114 research outputs found

    Controlling a robotic hip exoskeleton with noncontact capacitive sensors

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    For partial lower-limb exoskeletons, an accurate real-time estimation of the gait phase is paramount to provide timely and well-tailored assistance during gait. To this end, dedicated wearable sensors separate from the exoskeletons mechanical structure may be preferable because they are typically isolated from movement artifacts that often result from the transient dynamics of the physical human-robot interaction. Moreover, wearable sensors that do not require time-consuming calibration procedures are more easily acceptable by users. In this study a robotic hip orthosis was controlled using capacitive sensors placed in orthopedic cuffs on the shanks. The capacitive signals are zeroed after donning the cuffs and do not require any further calibration. The capacitive sensing-based controller was designed to perform online estimation of the gait cycle phase via adaptive oscillators, and to provide a phase-locked assistive torque. Two experimental activities were carried out to validate the effectiveness of the proposed control strategy. Experiments conducted with seven healthy subjects walking on a treadmill at different speeds demonstrated that the controller can estimate the gait phase with an average error of 4%, while also providing hip flexion assistance. Moreover, experiments carried out with four healthy subjects showed that the capacitive sensing-based controller could reduce the metabolic expenditure of subjects compared to the unassisted condition (mean ± SEM, -3.2% ± 1.1)

    Sensing Systems for Respiration Monitoring: A Technical Systematic Review

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    Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system

    Applications of the electric potential sensor for healthcare and assistive technologies

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    The work discussed in this thesis explores the possibility of employing the Electric Potential Sensor for use in healthcare and assistive technology applications with the same and in some cases better degrees of accuracy than those of conventional technologies. The Electric Potential Sensor is a generic and versatile sensing technology capable of working in both contact and non-contact (remote) modes. New versions of the active sensor were developed for specific surface electrophysiological signal measurements. The requirements in terms of frequency range, electrode size and gain varied with the type of signal measured for each application. Real-time applications based on electrooculography, electroretinography and electromyography are discussed, as well as an application based on human movement. A three sensor electrooculography eye tracking system was developed which is of interest to eye controlled assistive technologies. The system described achieved an accuracy at least as good as conventional wet gel electrodes for both horizontal and vertical eye movements. Surface recording of the electroretinogram, used to monitor eye health and diagnose degenerative diseases of the retina, was achieved and correlated with both corneal fibre and wet gel surface electrodes. The main signal components of electromyography lie in a higher bandwidth and surface signals of the deltoid muscle were recorded over the course of rehabilitation of a subject with an injured arm. Surface electromyography signals of the bicep were also recorded and correlated with the joint dynamics of the elbow. A related non-contact application of interest to assistive technologies was also developed. Hand movement within a defined area was mapped and used to control a mouse cursor and a predictive text interface

    Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU

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    Smart wearable robotic system, such as exoskeleton assist device and powered lower limb prostheses can rapidly and accurately realize man–machine interaction through locomotion mode recognition system. However, previous locomotion mode recognition studies usually adopted more sensors for higher accuracy and effective intelligent algorithms to recognize multiple locomotion modes simultaneously. To reduce the burden of sensors on users and recognize more locomotion modes, we design a novel decision tree structure (DTS) based on using an improved backpropagation neural network (IBPNN) as judgment nodes named IBPNN-DTS, after analyzing the experimental locomotion mode data using the original values with a 200-ms time window for a single inertial measurement unit to hierarchically identify nine common locomotion modes (level walking at three kinds of speeds, ramp ascent/descent, stair ascent/descent, Sit, and Stand). In addition, we reduce the number of parameters in the IBPNN for structure optimization and adopted the artificial bee colony (ABC) algorithm to perform global search for initial weight and threshold value to eliminate system uncertainty because randomly generated initial values tend to result in a failure to converge or falling into local optima. Experimental results demonstrate that recognition accuracy of the IBPNN-DTS with ABC optimization (ABC-IBPNN-DTS) was up to 96.71% (97.29% for the IBPNN-DTS). Compared to IBPNN-DTS without optimization, the number of parameters in ABC-IBPNN-DTS shrank by 66% with only a 0.58% reduction in accuracy while the classification model kept high robustness

    A Biomechanical and Physiological Signal Monitoring System for Four Degrees of Upper Limb Movement

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    A lack of adherence to prescribed physical therapy regimens in improper healing results in poor outcomes for those affected by musculoskeletal disorders (MSDs) of the upper limb. Societal and psychological barriers to proper adherence can be addressed through the system presented in this work consisting of the following components: an ambulatory biosignal acquisition sleeve, an electromyography (EMG) based motion repetition detection algorithm, and the design of a compatible capacitive EMG acquisition module. The biosignal acquisition sleeve was untethered, unobtrusive to motion, contained only modular components, and collected biomechanical and physiological sensor data to form full motion profiles of the following four degrees of freedom: elbow flexion—extension, forearm pronation—supination, wrist flexion—extension, and ulnar--radial deviation. The piloted sleeve simultaneously collected data from four inertial sensors, two electromyography (EMG) sensors and a flex-bend sensor. A visualization application was developed to present the information in a manner meaningful to the user. As well, an EMG based motion repetition detector was developed for use within the system. It was validated using an existing database of 23 subjects with varying musculoskeletal health, achieving a success rate of 95.43%. This algorithm was modified for use with the sleeve, resulting in a 95% success rate. An electrode and analog front end module was proposed, relying on unique material structures and low-noise, precision sensing techniques. The system prototype presented a resource-conscious tool for multi-modality tracking of elbow, forearm, and wrist motion, which could eventually be integrated into upper limb MSD rehabilitation

    Contribución al diseño de sensores vestibles y ambientales para medir la respiración y el salto vertical en adultos mayores y frágiles.

