Wearable pressure sensing for intelligent gesture recognition

The development of wearable sensors has become a major area of interest due to their wide range of promising applications, including health monitoring, human motion detection, human-machine interfaces, electronic skin and soft robotics. Particularly, pressure sensors have attracted considerable attention in wearable applications. However, traditional pressure sensing systems are using rigid sensors to detect the human motions. Lightweight and flexible pressure sensors are required to improve the comfortability of devices. Furthermore, in comparison with conventional sensing techniques without smart algorithm, machine learning-assisted wearable systems are capable of intelligently analysing data for classification or prediction purposes, making the system ‘smarter’ for more demanding tasks. Therefore, combining flexible pressure sensors and machine learning is a promising method to deal with human motion recognition. This thesis focuses on fabricating flexible pressure sensors and developing wearable applications to recognize human gestures. Firstly, a comprehensive literature review was conducted, including current state-of-the-art on pressure sensing techniques and machine learning algorithms. Secondly, a piezoelectric smart wristband was developed to distinguish finger typing movements. Three machine learning algorithms, K Nearest Neighbour (KNN), Decision Tree (DT) and Support Vector Machine (SVM), were used to classify the movement of different fingers. The SVM algorithm outperformed other classifiers with an overall accuracy of 98.67% and 100% when processing raw data and extracted features. Thirdly, a piezoresistive wristband was fabricated based on a flake-sphere composite configuration in which reduced graphene oxide fragments are doped with polystyrene spheres to achieve both high sensitivity and flexibility. The flexible wristband measured the pressure distribution around the wrist for accurate and comfortable hand gesture classification. The intelligent wristband was able to classify 12 hand gestures with 96.33% accuracy for five participants using a machine learning algorithm. Moreover, for demonstrating the practical applications of the proposed method, a realtime system was developed to control a robotic hand according to the classification results. Finally, this thesis also demonstrates an intelligent piezoresistive sensor to recognize different throat movements during pronunciation. The piezoresistive sensor was fabricated using two PolyDimethylsiloxane (PDMS) layers that were coated with silver nanowires and reduced graphene oxide films, where the microstructures were fabricated by the polystyrene spheres between the layers. The highly sensitive sensor was able to distinguish throat vibrations from five different spoken words with an accuracy of 96% using the artificial neural network algorithm

The 3rd International Conference on the Challenges, Opportunities, Innovations and Applications in Electronic Textiles

This reprint is a collection of papers from the E-Textiles 2021 Conference and represents the state-of-the-art from both academia and industry in the development of smart fabrics that incorporate electronic and sensing functionality. The reprint presents a wide range of applications of the technology including wearable textile devices for healthcare applications such as respiratory monitoring and functional electrical stimulation. Manufacturing approaches include printed smart materials, knitted e-textiles and flexible electronic circuit assembly within fabrics and garments. E-textile sustainability, a key future requirement for the technology, is also considered. Supplying power is a constant challenge for all wireless wearable technologies and the collection includes papers on triboelectric energy harvesting and textile-based water-activated batteries. Finally, the application of textiles antennas in both sensing and 5G wireless communications is demonstrated, where different antenna designs and their response to stimuli are presented

Revolutionizing digital healthcare networks with wearable strain sensors using sustainable fibers

Wearable strain sensors have attracted research interest owing to their potential within digital healthcare, offering smarter tracking, efficient diagnostics, and lower costs. Unlike rigid sensors, fiber‐based ones compete with their flexibility, durability, adaptability to body structures as well as eco‐friendliness to environment. Here, the sustainable fiber‐based wearable strain sensors for digital health are reviewed, and material, fabrication, and practical healthcare aspects are explored. Typical strain sensors predicated on various sensing modalities, be it resistive, capacitive, piezoelectric, or triboelectric, are explained and analyzed according to their strengths and weaknesses toward fabrication and applications. The applications in digital healthcare spanning from body area sensing networks, intelligent health management, and medical rehabilitation to multifunctional healthcare systems are also evaluated. Moreover, to create a more complete digital health network, wired and wireless methods of data collection and examples of machine learning are elaborated in detail. Finally, the prevailing challenges and prospective insights into the advancement of novel fibers, enhancement of sensing precision and wearability, and the establishment of seamlessly integrated systems are critically summarized and offered. This endeavor not only encapsulates the present landscape but also lays the foundation for future breakthroughs in fiber‐based wearable strain sensor technology within the domain of digital health

From wearable towards epidermal computing : soft wearable devices for rich interaction on the skin

