Capacitive fiber-meshed transducers for touch and proximity-sensing applications

Capacitive sensing is been used in E-Textiles for touch sensing and proximity sensing applications. The common approach is been to construct electrode on top of a non conducting fabric structure. Woven & knitted fabric structures are been used for the construction. Metallic wire and conductive material coated fibres are primarily been used. Due to the performance degradation and poor comfort of these constructions we had constructed electrodes with inherently conductive polymers and multifilament metallic fibres by integrating into fibre meshed structures such that the electrodes are a part of the base structure. We had used capacitive and resistive techniques for the measurements. Out of many mechanical methods of fibre integrating processors we had used flat bed knitting technology. In this paper we had discussed the construction, sensing and applications of capacitive fibre-meshed transducers and their applications

Construction kit for computationally enabled textiles

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Includes bibliographical references (p. 87-89).As technology moves forward, electronics have enmeshed with every aspect of daily life. Some pioneers have also embraced electronics as a means of expression and exploration, creating the fields of wearable computing and electronic textiles. While wearable computing and electronic textiles seem superficially connected as fields of investigation, in fact they are currently widely separated. However, as the field of electronic textiles grows and matures, it has become apparent that better tools and techniques are necessary in order for artists and designers interested in using electronic textiles as a means of expression and function to be able to use the full capabilities of the available technology. It remains generally outside the reach of the average designer or artist to create e-textile experiences, thus preventing them from appropriating the technology, and in turn allowing the general public to accept and exploit the technology. There is clearly a need to facilitate this cross-pollination between the technical and design domains both in order to foster greater creativity and depth in the field of electronic textiles, and in order to bring greater social acceptability to wearable computing.(cont.) This thesis introduces behavioral textiles, the intersection of wearable computing and electronic textiles that brings the interactive capability of wearable electronics to electronic textiles. As a means of harnessing this capability, the thesis also presents subTextile, a powerful and novel visual programming language and development. Design guidelines for hardware that can be used with the development environment to create complete behavioral textile systems are also presented. Using a rich, goal-oriented interface, subTextile makes it possible for novices to explore electronic textiles without concern for technical details. This thesis presents the design considerations and motivations that drove the creation of subTextile. Also presented are the result of a preliminary evaluation of the language, done with a sample chosen to represent users with varying capabilities in both the technical and design domains.by Sajid H. Sadi.S.M

An investigation of textile sensors and their application in wearable electronics

Using a garment as a wearable sensing device has become a reality. New methods and techniques in the field of wearable sensors are being developed and can now be incorporated into the wearer’s everyday attire. This research focuses on two types of textile based sensors – a wearable textile electrode used for ECG continuous monitoring, and a stitch sensor for monitoring body movement. These sensors were designed into a purposely engineered Smart Sports Bra (SSB) which can be regarded as a sensor itself. After a thorough investigation, two optimum textile electrodes were created; a plain electrode using cut and sew method (CSM) and a net type knitted electrode using knitting method (KM). The CSM electrode was made with conductive fabric (MedTexTM P-130) and the KM electrode was made with conductive thread (silver-plated nylon 234/34 four-ply), these materials having the lowest tested contact impedance; 450Ω and 500Ω, respectively. Both electrodes demonstrated a level of noise and baseline drift comparable with standard commercial wet-gel electrodes, which was corrected by optimising their size to 20x40 mm, holding pressure of 4 kPa (30 mmHg) and the electrode position at the 6th intercostal space on the right and left mid-clavicular, with one placed at the scapular line in the rear side (i.e. back horizontal formation) which gives clear and reliable ECG signal. These optimum electrodes were integrated directly into SSBs, in which a novel high shear, net structure, acting as a shock absorber to body movement that shows more stable electrode to skin contact by reducing the body motion artefact. During the investigation of the stitch stretch sensor the single jersey nylon fabric (4.44 tex two-ply) with 25% spandex (7.78 tex) had the highest elastic recovery (93%). Using this fabric, the work went on to show that the stitch type 304 (Zig-zag lock stitch) using the 117/17 two-ply thread demonstrated the best results i.e., maximum working range 50%, gauge factor 1.61, hysteresis 6.25% ΔR, linearity (R2 ) is 0.98, and good repeatability (drift in R2 is -0.00). The stitch stretch sensor was also incorporated into a sports bra SSB and positioned across the chest for respiration monitoring. This thesis contributes to a growing body of research in wearable E -textile solutions to support health and well-being, with fully functional sensors and easy-to-use design, for continues health monitoring

An investigation of the Matteucci effect on amorphous wires and its application to bend sensing

