A wireless ExG interface for patch-type ECG holter and EMG-controlled robot hand

This paper presents a wearable electrophysiological interface with enhanced immunity to motion artifacts. Anti-artifact schemes, including a patch-type modular structure and real-time automatic level adjustment, are proposed and verified in two wireless system prototypes of a patch-type electrocardiogram (ECG) module and an electromyogram (EMG)-based robot-hand controller. Their common ExG readout integrated circuit (ROIC), which is reconfigurable for multiple physiological interfaces, is designed and fabricated in a 0.18 ??m CMOS process. Moreover, analog pre-processing structures based on envelope detection are integrated with one another to mitigate signal processing burdens in the digital domain effectivel

Low-power Wearable Healthcare Sensors

Advances in technology have produced a range of on-body sensors and smartwatches that can be used to monitor a wearer’s health with the objective to keep the user healthy. However, the real potential of such devices not only lies in monitoring but also in interactive communication with expert-system-based cloud services to offer personalized and real-time healthcare advice that will enable the user to manage their health and, over time, to reduce expensive hospital admissions. To meet this goal, the research challenges for the next generation of wearable healthcare devices include the need to offer a wide range of sensing, computing, communication, and human–computer interaction methods, all within a tiny device with limited resources and electrical power. This Special Issue presents a collection of six papers on a wide range of research developments that highlight the specific challenges in creating the next generation of low-power wearable healthcare sensors

Multi-Biosignal Sensing Interface with Direct Sleep-Stage Classification

Department of Electrical EngineeringSleep is a time of mental and physical rest in a person???s daily cycle. It is an indispensable metabolic activity that helps the body grow and boosts immunity. Therefore, sleep disorders can cause illness in the body as well as just physical condition. Among these diseases are typically included rapid eye movement (REM) sleep behavior disorder, nocturnal enuresis, sleepwalking, etc., which can cause serious injury on sleep. Sleep disorders are a common disease. According to a survey, sleep deprivation and disability affect a significant part of the world???s population. It is a disease that affects tens of millions of people around the world. In general, treatment for sleep disorders checks the quality during sleep and prescribes various sleep diseases by checking the condition of sleep. Sleep quality and sleep disease are determined by the depth and time of the sleep phase. Therefore, the analysis and classification of the sleep stage are essential. According to the manual of the American Academy of Sleep Medicine (AASM), sleep stages are divided into five stages. Various methods for sleep analysis have been developed. Polysomnography (PSG), called the golden standard, is the most reliable measurement of sleep quality in hospitals for sleep disorders, but this conventional method requires the use of various human body signals, and it is difficult to access due to the complex interface and various electrodes. It is not economical because of its infrastructure, which does not lead to direct treatment of prospective patients. In addition, the conventional interface system process is not an integrated interface system. The integrated interface system refers to the integration of the interface in the measuring and analysis process. Conventional sensing and analysis take place on the instrument measuring the patient and on the analyst???s computer. Therefore, conventional treatments are not economical and make patient self-analysis difficult. Furthermore, this makes it difficult to increase the demand for prospective patients. This paper presents a multi-biosignal sensing interface system with direct sleep-stage classification. Unlike conventional systems, this work proposes an interface system that is an integrated interface system, measuring, and analysis based on the analog circuit and system. The proposed paper configures a multi-biosignal sensing interface consisting of single-channel EEG, EMG, and 2EoG. The multi-biosignal sensing readout integrated circuit (ROIC) collects analog signals from the electrodes and extracts features from the signal. The multi-biosignal sensing ROIC has a feature extraction stage that directly extracts the characteristic of sleep stages. The analog feature extraction stage consists of the optimized circuit for three multi-biosignal extracts the feature of each stage during sleep on the waveform. The extracted signal is scored by the rule-based decision tree sleep stage proposed by the micro controller unit (MCU). The multi-biosignal sensing ROIC can analyze the sleep stage through EEG, EMG, and 2EOG, and can simultaneously analyze four channels. The multi-biosignal sensing ROIC is implemented using a compensate metal-oxide-semiconductor 0.18um process. In addition, this system implements a low-power, integrated module for portable device configuration, and from this interface makes a smart headband for prospective patients. Depending on the purpose of use, it consists of 2 type paths, including raw data recording and analog feature extraction based direct sleep classification using decision tree algorithm. Finally, sleep stage scoring can be displayed, or raw data can be sent to the personal computer interface to increase accuracy. The sleep stage was verified by comparing the OpenBCI module-based MATLAB analysis using SVM with this system, and the result shows an overall accuracy of 74% for four sleep stages.clos

