Interaction Methods for Smart Glasses : A Survey

Since the launch of Google Glass in 2014, smart glasses have mainly been designed to support micro-interactions. The ultimate goal for them to become an augmented reality interface has not yet been attained due to an encumbrance of controls. Augmented reality involves superimposing interactive computer graphics images onto physical objects in the real world. This survey reviews current research issues in the area of human-computer interaction for smart glasses. The survey first studies the smart glasses available in the market and afterwards investigates the interaction methods proposed in the wide body of literature. The interaction methods can be classified into hand-held, touch, and touchless input. This paper mainly focuses on the touch and touchless input. Touch input can be further divided into on-device and on-body, while touchless input can be classified into hands-free and freehand. Next, we summarize the existing research efforts and trends, in which touch and touchless input are evaluated by a total of eight interaction goals. Finally, we discuss several key design challenges and the possibility of multi-modal input for smart glasses.Peer reviewe

Design and recognition of microgestures for always-available input

Gestural user interfaces for computing devices most commonly require the user to have at least one hand free to interact with the device, for example, moving a mouse, touching a screen, or performing mid-air gestures. Consequently, users find it difficult to operate computing devices while holding or manipulating everyday objects. This limits the users from interacting with the digital world during a significant portion of their everyday activities, such as, using tools in the kitchen or workshop, carrying items, or workout with sports equipment. This thesis pushes the boundaries towards the bigger goal of enabling always-available input. Microgestures have been recognized for their potential to facilitate direct and subtle interactions. However, it remains an open question how to interact using gestures with computing devices when both of the user’s hands are occupied holding everyday objects. We take a holistic approach and focus on three core contributions: i) To understand end-users preferences, we present an empirical analysis of users’ choice of microgestures when holding objects of diverse geometries. Instead of designing a gesture set for a specific object or geometry and to identify gestures that generalize, this thesis leverages the taxonomy of grasp types established from prior research. ii) We tackle the critical problem of avoiding false activation by introducing a novel gestural input concept that leverages a single-finger movement, which stands out from everyday finger motions during holding and manipulating objects. Through a data-driven approach, we also systematically validate the concept’s robustness with different everyday actions. iii) While full sensor coverage on the user’s hand would allow detailed hand-object interaction, minimal instrumentation is desirable for real-world use. This thesis addresses the problem of identifying sparse sensor layouts. We present the first rapid computational method, along with a GUI-based design tool that enables iterative design based on the designer’s high-level requirements. Furthermore, we demonstrate that minimal form-factor devices, like smart rings, can be used to effectively detect microgestures in hands-free and busy scenarios. Overall, the presented findings will serve as both conceptual and technical foundations for enabling interaction with computing devices wherever and whenever users need them.Benutzerschnittstellen für Computergeräte auf Basis von Gesten erfordern für eine Interaktion meist mindestens eine freie Hand, z.B. um eine Maus zu bewegen, einen Bildschirm zu berühren oder Gesten in der Luft auszuführen. Daher ist es für Nutzer schwierig, Geräte zu bedienen, während sie Gegenstände halten oder manipulieren. Dies schränkt die Interaktion mit der digitalen Welt während eines Großteils ihrer alltäglichen Aktivitäten ein, etwa wenn sie Küchengeräte oder Werkzeug verwenden, Gegenstände tragen oder mit Sportgeräten trainieren. Diese Arbeit erforscht neue Wege in Richtung des größeren Ziels, immer verfügbare Eingaben zu ermöglichen. Das Potential von Mikrogesten für die Erleichterung von direkten und feinen Interaktionen wurde bereits erkannt. Die Frage, wie der Nutzer mit Geräten interagiert, wenn beide Hände mit dem Halten von Gegenständen belegt sind, bleibt jedoch offen. Wir verfolgen einen ganzheitlichen Ansatz und konzentrieren uns auf drei Kernbeiträge: i) Um die Präferenzen der Endnutzer zu verstehen, präsentieren wir eine empirische Analyse der Wahl von Mikrogesten beim Halten von Objekte mit diversen Geometrien. Anstatt einen Satz an Gesten für ein bestimmtes Objekt oder eine bestimmte Geometrie zu entwerfen, nutzt diese Arbeit die aus früheren Forschungen stammenden Taxonomien an Griff-Typen. ii) Wir adressieren das Problem falscher Aktivierungen durch ein neuartiges Eingabekonzept, das die sich von alltäglichen Fingerbewegungen abhebende Bewegung eines einzelnen Fingers nutzt. Durch einen datengesteuerten Ansatz validieren wir zudem systematisch die Robustheit des Konzepts bei diversen alltäglichen Aktionen. iii) Auch wenn eine vollständige Sensorabdeckung an der Hand des Nutzers eine detaillierte Hand-Objekt-Interaktion ermöglichen würde, ist eine minimale Ausstattung für den Einsatz in der realen Welt wünschenswert. Diese Arbeit befasst sich mit der Identifizierung reduzierter Sensoranordnungen. Wir präsentieren die erste, schnelle Berechnungsmethode in einem GUI-basierten Designtool, das iteratives Design basierend auf den Anforderungen des Designers ermöglicht. Wir zeigen zudem, dass Geräte mit minimalem Formfaktor wie smarte Ringe für die Erkennung von Mikrogesten verwendet werden können. Insgesamt dienen die vorgestellten Ergebnisse sowohl als konzeptionelle als auch als technische Grundlage für die Realisierung von Interaktion mit Computergeräten wo und wann immer Nutzer sie benötigen.Bosch Researc

Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets

Recognition of activities of daily living plays an important role in monitoring elderly people and helping caregivers in controlling and detecting changes in daily behaviors. Thanks to the miniaturization and low cost of Microelectromechanical systems (MEMs), in particular of Inertial Measurement Units, in recent years body-worn activity recognition has gained popularity. In this context, the proposed work aims to recognize nine different gestures involved in daily activities using hand and wrist wearable sensors. Additionally, the analysis was carried out also considering different combinations of wearable sensors, in order to find the best combination in terms of unobtrusiveness and recognition accuracy. In order to achieve the proposed goals, an extensive experimentation was performed in a realistic environment. Twenty users were asked to perform the selected gestures and then the data were off-line analyzed to extract significant features. In order to corroborate the analysis, the classification problem was treated using two different and commonly used supervised machine learning techniques, namely Decision Tree and Support Vector Machine, analyzing both personal model and Leave-One-Subject-Out cross validation. The results obtained from this analysis show that the proposed system is able to recognize the proposed gestures with an accuracy of 89.01% in the Leave-One-Subject-Out cross validation and are therefore promising for further investigation in real life scenarios

Proceedings of the 4th Workshop on Interacting with Smart Objects 2015

These are the Proceedings of the 4th IUI Workshop on Interacting with Smart Objects. Objects that we use in our everyday life are expanding their restricted interaction capabilities and provide functionalities that go far beyond their original functionality. They feature computing capabilities and are thus able to capture information, process and store it and interact with their environments, turning them into smart objects

GestureGPT: Zero-shot Interactive Gesture Understanding and Grounding with Large Language Model Agents

Current gesture recognition systems primarily focus on identifying gestures within a predefined set, leaving a gap in connecting these gestures to interactive GUI elements or system functions (e.g., linking a 'thumb-up' gesture to a 'like' button). We introduce GestureGPT, a novel zero-shot gesture understanding and grounding framework leveraging large language models (LLMs). Gesture descriptions are formulated based on hand landmark coordinates from gesture videos and fed into our dual-agent dialogue system. A gesture agent deciphers these descriptions and queries about the interaction context (e.g., interface, history, gaze data), which a context agent organizes and provides. Following iterative exchanges, the gesture agent discerns user intent, grounding it to an interactive function. We validated the gesture description module using public first-view and third-view gesture datasets and tested the whole system in two real-world settings: video streaming and smart home IoT control. The highest zero-shot Top-5 grounding accuracies are 80.11% for video streaming and 90.78% for smart home tasks, showing potential of the new gesture understanding paradigm