Human skin provides a large, always available, and easy to access real-estate for interaction. Recent advances in new materials, electronics, and human-computer interaction have led to the emergence of electronic devices that reside directly on the user's skin. These conformal devices, referred to as Epidermal Devices, have mechanical properties compatible with human skin: they are very thin, often thinner than human hair; they elastically deform when the body is moving, and stretch with the user's skin. Firstly, this thesis provides a conceptual understanding of Epidermal Devices in the HCI literature. We compare and contrast them with other technical approaches that enable novel on-skin interactions. Then, through a multi-disciplinary analysis of Epidermal Devices, we identify the design goals and challenges that need to be addressed for advancing this emerging research area in HCI. Following this, our fundamental empirical research investigated how epidermal devices of different rigidity levels affect passive and active tactile perception. Generally, a correlation was found between the device rigidity and tactile sensitivity thresholds as well as roughness discrimination ability. Based on these findings, we derive design recommendations for realizing epidermal devices. Secondly, this thesis contributes novel Epidermal Devices that enable rich on-body interaction. SkinMarks contributes to the fabrication and design of novel Epidermal Devices that are highly skin-conformal and enable touch, squeeze, and bend sensing with co-located visual output. These devices can be deployed on highly challenging body locations, enabling novel interaction techniques and expanding the design space of on-body interaction. Multi-Touch Skin enables high-resolution multi-touch input on the body. We present the first non-rectangular and high-resolution multi-touch sensor overlays for use on skin and introduce a design tool that generates such sensors in custom shapes and sizes. Empirical results from two technical evaluations confirm that the sensor achieves a high signal-to-noise ratio on the body under various grounding conditions and has a high spatial accuracy even when subjected to strong deformations. Thirdly, Epidermal Devices are in contact with the skin, they offer opportunities for sensing rich physiological signals from the body. To leverage this unique property, this thesis presents rapid fabrication and computational design techniques for realizing Multi-Modal Epidermal Devices that can measure multiple physiological signals from the human body. Devices fabricated through these techniques can measure ECG (Electrocardiogram), EMG (Electromyogram), and EDA (Electro-Dermal Activity). We also contribute a computational design and optimization method based on underlying human anatomical models to create optimized device designs that provide an optimal trade-off between physiological signal acquisition capability and device size. The graphical tool allows for easily specifying design preferences and to visually analyze the generated designs in real-time, enabling designer-in-the-loop optimization. Experimental results show high quantitative agreement between the prediction of the optimizer and experimentally collected physiological data. Finally, taking a multi-disciplinary perspective, we outline the roadmap for future research in this area by highlighting the next important steps, opportunities, and challenges. Taken together, this thesis contributes towards a holistic understanding of Epidermal Devices}: it provides an empirical and conceptual understanding as well as technical insights through contributions in DIY (Do-It-Yourself), rapid fabrication, and computational design techniques.Die menschliche Haut bietet eine große, stets verfügbare und leicht zugängliche Fläche für Interaktion. Jüngste Fortschritte in den Bereichen Materialwissenschaft, Elektronik und Mensch-Computer-Interaktion (Human-Computer-Interaction, HCI) [so that you can later use the Englisch abbreviation] haben zur Entwicklung elektronischer Geräte geführt, die sich direkt auf der Haut des Benutzers befinden. Diese sogenannten Epidermisgeräte haben mechanische Eigenschaften, die mit der menschlichen Haut kompatibel sind: Sie sind sehr dünn, oft dünner als ein menschliches Haar; sie verformen sich elastisch, wenn sich der Körper bewegt, und dehnen sich mit der Haut des Benutzers. Diese Thesis bietet, erstens, ein konzeptionelles Verständnis von Epidermisgeräten in der HCI-Literatur. Wir vergleichen sie mit anderen technischen Ansätzen, die neuartige Interaktionen auf der Haut ermöglichen. Dann identifizieren wir durch eine multidisziplinäre Analyse von Epidermisgeräten die Designziele und Herausforderungen, die angegangen werden müssen, um diesen aufstrebenden Forschungsbereich voranzubringen. Im Anschluss daran untersuchten wir in unserer empirischen Grundlagenforschung, wie epidermale Geräte unterschiedlicher Steifigkeit die passive und aktive taktile Wahrnehmung beeinflussen. Im Allgemeinen wurde eine Korrelation zwischen der Steifigkeit des Geräts und den taktilen Empfindlichkeitsschwellen sowie der Fähigkeit zur Rauheitsunterscheidung festgestellt. Basierend auf diesen Ergebnissen leiten wir Designempfehlungen für die Realisierung epidermaler Geräte ab. Zweitens trägt diese Thesis zu neuartigen Epidermisgeräten bei, die eine reichhaltige Interaktion am Körper ermöglichen. SkinMarks trägt zur Herstellung und zum Design neuartiger Epidermisgeräte bei, die hochgradig an die Haut angepasst sind und Berührungs-, Quetsch- und Biegesensoren mit gleichzeitiger visueller Ausgabe ermöglichen. Diese Geräte können an sehr schwierigen Körperstellen eingesetzt werden, ermöglichen neuartige Interaktionstechniken und erweitern den Designraum für die Interaktion am Körper. Multi-Touch Skin ermöglicht hochauflösende Multi-Touch-Eingaben am Körper. Wir präsentieren die ersten nicht-rechteckigen und hochauflösenden Multi-Touch-Sensor-Overlays zur Verwendung auf der Haut und stellen ein Design-Tool vor, das solche Sensoren in benutzerdefinierten Formen und Größen erzeugt. Empirische Ergebnisse aus zwei technischen Evaluierungen bestätigen, dass der Sensor auf dem Körper unter verschiedenen Bedingungen ein hohes Signal-Rausch-Verhältnis erreicht und eine hohe räumliche Auflösung aufweist, selbst wenn er starken Verformungen ausgesetzt ist. Drittens, da Epidermisgeräte in Kontakt mit der Haut stehen, bieten sie die Möglichkeit, reichhaltige physiologische Signale des Körpers zu erfassen. Um diese einzigartige Eigenschaft zu nutzen, werden in dieser Arbeit Techniken zur schnellen Herstellung und zum computergestützten Design von multimodalen Epidermisgeräten vorgestellt, die mehrere physiologische Signale des menschlichen Körpers messen können. Die mit diesen Techniken hergestellten Geräte können EKG (Elektrokardiogramm), EMG (Elektromyogramm) und EDA (elektrodermale Aktivität) messen. Darüber hinaus stellen wir eine computergestützte Design- und Optimierungsmethode vor, die auf den zugrunde liegenden anatomischen Modellen des Menschen basiert, um optimierte Gerätedesigns zu erstellen. Diese Designs bieten einen optimalen Kompromiss zwischen der Fähigkeit zur Erfassung physiologischer Signale und der Größe des Geräts. Das grafische Tool ermöglicht die einfache Festlegung von Designpräferenzen und die visuelle Analyse der generierten Designs in Echtzeit, was eine Optimierung durch den Designer im laufenden Betrieb ermöglicht. Experimentelle Ergebnisse zeigen eine hohe quantitative Übereinstimmung zwischen den Vorhersagen des Optimierers und den experimentell erfassten physiologischen Daten. Schließlich skizzieren wir aus einer multidisziplinären Perspektive einen Fahrplan für zukünftige Forschung in diesem Bereich, indem wir die nächsten wichtigen Schritte, Möglichkeiten und Herausforderungen hervorheben. Insgesamt trägt diese Arbeit zu einem ganzheitlichen Verständnis von Epidermisgeräten bei: Sie liefert ein empirisches und konzeptionelles Verständnis sowie technische Einblicke durch Beiträge zu DIY (Do-It-Yourself), schneller Fertigung und computergestützten Entwurfstechniken