A HARDWARE-SOFTWARE CO-DESIGNED WEARABLE FOR REAL-TIME PHYSIOLOGICAL DATA COLLECTION AND SIGNAL QUALITY ASSESSMENT

In the future, Smart and Connected Communities (S&CC) will use distributed wireless sensors and embedded computing platforms to produce meaningful data that can help individuals, and communities. Here, we presented a scanner, a data reliability estimation algorithm and Electrocardiogram (ECG) beat classification algorithm which contributes to the S&CC framework .In part 1, we report the design, prototyping, and functional validation of a low-power, small, and portable signal acquisition device for these sensors. The scanner was fully tested, characterized, and validated in the lab, as well as through deployment to users homes. As a test case, we show results of the scanner measuring WRAP temperature sensors with relative error within the 0.01% range. The scanner measurement shows distinguish temperature of 1F difference and excellent linear dependence between actual and measured resistance (R2 = 0.998). This device hasdemonstrated the possibility of a small, low-power portable scanner for WRAP sensors.Additionally, we explored the statistical data reliability metric (DReM) to explain the quality of bio-signal quantitatively on a scale between 0.0 -1.0. As proof of concept, we analyzed the ECG signal. Our DReM prediction algorithm measures the reliability of the ECG signals effectively with low Root mean square error = 0.010 and Mean absolute error = 0.008 and coefficient of determination R2 value of 0.990. Finally, we tested our model against the opinions of three independent judges and presented R2 value to determine the agreement between judgments vs our prediction model.We concluded our contribution to the S&CC framework by analyzing ECG beat classification with a pipeline of classifiers that focuses on improving the models performance on identifying minority classes (ventricular ectopic beat, supraventricular ectopic beat). Moreover, we intended to minimize morphological distortion introduced due to indiscriminate use of filtering techniques on ECG signals. Our approach shows an average positive predictive value 95.21%, sensitivity of95.28%, and F-1 score 95.76% respectively

Wired, wireless and wearable bioinstrumentation for high-precision recording of bioelectrical signals in bidirectional neural interfaces

It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Furthermore, electrical stimulation of specific target brain regions has been shown to alleviate symptoms of neurological disorders, such as Parkinson’s disease, essential tremor, dystonia, epilepsy etc. In recent years, the traditional practice of continuously stimulating the brain using static stimulation parameters has shifted to the use of disease biomarkers to determine the intensity and timing of stimulation. The main motivation behind closed-loop stimulation is minimization of treatment side effects by providing only the necessary stimulation required within a certain period of time, as determined from a guiding biomarker. Hence, it is clear that high-quality recording of local field potentials (LFPs) or electrocorticographic (ECoG) signals during deep brain stimulation (DBS) is necessary to investigate the instantaneous brain response to stimulation, minimize time delays for closed-loop neurostimulation and maximise the available neural data. To our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording ECoG signals, which are of particular importance in closed-loop DBS and epilepsy DBS. In addition, existing recording systems lack the ability to provide artefact-free high-frequency (> 100 Hz) LFP recordings during DBS in real time primarily because of the contamination of the neural signals of interest by the stimulation artefacts. To address the problem of limited mobility often encountered by patients in the clinic and to provide a wide variety of high-precision sensor data to a closed-loop neurostimulation platform, a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless multi-instrument (55 × 80 mm2) was designed and developed. The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments. The combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5 – 500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile tool to be utilized in a wide range of applications and environments. Moreover, in order to offer the capability of sensing and stimulating via the same electrode, novel real-time artefact suppression methods that could be used in bidirectional (recording and stimulation) system architectures are proposed and validated. More specifically, a novel, low-noise and versatile analog front-end (AFE), which uses a high-order (8th) analog Chebyshev notch filter to suppress the artefacts originating from the stimulation frequency, is presented. After defining the system requirements for concurrent LFP recording and DBS artefact suppression, the performance of the realised AFE is assessed by conducting both in vitro and in vivo experiments using unipolar and bipolar DBS (monophasic pulses, amplitude ranging from 3 to 6 V peak-to-peak, frequency 140 Hz and pulse width 100 µs). Under both in vitro and in vivo experimental conditions, the proposed AFE provided real-time, low-noise and artefact-free LFP recordings (in the frequency range 0.5 – 250 Hz) during stimulation. Finally, a family of tunable hardware filter designs and a novel method for real-time artefact suppression that enables wide-bandwidth biosignal recordings during stimulation are also presented. This work paves the way for the development of miniaturized research tools for closed-loop neuromodulation that use a wide variety of bioelectrical signals as control signals.Open Acces

