Towards a high accuracy wearable hand gesture recognition system using EIT

This paper presents a high accuracy hand gesture recognition system based on electrical impedance tomography (EIT). The system interfaces the forearm using a wrist wrap with embedded electrodes. It measures the inner conductivity distributions caused by bone and muscle movement of the forearm in real-time and passes the data to a deep learning neural network for gesture recognition. The system has an EIT bandwidth of 500 kHz and a measured sensitivity in excess of 6.4 Ω per frame. Nineteen hand gestures are designed for recognition, and with the proposed round robin sub-grouping method, an accuracy of over 98% is achieved

Live Demonstration: A Wearable EIT System for Hand Prosthesis Motion Controls

A wearable electrical impedance tomography (EIT) system for hand prosthesis motion control is demonstrated. The system captures the user's hand motion by measuring the impedance alterations caused by muscle and bone movement inside the forearm. These impedance data are sent to an artificial neural network for motion classification which is then used to manipulate a hand prosthesis. During the live demonstration, a sensor band is put on a volunteers' forearm for data acquisition. After signal processing, hand gestures learnt by the neural network can be recognized and the same hand motion can be recreated through the hand prosthesis in real-time

A Low Power, Low THD Current Driver with Discrete Common-Mode Feedback for EIT Applications

A low THD sinusoidal current driver for electrical impedance tomography (EIT) applications is proposed and analyzed in this paper. A discrete common-mode feedback method is proposed to increase the accuracy of the output current amplitude and output impedance. The current driver is designed in 65 nm technology under 3.3 V supply with a chip area of 0.0843 mm2. The maximum output current amplitude is 1.2 mA. In simulations the current driver achieves an average THD of 0.098% at 1 mA output current amplitude and 500 kHz output current frequency. The simulated output impedance is higher than 4 MΩ at a load impedance lower than 3.5 kΩ. The current consumption of the circuit is 1.47 mA and provides a current efficiency of 81.6%

A Gesture-based Recognition System for Augmented Reality

With the geometrical improvement in Information Technology, current conventional input devices are becoming increasingly obsolete and lacking. Experts in Human Computer Interaction (HCI) are convinced that input devices remain the bottleneck of information acquisition specifically in when using Augmented Reality (AR) technology. Current input mechanisms are unable to compete with this trend towards naturalness and expressivity which allows users to perform natural gestures or operations and convert them as input. Hence, a more natural and intuitive input device is imperative, specifically gestural inputs that have been widely perceived by HCI experts as the next big input device. To address this gap, this project is set to develop a prototype of hand gesture recognition system based on computer vision in modeling basic human-computer interactions. The main motivation in this work is a technology that requires no outfitting of additional equipment whatsoever by the users. The gesture-based had recognition system was implemented using the Rapid Application Development (RAD) methodology and was evaluated in terms of its usability and performance through five levels of testing, which are unit testing, integration testing, system testing, recognition accuracy testing, and user acceptance testing. The test results of unit, integration, system testing as well as user acceptance testing produced favorable results. In conclusion, current conventional input devices will continue to bottleneck this advancement in technology; therefore, a better alternative input technique should be looked into, in particularly, gesture-based input technique which offers user a more natural and intuitive control

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

Design and implementation of a multi-modal sensor with on-chip security

With the advancement of technology, wearable devices for fitness tracking, patient monitoring, diagnosis, and disease prevention are finding ways to be woven into modern world reality. CMOS sensors are known to be compact, with low power consumption, making them an inseparable part of wireless medical applications and Internet of Things (IoT). Digital/semi-digital output, by the translation of transmitting data into the frequency domain, takes advantages of both the analog and digital world. However, one of the most critical measures of communication, security, is ignored and not considered for fabrication of an integrated chip. With the advancement of Moore\u27s law and the possibility of having a higher number of transistors and more complex circuits, the feasibility of having on-chip security measures is drawing more attention. One of the fundamental means of secure communication is real-time encryption. Encryption/ciphering occurs when we encode a signal or data, and prevents unauthorized parties from reading or understanding this information. Encryption is the process of transmitting sensitive data securely and with privacy. This measure of security is essential since in biomedical devices, the attacker/hacker can endanger users of IoT or wearable sensors (e.g. attacks at implanted biosensors can cause fatal harm to the user). This work develops 1) A low power and compact multi-modal sensor that can measure temperature and impedance with a quasi-digital output and 2) a low power on-chip signal cipher for real-time data transfer

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%