High performance circuit techniques for neural front-end design in 65nm CMOS

Integrated low noise neural amplifiers become recently practical in CMOS technologies. In this paper, a low noise OTA technique has been proposed while keeping the power consumption constant. A capacitive feedback, ac coupled 46dB amplifier with high pass cutoff frequency close to the 90Hz has been achieved. The proposed amplifier has been implemented in 65nm CMOS technology; at room temperature circuit consumes 323uA current from 1.2V power supply. The circuit occupies 2627um 2 silicon area

Biomedical Engineering

Biomedical engineering is currently relatively wide scientific area which has been constantly bringing innovations with an objective to support and improve all areas of medicine such as therapy, diagnostics and rehabilitation. It holds a strong position also in natural and biological sciences. In the terms of application, biomedical engineering is present at almost all technical universities where some of them are targeted for the research and development in this area. The presented book brings chosen outputs and results of research and development tasks, often supported by important world or European framework programs or grant agencies. The knowledge and findings from the area of biomaterials, bioelectronics, bioinformatics, biomedical devices and tools or computer support in the processes of diagnostics and therapy are defined in a way that they bring both basic information to a reader and also specific outputs with a possible further use in research and development

An Energy Efficient Power Converter for Zero Power Wearable Devices

Early diagnosis of Alzheimer's and epilepsy requires monitoring a subject's development of symptoms through electroencephalography (EEG) signals over long periods. Wearable devices enable convenient monitoring of biosignals, unlike complex and costly hospital equipment. The key to achieving a fit and forgettable wearable device is to increase its operating cycle and decrease its size and weight. Instead of batteries, which limit the life cycle of electronic devices and set their form factor, body heat and environmental light can power wearable devices through energy-scavenging technologies. The harvester transducers should be tailored according to on the application and the sensor placement. This leaves a wide variety of transducers with an extensive range of impedances and voltages. To realize an autonomous wearable device, the power converter energy harvester, has to be very efficient and maintain its efficiency despite potential transducer replacement or variations in environmental conditions. This thesis presents a detailed design of an efficient integrated power converter for use in an autonomous wearable device. The design is based on the examination of both power losses and power transfer in the power converter. The efficiency bound of the converter is derived from the specifications of its transducer. The tuning ranges for the reconfigurable parameters are extracted to keep the converter efficient with variations in the transducer specifications. With the efficient design and the manual tuning of the reconfigurable parameters, the converter can work optimally with different types of transducers, and keeps its efficiency in the conversion of low voltages from the harvesters. Measurements of the designed converter demonstrate an efficiency of higher than 50% and 70% with two different transducers having an open-circuit voltage as low as 20 mV and 100 mV, respectively. The power converter should be able to reconfigure itself without manual tunings to keep its efficiency despite changes in the harvesters' specifications. The second portion of this dissertation addresses this issue with a proposed design methodology to implement a control section. The control section adjusts the converter's reconfigurable parameters by examining the power transfer and loss and through concurrent closed loops. The concurrent loops working together raise a serious concern regarding stability. The system is designed and analyzed in the time domain with the state-space averaging (SSA) model to address the stability issue. The ultra-low-power control section obtained from the SSA model estimates the power and loss with a reasonable accuracy, and adjusts the timings in a stable manner. The entire control section consumes only 30 nW dynamic power at 10 kHz. The control section tunes the converter's speed or its working frequency depending on the available power. The frequency clocks the entire architecture, which is designed asynchronously; therefore, the power consumption of the system depends on the power available from the transducer. The system is implemented using 0.18 µm CMOS technology. For an input as low as 7 mV, the converter is not only functional but also has an efficiency of more than 40%. The efficiency can reach 70% with an input voltage of 50 mV. The system operates in a range of just a few of millivolts to half a volt with ample efficiencies. It can work at an optimal point with different transducers and environmental conditions

Guidage non-intrusif d'un bras robotique à l'aide d'un bracelet myoélectrique à électrode sèche

