A marcha humana, ou caminhada, é um padrão cíclico de movimentos corporais que se repetem a cada passo que desloca um indivíduo de um local a outro. Atualmente, avaliações biomecânicas da marcha humana tem sido utilizado no diagnóstico de alterações neuromusculares, músculo-esqueléticas e como forma de avaliação pré e pós-tratamento cirúrgico, medicamentoso e/ou fisioterapêutico. O presente trabalho apresenta o desenvolvimento de uma ferramenta acadêmica de baixo custo para o estudo da marcha humana. Esse sistema consiste no sensoriamento da marcha de um usuário através de sensores inerciais e de um modelo virtual do corpo humano para permitir a visualização do movimento gerado. Dessa maneira o usuário poderá ter suas ações corrigidas por sua percepção visual e também corrigida pelas orientações de um fisiatra ou fisioterapeuta que terá a reprodução do modelo virtual conforme a movimentação detalhada do paciente para análise. O sistema ainda efetuará os registros das variáveis cinemáticas da marcha (tais como aceleração, velocidade angular, angulações dos membros sensoriados) para estudos e acompanhamento mais detalhado da sua recuperação e/ou tratamento. Como resultado, o sistema desenvolvido obteve erros médios de X 0,52º Y 1,20º Z 1,80º e erros em RMS de X 3,01º Y 3,30º Z 5,70º quando comparados com um sistema comercial, sendo esse resultado próximo à literatura e aplicável em exames biomecânicos de marcha. / The human gait is a cyclical pattern of body movements that are repeated every step that moves a subject from one location to another. Currently, biomechanical assessments of human gait has been used for diagnosing neuromuscular disorders, musculoskeletal and as a way of pre and post-surgical treatment, medication and/or physical therapy. This paper presents the development of a low cost academic tool for the study of human gait. This system consists of sensing the motion of a user through inertial sensors and a virtual model of the human body to allow the visualization of the generated movement. In this way, the user can have its actions corrected by his visual perception and also corrected by therapist or physiotherapist who will visualize the virtual model as the detailed movements of patient. The system will also record the kinematic gait variables (as acceleration, angular velocity, angles of the sensed members) for studies and more detailed monitoring of their recovery and/or treatment. As result, the developed system obtained average errors of X 0,52º Y 1,20º Z 1,80º and errors in RMS X 3,01º Y 3,30º Z 5,70º compared to a commercial system, and these results close to the ones seen in literature and applicable in biomechanical tests of gait.

Orientador: Prof. Dr. Michel Oliveira da Silva Dantas / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia Elétrica, 2017. / As atividades realizadas através de movimentações do corpo são fundamentais para manter e promover a saúde humana, permitindo melhorar nossa qualidade de vida. Neste contexto, são cada vez mais desenvolvidos sistemas comerciais para monitorar movimentos humanos. Entretanto, os sistemas existentes, compostos normalmente por análise de imagens capturadas por câmeras de vídeo, plataformas de força, eletromiografia (EMG), etc, necessitam de ambientes especiais para serem aplicados, limitando o resultado e a fidelidade da análise dos movimentos. Desta forma, o objetivo desta pesquisa foi apresentar o estudo de um sistema de classificação de movimentos humanos utilizando sensores inerciais dos tipos acelerômetro e giroscópio de um Smartphone, sendo uma tecnologia atraente, de baixo custo e viável para substituir os métodos tradicionais aplicados neste tipo de análise. Utilizando os sensores inerciais de um Smartphone, e com o auxílio do aplicativo Matlab Mobile, foi possível enviar, através da comunicação Wi Fi, os sinais de aceleração e