Advanced Materials and Technologies in Nanogenerators

This reprint discusses the various applications, new materials, and evolution in the field of nanogenerators. This lays the foundation for the popularization of their broad applications in energy science, environmental protection, wearable electronics, self-powered sensors, medical science, robotics, and artificial intelligence

Physical Diagnosis and Rehabilitation Technologies

The book focuses on the diagnosis, evaluation, and assistance of gait disorders; all the papers have been contributed by research groups related to assistive robotics, instrumentations, and augmentative devices

Design and Characterization of Crossbar architecture Velostat-based Flexible Writing Pad

Pressure sensors are popular in a large variety of industries. For some applications, it is critical for these sensors to come in a flexible form factor. With the development of new synthetic polymers and novel fabrication techniques, flexible pressure sensing arrays are more easily accessible and can serve a variety of applications. As part of this dissertation, we demonstrate one such application of the same by developing a low-cost flexible writing pad and doing crosstalk analysis on sensors with similar working principles. We present a low-cost, flexible writing pad that uses a 16x16 pressure sensing matrix based on the piezoresistive thin film of velostat. The writing area is 5 cm x 5 cm with an effective pixel area of 0.06 mm^2. A read-out circuit is designed to detect the change in resistance of the velostat pixel using a voltage divider. A microprocessor raster scans through the sensor pixel matrix to obtain a data frame of 256 numbers. This data is processed using techniques like squaring and normalising (S\&N), Gaussian blurring, and adaptive thresholding to generate a more readable output. The writing pad is able to resolve characters larger than 2 cm in length. The flexible writing pad produces legible output while flexed at a bending radius of up to 4 cm. Such flexibility promises to enhance the usability and portability of the writing pad significantly. We noticed that the raw data produced by the writing pad had a lot of crosstalk which we were subsequently able to resolve using the algorithms mentioned above. Such crosstalk has been reported in literature multiple times and is common, especially for sensors of the crossbar architecture.Crosstalk, in a sensor matrix, is the unwanted signal obtained at a sensor pixel that is not directly related to the stimulus. This paper presents a novel approach towards quantifying the crosstalk characteristics of a sensor matrix