Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

Automotive gestures recognition based on capacitive sensing

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e ComputadoresDriven by technological advancements, vehicles have steadily increased in sophistication, specially in the way drivers and passengers interact with their vehicles. For example, the BMW 7 series driver-controlled systems, contains over 700 functions. Whereas, it makes easier to navigate streets, talk on phone and more, this may lead to visual distraction, since when paying attention to a task not driving related, the brain focus on that activity. That distraction is, according to studies, the third cause of accidents, only surpassed by speeding and drunk driving. Driver distraction is stressed as the main concern by regulators, in particular, National Highway Transportation Safety Agency (NHTSA), which is developing recommended limits for the amount of time a driver needs to spend glancing away from the road to operate in-car features. Diverting attention from driving can be fatal; therefore, automakers have been challenged to design safer and comfortable human-machine interfaces (HMIs) without missing the latest technological achievements. This dissertation aims to mitigate driver distraction by developing a gestural recognition system that allows the user a more comfortable and intuitive experience while driving. The developed system outlines the algorithms to recognize gestures using the capacitive technology.Impulsionados pelos avanços tecnológicos, os automóveis tem de forma continua aumentado em complexidade, sobretudo na forma como os conductores e passageiros interagem com os seus veículos. Por exemplo, os sistemas controlados pelo condutor do BMW série 7 continham mais de 700 funções. Embora, isto facilite a navegação entre locais, falar ao telemóvel entre outros, isso pode levar a uma distração visual, já que ao prestar atenção a uma tarefa não relacionados com a condução, o cérebro se concentra nessa atividade. Essa distração é, de acordo com os estudos, a terceira causa de acidentes, apenas ultrapassada pelo excesso de velocidade e condução embriagada. A distração do condutor é realçada como a principal preocupação dos reguladores, em particular, a National Highway Transportation Safety Agency (NHTSA), que está desenvolvendo os limites recomendados para a quantidade de tempo que um condutor precisa de desviar o olhar da estrada para controlar os sistemas do carro. Desviar a atenção da conducção, pode ser fatal; portanto, os fabricante de automóveis têm sido desafiados a projetar interfaces homemmáquina (HMIs) mais seguras e confortáveis, sem perder as últimas conquistas tecnológicas. Esta dissertação tem como objetivo minimizar a distração do condutor, desenvolvendo um sistema de reconhecimento gestual que permite ao utilizador uma experiência mais confortável e intuitiva ao conduzir. O sistema desenvolvido descreve os algoritmos de reconhecimento de gestos usando a tecnologia capacitiva.It is worth noting that this work has been financially supported by the Portugal Incentive System for Research and Technological Development in scope of the projects in co-promotion number 036265/2013 (HMIExcel 2013-2015), number 002814/2015 (iFACTORY 2015-2018) and number 002797/2015 (INNOVCAR 2015-2018)

