Wearable electromyography measurement system using cable-free network system on conductive fabric

金沢大学大学院自然科学研究科情報システムObjective: To solve the complicated wires and battery maintenance problems in the application of wearable computing for biomedical monitoring, the electromyography (EMG) measurement system using conductive fabric for power supply and electric shield for noise reduction is proposed. Material and methods: The basic cable-free network system using conductive fabric, named as "TextileNet" is developed. The conductive fabric has the function of electric shield for noise reduction in EMG measurement, and it enables the precise EMG measurement with wearable system. Results: The specifications of the developed prototype TextileNet system using wear with conductive fabric were communication speed of 9600 bit/s and power supply of 3 W for each device. The electric shield effect was evaluated for precise EMG measurement, and the shield efficacy of conductive fabric was estimated as high as that of shield room. Conclusions: TextileNet system solves both the problems of complicated wires and battery maintenance in wearable computing systems. Conductive fabric used in TextileNet system is also effective for precise EMG measurement as electric shield. The combination of TextileNet system and EMG measurement device will implement the cable-free, battery-free wearable EMG measurement system. © 2007 Elsevier B.V. All rights reserved

Biosignal monitoring implemented in a swimsuit for athlete performance evaluation

Monitor athletes during exercise has always been a major challenge for engineers and researchers due to the restrictions involving the measurement of physiological and performance parameters. An athlete should have complete freedom to perform his normal activity, in order to be correctly monitored. The advent of e-textiles can give an important contribution to overcome these limitations since it is possible to integrate sensors in garments and thus perform monitoring without limiting the freedom of movements. This paper presents part of the work that is being carried out in the project entitled BIOSWIM, which envisions the development of an instrumented swimsuit, capable of acquiring several physiological and performance related signals with the purpose of aiding the trainer in improving the technical component of the swimmer and improve his performance. This paper will give an overview of the monitoring system and the textile sensors that were developed, namely for biopotential measurement.Fundação para a Ciência e a Tecnologia (FCT) - projeto Bioswim (PTDC/EEAELC/70803/2006

Glove-based systems for medical applications: review of recent advancements

Human hand motion analysis is attracting researchers in the areas of neuroscience, biomedical engineering, robotics, human-machines interfaces (HMI), human-computer interaction (HCI), and artificial intelligence (AI). Among the others, the fields of medical rehabilitation and physiological assessments are suggesting high impact applications for wearable sensing systems. Glove-based systems are one of the most significant devices in assessing quantities related to hand movements. This paper provides updated survey among the main glove solutions proposed in literature for hand rehabilitation. Then, the process for designing glove-based systems is defined, by including all relevant design issues for researchers and makers. The main goal of the paper is to describe the basics of glove-based systems and to outline their potentialities and limitations. At the same time, roadmap to design and prototype the next generation of these devices is defined, according to the results of previous experiences in the scientific community

Graphene textile smart clothing for wearable cardiac monitoring

Wearable electronics is a rapidly growing field that recently started to introduce successful commercial products into the consumer electronics market. Employment of biopotential signals in wearable systems as either biofeedbacks or control commands are expected to revolutionize many technologies including point of care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMIs), and brain–computer interfaces (BCIs). Since electrodes are regarded as a decisive part of such products, they have been studied for almost a decade now, resulting in the emergence of textile electrodes. This study reports on the synthesis and application of graphene nanotextiles for the development of wearable electrocardiography (ECG) sensors for personalized health monitoring applications. In this study, we show for the first time that the electrocardiogram was successfully obtained with graphene textiles placed on a single arm. The use of only one elastic armband, and an “all-textile-approach” facilitates seamless heart monitoring with maximum comfort to the wearer. The functionality of graphene textiles produced using dip coating and stencil printing techniques has been demonstrated by the non-invasive measurement of ECG signals, up to 98% excellent correlation with conventional pre-gelled, wet, silver/silver-chloride (Ag / AgCl) electrodes. Heart rate have been successfully determined with ECG signals obtained in different situations. The system-level integration and holistic design approach presented here will be effective for developing the latest technology in wearable heart monitoring devices

PhysioAR: smart sensing and augmented reality for physical rehabilitation

The continuous evolution of technology allows for a better analysis of the human being. In certain medical areas such as physiotherapy is required a correct analysis of the patient's evolution. The development of Information and Communication Technologies and recent innovations in the Internet of Things opens new possibilities in the medical field as systems of remote monitoring of patients with new sensors that allow the correct analysis of the health data of patients. In physiotherapy one of the most common problems in the application of treatments is the patient demotivation, something that today can be reduced with the introduction of Augmented Reality that provides a new experience to the patient. For this purpose, a system was developed that combines intelligent sensors with Augmented Reality application that will help monitor patient performance. This system is capable of monitoring lower limb movements acceleration, knee joint angle, patient equilibrium, muscular activity and cardiac activity using electromyography and electrocardiography with a wearable set of tools for easy utilization.A evolução continua da tecnologia permite cada vez mais uma melhor análise do ser humano. Em certas áreas médicas, como a fisioterapia, é necessária uma correta análise da evolução do paciente. O desenvolvimento das Tecnologias de Informação e Comunicação, e as inovações no domínio de Internet das Coisas novas possibilidades no ramo da medicina, como sistemas de monitorização remota de pacientes com novos sensores que permitem a correta análise dos dados de saúde dos pacientes. Na fisioterapia um dos problemas mais comuns na aplicação dos tratamentos é a desmotivação do paciente, algo que hoje pode ser reduzido com introdução da aplicação da Realidade Aumentada que proporciona uma nova experiência ao paciente. Para isso nesta dissertação foi desenvolvido um sistema que combina sensores inteligentes com Realidade Aumentada que vai ajudar o paciente monitorizando o seu desempenho. Este sistema é capaz de monitorizar o ângulo do joelho, captar acelaração de movimentos dos membros inferiores, equilíbrio do paciente, atividade muscular e atividade cárdica usando electromiografia e electrocardiografia num conjunto wearable de fácil utilização

A Biomechanical and Physiological Signal Monitoring System for Four Degrees of Upper Limb Movement

A lack of adherence to prescribed physical therapy regimens in improper healing results in poor outcomes for those affected by musculoskeletal disorders (MSDs) of the upper limb. Societal and psychological barriers to proper adherence can be addressed through the system presented in this work consisting of the following components: an ambulatory biosignal acquisition sleeve, an electromyography (EMG) based motion repetition detection algorithm, and the design of a compatible capacitive EMG acquisition module. The biosignal acquisition sleeve was untethered, unobtrusive to motion, contained only modular components, and collected biomechanical and physiological sensor data to form full motion profiles of the following four degrees of freedom: elbow flexion—extension, forearm pronation—supination, wrist flexion—extension, and ulnar--radial deviation. The piloted sleeve simultaneously collected data from four inertial sensors, two electromyography (EMG) sensors and a flex-bend sensor. A visualization application was developed to present the information in a manner meaningful to the user. As well, an EMG based motion repetition detector was developed for use within the system. It was validated using an existing database of 23 subjects with varying musculoskeletal health, achieving a success rate of 95.43%. This algorithm was modified for use with the sleeve, resulting in a 95% success rate. An electrode and analog front end module was proposed, relying on unique material structures and low-noise, precision sensing techniques. The system prototype presented a resource-conscious tool for multi-modality tracking of elbow, forearm, and wrist motion, which could eventually be integrated into upper limb MSD rehabilitation

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%