Applications of Silicon Retinas: from Neuroscience to Computer Vision

Traditional visual sensor technology is firmly rooted in the concept of sequences of image frames. The sequence of stroboscopic images in these "frame cameras" is very different compared to the information running from the retina to the visual cortex. While conventional cameras have improved in the direction of smaller pixels and higher frame rates, the basics of image acquisition have remained the same. Event-based vision sensors were originally known as "silicon retinas" but are now widely called "event cameras." They are a new type of vision sensors that take inspiration from the mechanisms developed by nature for the mammalian retina and suggest a different way of perceiving the world. As in the neural system, the sensed information is encoded in a train of spikes, or so-called events, comparable to the action potential generated in the nerve. Event-based sensors produce sparse and asynchronous output that represents in- formative changes in the scene. These sensors have advantages in terms of fast response, low latency, high dynamic range, and sparse output. All these char- acteristics are appealing for computer vision and robotic applications, increasing the interest in this kind of sensor. However, since the sensor’s output is very dif- ferent, algorithms applied for frames need to be rethought and re-adapted. This thesis focuses on several applications of event cameras in scientific scenarios. It aims to identify where they can make the difference compared to frame cam- eras. The presented applications use the Dynamic Vision Sensor (event camera developed by the Sensors Group of the Institute of Neuroinformatics, University of Zurich and ETH). To explore some applications in more extreme situations, the first chapters of the thesis focus on the characterization of several advanced versions of the standard DVS. The low light condition represents a challenging situation for every vision sensor. Taking inspiration from standard Complementary Metal Oxide Semiconductor (CMOS) technology, the DVS pixel performances in a low light scenario can be improved, increasing sensitivity and quantum efficiency, by using back-side illumination. This thesis characterizes the so-called Back Side Illumination DAVIS (BSI DAVIS) camera and shows results from its application in calcium imaging of neural activity. The BSI DAVIS has shown better performance in the low light scene due to its high Quantum Efficiency (QE) of 93% and proved to be the best type of technology for microscopy application. The BSI DAVIS allows detecting fast dynamic changes in neural fluorescent imaging using the green fluorescent calcium indicator GCaMP6f. Event camera advances have pushed the exploration of event-based cameras in computer vision tasks. Chapters of this thesis focus on two of the most active research areas in computer vision: human pose estimation and hand gesture classification. Both chapters report the datasets collected to achieve the task, fulfilling the continuous need for data for this kind of new technology. The Dynamic Vision Sensor Human Pose dataset (DHP19) is an extensive collection of 33 whole-body human actions from 17 subjects. The chapter presents the first benchmark neural network model for 3D pose estimation using DHP19. The network archives a mean error of less than 8 mm in the 3D space, which is comparable with frame-based Human Pose Estimation (HPE) methods using frames. The gesture classification chapter reports an application running on a mobile device and explores future developments in the direction of embedded portable low power devices for online processing. The sparse output from the sensor suggests using a small model with a reduced number of parameters and low power consumption. The thesis also describes pilot results from two other scientific imaging applica- tions for raindrop size measurement and laser speckle analysis presented in the appendices

Design of a robotic transcranial magnetic stimulation system

Transcranial Magnetic Stimulation (TMS) is an excellent and non-invasive technique for studying the human brain. Accurate placement of the magnetic coil is required by this technique in order to induce a specific cortical activity. Currently, the coil is manually held in most of stimulation procedures, which does not achieve the precise clinical evaluation of the procedure. This thesis proposes a robotic TMS system to resolve these problems as a robot has excellent locating and holding capabilities. The proposed system can track in real-time the subject’s head position and simultaneously maintain a constant contact force between the coil and the subject’s head so that it does not need to be restrained and thus ensure the accuracy of the stimulation result. Requirements for the robotic TMS system are proposed initially base on analysis of a serial of TMS experiments on real subjects. Both hardware and software design are addressed according to these requirements in this thesis. An optical tracking system is used in the system for guiding and tracking the motion of the robot and inadvertent small movements of the subject’s head. Two methods of coordinate system registration are developed base on DH and Tsai-lenz’s method, and it is found that DH method has an improved accuracy (RMS error is 0.55mm). In addition, the contact force is controlled using a Force/Torque sensor; and a combined position and force tracking controller is applied in the system. This combined controller incorporates the position tracking and conventional gain scheduling force control algorithms to monitor both position and force in real-time. These algorithms are verified through a series of experiments. And it is found that the maximum position and force error are 3mm and 5N respectively when the subject moves at a speed of 20mm/s. Although the performance still needs to be improved to achieve a better system, the robotic system has shown the significant advantage compared with the manual TMS system. Keywords—Transcranial Magnetic Stimulation, Robot arm, Medical system, Calibration, TrackingEThOS - Electronic Theses Online ServiceGBUnited Kingdo

