1,816 research outputs found

    Recognition of elementary arm movements using orientation of a tri-axial accelerometer located near the wrist

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    In this paper we present a method for recognising three fundamental movements of the human arm (reach and retrieve, lift cup to mouth, rotation of the arm) by determining the orientation of a tri-axial accelerometer located near the wrist. Our objective is to detect the occurrence of such movements performed with the impaired arm of a stroke patient during normal daily activities as a means to assess their rehabilitation. The method relies on accurately mapping transitions of predefined, standard orientations of the accelerometer to corresponding elementary arm movements. To evaluate the technique, kinematic data was collected from four healthy subjects and four stroke patients as they performed a number of activities involved in a representative activity of daily living, 'making-a-cup-of-tea'. Our experimental results show that the proposed method can independently recognise all three of the elementary upper limb movements investigated with accuracies in the range 91–99% for healthy subjects and 70–85% for stroke patients

    Magnetic sensors and gradiometers for detection of objects

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    Disertační práce popisuje vývoj nových detekčních zařízení s anizotropními magnetorezistoryThis thesis describes development of innovative sensor systems based on anisotropi

    Vector magnetometer design study: Analysis of a triaxial fluxgate sensor design demonstrates that all MAGSAT Vector Magnetometer specifications can be met

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    The design of the vector magnetometer selected for analysis is capable of exceeding the required accuracy of 5 gamma per vector field component. The principal elements that assure this performance level are very low power dissipation triaxial feedback coils surrounding ring core flux-gates and temperature control of the critical components of two-loop feedback electronics. An analysis of the calibration problem points to the need for improved test facilities

    Magnetic Local Positioning System with Supplemental Magnetometer-Accelerometer Data Fusion

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    Geo-location and tracking technology, once confined to the industrial and military sectors, have been widely proliferated to the consumer world since early in the twenty-first century. The commoditization of Global Positioning System (GPS) and inertial measurement integrated circuits has made this possible, with devices small enough to fit in a cellular phone. However, GPS technology is not without its drawbacks: Its power use is high, and it can fail in smaller, obstructed spaces. Magnetic positioning, which exploits the magnetic field coupling between a set of transmitter beacon coils and a set of receiver coils, is an often overlooked, complementary technology that does not suffer from these problems. Magnetic positioning is strong where GPS is weak; however, it has some weaknesses of its own. Namely, it is subject to distortions due to metal objects in its immediate vicinity. In much of the prior art, these distortions are ignored or either statically measured and then corrected. This work presents a novel technique to dynamically correct for distorted fields. Specifically, a tri-axial magnetometer and a tri-axial accelerometer are integrated with the magnetic positioning system using a complementary Kalman filter. The end result resembles a tightly-coupled integrated GPS/inertial navigation system. The results achieved by this integrated magnetic positioning system prove the viability of the approach. The results are demonstrated in a real-world environment, where both strong, localized distortions and spatially broad distortions are corrected. In addition to the integrated magnetic position system, this work presents a novel scheme for calibrating the magnetic receiver; this technique is termed application domain calibration. In many real-world situations, low-level measurement and calibration will not be possible; therefore, this new technique uses the same set of demodulated and down-mixed data that is used by the magnetic positioning algorithms

    Gait rehabilitation monitor

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    This work presents a simple wearable, non-intrusive affordable mobile framework that allows remote patient monitoring during gait rehabilitation, by doctors and physiotherapists. The system includes a set of 2 Shimmer3 9DoF Inertial Measurement Units (IMUs), Bluetooth compatible from Shimmer, an Android smartphone for collecting and primary processing of data and persistence in a local database. Low computational load algorithms based on Euler angles and accelerometer, gyroscope and magnetometer signals were developed and used for the classification and identification of several gait disturbances. These algorithms include the alignment of IMUs sensors data by means of a common temporal reference as well as heel strike and stride detection algorithms to help segmentation of the remotely collected signals by the System app to identify gait strides and extract relevant features to feed, train and test a classifier to predict gait abnormalities in gait sessions. A set of drivers from Shimmer manufacturer is used to make the connection between the app and the set of IMUs using Bluetooth. The developed app allows users to collect data and train a classification model for identifying abnormal and normal gait types. The system provides a REST API available in a backend server along with Java and Python libraries and a PostgreSQL database. The machine-learning type is Supervised using Extremely Randomized Trees method. Frequency, time and time-frequency domain features were extracted from the collected and processed signals to train the classifier. To test the framework a set of gait abnormalities and normal gait were used to train a model and test the classifier.Este trabalho apresenta uma estrutura móvel acessível, simples e não intrusiva, que permite a monitorização e a assistência remota de pacientes durante a reabilitação da marcha, por médicos e fisioterapeutas que monitorizam a reabilitação da marcha do paciente. O sistema inclui um conjunto de 2 IMUs (Inertial Mesaurement Units) Shimmer3 da marca Shimmer, compatíveís com Bluetooth, um smartphone Android para recolha, e pré-processamento de dados e armazenamento numa base de dados local. Algoritmos de baixa carga computacional baseados em ângulos Euler e sinais de acelerómetros, giroscópios e magnetómetros foram desenvolvidos e utilizados para a classificação e identificação de diversas perturbações da marcha. Estes algoritmos incluem o alinhamento e sincronização dos dados dos sensores IMUs usando uma referência temporal comum, além de algoritmos de detecção de passos e strides para auxiliar a segmentação dos sinais recolhidos remotamente pelaappdestaframeworke identificar os passos da marcha extraindo as características relevantes para treinar e testar um classificador que faça a predição de deficiências na marcha durante as sessões de monitorização. Um conjunto de drivers do fabricante Shimmer é usado para fazer a conexão entre a app e o conjunto de IMUs através de Bluetooth. A app desenvolvida permite aos utilizadores recolher dados e treinar um modelo de classificação para identificar os tipos de marcha normais e patológicos. O sistema fornece uma REST API disponível num servidor backend recorrendo a bibliotecas Java e Python e a uma base de dados PostgreSQL. O tipo de machine-learning é Supervisionado usando Extremely Randomized Trees. Features no domínio do tempo, da frequência e do tempo-frequência foram extraídas dos sinais recolhidos e processados para treinar o classificador. Para testar a estrutura, um conjunto de marchas patológicas e normais foram utilizadas para treinar um modelo e testar o classificador

    Hand-finger pose tracking using inertial and magnetic sensors

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