SWiM: Shape Writing in Motion [CHI17 Paper, to appear]
We propose and evaluate a novel design point around a tilt-based text entry technique which supports single handed usage. Our technique is based on the gesture keyboard (shape writing). However, instead of drawing gestures with a finger or stylus, users articulate a gesture by tilting the device. This can be especially useful when the user’s other hand is otherwise encumbered or unavailable.
Mirror Mirror: An On-Body Clothing Design System [CHI16 Note]
We contribute the Mirror Mirror system that supports not only mixing and matching existing fashion items, but also lets users design new items in front of the mirror and export designs to fabrication devices. Mirror Mirror makes use of spatial augmented reality and a mirror Virtual garments are visible both on the body for precise manipulation as well as in the reflection to obtain a third person perspective. While much previous work deals with re-texturing and registering virtual garments to live user data, we focus on collaborative design and show various ways of designing using real bodies as mannequins. Sample: Facebook, Google Drive, Twitter
We 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 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, instead of limiting the interaction to the small touch screen only.
We present a virtual keyboard system that enables freehand midair text entry for distant display while only requiring a low-cost depth sensor. Leveraging user’s spatial familiarity with the QWERTY layout, our system allows users to input text in thin air by mimicking the typing action they usually perform on a physical keyboard or touchscreen device. Both hands and ten fingers are individually tracked, along with clicking action detection to enable a wide variety of interactions. We propose three midair text entry techniques: bi-manual hunt-and-peck, ten fingers touch-typing and one hand shape writing.
12 Feb 17: SWiM & TiTAN accepted at CHI17
16 Oct 16: RadarCat presented at UIST17
09 Sep 16: WatchMI won Honorable mention!
31 Aug 16: Gave a Tech talk at Google UK
20 May 16: Soli project featured in Google I/O
We aim to improve the peripheral vision on VR HMD (Oculus Rift) using embedded LEDs.
Material sensing using front camera.
We present a novel framework for mixed reality based remote collaboration system, which enables a local user to interact and collaborate with another user from remote space using natural hand motion. Unlike conventional system where the remote user appears only inside the screen, our system is able to summon the remote user into the local space, which appears as a virtual avatar in the real world view seen by the local user.
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, all seamlessly integrated in an unmodified smart watch.
In RadarCat we present a small, versatile radar-based system for material and object classification which enables new forms of everyday proximate interaction with digital devices. We further demonstrate four working examples including a physical object dictionary, painting and photo editing application, body shortcuts and automatic refill based on RadarCat.
We present a robust marker-less hand/finger tracking and gesture recognition system using low-cost hardware. We propose a simple but efficient method that allows robust and fast hand tracking despite complex background and motion blur. Our system is able to translate the detected hands or gestures into different functional inputs and interfaces with other applications via several methods. We also developed sample applications that can utilize the inputs from the hand tracking system.
We present a solution for mobile devices that unifies storage from multiple cloud providers into a centralized storage pool that is better in terms of availability, capacity, performance, reliability and security. First, we explore the feasibility of applying various storage technologies to address the aforementioned issues. Then, we validate our solution in comparisons with single cloud storage by implementation of a working prototype on mobile device. Our results show that it can improve the usage of consumer cloud storage at zero monetary cost, while the minimal overheads incurred are actually compensated by the performance gained.
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