Advanced photonic and electronic systems - WILGA 2017

WILGA annual symposium on advanced photonic and electronic systems has been organized by young scientist for young scientists since two decades. It traditionally gathers more than 350 young researchers and their tutors. Ph.D students and graduates present their recent achievements during well attended oral sessions. Wilga is a very good digest of Ph.D. works carried out at technical universities in electronics and photonics, as well as information sciences throughout Poland and some neighboring countries. Publishing patronage over Wilga keep Elektronika technical journal by SEP, IJET by PAN and Proceedings of SPIE. The latter world editorial series publishes annually more than 200 papers from Wilga. Wilga 2017 was the XL edition of this meeting. The following topical tracks were distinguished: photonics, electronics, information technologies and system research. The article is a digest of some chosen works presented during Wilga 2017 symposium. WILGA 2017 works were published in Proc. SPIE vol.10445

Design of a robotic transcranial magnetic stimulation system

Transcranial Magnetic Stimulation (TMS) is an excellent and non-invasive technique for studying the human brain. Accurate placement of the magnetic coil is required by this technique in order to induce a specific cortical activity. Currently, the coil is manually held in most of stimulation procedures, which does not achieve the precise clinical evaluation of the procedure. This thesis proposes a robotic TMS system to resolve these problems as a robot has excellent locating and holding capabilities. The proposed system can track in real-time the subject’s head position and simultaneously maintain a constant contact force between the coil and the subject’s head so that it does not need to be restrained and thus ensure the accuracy of the stimulation result. Requirements for the robotic TMS system are proposed initially base on analysis of a serial of TMS experiments on real subjects. Both hardware and software design are addressed according to these requirements in this thesis. An optical tracking system is used in the system for guiding and tracking the motion of the robot and inadvertent small movements of the subject’s head. Two methods of coordinate system registration are developed base on DH and Tsai-lenz’s method, and it is found that DH method has an improved accuracy (RMS error is 0.55mm). In addition, the contact force is controlled using a Force/Torque sensor; and a combined position and force tracking controller is applied in the system. This combined controller incorporates the position tracking and conventional gain scheduling force control algorithms to monitor both position and force in real-time. These algorithms are verified through a series of experiments. And it is found that the maximum position and force error are 3mm and 5N respectively when the subject moves at a speed of 20mm/s. Although the performance still needs to be improved to achieve a better system, the robotic system has shown the significant advantage compared with the manual TMS system. Keywords—Transcranial Magnetic Stimulation, Robot arm, Medical system, Calibration, TrackingEThOS - Electronic Theses Online ServiceGBUnited Kingdo

Electronic bidirectional interfaces to the peripheral nervous system for prosthetic applications

The research presented in this thesis concerns the field of bioelectronics, in particular the work has been focused on the development of special electronic devices for neural signal acquisition and Peripheral Nervous System (PNS) stimulation. The final aim of the project in which this work is involved is in fact the realization of a prosthetic hand controlled using neural signals. The commercially available prosthesis are based on Electromyographic (EMG) signals, their use implies unnatural movements for the patient that needs a special training to develop the control capabilities over the mechanical limb. The proposed approach offers a number of advantages compared to the traditional prosthesis, first because the signals used are the same used to control the biologic limb, allowing a more comfortable solution for the patient that gets closer to feel the robotic hand as a natural extension of his/her body. Secondly, placing temperature and pressure sensors on the limb surface, it is possible to trasduce such information in an electrical current that, injected into the PNS, can restore the sensory feedback in amputees. The final goal of this research is the development of a fully implantable device able to perform a bidirectional communication between the robotic hand and the patient. Due to small area, low noise and low power constraints, the only possible way to reach this aim is the design of a full custom Integrated Circuit (IC). However a preliminary evaluation of the key design features, such as neural signal amplitudes and frequencies as well as stimulation shape parameters, is necessary in order to define clearly and precisely the design specifications. A low-cost and short implementation time device is then needed for this aim, the Components Off The Shelf (COTS) approach seems to be the best solution for this purpose. A Printed Circuit Board (PCB) with discrete components has been designed, developed and tested, the information extracted by the test results have been used to guide the IC design. The generation of electrical signals in biological cells, such as neural spikes, is possible thanks to ions that move across the cell membrane. In many applications it is important, not only to record the spikes, but also to measure these small currents in order to understand which electro-chemical processes are involved in the signal generation and to have a direct measurement of the ion channels involved in the reaction. Ion currents, in fact, play a key role in several physiological processes, in neural signal generation, but also in the maintenance of heartbeat and in muscle contraction. For this purpose, a system level implementation of a Read out circuit for ion channel current detection has been developed