Depuis plusieurs années la robotique est vue comme une solution clef pour améliorer la qualité de vie des personnes ayant subi une amputation. Pour créer de nouvelles prothèses intelligentes qui peuvent être facilement intégrées à la vie quotidienne et acceptée par ces personnes, celles-ci doivent être non-intrusives, fiables et peu coûteuses. L’électromyographie de surface fournit une interface intuitive et non intrusive basée sur l’activité musculaire de l’utilisateur permettant d’interagir avec des robots. Cependant, malgré des recherches approfondies dans le domaine de la classification des signaux sEMG, les classificateurs actuels manquent toujours de fiabilité, car ils ne sont pas robustes face au bruit à court terme (par exemple, petit déplacement des électrodes, fatigue musculaire) ou à long terme (par exemple, changement de la masse musculaire et des tissus adipeux) et requiert donc de recalibrer le classifieur de façon périodique. L’objectif de mon projet de recherche est de proposer une interface myoélectrique humain-robot basé sur des algorithmes d’apprentissage par transfert et d’adaptation de domaine afin d’augmenter la fiabilité du système à long-terme, tout en minimisant l’intrusivité (au niveau du temps de préparation) de ce genre de système. L’aspect non intrusif est obtenu en utilisant un bracelet à électrode sèche possédant dix canaux. Ce bracelet (3DC Armband) est de notre (Docteur Gabriel Gagnon-Turcotte, mes co-directeurs et moi-même) conception et a été réalisé durant mon doctorat. À l’heure d’écrire ces lignes, le 3DC Armband est le bracelet sans fil pour l’enregistrement de signaux sEMG le plus performant disponible. Contrairement aux dispositifs utilisant des électrodes à base de gel qui nécessitent un rasage de l’avant-bras, un nettoyage de la zone de placement et l’application d’un gel conducteur avant l’utilisation, le brassard du 3DC peut simplement être placé sur l’avant-bras sans aucune préparation. Cependant, cette facilité d’utilisation entraîne une diminution de la qualité de l’information du signal. Cette diminution provient du fait que les électrodes sèches obtiennent un signal plus bruité que celle à base de gel. En outre, des méthodes invasives peuvent réduire les déplacements d’électrodes lors de l’utilisation, contrairement au brassard. Pour remédier à cette dégradation de l’information, le projet de recherche s’appuiera sur l’apprentissage profond, et plus précisément sur les réseaux convolutionels. Le projet de recherche a été divisé en trois phases. La première porte sur la conception d’un classifieur permettant la reconnaissance de gestes de la main en temps réel. La deuxième porte sur l’implémentation d’un algorithme d’apprentissage par transfert afin de pouvoir profiter des données provenant d’autres personnes, permettant ainsi d’améliorer la classification des mouvements de la main pour un nouvel individu tout en diminuant le temps de préparation nécessaire pour utiliser le système. La troisième phase consiste en l’élaboration et l’implémentation des algorithmes d’adaptation de domaine et d’apprentissage faiblement supervisé afin de créer un classifieur qui soit robuste au changement à long terme.For several years, robotics has been seen as a key solution to improve the quality of life of people living with upper-limb disabilities. To create new, smart prostheses that can easily be integrated into everyday life, they must be non-intrusive, reliable and inexpensive. Surface electromyography provides an intuitive interface based on a user’s muscle activity to interact with robots. However, despite extensive research in the field of sEMG signal classification, current classifiers still lack reliability due to their lack of robustness to short-term (e.g. small electrode displacement, muscle fatigue) or long-term (e.g. change in muscle mass and adipose tissue) noise. In practice, this mean that to be useful, classifier needs to be periodically re-calibrated, a time consuming process. The goal of my research project is to proposes a human-robot myoelectric interface based on transfer learning and domain adaptation algorithms to increase the reliability of the system in the long term, while at the same time reducing the intrusiveness (in terms of hardware and preparation time) of this kind of systems. The non-intrusive aspect is achieved from a dry-electrode armband featuring ten channels. This armband, named the 3DC Armband is from our (Dr. Gabriel Gagnon-Turcotte, my co-directors and myself) conception and was realized during my doctorate. At the time of writing, the 3DC Armband offers the best performance for currently available dry-electrodes, surface electromyographic armbands. Unlike gel-based electrodes which require intrusive skin preparation (i.e. shaving, cleaning the skin and applying conductive gel), the 3DC Armband can simply be placed on the forearm without any preparation. However, this ease of use results in a decrease in the quality of information. This decrease is due to the fact that the signal recorded by dry electrodes is inherently noisier than gel-based ones. In addition, other systems use invasive methods (intramuscular electromyography) to capture a cleaner signal and reduce the source of noises (e.g. electrode shift). To remedy this degradation of information resulting from the non-intrusiveness of the armband, this research project will rely on deep learning, and more specifically on convolutional networks. The research project was divided into three phases. The first is the design of a classifier allowing the recognition of hand gestures in real-time. The second is the implementation of a transfer learning algorithm to take advantage of the data recorded across multiple users, thereby improving the system’s accuracy, while decreasing the time required to use the system. The third phase is the development and implementation of a domain adaptation and self-supervised learning to enhance the classifier’s robustness to long-term changes