Measurements, Models, Systems and Design

531 s.

Improving the mechanistic study of neuromuscular diseases through the development of a fully wireless and implantable recording device

Neuromuscular diseases manifest by a handful of known phenotypes affecting the peripheral nerves, skeletal muscle fibers, and neuromuscular junction. Common signs of these diseases include demyelination, myasthenia, atrophy, and aberrant muscle activity—all of which may be tracked over time using one or more electrophysiological markers. Mice, which are the predominant mammalian model for most human diseases, have been used to study congenital neuromuscular diseases for decades. However, our understanding of the mechanisms underlying these pathologies is still incomplete. This is in part due to the lack of instrumentation available to easily collect longitudinal, in vivo electrophysiological activity from mice. There remains a need for a fully wireless, batteryless, and implantable recording system that can be adapted for a variety of electrophysiological measurements and also enable long-term, continuous data collection in very small animals. To meet this need a miniature, chronically implantable device has been developed that is capable of wirelessly coupling energy from electromagnetic fields while implanted within a body. This device can both record and trigger bioelectric events and may be chronically implanted in rodents as small as mice. This grants investigators the ability to continuously observe electrophysiological changes corresponding to disease progression in a single, freely behaving, untethered animal. The fully wireless closed-loop system is an adaptable solution for a range of long-term mechanistic and diagnostic studies in rodent disease models. Its high level of functionality, adjustable parameters, accessible building blocks, reprogrammable firmware, and modular electrode interface offer flexibility that is distinctive among fully implantable recording or stimulating devices. The key significance of this work is that it has generated novel instrumentation in the form of a fully implantable bioelectric recording device having a much higher level of functionality than any other fully wireless system available for mouse work. This has incidentally led to contributions in the areas of wireless power transfer and neural interfaces for upper-limb prosthesis control. Herein the solution space for wireless power transfer is examined including a close inspection of far-field power transfer to implanted bioelectric sensors. Methods of design and characterization for the iterative development of the device are detailed. Furthermore, its performance and utility in remote bioelectric sensing applications is demonstrated with humans, rats, healthy mice, and mouse models for degenerative neuromuscular and motoneuron diseases

Pattern recognition-based real-time myoelectric control for anthropomorphic robotic systems : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronics at Massey University, Manawatū, New Zealand