Grasp-sensitive surfaces

Grasping objects with our hands allows us to skillfully move and manipulate them. Hand-held tools further extend our capabilities by adapting precision, power, and shape of our hands to the task at hand. Some of these tools, such as mobile phones or computer mice, already incorporate information processing capabilities. Many other tools may be augmented with small, energy-efficient digital sensors and processors. This allows for graspable objects to learn about the user grasping them - and supporting the user's goals. For example, the way we grasp a mobile phone might indicate whether we want to take a photo or call a friend with it - and thus serve as a shortcut to that action. A power drill might sense whether the user is grasping it firmly enough and refuse to turn on if this is not the case. And a computer mouse could distinguish between intentional and unintentional movement and ignore the latter. This dissertation gives an overview of grasp sensing for human-computer interaction, focusing on technologies for building grasp-sensitive surfaces and challenges in designing grasp-sensitive user interfaces. It comprises three major contributions: a comprehensive review of existing research on human grasping and grasp sensing, a detailed description of three novel prototyping tools for grasp-sensitive surfaces, and a framework for analyzing and designing grasp interaction: For nearly a century, scientists have analyzed human grasping. My literature review gives an overview of definitions, classifications, and models of human grasping. A small number of studies have investigated grasping in everyday situations. They found a much greater diversity of grasps than described by existing taxonomies. This diversity makes it difficult to directly associate certain grasps with users' goals. In order to structure related work and own research, I formalize a generic workflow for grasp sensing. It comprises *capturing* of sensor values, *identifying* the associated grasp, and *interpreting* the meaning of the grasp. A comprehensive overview of related work shows that implementation of grasp-sensitive surfaces is still hard, researchers often are not aware of related work from other disciplines, and intuitive grasp interaction has not yet received much attention. In order to address the first issue, I developed three novel sensor technologies designed for grasp-sensitive surfaces. These mitigate one or more limitations of traditional sensing techniques: **HandSense** uses four strategically positioned capacitive sensors for detecting and classifying grasp patterns on mobile phones. The use of custom-built high-resolution sensors allows detecting proximity and avoids the need to cover the whole device surface with sensors. User tests showed a recognition rate of 81%, comparable to that of a system with 72 binary sensors. **FlyEye** uses optical fiber bundles connected to a camera for detecting touch and proximity on arbitrarily shaped surfaces. It allows rapid prototyping of touch- and grasp-sensitive objects and requires only very limited electronics knowledge. For FlyEye I developed a *relative calibration* algorithm that allows determining the locations of groups of sensors whose arrangement is not known. **TDRtouch** extends Time Domain Reflectometry (TDR), a technique traditionally used for inspecting cable faults, for touch and grasp sensing. TDRtouch is able to locate touches along a wire, allowing designers to rapidly prototype and implement modular, extremely thin, and flexible grasp-sensitive surfaces. I summarize how these technologies cater to different requirements and significantly expand the design space for grasp-sensitive objects. Furthermore, I discuss challenges for making sense of raw grasp information and categorize interactions. Traditional application scenarios for grasp sensing use only the grasp sensor's data, and only for mode-switching. I argue that data from grasp sensors is part of the general usage context and should be only used in combination with other context information. For analyzing and discussing the possible meanings of grasp types, I created the GRASP model. It describes five categories of influencing factors that determine how we grasp an object: *Goal* -- what we want to do with the object, *Relationship* -- what we know and feel about the object we want to grasp, *Anatomy* -- hand shape and learned movement patterns, *Setting* -- surrounding and environmental conditions, and *Properties* -- texture, shape, weight, and other intrinsics of the object I conclude the dissertation with a discussion of upcoming challenges in grasp sensing and grasp interaction, and provide suggestions for implementing robust and usable grasp interaction.Die Fähigkeit, Gegenstände mit unseren Händen zu greifen, erlaubt uns, diese vielfältig zu manipulieren. Werkzeuge erweitern unsere Fähigkeiten noch, indem sie Genauigkeit, Kraft und Form unserer Hände an die Aufgabe anpassen. Digitale Werkzeuge, beispielsweise Mobiltelefone oder Computermäuse, erlauben uns auch, die Fähigkeiten unseres Gehirns und unserer Sinnesorgane zu erweitern. Diese Geräte verfügen bereits über Sensoren und Recheneinheiten. Aber