The study of wearable sensors for human biometrics has recently developed into an important research area due to its potential for personalised health monitoring. To measure bending parameters in humans such as joint movement or posture, several techniques have been proposed however, the majority of these suffer from poor accuracy, sensitivity and linearity. To overcome these limitations, this research aims to develop a novel flexible sensor for the measurement of bending by utilising the Matteucci effect on amorphous wires. The Matteucci effect occurs in all ferromagnetic wires but the advantages of amorphous wires are their superior soft magnetic and magnetoelastic properties and a Matteucci effect that is very sensitive to applied stresses like tensile and torsion. For these reasons a sensor based on Matteucci effect was investigated for use as a wearable bending sensor. Previous studies of the Matteucci effect have been interpreted in terms of simple phenomenological models using conveniently sized lengths of amorphous wire. In this work, the Matteucci effect has been characterised in short, sensor-compatible, wires. In addition, a thorough examination of the stress dependency of the Matteucci effect was also investigated as this is an area that has been neglected in the past. The main aim of this work was to study the effect of tensile and torsion stresses on the Matteucci effect in both highly positive magnetostrictive and nearly zero magnetostrictive amorphous wires. A measurement rig was specifically built to characterise the Matteucci effect for a range of magnetic field amplitudes, frequencies, torsions and axial stresses. The second major aim was to use this characterisation data to ascertain the optimum working parameters to design and construct a novel flexible bending sensor. In this work, the Matteucci effect in amorphous wires was found to be very sensitive to both axial and torsional applied stresses and dependent upon the sign of the magnetostriction. Insights gained here were used to develop the bend sensor in three steps. The initial prototype was a non-flexible strain sensor for measuring tensile stress and exhibited a very high gauge factor equal to 601± 30. The second step resulted in a strain sensor prototype utilising a flexible planar coil to magnetise the amorphous wire. The final step produced a bend sensor this time consisting of a flexible solenoid with greater magnetising capability. It resulted in a bend sensor IV with a high output voltage sensitivity of 5.62 ± 0.02 mV/cm which is the slope of the voltage due to curvature and excellent linearity (R2 = 0.98). In this case the sensor’s operating range was 1.11 rad to 2.49 rad with ± 0.003 rad uncertainty. This range is scalable and dependent on the sensor configuration. This work has demonstrated the feasibility of utilising the Matteucci effect as a bend sensor with a performance exceeding that found in many commercial sensors

Técnicas de computación evolutiva aplicadas a la clasificación a partir de monitores de actividad física

Actualmente, varios factores están haciendo que el campo de reconocimiento de actividades humanas cobre una mayor importancia, como por ejemplo, la proliferación de dispositivos “wearables” que permiten monitorizar la actividad física o la tendencia de la población mundial hacia un estilo de vida cada vez más sedentario. Este estilo de vida sedentario propio de la sociedad actual se traduce en insuficiente actividad física y se considera uno de los mayores factores de riesgo para la salud, estando entre los primeros puestos de factores de riesgo de mortalidad a nivel mundial, según la OMS [11]. De esta manera, dentro del ámbito de la salud y el bienestar, gracias al avance en la miniaturización de sensores, que incluso permite su uso incorporados a la ropa de las personas, el reconocimiento automático de actividades se presenta como una solución a problemas de diversa índole, como por ejemplo, prevención de enfermedades, envejecimiento activo, monitorización remota de enfermos, además de un amplio espectro de aplicaciones en el ámbito deportivo. Es por esto que se convierten en dispositivos de monitorización sumamente útiles en otras áreas de investigación, introduciendo el reconocimiento de actividades humanas en la computación ubicua, el entretenimiento, el registro de actividades diarias personales o el seguimiento del rendimiento deportivo o profesional. Con la principal motivación de explorar nuevos frentes de investigación del reconocimiento de actividades, con un enfoque distinto a los planteados hasta ahora, en este trabajo se propone un sistema de reconocimiento automático de actividades que integra un algoritmo evolutivo, para la tarea de clasificación de actividades, y un enjambre de partículas, para la realización de un clustering que mejore el aprendizaje automático. El sistema ha sido evaluado mediante validación cruzada del tipo leave-one-subject-out, para comprobar su rendimiento en situaciones de reconocimiento independiente del sujeto, obteniendo un 52,37% de acierto. También, se ha evaluado el sistema con validación cruzada estándar de 10-folds en cada sujeto, para analizar la capacidad del sistema en casos de clasificación dependiente del sujeto, alcanzando un 98,07% de acierto. Un resultado significativamente más positivo que el primero, que muestra que el sistema puede tender a la personalización del reconocimiento de actividades. Además, se ha llevado a cabo la evaluación del sistema con validación cruzada estándar de 10-folds en el conjunto de todos los sujetos, con un 70,2267% de acierto, abundándose en la conclusión expuesta más arriba, de que el sistema presenta un mejor funcionamiento en situaciones de personalización del reconocimiento de actividades.In the current time, various factors are making the field of activity recognition become more important, such as the proliferation of wearable devices that allow to monitor physical activity or global population’s tendency towards a more sendentary lifestyle. This sedentary lifestyle is turning into insufficient physical activity and is considered one of the factors with a highest risk for health, being among the leading risk factors of mortality, regarding the WHO [11]. This way, within health and wellness field, thanks to the advance in sensor miniaturization, which even allows sensor usage incorporated to people clothes, activity automatic recognition is presented as a solution to very diverse problems, such as diseases prevention, active aging, patient remote monitoring, as well as a wide range of applications in sports. For that reason, wearable sensors happen to be extremely useful monitorizing devices in other research areas, introducing human activity recognition to ubiquitous computing, entertainment industry, daily life activities logging and sportive and professional perfomance monitoring, among others. With the main motivation of exploring new research horizons, through a different approach to the previous works, in this project, an activity automatic recognition system that integrates an evolutionary algorithm, for the activity classification task, and a particle swarm, for a clustering that improves the automatic learning, is proposed. The system has been evaluated with leave-one-subject-out (LOSO) cross validation, in order to assess its performance in situations where the recognition is subject independent, obtaining an accuracy rate of 52,37%. Also, the system has been evaluated with 10-fold standard cross validation within each subject, to analyze the system’s capacity in subject dependent classification cases, reaching an accuracy rate of 98,07%. A significantly more positive result than the first one, that shows the system might tend to personalization of activity recognition. In addition, the system evaluation has been carried with 10-fold standard cross validation within the whole set of all the subjects, getting an accuracy rate of 70,2267%, which supports the conclusion presented above that the system works better in situations of personalization of the activity recognition.Grado en Ingeniería Informátic