4,669 research outputs found

    Hydraulophone design considerations : absement, displacement, and velocity-sensitive music keyboard in which each key is a water jet

    Get PDF
    We present a musical keyboard that is not only velocity-sensitive, but in fact responds to absement (presement), displacement (placement), velocity, acceleration, jerk, jounce, etc. (i.e. to all the derivatives, as well as the integral, of displacement). Moreover, unlike a piano keyboard in which the keys reach a point of maximal displacement, our keys are essentially infinite in length, and thus never reach an end to their key travel. Our infinite length keys are achieved by using water jet streams that continue to flow past the fingers of a person playing the instrument. The instrument takes the form of a pipe with a row of holes, in which water flows out of each hole, while a user is invited to play the instrument by interfering with the flow of water coming out of the holes. The instrument resembles a large flute, but, unlike a flute, there is no complicated fingering pattern. Instead, each hole (each water jet) corresponds to one note (as with a piano or pipe organ). Therefore, unlike a flute, chords can be played by blocking more than one water jet hole at the same time. Because each note corresponds to only one hole, different fingers of the musician can be inserted into, onto, around, or near several of the instrument’s many water jet holes, in a variety of different ways, resulting in an ability to independently control the way in which each note in a chord sounds. Thus the hydraulophone combines the intricate embouchure control of woodwind instruments with the polyphony of keyboard instruments. Various forms of our instrument include totally acoustic, totally electronic, as well as hybrid instruments that are acoustic but also include an interface to a multimedia computer to produce a mixture of sounds that are produced by the acoustic properties of water screeching through orific plates, as well as synthesized sounds

    Resonating Experiences of Self and Others enabled by a Tangible Somaesthetic Design

    Get PDF
    Digitalization is penetrating every aspect of everyday life including a human's heart beating, which can easily be sensed by wearable sensors and displayed for others to see, feel, and potentially "bodily resonate" with. Previous work in studying human interactions and interaction designs with physiological data, such as a heart's pulse rate, have argued that feeding it back to the users may, for example support users' mindfulness and self-awareness during various everyday activities and ultimately support their wellbeing. Inspired by Somaesthetics as a discipline, which focuses on an appreciation of the living body's role in all our experiences, we designed and explored mobile tangible heart beat displays, which enable rich forms of bodily experiencing oneself and others in social proximity. In this paper, we first report on the design process of tangible heart displays and then present results of a field study with 30 pairs of participants. Participants were asked to use the tangible heart displays during watching movies together and report their experience in three different heart display conditions (i.e., displaying their own heart beat, their partner's heart beat, and watching a movie without a heart display). We found, for example that participants reported significant effects in experiencing sensory immersion when they felt their own heart beats compared to the condition without any heart beat display, and that feeling their partner's heart beats resulted in significant effects on social experience. We refer to resonance theory to discuss the results, highlighting the potential of how ubiquitous technology could utilize physiological data to provide resonance in a modern society facing social acceleration.Comment: 18 page

    deForm: An interactive malleable surface for capturing 2.5D arbitrary objects, tools and touch

    Get PDF
    We introduce a novel input device, deForm, that supports 2.5D touch gestures, tangible tools, and arbitrary objects through real-time structured light scanning of a malleable surface of interaction. DeForm captures high-resolution surface deformations and 2D grey-scale textures of a gel surface through a three-phase structured light 3D scanner. This technique can be combined with IR projection to allow for invisible capture, providing the opportunity for co-located visual feedback on the deformable surface. We describe methods for tracking fingers, whole hand gestures, and arbitrary tangible tools. We outline a method for physically encoding fiducial marker information in the height map of tangible tools. In addition, we describe a novel method for distinguishing between human touch and tangible tools, through capacitive sensing on top of the input surface. Finally we motivate our device through a number of sample applications