velocidade angular para uma central de processamento. Os sinais destes sensores foram janelados para extrair características distintas para cada movimento e foram utilizadas nos algoritmos classificadores baseados em inteligência artificial Arvores de Decisão e o KNN (K Nearest Neighbor). Três abordagens foram realizadas com o intuito de verificar a sensibilidade dos sensores inerciais quando submetidos a classificação individualmente e combinando os sinais de aceleração e velocidade angular. O sensor acelerômetro se mostrou com alta eficiência em conjunto com a técnica KNN, obtendo 92,8 % de acertos gerais. Utilizando apenas o giroscópio, novamente o classificador KNN se mostrou superior em comparação ao classificador Árvores de Decisão, com uma taxa de acertos gerais de 80,1 %. Por fim, combinando os sinais dos sensores, os dois classificadores apresentaram um alto desempenho, obtendo 90,7 % para o classificador KNN e 89,7 % utilizando o classificador Árvores de Decisão. Procurou-se analisar e comparar diferentes técnicas e sistemas de classificação de movimentos humanos que podem ser consideradas alternativas à opção realizada neste trabalho. Foram analisados, ainda, outros trabalhos relacionados à classificação de movimentos, buscando comparar as vantagens e desvantagens de cada método. Estas comparações mostram que os resultados obtidos neste trabalho são similares, provando que um único dispositivo permite uma classificação de movimentos humanos com alto desempenho e baixo custo. / The activities carried out through body movements are important to maintain and to promote human health, increasing our life quality. In this context, commercial systems have been developed increasingly for human movement monitoring. However, the existing systems, which are usually composed by analysis of images captured by video cameras, power platforms, electromyography, etc., require special room to be applied, thus limiting the outcome and the fidelity of the motion in analysis. Based on the exposed, the objective of this research was to present the study of a human movement classification system using the accelerometer and gyroscope inertial sensors of a Smartphone. Such alternative aims to be an attractive, low cost and viable technology to replace the traditional methods applied in this type of analysis. By using the inertial sensors of a Galaxy S6 Smartphone coupled with Matlab Mobile application, it was possible to send the signals of acceleration and angular velocity to a processing center through Wi Fi communication. The signals from these sensors were windowed to extract features for each movement, and they were used in the algorithms based on Artificial Intelligence Decision Trees and KNN (K Nearest Neighbor). Three approaches were carried out with the purpose of verifying the sensitivity of the inertial sensors when subjected to individual classification and combining the signs of acceleration and angular velocity. The accelerometer sensor presented high efficiency when coupled with the KNN technique, obtaining 92.8 % of general hit rate. By using only the gyroscope, the KNN was superior in comparison to the Decision Trees, with a general hit rate of 80.1%. Finally, by combining the signals from booth sensors, the two classifiers presented a high performance: 90.7% for the KNN and 89.7% for the Decision Trees. We have analyzed and compared different techniques and systems of classification of human movements that are alternatives to that used in this work. Other works related to the classification of movements were also analyzed, aiming to compare the advantages and disadvantages of each method. These comparisons showed that the results obtained in this work are quite similar, which indicates that is possible to use a single device to obtain a classification of human movements with high performance and low cost.