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    Con el avance de la tecnología, se ha popularizado entre la población el uso de dispositivos para medir su estado de salud. Para lograr esto, se suelen utilizar dispositivos vestibles como los smartwatch y smartbands, dispositivos ambientales embebidos en los alrededores, e incluso dispositivos conectados a aplicaciones móviles. El uso de estas tecnologías también se ha popularizado entre los profesionales de la salud.Esta tesis se centra en el desarrollo de dispositivos para monitorizar la salud de adultos mayores y adultos frágiles. Se desarrollaron dos líneas de trabajo: en la primera se diseñó e implementó un sistema vestible para monitorizar en tiempo real la respiración de los usuarios; en la segunda se desarrolló un sistema ambiental capaz de medir la altura del salto vertical efectuado por los usuarios sobre él.Sistema vestible para monitorizar la respiración:- Dentro de esta línea de trabajo se investigó un nuevo sensor de respiración que venía a cubrir algunas lagunas existentes en el estado de la técnica: la integración de todos los elementos electrónicos del sistema en un encapsulado compacto, la liberación del diseño para su reutilización y mejora por parte de otros investigadores y el bajo coste de los elementos que componen el sistema, entre otros. El sistema vestible consiste en un dispositivo que se coloca alrededor del pecho mediante una cinta ajustable. Este sistema funciona mediante un sensor piezoresistivo que detecta las variaciones en el diámetro del pecho ocasionadas al inhalar y exhalar; las variaciones detectadas son enviadas de forma inalámbrica mediante Bluetooth a una estación de visualización elegida por el usuario (PC, Tablet o Smartphone). El sistema se encuentra embebido en un armazón impreso en 3D. Para validar el funcionamiento de este sistema, se realizaron pruebas con 21 voluntarios que efectuaron diferentes ritmos de respiración. Para obtener los ritmos respiratorios de cada señal generada, se utilizaron dos algoritmos. Estos algoritmos calculan el ritmo respiratorio al segmentar la señal original en ventanas de tiempo desde 6 hasta 30 segundos. Los resultados obtenidos muestran que, con una ventana de tiempo de 27 segundos, se obtiene el menor error para cada algoritmo (4,02% y 3,40 %).Sistema ambiental para medir el salto vertical:- Dentro de esta segunda línea de trabajo se investigó en un novedoso sistema ambiental para medir la altura del salto, lo que supuso una innovación respecto a los sensores utilizados actualmente para este fin. El sistema ambiental consiste en una plataforma que detecta objetos sobre ella mediante la presión, y mide el tiempo transcurrido desde que un objeto se retira y se coloca de nuevo. El sistema detecta los objetos mediante una matriz de sensores piezoresitivos (Force Sensitive Resistors - FSR realizados con velostat). Las dimensiones de la plataforma son 30 cm x 30 cm, área sobre la cual se distribuyen un total de 256 sensores FSR. El salto vertical se calcula mediante la fórmula de tiempo de vuelo, y el resultado es enviado mediante Bluetooth a un PC o Smartphone. Se realizaron dos experimentos: en el primero participaron un total de 38 voluntarios, con el objetivo de validar el funcionamiento del sistema con una cámara de alta velocidad como referencia (120 fps); en el segundo experimento se capturaron los datos en crudo de 15 voluntarios, con estos datos se emularon 10 frecuencias de muestreo (desde 20 Hz hasta 200 Hz) y se analizaron los efectos de utilizar frecuencias más bajas. Del primer experimento se obtuvo un error relativo medio de 1.98% con un coeficiente de determinación r2= 0,996. Del segundo experimento se determinó que las frecuencias de muestreo de 200 Hz y 100 Hz muestran un desempeño similar al mantener un error relativo por debajo del 5% en el 95% de las mediciones.Finalmente, este trabajo de tesis concluye indicando las principales aportaciones realizadas para cada una de las dos líneas de trabajo, así como el trabajo futuro que podría desarrollarse en cada una de ellas.<br /

    IMU-Based Classification of Locomotion Modes, Transitions, and Gait Phases with Convolutional Recurrent Neural Networks

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    This paper focuses on the classification of seven locomotion modes (sitting, standing, level ground walking, ramp ascent and descent, stair ascent and descent), the transitions among these modes, and the gait phases within each mode, by only using data in the frequency domain from one or two inertial measurement units. Different deep neural network configurations are investigated and compared by combining convolutional and recurrent layers. The results show that a system composed of a convolutional neural network followed by a long short-term memory network is able to classify with a mean [Formula: see text]-score of 0.89 and 0.91 for ten healthy subjects, and of 0.92 and 0.95 for one osseointegrated transfemoral amputee subject (excluding the gait phases because they are not labeled in the data-set), using one and two inertial measurement units, respectively, with a 5-fold cross-validation. The promising results obtained in this study pave the way for using deep learning for the control of transfemoral prostheses with a minimum number of inertial measurement units

    The Emerging Wearable Solutions in mHealth

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    The marriage of wearable sensors and smartphones have fashioned a foundation for mobile health technologies that enable healthcare to be unimpeded by geographical boundaries. Sweeping efforts are under way to develop a wide variety of smartphone-linked wearable biometric sensors and systems. This chapter reviews recent progress in the field of wearable technologies with a focus on key solutions for fall detection and prevention, Parkinson’s disease assessment and cardiac disease, blood pressure and blood glucose management. In particular, the smartphone-based systems, without any external wearables, are summarized and discussed
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