, 1 додаток, 71 літературних джерел. Наповнення конструкторського розділу це в першу чергу структура м’язового та рухового апарату людини, основи руху та методи ампутації верхніх кінцівок, для кращого розуміння схеми руху та функціоналу протезу. Наступні етапи це огляд побудов систем керування, та класифікація самих протезів, з оглядами патентів та інтелектуальної власності. Одним з важливих етапів буде аналіз сучасних пристроїв та після цього перехід до розробки загального вигляду конструкції автоматизованого протезу руки. Після чого вже ідуть етапи розробки електричних схем, систем руху, та повірки компонентів протезу. Наповненням технологічного розділу являється аналіз конструкції протезу з розрахунками технологічного процесу складання. Паралельно розробляються технологічний процес складання з розробками схем структурного складу та технологічного складання.The diploma project considered the problem of restoring a lost or amputated limb by designing and developing a limb prosthesis, namely a hand with the restoration of its cosmetic and functional capabilities. This project is divided into two sections, one of them is a design section and a technological section. Where the design section is a review of the literature and patents for solving the given tasks and the development of this project, the design section is where the construction of prosthesis schemes, its movement systems, work, and so on takes place. The project is placed on 83 pages and contains 33 figures, 3 tables, 18 formulas, 1 appendix, and 71 literary sources. The content of the design section is primarily the structure of the human muscular and motor apparatus, the basics of movement and methods of amputation of the upper limbs, for a better understanding of the movement scheme and functionality of the prosthesis. The next stages are a review of the construction of the control systems, and the classification of the prostheses themselves, with reviews of patents and intellectual property. One of the important stages will be the analysis of modern devices and, after that, the transition to the development of the general design of the automated hand prosthesis. After that, the stages of development of electrical circuits, motion systems, and verification of prosthesis components are already underway. The filling of the technological section is an analysis of the design of the prosthesis with calculations of the technological assembly process. In parallel, the technological assembly process is being developed with the development of schemes of structural composition and technological assembly

Advances in Integrated Circuits and Systems for Wearable Biomedical Electrical Impedance Tomography

Electrical impedance tomography (EIT) is an impedance mapping technique that can be used to image the inner impedance distribution of the subject under test. It is non-invasive, inexpensive and radiation-free, while at the same time it can facilitate long-term and real-time dynamic monitoring. Thus, EIT lends itself particularly well to the development of a bio-signal monitoring/imaging system in the form of wearable technology. This work focuses on EIT system hardware advancement using complementary metal oxide semiconductor (CMOS) technology. It presents the design and testing of application specific integrated circuit (ASIC) and their successful use in two bio-medical applications, namely, neonatal lung function monitoring and human-machine interface (HMI) for prosthetic hand control. Each year fifteen million babies are born prematurely, and up to 30% suffer from lung disease. Although respiratory support, especially mechanical ventilation, can improve their survival, it also can cause injury to their vulnerable lungs resulting in severe and chronic pulmonary morbidity lasting into adulthood, thus an integrated wearable EIT system for neonatal lung function monitoring is urgently needed. In this work, two wearable belt systems are presented. The first belt features a miniaturized active electrode module built around an analog front-end ASIC which is fabricated with 0.35-µm high-voltage process technology with ±9 V power supplies and occupies a total die area of 3.9 mm². The ASIC offers a high power active current driver capable of up to 6 mAp-p output, and wideband active buffer for EIT recording as well as contact impedance monitoring. The belt has a bandwidth of 500 kHz, and an image frame rate of 107 frame/s. To further improve the system, the active electrode module is integrated into one ASIC. It contains a fully differential current driver, a current feedback instrumentation amplifier (IA), a digital controller and multiplexors with a total die area of 9.6 mm². Compared to the conventional active electrode architecture employed in the first EIT belt, the second belt features a new architecture. It allows programmable flexible electrode current drive and voltage sense patterns under simple digital control. It has intimate connections to the electrodes for the current drive and to the IA for direct differential voltage measurement providing superior common-mode rejection ratio (CMRR) up to 74 dB, and with active gain, the noise level can be reduced by a factor of √3 using the adjacent scan. The second belt has a wider operating bandwidth of 1 MHz and multi-frequency operation. The image frame rate is 122 frame/s, the fastest wearable EIT reported to date. It measures impedance with 98% accuracy and has less than 0.5 Ω and 1° variation across all channels. In addition the ASIC facilitates several other functionalities to provide supplementary clinical information at the bedside. With the advancement of technology and the ever-increasing fusion of computer and machine into daily life, a seamless HMI system that can recognize hand gestures and motions and allow the control of robotic machines or prostheses to perform dexterous tasks, is a target of research. Originally developed as an imaging technique, EIT can be used with a machine learning technique to track bones and muscles movement towards understanding the human user’s intentions and ultimately controlling prosthetic hand applications. For this application, an analog front-end ASIC is designed using 0.35-µm standard process technology with ±1.65 V power supplies. It comprises a current driver capable of differential drive and a low noise (9μVrms) IA with a CMRR of 80 dB. The function modules occupy an area of 0.07 mm². Using the ASIC, a complete HMI system based on the EIT principle for hand prosthesis control has been presented, and the user’s forearm inner bio-impedance redistribution is assessed. Using artificial neural networks, bio-impedance redistribution can be learned so as to recognise the user’s intention in real-time for prosthesis operation. In this work, eleven hand motions are designed for prosthesis operation. Experiments with five subjects show that the system can achieve an overall recognition accuracy of 95.8%

Advanced Interfaces for HMI in Hand Gesture Recognition

The present thesis investigates techniques and technologies for high quality Human Machine Interfaces (HMI) in biomedical applications. Starting from a literature review and considering market SoA in this field, the thesis explores advanced sensor interfaces, wearable computing and machine learning techniques for embedded resource-constrained systems. The research starts from the design and implementation of a real-time control system for a multifinger hand prosthesis based on pattern recognition algorithms. This system is capable to control an artificial hand using a natural gesture interface, considering the challenges related to the trade-off between responsiveness, accuracy and light computation. Furthermore, the thesis addresses the challenges related to the design of a scalable and versatile system for gesture recognition with the integration of a novel sensor interface for wearable medical and consumer application