A marcha humana, ou caminhada, é um padrão cíclico de movimentos corporais que se repetem a cada passo que desloca um indivíduo de um local a outro. Atualmente, avaliações biomecânicas da marcha humana tem sido utilizado no diagnóstico de alterações neuromusculares, músculo-esqueléticas e como forma de avaliação pré e pós-tratamento cirúrgico, medicamentoso e/ou fisioterapêutico. O presente trabalho apresenta o desenvolvimento de uma ferramenta acadêmica de baixo custo para o estudo da marcha humana. Esse sistema consiste no sensoriamento da marcha de um usuário através de sensores inerciais e de um modelo virtual do corpo humano para permitir a visualização do movimento gerado. Dessa maneira o usuário poderá ter suas ações corrigidas por sua percepção visual e também corrigida pelas orientações de um fisiatra ou fisioterapeuta que terá a reprodução do modelo virtual conforme a movimentação detalhada do paciente para análise. O sistema ainda efetuará os registros das variáveis cinemáticas da marcha (tais como aceleração, velocidade angular, angulações dos membros sensoriados) para estudos e acompanhamento mais detalhado da sua recuperação e/ou tratamento. Como resultado, o sistema desenvolvido obteve erros médios de X 0,52º Y 1,20º Z 1,80º e erros em RMS de X 3,01º Y 3,30º Z 5,70º quando comparados com um sistema comercial, sendo esse resultado próximo à literatura e aplicável em exames biomecânicos de marcha. / The human gait is a cyclical pattern of body movements that are repeated every step that moves a subject from one location to another. Currently, biomechanical assessments of human gait has been used for diagnosing neuromuscular disorders, musculoskeletal and as a way of pre and post-surgical treatment, medication and/or physical therapy. This paper presents the development of a low cost academic tool for the study of human gait. This system consists of sensing the motion of a user through inertial sensors and a virtual model of the human body to allow the visualization of the generated movement. In this way, the user can have its actions corrected by his visual perception and also corrected by therapist or physiotherapist who will visualize the virtual model as the detailed movements of patient. The system will also record the kinematic gait variables (as acceleration, angular velocity, angles of the sensed members) for studies and more detailed monitoring of their recovery and/or treatment. As result, the developed system obtained average errors of X 0,52º Y 1,20º Z 1,80º and errors in RMS X 3,01º Y 3,30º Z 5,70º compared to a commercial system, and these results close to the ones seen in literature and applicable in biomechanical tests of gait.

Ce travail de thèse s’intéresse à l’estimation des angles articulaires et des positions segmentaires du membre supérieur à partir de capteurs inertiels (MIMU). Malgré l’intérêt grandissant de la communauté scientifique pour cette technologie, plusieurs questions de recherche restent en suspens. Ce travail de thèse contribue à l’avancée de connaissances scientifiques à la fois au niveau de la modélisation cinématique du membre supérieur associée aux capteurs inertiels et au niveau de la validation même des données cinématiques de sortie (angles articulaires et positions segmentaires). Au travers d’une approche méthodologique complète, des recommandations de calibration anatomique sont avancées. De plus, des valeurs clefs de caractérisation sont proposées, telles qu’une reproductibilité des données angulaires de l’ordre de 5-10° et une erreur de positionnement de la main de 7-15 cm. La finalité de ce travail de thèse est la mise à disposition d’un système ambulatoire pour l’évaluation des postures et des mouvements du membre supérieur dans une optique d’évaluation des risques de troubles musculo-squelettiques en milieu professionnel. Une modélisation cinématique avancée prenant en compte un capteur inertiel sur la scapula et la caractérisation du système en milieu perturbé magnétiquement apparaissent comme une suite logique à ce travail de thèse / This PhD work is focused on the estimation of joint angles and segment positions of the upper limb based on inertial sensor technology (MIMU). Despite much interest from the scientific community in this topic, several aspects of research deserve more investigation. This work contributes to the enhancement of scientific knowledge related to (1) the kinematic modeling associated to MIMU and (2) the validation of final kinematic outputs (joint angles and segment positions). Based on an exhaustive methodological approach, recommendations related to the anatomical calibration of MIMU are highlighted. Moreover, key-values related to the characterization of kinematic outputs are proposed, such as a precision of joint angles of 5-10° and a hand positioning error of 7-15 cm. The aim of this study is the development of an ambulatory system for the assessment of postures and movements of the upper limb, in a general context of musculoskeletal disorders risk assessment at work. From now on, an advanced kinematic modeling that uses a MIMU placed on the scapula as well as a characterization of the system under magnetic disturbances represent two of the main scientific questions to explore