    Grasp-sensitive surfaces

    Get PDF
    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 FĂ€higkeit, GegenstĂ€nde mit unseren HĂ€nden zu greifen, erlaubt uns, diese vielfĂ€ltig zu manipulieren. Werkzeuge erweitern unsere FĂ€higkeiten noch, indem sie Genauigkeit, Kraft und Form unserer HĂ€nde an die Aufgabe anpassen. Digitale Werkzeuge, beispielsweise Mobiltelefone oder ComputermĂ€use, erlauben uns auch, die FĂ€higkeiten unseres Gehirns und unserer Sinnesorgane zu erweitern. Diese GerĂ€te verfĂŒgen bereits ĂŒber Sensoren und Recheneinheiten. Aber auch viele andere Werkzeuge und Objekte lassen sich mit winzigen, effizienten Sensoren und Recheneinheiten erweitern. Dies erlaubt greifbaren Objekten, mehr ĂŒber den Benutzer zu erfahren, der sie greift - und ermöglicht es, ihn bei der Erreichung seines Ziels zu unterstĂŒtzen. 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 fĂŒr diese Aktionen dienen. Eine Bohrmaschine könnte erkennen, ob der Benutzer sie auch wirklich sicher hĂ€lt 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 ĂŒber Grifferkennung (*grasp sensing*) fĂŒr die Mensch-Maschine-Interaktion, mit einem Fokus auf Technologien zur Implementierung griffempfindlicher OberflĂ€chen und auf Herausforderungen beim Design griffempfindlicher Benutzerschnittstellen. Sie umfasst drei primĂ€re BeitrĂ€ge zum wissenschaftlichen Forschungsstand: einen umfassenden Überblick ĂŒber die bisherige Forschung zu menschlichem Greifen und Grifferkennung, eine detaillierte Beschreibung dreier neuer Prototyping-Werkzeuge fĂŒr griffempfindliche OberflĂ€chen und ein Framework fĂŒr Analyse und Design von griff-basierter Interaktion (*grasp interaction*). Seit nahezu einem Jahrhundert erforschen Wissenschaftler menschliches Greifen. Mein Überblick ĂŒber den Forschungsstand beschreibt Definitionen, Klassifikationen und Modelle menschlichen Greifens. In einigen wenigen Studien wurde bisher Greifen in alltĂ€glichen Situationen untersucht. Diese fanden eine deutlich grĂ¶ĂŸere DiversitĂ€t in den Griffmuster als in existierenden Taxonomien beschreibbar. Diese DiversitĂ€t 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 verknĂŒpften Griffe und der *Interpretation* der Bedeutung des Griffes. In einem umfassenden Überblick ĂŒber verwandte Arbeiten zeige ich, dass die Implementierung von griffempfindlichen OberflĂ€chen immer noch ein herausforderndes Problem ist, dass Forscher regelmĂ€ĂŸig 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 fĂŒr griffempfindliche OberflĂ€chen entwickelt. Diese mindern jeweils eine oder mehrere SchwĂ€chen 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 AnnĂ€herung an das Objekt zu erkennen. Außerdem muss nicht die komplette OberflĂ€che des Objekts mit Sensoren bedeckt werden. Benutzertests ergaben eine Erkennungsrate, die vergleichbar mit einem System mit 72 binĂ€ren Sensoren ist. **FlyEye** verwendet LichtwellenleiterbĂŒndel, die an eine Kamera angeschlossen werden, um AnnĂ€herung und BerĂŒhrung auf beliebig geformten OberflĂ€chen zu erkennen. Es ermöglicht auch Designern mit begrenzter Elektronikerfahrung das Rapid Prototyping von berĂŒhrungs- und griffempfindlichen Objekten. FĂŒr 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 ĂŒblicherweise zur Analyse von KabelbeschĂ€digungen eingesetzt wird. TDRtouch erlaubt es, BerĂŒhrungen entlang eines Drahtes zu lokalisieren. Dies ermöglicht es, schnell modulare, extrem dĂŒnne und flexible griffempfindliche OberflĂ€chen zu entwickeln. Ich beschreibe, wie diese Techniken verschiedene Anforderungen erfĂŒllen und den *design space* fĂŒr griffempfindliche Objekte deutlich erweitern. Desweiteren bespreche ich die Herausforderungen beim Verstehen von Griffinformationen und stelle eine Einteilung von Interaktionsmöglichkeiten vor. Bisherige Anwendungsbeispiele fĂŒr die Grifferkennung nutzen nur Daten der Griffsensoren und beschrĂ€nken 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 fĂŒnf Kategorien von Einflussfaktoren, die bestimmen wie wir ein Objekt greifen: *Goal* -- das Ziel, das wir mit dem Griff erreichen wollen, *Relationship* -- das VerhĂ€ltnis zum Objekt, *Anatomy* -- Handform und Bewegungsmuster, *Setting* -- Umgebungsfaktoren und *Properties* -- Eigenschaften des Objekts, wie OberflĂ€chenbeschaffenheit, Form oder Gewicht. Ich schließe mit einer Besprechung neuer Herausforderungen bei der Grifferkennung und Griffinteraktion und mache VorschlĂ€ge zur Entwicklung von zuverlĂ€ssiger und benutzbarer Griffinteraktion
    • 

    corecore