All copyrighted Figures have been removed but may be accessed via their source cited in their respective captions.Advanced human-computer interaction (HCI) or human-machine interaction (HMI) aims to help humans interact with computers smartly. Biosignal-based technology is one of the most promising approaches in developing intelligent HCI systems. As a means of convenient and non-invasive biosignal-based intelligent control, myoelectric control identifies human movement intentions from electromyogram (EMG) signals recorded on muscles to realise intelligent control of robotic systems. Although the history of myoelectric control research has been more than half a century, commercial myoelectric-controlled devices are still mostly based on those early threshold-based methods. The emerging pattern recognition-based myoelectric control has remained an active research topic in laboratories because of insufficient reliability and robustness. This research focuses on pattern recognition-based myoelectric control. Up to now, most of effort in pattern recognition-based myoelectric control research has been invested in improving EMG pattern classification accuracy. However, high classification accuracy cannot directly lead to high controllability and usability for EMG-driven systems. This suggests that a complete system that is composed of relevant modules, including EMG acquisition, pattern recognition-based gesture discrimination, output equipment and its controller, is desirable and helpful as a developing and validating platform that is able to closely emulate real-world situations to promote research in myoelectric control. This research aims at investigating feasible and effective EMG signal processing and pattern recognition methods to extract useful information contained in EMG signals to establish an intelligent, compact and economical biosignal-based robotic control system. The research work includes in-depth study on existing pattern recognition-based methodologies, investigation on effective EMG signal capturing and data processing, EMG-based control system development, and anthropomorphic robotic hand design. The contributions of this research are mainly in following three aspects: Developed precision electronic surface EMG (sEMG) acquisition methods that are able to collect high quality sEMG signals. The first method was designed in a single-ended signalling manner by using monolithic instrumentation amplifiers to determine and evaluate the analog sEMG signal processing chain architecture and circuit parameters. This method was then evolved into a fully differential analog sEMG detection and collection method that uses common commercial electronic components to implement all analog sEMG amplification and filtering stages in a fully differential way. The proposed fully differential sEMG detection and collection method is capable of offering a higher signal-to-noise ratio in noisy environments than the single-ended method by making full use of inherent common-mode noise rejection capability of balanced signalling. To the best of my knowledge, the literature study has not found similar methods that implement the entire analog sEMG amplification and filtering chain in a fully differential way by using common commercial electronic components. Investigated and developed a reliable EMG pattern recognition-based real-time gesture discrimination approach. Necessary functional modules for real-time gesture discrimination were identified and implemented using appropriate algorithms. Special attention was paid to the investigation and comparison of representative features and classifiers for improving accuracy and robustness. A novel EMG feature set was proposed to improve the performance of EMG pattern recognition. Designed an anthropomorphic robotic hand construction methodology for myoelectric control validation on a physical platform similar to in real-world situations. The natural anatomical structure of the human hand was imitated to kinematically model the robotic hand. The proposed robotic hand is a highly underactuated mechanism, featuring 14 degrees of freedom and three degrees of actuation. This research carried out an in-depth investigation into EMG data acquisition and EMG signal pattern recognition. A series of experiments were conducted in EMG signal processing and system development. The final myoelectric-controlled robotic hand system and the system testing confirmed the effectiveness of the proposed methods for surface EMG acquisition and human hand gesture discrimination. To verify and demonstrate the proposed myoelectric control system, real-time tests were conducted onto the anthropomorphic prototype robotic hand. Currently, the system is able to identify five patterns in real time, including hand open, hand close, wrist flexion, wrist extension and the rest state. With more motion patterns added in, this system has the potential to identify more hand movements. The research has generated a few journal and international conference publications

Optical wireless data transfer for rotor detection and diagnostics

A special application of optical wireless data transfer, namely on-line monitoring and diagnostic of rotors in turbines and engines, has been considered in this thesis. In this application, to maintain line of sight, i.e. data transfer, between a sensor placed on a rotating component inside the turbine and a monitoring point placed in a fixed position outside the turbine, a periodic fast fading channel is generated, which gives the transceivers more flexibility regarding their mounting location. The communication in such a channel is affected by the intermittency and variation of the signal power, which produces a unique channel condition that influences the performance of the optical transceiver. To investigate the channel condition and the error rate of the periodic fast fading channel with signal fluctuation, a model is developed to simulate the optical channel by considering the variation of signal power as a result of the change in the relative position of the photodiode with respect to the Lambertian radiation pattern of the LED, in a simplified linear geometry. The error rate is estimated using the Saddlepoint approximation on a specific threshold strategy. The results show that the channel can afford the sensor data transmission and the performance can be improved by modifying several parameters, such as geometrical distance, transmitter power and load resistor. Compared to a normal channel, a higher load resistor on the photodiode front end has the advantage of decreasing the noise level and increasing the data capacity in the fast fading channel. The analysis of the automatic gain control amplifier indicates that a higher load resistor needs a lower loop gain and from the model of the Transimpedance amplifier (TIA), the bandwidth extension from the amplifier is more significant for a higher resistor. In addition to the theoretical model, an experimental setup is built to emulate the channel in practice. The degree of similarity between the experimental setup and the theoretical model of the channel is estimated from the comparison of the generated communication windows. Since it has been found that differences exist in the duration of the communication window and the variation of the signal power, scaling factors to ensure their compatibility have been derived. Transceiver hardware which implemented the modelled functionality has been developed and a protocol to establish the communication with the required error rate has been proposed. Using the hardware implementation, a detection method for both rising and falling edges of the signal pulses and a threshold strategy have been demonstrated. The device power consumption is also estimated. What is more, the electromagnetic environment of a squirrel cage motor is simulated using the finite element method to investigate the interference and the possibility of providing power to the IR communication devices using power scavenging. In the conclusion, the key findings of the thesis are summarised. A solution is proposed for sensor data transfer using an optical channel for rotor monitoring applications, which involves the design of the IR transceiver, the implementation of the developed protocol and the power consumption estimation

A Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG

Includes bibliographical references.The patent developed by Dr. L. John [1] allows for the the detection of deep muscle activation through the combination of specially positioned monopolar surface Electromyography (sEMG) electrodes and a Blind Source Separation algorithm. This concept was then proved by Morowasi and John [2] in a 12 electrode prototype system around the bicep. This proof of concept showed that it was possible to extract the deep tissue activity of the brachialis muscle in the upper arm, however, the effect of surface electrode positioning and effectual number of electrodes on signal quality is still unclear. The hope of this research is to extend this work. In this research, a genetic algorithm (GA) is implemented on top of the Fast Independent Component Analysis (FastICA) algorithm to reduce the number of electrodes needed to isolate the activity from all muscles in the upper arm, including deep tissue. The GA selects electrodes based on the amount of significant information they contribute to the ICA solution and by doing so, a reduced electrode set is generated and alternative electrode positions are identified. This allows a near optimal electrode configuration to be produced for each user. The benefits of this approach are: 1.The generalized electrode array and this algorithm can select the near optimal electrode arrangement with very minimal understanding of the underlying anatomy. 2. It can correct for small anatomical differences between test subjects and act as a calibration phase for individuals. As with any design there are also disadvantages, such as each user needs to have the electrode placement specifically customised for him or her and this process needs to be conducted using a higher number of electrodes to begin with

NASA Tech Briefs, August 1991

Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

Current derivative estimation for sensorless motor drives

The work presented in this thesis aims to improve the performance of the Fundamental PWM sensorless control technique by proposing a new way to estimate current derivatives in the presence of high frequency oscillations. The Fundamental PWM technique offers performance across the entire speed range (including zero speed). The method requires current derivative measurements when certain PWM (Pulse Width Modulation) active and null vectors are applied to the machine. However the switching action of the active devices in the inverter and the associated large dv/dt result in current and current derivative waveforms being affected by high frequency oscillations which prevent accurate measurement of the current derivative. Other approaches have allowed these oscillations to decay before attempting to take a derivative measurement. This requires that the PWM vectors are applied to the machine for a time sufficient to allow the oscillations to decay and a derivative measurement to be made (the minimum pulse width). On some occasions this time is longer than the time a vector would have normally been applied for (for example when operating at low speed) and the vectors must be extended and later compensated. Vector extension introduces undesirable current distortion, audible noise, torque ripple and vibration. In this thesis the high frequency oscillations and their sources are investigated and a method of using Artificial Neural Networks to estimate current derivatives using only a short window of the transient current response is proposed. The method is able to estimate the derivative directly from phase current measurements affected by high frequency oscillations and thus allows a reduction in the minimum pulse width to be achieved (since it is no longer necessary to wait for the oscillations to fully decay) without the need for dedicated current derivative sensors. The performance of the technique is validated with experimental results

Modeling, Measurement and Mitigation of Fast Switching Issues in Voltage Source Inverters

Wide-bandgap devices are enjoying wider adoption across the power electronics industry for their superior properties and the resulting opportunities for higher efficiency and power density. However, various issues arise due to the faster switching speed, including switching transient voltage overshoot, unstable oscillation, gate driving and evaluation difficulty, measurement and monitoring challenge, and potential load insulation degradation. This dissertation first sets out to model and understand the switching transient voltage overshoots. Unique oscillation patterns and features of the turn-on and turn-off overvoltage are discovered and analyzed, which provides new insights into the switching transient. During the experimental characterization, a new unstable oscillation pattern is found during the trench MOSFET\u27s turn-off transient. The MOSFET channel may be falsely turned back on, resulting in severe oscillation and possible loss of control. Time-domain and large-signal analytical models are established, which reveals the negative impact of common-source inductances and unconventional capacitance curve of trench MOSFET. Besides the devices themselves, another determining part in their switching transient behavior is the gate driver. A programmable gate driver platform is proposed to readily adapt to different power semiconductors and driving schemes, which can greatly facilitate the evaluation and comparison of different devices and driving schemes. The faster switching speed of wide-bandgap devices also requires more demanding measurement and monitoring solutions. A novel combinational Rogowski coil concept is proposed, which leverages the self-integrating feature to further increase the bandwidth. Prototypes achieved more than 300 MHz bandwidth, while keeping the cross-sectional area less than 2.5 mm$^2$. Finally, the very high voltage slew rate of wide-bandgap devices may negatively impact the motor load insulation. Attempting to fully utilize the higher switching frequency capability, sinewave and $dv/dt$ filters are compared. It is shown that sinewave filters can achieve higher efficiency and power density than $dv/dt$ filters, especially for high frequency applications