Abstract Inertial sensors are devices that measure movement, and therefore, when they are attached to a body, they can be used to measure human movements. In this thesis, data from these sensors are studied to recognize human activities user-independently. This is possible if the following two hypotheses are valid: firstly, as human movements are dissimilar between activities, also inertial sensor data between activities is so different that this data can be used to recognize activities. Secondly, while movements and inertial data are dissimilar between activities, they are so similar when different persons are performing the same activity that they can be recognized as the same activity. In this thesis, pattern recognition -based solutions are applied to inertial data to find these dissimilarities and similarities, and therefore, to build models to recognize activities user-independently. Activity recognition within this thesis is studied in two contexts: daily activity recognition using mobile phones, and activity recognition in industrial context. Both of these contexts have special requirements and these are considered in the presented solutions. Mobile phones are optimal devices to measure daily activity: they include a wide range of useful sensors to detect activities, and people carry them with them most of the time. On the other hand, the usage of mobile phones in active recognition includes several challenges; for instance, a person can carry a phone in any orientation, and there are hundreds of smartphone models, and each of them have specific hardware and software. Moreover, as battery life is always as issue with smartphones, techniques to lighten the classification process are proposed. Industrial context is different from daily activity context: when daily activities are recognized, occasional misclassifications may disturb the user, but they do not cause any other type of harm. This is not the case when activities are recognized in industrial context and the purpose is to recognize if the assembly line worker has performed tasks correctly. In this case, false classifications may be much more harmful. Solutions to these challenges are presented in this thesis. The solutions introduced in this thesis are applied to activity recognition data sets. However, as the basic idea of the activity recognition problem is the same as in many other pattern recognition procedures, most of the solutions can be applied to any pattern recognition problem, especially to ones where time series data is studied. / Tiivistelmä Liikettä mittaavista antureista, kuten kiihtyvyysantureista, saatavaa tietoa voidaan käyttää ihmisten liikkeiden mittaamiseen kiinnittämällä ne johonkin kohtaan ihmisen kehoa. Väitöskirjassani tavoitteena on opettaa tähän tietoon perustuvia käyttäjäriippumattomia malleja, joiden avulla voidaan tunnistaa ihmisten toimia, kuten käveleminen ja juokseminen. Näiden mallien toimivuus perustuu seuraavaan kahteen oletukseen: (1) koska henkilöiden liikkeet eri toimissa ovat erilaisia, myös niistä mitattava anturitieto on erilaista, (2) useamman henkilön liikkeet samassa toimessa ovat niin samanlaisia, että liikkeistä mitatun anturitiedon perusteella nämä liikkeet voidaan päätellä kuvaavan samaa toimea. Tässä väitöskirjassa käyttäjäriippumaton ihmisten toimien tunnistus perustuu hahmontunnistusmenetelmiin ja tunnistusta on sovellettu kahteen eri asiayhteyteen: arkitoimien tunnistamiseen älypuhelimella sekä toimintojen tunnistamiseen teollisessa ympäristössä. Molemmilla sovellusalueilla on omat erityisvaatimuksensa ja -haasteensa. Älypuhelimien liikettä mittaavien antureihin perustuva tunnistus on haastavaa esimerkiksi siksi, että puhelimen asento ja paikka voivat vaihdella. Se voi olla esimerkiksi laukussa tai taskussa, lisäksi se voi olla missä tahansa asennossa. Myös puhelimen akun rajallinen kesto luo omat haasteensa. Tämän vuoksi tunnistus tulisi tehdä mahdollisimman kevyesti ja vähän virtaa kuluttavalla tavalla. Teollisessa ympäristössä haasteet ovat toisenlaisia. Kun tarkoituksena on tunnistaa esimerkiksi työvaiheiden oikea suoritusjärjestys kokoamislinjastolla, yksikin virheellinen tunnistus voi aiheuttaa suuren vahingon. Teollisessa ympäristössä tavoitteena onkin tunnistaa toimet mahdollisimman tarkasti välittämättä siitä kuinka paljon virtaa ja tehoa tunnistus vaatii. Väitöskirjassani kerrotaan kuinka nämä erityisvaatimukset ja -haasteet voidaan ottaa huomioon suunniteltaessa malleja ihmisten toimien tunnistamiseen. Väitöskirjassani esiteltyjä uusia menetelmiä on sovellettu ihmisten toimien tunnistamiseen. Samoja menetelmiä voidaan kuitenkin käyttää monissa muissa hahmontunnistukseen liittyvissä ongelmissa, erityisesti sellaisissa, joissa analysoitava tieto on aikasarjamuotoista.