auch viele andere Werkzeuge und Objekte lassen sich mit winzigen, effizienten Sensoren und Recheneinheiten erweitern. Dies erlaubt greifbaren Objekten, mehr über den Benutzer zu erfahren, der sie greift - und ermöglicht es, ihn bei der Erreichung seines Ziels zu unterstützen. Zum Beispiel könnte die Art und Weise, in der wir ein Mobiltelefon halten, verraten, ob wir ein Foto aufnehmen oder einen Freund anrufen wollen - und damit als Shortcut für diese Aktionen dienen. Eine Bohrmaschine könnte erkennen, ob der Benutzer sie auch wirklich sicher hält und den Dienst verweigern, falls dem nicht so ist. Und eine Computermaus könnte zwischen absichtlichen und unabsichtlichen Mausbewegungen unterscheiden und letztere ignorieren. Diese Dissertation gibt einen Überblick über Grifferkennung (*grasp sensing*) für die Mensch-Maschine-Interaktion, mit einem Fokus auf Technologien zur Implementierung griffempfindlicher Oberflächen und auf Herausforderungen beim Design griffempfindlicher Benutzerschnittstellen. Sie umfasst drei primäre Beiträge zum wissenschaftlichen Forschungsstand: einen umfassenden Überblick über die bisherige Forschung zu menschlichem Greifen und Grifferkennung, eine detaillierte Beschreibung dreier neuer Prototyping-Werkzeuge für griffempfindliche Oberflächen und ein Framework für Analyse und Design von griff-basierter Interaktion (*grasp interaction*). Seit nahezu einem Jahrhundert erforschen Wissenschaftler menschliches Greifen. Mein Überblick über den Forschungsstand beschreibt Definitionen, Klassifikationen und Modelle menschlichen Greifens. In einigen wenigen Studien wurde bisher Greifen in alltäglichen Situationen untersucht. Diese fanden eine deutlich größere Diversität in den Griffmuster als in existierenden Taxonomien beschreibbar. Diese Diversität erschwert es, bestimmten Griffmustern eine Absicht des Benutzers zuzuordnen. Um verwandte Arbeiten und eigene Forschungsergebnisse zu strukturieren, formalisiere ich einen allgemeinen Ablauf der Grifferkennung. Dieser besteht aus dem *Erfassen* von Sensorwerten, der *Identifizierung* der damit verknüpften Griffe und der *Interpretation* der Bedeutung des Griffes. In einem umfassenden Überblick über verwandte Arbeiten zeige ich, dass die Implementierung von griffempfindlichen Oberflächen immer noch ein herausforderndes Problem ist, dass Forscher regelmäßig keine Ahnung von verwandten Arbeiten in benachbarten Forschungsfeldern haben, und dass intuitive Griffinteraktion bislang wenig Aufmerksamkeit erhalten hat. Um das erstgenannte Problem zu lösen, habe ich drei neuartige Sensortechniken für griffempfindliche Oberflächen entwickelt. Diese mindern jeweils eine oder mehrere Schwächen traditioneller Sensortechniken: **HandSense** verwendet vier strategisch positionierte kapazitive Sensoren um Griffmuster zu erkennen. Durch die Verwendung von selbst entwickelten, hochauflösenden Sensoren ist es möglich, schon die Annäherung an das Objekt zu erkennen. Außerdem muss nicht die komplette Oberfläche des Objekts mit Sensoren bedeckt werden. Benutzertests ergaben eine Erkennungsrate, die vergleichbar mit einem System mit 72 binären Sensoren ist. **FlyEye** verwendet Lichtwellenleiterbündel, die an eine Kamera angeschlossen werden, um Annäherung und Berührung auf beliebig geformten Oberflächen zu erkennen. Es ermöglicht auch Designern mit begrenzter Elektronikerfahrung das Rapid Prototyping von berührungs- und griffempfindlichen Objekten. Für FlyEye entwickelte ich einen *relative-calibration*-Algorithmus, der verwendet werden kann um Gruppen von Sensoren, deren Anordnung unbekannt ist, semi-automatisch anzuordnen. **TDRtouch** erweitert Time Domain Reflectometry (TDR), eine Technik die üblicherweise zur Analyse von Kabelbeschädigungen eingesetzt wird. TDRtouch erlaubt es, Berührungen entlang eines Drahtes zu lokalisieren. Dies ermöglicht es, schnell modulare, extrem dünne und flexible griffempfindliche Oberflächen zu entwickeln. Ich beschreibe, wie diese Techniken verschiedene Anforderungen erfüllen und den *design space* für griffempfindliche Objekte deutlich erweitern. Desweiteren bespreche ich die Herausforderungen beim Verstehen von Griffinformationen und stelle eine Einteilung von Interaktionsmöglichkeiten vor. Bisherige Anwendungsbeispiele für die Grifferkennung nutzen nur Daten der Griffsensoren und beschränken sich auf Moduswechsel. Ich argumentiere, dass diese Sensordaten Teil des allgemeinen Benutzungskontexts sind und nur in Kombination mit anderer Kontextinformation verwendet werden sollten. Um die möglichen Bedeutungen von Griffarten analysieren und diskutieren zu können, entwickelte ich das GRASP-Modell. Dieses beschreibt fünf Kategorien von Einflussfaktoren, die bestimmen wie wir ein Objekt greifen: *Goal* -- das Ziel, das wir mit dem Griff erreichen wollen, *Relationship* -- das Verhältnis zum Objekt, *Anatomy* -- Handform und Bewegungsmuster, *Setting* -- Umgebungsfaktoren und *Properties* -- Eigenschaften des Objekts, wie Oberflächenbeschaffenheit, Form oder Gewicht. Ich schließe mit einer Besprechung neuer Herausforderungen bei der Grifferkennung und Griffinteraktion und mache Vorschläge zur Entwicklung von zuverlässiger und benutzbarer Griffinteraktion