Bidirectional Neural Interface Circuits with On-Chip Stimulation Artifact Reduction Schemes

Bidirectional neural interfaces are tools designed to “communicate” with the brain via recording and modulation of neuronal activity. The bidirectional interface systems have been adopted for many applications. Neuroscientists employ them to map neuronal circuits through precise stimulation and recording. Medical doctors deploy them as adaptable medical devices which control therapeutic stimulation parameters based on monitoring real-time neural activity. Brain-machine-interface (BMI) researchers use neural interfaces to bypass the nervous system and directly control neuroprosthetics or brain-computer-interface (BCI) spellers. In bidirectional interfaces, the implantable transducers as well as the corresponding electronic circuits and systems face several challenges. A high channel count, low power consumption, and reduced system size are desirable for potential chronic deployment and wider applicability. Moreover, a neural interface designed for robust closed-loop operation requires the mitigation of stimulation artifacts which corrupt the recorded signals. This dissertation introduces several techniques targeting low power consumption, small size, and reduction of stimulation artifacts. These techniques are implemented for extracellular electrophysiological recording and two stimulation modalities: direct current stimulation for closed-loop control of seizure detection/quench and optical stimulation for optogenetic studies. While the two modalities differ in their mechanisms, hardware implementation, and applications, they share many crucial system-level challenges. The first method aims at solving the critical issue of stimulation artifacts saturating the preamplifier in the recording front-end. To prevent saturation, a novel mixed-signal stimulation artifact cancellation circuit is devised to subtract the artifact before amplification and maintain the standard input range of a power-hungry preamplifier. Additional novel techniques have been also implemented to lower the noise and power consumption. A common average referencing (CAR) front-end circuit eliminates the cross-channel common mode noise by averaging and subtracting it in analog domain. A range-adapting SAR ADC saves additional power by eliminating unnecessary conversion cycles when the input signal is small. Measurements of an integrated circuit (IC) prototype demonstrate the attenuation of stimulation artifacts by up to 42 dB and cross-channel noise suppression by up to 39.8 dB. The power consumption per channel is maintained at 330 nW, while the area per channel is only 0.17 mm2. The second system implements a compact headstage for closed-loop optogenetic stimulation and electrophysiological recording. This design targets a miniaturized form factor, high channel count, and high-precision stimulation control suitable for rodent in-vivo optogenetic studies. Monolithically integrated optoelectrodes (which include 12 µLEDs for optical stimulation and 12 electrical recording sites) are combined with an off-the-shelf recording IC and a custom-designed high-precision LED driver. 32 recording and 12 stimulation channels can be individually accessed and controlled on a small headstage with dimensions of 2.16 x 2.38 x 0.35 cm and mass of 1.9 g. A third system prototype improves the optogenetic headstage prototype by furthering system integration and improving power efficiency facilitating wireless operation. The custom application-specific integrated circuit (ASIC) combines recording and stimulation channels with a power management unit, allowing the system to be powered by an ultra-light Li-ion battery. Additionally, the µLED drivers include a high-resolution arbitrary waveform generation mode for shaping of µLED current pulses to preemptively reduce artifacts. A prototype IC occupies 7.66 mm2, consumes 3.04 mW under typical operating conditions, and the optical pulse shaping scheme can attenuate stimulation artifacts by up to 3x with a Gaussian-rise pulse rise time under 1 ms.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147674/1/mendrela_1.pd