This dissertation focuses on the development of three classes of brain-inspired machine learning classification models. The models attempt to emulate (a) multi-sensory integration, (b) context-integration, and (c) visual information processing in the brain.The multi-sensory integration models are aimed at enhancing object classification through the integration of semantically congruent unimodal stimuli. Two multimodal classification models are introduced: the feature integrating (FI) model and the decision integrating (DI) model. The FI model, inspired by multisensory integration in the subcortical superior colliculus, combines unimodal features which are subsequently classified by a multimodal classifier. The DI model, inspired by integration in primary cortical areas, classifies unimodal stimuli independently using unimodal classifiers and classifies the combined decisions using a multimodal classifier. The multimodal classifier models are be implemented using multilayer perceptrons and multivariate statistical classifiers. Experiments involving the classification of noisy and attenuated auditory and visual representations of ten digits are designed to demonstrate the properties of the multimodal classifiers and to compare the performances of multimodal and unimodal classifiers. The experimental results show that the multimodal classification systems exhibit an important aspect of the “inverse effectiveness principle” by yielding significantly higher classification accuracies when compared with those of the unimodal classifiers. Furthermore, the flexibility offered by the generalized models enables the simulations and evaluations of various combinations of multimodal stimuli and classifiers under varying uncertainty conditions. The context-integrating model emulates the brain’s ability to use contextual information to uniquely resolve the interpretation of ambiguous stimuli. A deep learning neural network classification model that emulates this ability by integrating weighted bidirectional context into the classification process is introduced. The model, referred to as the CINET, is implemented using a convolution neural network (CNN), which is shown to be ideal for combining target and context stimuli and for extracting coupled target-context features. The CINET parameters can be manipulated to simulate congruent and incongruent context environments and to manipulate target-context stimuli relationships. The formulation of the CINET is quite general; consequently, it is not restricted to stimuli in any particular sensory modality nor to the dimensionality of the stimuli. A broad range of experiments are designed to demonstrate the effectiveness of the CINET in resolving ambiguous visual stimuli and in improving the classification of non-ambiguous visual stimuli in various contextual environments. The fact that the performance improves through the inclusion of context can be exploited to design robust brain-inspired machine learning algorithms. It is interesting to note that the CINET is a classification model that is inspired by a combination of brain’s ability to integrate contextual information and the CNN, which is inspired by the hierarchical processing of visual information in the visual cortex. A convolution neural network (CNN) model, inspired by the hierarchical processing of visual information in the brain, is introduced to fuse information from an ensemble of multi-axial sensors in order to classify strikes such as boxing punches and taekwondo kicks in combat sports. Although CNNs are not an obvious choice for non-array data nor for signals with non-linear variations, it will be shown that CNN models can effectively classify multi-axial multi-sensor signals. Experiments involving the classification of three-axis accelerometer and three-axes gyroscope signals measuring boxing punches and taekwondo kicks showed that the performance of