1,720,982 research outputs found
WatchMI: applications of watch movement input on unmodified smartwatches
No full textIn this demo, we show that it is possible to enhance touch interaction on unmodified smartwatch to support continuous pressure touch, twist and pan gestures, by only analyzing the real-time data of Inertial Measurement Unit (IMU). Our evaluation results show that the three proposed input interfaces are accurate, noise-resistant, easy to use and can be deployed to a variety of smartwatches. We then showcase the potential of this work with seven example applications. During the demo session, users can try the prototype
Sidetap & slingshot gestures on unmodified smartwatches
No full textWe present a technique for detecting gestures on the edge of an unmodified smartwatch. We demonstrate two exemplary gestures, i) Sidetap - tapping on any side and ii) Slingshot - pressing on the edge and then releasing quickly. Our technique is lightweight, as it relies on measuring the data from the internal Inertial measurement unit (IMU) only. With these two gestures, we expand the input expressiveness of a smartwatch, allowing users to use intuitive gestures with natural tactile feedback, e.g., for the rapid navigation of a long list of items with a tap, or act as shortcut commands to launch applications. It can also allow for eyes-free interaction or subtle interaction where visual attention is not available
SpeCam: sensing surface color and material with the front-facing camera of mobile device
No full textSpeCam is a lightweight surface color and material sensing approach for mobile devices which only uses the front-facing camera and the display as a multi-spectral light source. We leverage the natural use of mobile devices (placing it face-down) to detect the material underneath and therefore infer the location or placement of the device. SpeCam can then be used to support discreet micro-interactions to avoid the numerous distractions that users daily face with today's mobile devices. Our two-parts study shows that SpeCam can i) recognize colors in the HSB space with 10 degrees apart near the 3 dominant colors and 4 degrees otherwise and ii) 30 types of surface materials with 99% accuracy. These findings are further supported by a spectroscopy study. Finally, we suggest a series of applications based on simple mobile micro-interactions suitable for using the phone when placed face-down
WatchMI: pressure touch, twist and pan gesture input on unmodified smartwatches
No full textThe screen size of a smartwatch provides limited space to enable expressive multi-touch input, resulting in a markedly difficult and limited experience. We present WatchMI: Watch Movement Input that enhances touch interaction on a smartwatch to support continuous pressure touch, twist, pan gestures and their combinations. Our novel approach relies on software that analyzes, in real-time, the data from a built-in Inertial Measurement Unit (IMU) in order to determine with great accuracy and different levels of granularity the actions performed by the user, without requiring additional hardware or modification of the watch. We report the results of an evaluation with the system, and demonstrate that the three proposed input interfaces are accurate, noise-resistant, easy to use and can be deployed on a variety of smartwatches. We then showcase the potential of this work with seven different applications including, map navigation, an alarm clock, a music player, pan gesture recognition, text entry, file explorer and controlling remote devices or a game character
Mirror mirror
No full textWhen choosing what to wear, people often use mirrors to try clothing items and see the fit on their body. What if we can not only evaluate items in front of the mirror but also design items and have them fabricated on the spot
OmniSense: Exploring Novel Input Sensing and Interaction Techniques on Mobile Device with an Omni-Directional Camera
No full textAn omni-directional (360°) camera captures the entire viewing sphere surrounding its optical center. Such cameras are growing in use to create highly immersive content and viewing experiences. When such a camera is held by a user, the view includes the user's hand grip, finger, body pose, face, and the surrounding environment, providing a complete understanding of the visual world and context around it. This capability opens up numerous possibilities for rich mobile input sensing. In OmniSense, we explore the broad input design space for mobile devices with a built-in omni-directional camera and broadly categorize them into three sensing pillars: i) near device ii) around device and iii) surrounding device. In addition we explore potential use cases and applications that leverage these sensing capabilities to solve user needs. Following this, we develop a working system to put these concepts into action, by leveraging these sensing capabilities to enable potential use cases and applications. We studied the system in a technical evaluation and a preliminary user study to gain initial feedback and insights. Collectively these techniques illustrate how a single, omni-purpose sensor on a mobile device affords many compelling ways to enable expressive input, while also affording a broad range of novel applications that improve user experience during mobile interaction
Itchy Nose : discreet gesture interaction using EOG sensors in smart eyewear
Get PDFWe propose a sensing technique for detecting finger movements on the nose, using EOG sensors embedded in the frame of a pair of eyeglasses. Eyeglasses wearers can use their fingers to exert different types of movement on the nose, such as flicking, pushing or rubbing. These subtle gestures can be used to control a wearable computer without calling attention to the user in public. We present two user studies where we test recognition accuracy for these movements
WRIST : Watch-Ring Interaction and Sensing Technique for wrist gestures and macro-micro pointing
Get PDFFunding: Next-Generation In-ormation Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (NRF-2017M3C4A7066316) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2019-0-01270, WISE AR UI/UX Platform Development for Smartglasses).To better explore the incorporation of pointing and gesturing into ubiquitous computing, we introduce WRIST, an interaction and sensing technique that leverages the dexterity of human wrist motion. WRIST employs a sensor fusion approach which combines inertial measurement unit (IMU) data from a smartwatch and a smart ring. The relative orientation difference of the two devices is measured as the wrist rotation that is independent from arm rotation, which is also position and orientation invariant. Employing our test hardware, we demonstrate that WRIST affords and enables a number of novel yet simplistic interaction techniques, such as (i) macro-micro pointing without explicit mode switching and (ii) wrist gesture recognition when the hand is held in different orientations (e.g., raised or lowered). We report on two studies to evaluate the proposed techniques and we present a set of applications that demonstrate the benefits of WRIST. We conclude with a discussion of the limitations and highlight possible future pathways for research in pointing and gesturing with wearable devices
Single-handed interaction techniques for mobile and wearable computing
Get PDFThe past decade has seen the proliferation of mobile and wearable computing devices into our everyday life. Such devices are now used throughout the day for both productivity and entertainment purposes. As a result, it is important that input techniques for these devices are efficient, effective and intuitive. Further, it is important that these techniques reflect the reality of common usage patterns. In particular, supporting single-handed usage is of paramount importance, given that in many scenarios only one hand is available. As the screen size of mobile devices are getting larger, single-handed usage becomes even more problematic. At the opposite end of the scale, using small wearable devices such as smartwatches or fitness trackers often requires two hands. This thesis is concerned with the exploration, design, and evaluation of input techniques that enable practical and effective single-handed interaction on mobile and wearable devices, which empower users to achieve more with their smart devices when only one hand is available. In particular, the thesis focuses on the practicability and actual implementation of such techniques, by using built-in or low-cost sensors that are readily available. The work first motivates the thesis topic that was encountered during the early phase of study. Then, the single-handed interaction problem is tackled with two types of device form factor, both mobile and wearable. This thesis studies the problem on three types of input modalities – mid-air gesture, hand posture, on-surface gesture and three types of interaction techniques – text input, gesture, pointing. This thesis provides several techniques, interaction methods and exemplars required to explore the single-handed interaction problem. The effectiveness and efficiency of the techniques are evaluated with rigorous studies
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