Physical sketching tools and techniques for customized sensate surfaces

Sensate surfaces are a promising avenue for enhancing human interaction with digital systems due to their inherent intuitiveness and natural user interface. Recent technological advancements have enabled sensate surfaces to surpass the constraints of conventional touchscreens by integrating them into everyday objects, creating interactive interfaces that can detect various inputs such as touch, pressure, and gestures. This allows for more natural and intuitive control of digital systems. However, prototyping interactive surfaces that are customized to users' requirements using conventional techniques remains technically challenging due to limitations in accommodating complex geometric shapes and varying sizes. Furthermore, it is crucial to consider the context in which customized surfaces are utilized, as relocating them to fabrication labs may lead to the loss of their original design context. Additionally, prototyping high-resolution sensate surfaces presents challenges due to the complex signal processing requirements involved. This thesis investigates the design and fabrication of customized sensate surfaces that meet the diverse requirements of different users and contexts. The research aims to develop novel tools and techniques that overcome the technical limitations of current methods and enable the creation of sensate surfaces that enhance human interaction with digital systems.Sensorische Oberflächen sind aufgrund ihrer inhärenten Intuitivität und natürlichen Benutzeroberfläche ein vielversprechender Ansatz, um die menschliche Interaktionmit digitalen Systemen zu verbessern. Die jüngsten technologischen Fortschritte haben es ermöglicht, dass sensorische Oberflächen die Beschränkungen herkömmlicher Touchscreens überwinden, indem sie in Alltagsgegenstände integriert werden und interaktive Schnittstellen schaffen, die diverse Eingaben wie Berührung, Druck, oder Gesten erkennen können. Dies ermöglicht eine natürlichere und intuitivere Steuerung von digitalen Systemen. Das Prototyping interaktiver Oberflächen, die mit herkömmlichen Techniken an die Bedürfnisse der Nutzer angepasst werden, bleibt jedoch eine technische Herausforderung, da komplexe geometrische Formen und variierende Größen nur begrenzt berücksichtigt werden können. Darüber hinaus ist es von entscheidender Bedeutung, den Kontext, in dem diese individuell angepassten Oberflächen verwendet werden, zu berücksichtigen, da eine Verlagerung in Fabrikations-Laboratorien zum Verlust ihres ursprünglichen Designkontextes führen kann. Zudem stellt das Prototyping hochauflösender sensorischer Oberflächen aufgrund der komplexen Anforderungen an die Signalverarbeitung eine Herausforderung dar. Diese Arbeit erforscht dasDesign und die Fabrikation individuell angepasster sensorischer Oberflächen, die den diversen Anforderungen unterschiedlicher Nutzer und Kontexte gerecht werden. Die Forschung zielt darauf ab, neuartigeWerkzeuge und Techniken zu entwickeln, die die technischen Beschränkungen derzeitigerMethoden überwinden und die Erstellung von sensorischen Oberflächen ermöglichen, die die menschliche Interaktion mit digitalen Systemen verbessern

Musical Gesture through the Human Computer Interface: An Investigation using Information Theory

This study applies information theory to investigate human ability to communicate using continuous control sensors with a particular focus on informing the design of digital musical instruments. There is an active practice of building and evaluating such instruments, for instance, in the New Interfaces for Musical Expression (NIME) conference community. The fidelity of the instruments can depend on the included sensors, and although much anecdotal evidence and craft experience informs the use of these sensors, relatively little is known about the ability of humans to control them accurately. This dissertation addresses this issue and related concerns, including continuous control performance in increasing degrees-of-freedom, pursuit tracking in comparison with pointing, and the estimations of musical interface designers and researchers of human performance with continuous control sensors. The methodology used models the human-computer system as an information channel while applying concepts from information theory to performance data collected in studies of human subjects using sensing devices. These studies not only add to knowledge about human abilities, but they also inform on issues in musical mappings, ergonomics, and usability