the fusion classifiers were significantly superior to the uni-axial classifiers. Interestingly, the classification accuracies of the CNN fusion classifiers were significantly higher than those of the DTW fusion classifiers. Through training with representative signals and the local feature extraction property, the CNNs tend to be invariant to the latency shifts and non-linear variations. Moreover, by increasing the number of network layers and the training set, the CNN classifiers offer the potential for even better performance as well as the ability to handle a larger number of classes. Finally, due to the generalized formulations, the classifier models can be easily adapted to classify multi-dimensional signals of multiple sensors in various other applications.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第22077号 / 農博第2369号 / 新制||農||1072(附属図書館) / 学位論文||R1||N5231(農学部図書室) / 京都大学大学院農学研究科地域環境科学専攻 / (主査)教授 近藤 直, 教授 清水 浩, 教授 飯田 訓久 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

Whilst GPS positioning has been a well used technology for many years in outdoor environments,a ubiquitous solution for indoor positioning is yet to be found, as GPS positioning is unreliableindoors. This thesis focuses on the combination of Inertial Sensor Dead Reckoning and positionsobtained from the Bluetooth Low Energy (BLE) Direction Finding technique. The main objectiveis to reduce the error rate and size of a BLE Direction Finding system. The positioned object is aMicro-Electrical Mechanical System (MEMS) with an accelerometer and a gyroscope, placed on atrolley. The accelerometer and gyroscope are used to obtain an orientation, velocity vector, andin turn a position which is combined with the BLE Direction Finding position. To further reducethe error rate of the system, a Stationary Detection functionality is implemented. Because of thetrolley movement pattern causing noise in the sensor signals, and the limited sensor setup, it is notpossible to increase the accuracy of the system using the proposed method. However, the StationaryDetection is able to correctly determine a stationary state and thus decreasing error rate and powerconsumption. / GPS är en väl använd teknologi sedan många år, men på grund av dess bristande precision vid inomhuspositionering, behöver en ny teknologi för detta område hittas. Denna studie är fokuserad på Dead Reckoning som ett stöd till ett Bluetooth Direction Finding positioneringssystem. Det främsta målet är att minska felfrekvensen och felstorleken i BLE Direction Finding systemet. Föremålet som positioneras är en Micro-Electrical Mechanical System (MEMS) med en accelerometer och ett gyroskop, placerad på en vagn. Accelerometern och gyroskopet används för att erhålla en orientering, hastighetsvektor och därefter en position som kombineras med den position som ges av BLE Direction Finding. För att minska felfrekvensen ytterligare hos systemet, implementeras en funktionalitet som detekterar om MEMS-enheten är stillastående, kallad Stationary Detection. På grund av vagnens rörelsemönster, som bidrar till brus hos sensorsignalerna, samt den begränsade sensorkonfigurationen, är det inte möjligt att förbättra systemets precision med den föreslagna metoden. Dock kan Stationary Detection korrekt fastställa ett stationärt tillstånd och därmed minska felfrekvensen och energiförbrukningen för enheten.

This thesis is concerned with attitude estimation of small flying robots using low cost, small-sized inertial and magnetic sensors. A quaternion-based extended Kalman filter is used, which adaptively compensates external acceleration. External acceleration is the main source of estimation error. If external acceleration is detected, the accelerometer measurement covariance matrix of the Kalman filter is adjusted. The proposed algorithms are verified through experiments. Selected algorithm is implemented on STM32F4DISCOVERY development board.

L’intégration de plusieurs capteurs inertiels au sein d’un même dispositif de type MEMS afin de pouvoir estimer plusieurs degrés de liberté devient un enjeu important pour le marché de l’électronique grand public à cause de l’augmentation et de la popularité croissante des applications embarquées. Aujourd’hui, les efforts d'intégration se concentrent autour de la réduction de la taille, du coût et de la puissance consommée. Dans ce contexte, la co-intégration d’un accéléromètre trois-axes avec un gyromètre trois-axes est cohérente avec la quête conjointe de ces trois objectifs. Toutefois, cette co-intégration doit s’opérer dans une même cavité basse pression afin de préserver un facteur de qualité élevé nécessaire au bon fonctionnement du gyromètre. Dans cette optique, un nouveau système de contrôle, qui utilise le principe de l’amortissement électrostatique, a été conçu pour permettre l’utilisation d’un accéléromètre sous amorti naturellement. Le principe utilisé pour contrôler l’accéléromètre est d’appliquer dans la contre-réaction une force électrostatique générée à partir de l’estimation de la vitesse du MEMS. Cette technique permet d’augmenter le facteur d’amortissement et de diminuer le temps d’établissement de l’accéléromètre. L’architecture proposée met en oeuvre une méthode novatrice pour détecter et contrôler le mouvement d’un accéléromètre capacitif en technologie MEMS selon trois degrés de liberté : x, y et z. L'accélération externe appliquée au capteur peut être lue en utilisant la variation de capacité qui apparaît lorsque la masse se déplace. Lors de la phase de mesure, quand une tension est appliquée sur les électrodes du MEMS, une variation de charge est appliquée à l’entrée de l’amplificateur de charge (Charge-to-Voltage : C2V). La particularité de cette architecture est que le C2V est partagé entre les trois axes, ce qui permet une réduction de surface et de puissance consommée. Cependant, étant donné que le circuit ainsi que l’électrode mobile (commune aux trois axes du MEMS) sont partagés, on ne peut mesurer qu’un seul axe à lafois. Ainsi, pendant la phase d'amortissement, une tension de commande, calculée pendant les phases de mesure précédentes, est appliquée sur les électrodes d'excitation du MEMS. Cette tension de commande représente la différence entre deux échantillons successifs de la tension de sortie du C2V et elle est mémorisée et appliquée trois fois sur les électrodes d’excitation pendantla même période d’échantillonnage. Afin d’étudier la faisabilité de cette technique, des modèles mathématiques, Matlab-Simulink et VerilogA ont été développés. Le principe de fonctionnement basé sur l’amortissement électrostatique simultané a été validé grâce à ces modèles. Deux approches consécutives ont été considérées pour valider expérimentalement cette nouvelle technique : dans un premier temps l’implémentation du circuit en éléments discrets associé à un accéléromètre sous vide est présentée. En perspective, un accéléromètre sera intégré dans la même cavité qu’un gyromètre, les capteurs étant instrumentés à l’aide de circuits CMOS intégrés. Dans cette cadre, la conception en technologie CMOS 0.18μm de l’interface analogique d’amortissement est présentée et validée par simulation dans le manuscrit. / Recently, consumer electronics industry has known a spectacular growth that would have not been possible without pushing the integration barrier further and further. Micro Electro Mechanical Systems (MEMS) inertial sensors (e.g. accelerometers, gyroscopes) provide high performance, low power, low die cost solutions and are, nowadays, embedded in most consumer applications. In addition, the sensors fusion has become a new trend and combo sensors are gaining growing popularity since the co-integration of a three-axis MEMS accelerometer and a three-axis MEMS gyroscope provides complete navigation information. The resulting device is an Inertial measurement unit (IMU) able to sense multiple Degrees of Freedom (DoF). Nevertheless, the performances of the accelerometers and the gyroscopes are conditioned by the MEMS cavity pressure: the accelerometer is usually a damped system functioning under an atmospheric pressure while the gyroscope is a highly resonant system. Thus, to conceive a combo sensor, aunique low cavity pressure is required. The integration of both transducers within the same low pressure cavity necessitates a method to control and reduce the ringing phenomena by increasing the damping factor of the MEMS accelerometer. Consequently, the aim of the thesis is the design of an analog front-end interface able to sense and control an underdamped three-axis MEMSaccelerometer. This work proposes a novel closed-loop accelerometer interface achieving low power consumption The design challenge consists in finding a trade-off between the sampling frequency, the settling time and the circuit complexity since the sensor excitation plates are multiplexed between the measurement and the damping phases. In this context, a patenteddamping sequence (simultaneous damping) has been conceived to improve the damping efficiency over the state of the art approach performances (successive damping). To investigate the feasibility of the novel electrostatic damping control architecture, several mathematical models have been developed and the settling time method is used to assess the damping efficiency. Moreover, a new method that uses the multirate signal processing theory and allows the system stability study has been developed. This very method is used to conclude on the loop stability for a certain sampling frequency and loop gain value. Next, a 0.18μm CMOS implementation of the entire accelerometer signal chain is designed and validated.