Design and Development of Sensor Integrated Robotic Hand

Most of the automated systems using robots as agents do use few sensors according to the need. However, there are situations where the tasks carried out by the end-effector, or for that matter by the robot hand needs multiple sensors. The hand, to make the best use of these sensors, and behave autonomously, requires a set of appropriate types of sensors which could be integrated in proper manners. The present research work aims at developing a sensor integrated robot hand that can collect information related to the assigned tasks, assimilate there correctly and then do task action as appropriate. The process of development involves selection of sensors of right types and of right specification, locating then at proper places in the hand, checking their functionality individually and calibrating them for the envisaged process. Since the sensors need to be integrated so that they perform in the desired manner collectively, an integration platform is created using NI PXIe-1082. A set of algorithm is developed for achieving the integrated model. The entire process is first modelled and simulated off line for possible modification in order to ensure that all the sensors do contribute towards the autonomy of the hand for desired activity. This work also involves design of a two-fingered gripper. The design is made in such a way that it is capable of carrying out the desired tasks and can accommodate all the sensors within its fold. The developed sensor integrated hand has been put to work and its performance test has been carried out. This hand can be very useful for part assembly work in industries for any shape of part with a limit on the size of the part in mind. The broad aim is to design, model simulate and develop an advanced robotic hand. Sensors for pick up contacts pressure, force, torque, position, surface profile shape using suitable sensing elements in a robot hand are to be introduced. The hand is a complex structure with large number of degrees of freedom and has multiple sensing capabilities apart from the associated sensing assistance from other organs. The present work is envisaged to add multiple sensors to a two-fingered robotic hand having motion capabilities and constraints similar to the human hand. There has been a good amount of research and development in this field during the last two decades a lot remains to be explored and achieved. The objective of the proposed work is to design, simulate and develop a sensor integrated robotic hand. Its potential applications can be proposed for industrial environments and in healthcare field. The industrial applications include electronic assembly tasks, lighter inspection tasks, etc. Application in healthcare could be in the areas of rehabilitation and assistive techniques. The work also aims to establish the requirement of the robotic hand for the target application areas, to identify the suitable kinds and model of sensors that can be integrated on hand control system. Functioning of motors in the robotic hand and integration of appropriate sensors for the desired motion is explained for the control of the various elements of the hand. Additional sensors, capable of collecting external information and information about the object for manipulation is explored. Processes are designed using various software and hardware tools such as mathematical computation MATLAB, OpenCV library and LabVIEW 2013 DAQ system as applicable, validated theoretically and finally implemented to develop an intelligent robotic hand. The multiple smart sensors are installed on a standard six degree-of-freedom industrial robot KAWASAKI RS06L articulated manipulator, with the two-finger pneumatic SHUNK robotic hand or designed prototype and robot control programs are integrated in such a manner that allows easy application of grasping in an industrial pick-and-place operation where the characteristics of the object can vary or are unknown. The effectiveness of the actual recommended structure is usually proven simply by experiments using calibration involving sensors and manipulator. The dissertation concludes with a summary of the contribution and the scope of further work

Control system design for a C-130 Ro-Ro sensor deployment platform

A WVU team of engineers designed and built a palletized system that will be used to deploy surveillance sensors from a C-130 cargo airplane. There will be two pallets, one that will house the Operator Station and one that will carry a mechanical arm with a Sensor Pod, where the sensors will reside. This pallet will be placed on the C-130 rear door, which will be opened while in flight. The mechanical arm is designed to rotate the Sensor Pod underneath the door so the sensors can observe the ground.;Computer/Electrical engineers were asked to design the control circuit for the Sensor Pallet, providing the user with a user interface to control deployment of the mechanical arm and Sensor Pod. The mechanical arm should also be deployable in an automated process, controlled by a computerized system. They were also responsible for designing the circuit to provide power to the system, interfacing with the power generated on the C-130 cargo airplane.;The thesis Control System Design for a C-130 Ro-Ro Sensor Deployment Platform details the power distribution circuit design, the control circuit design and the design of the automated process program