Human-computer interaction (HCI) is being revolutionized by computational design and artificial intelligence. As the diversity of user interfaces shifts from personal desktops to mobile and wearable devices, yesterday’s tools and interfaces are insufficient to meet the demands of tomorrow’s devices. This dissertation describes my research on leveraging different physical channels (e.g., vibration, light, capacitance) to enable novel interaction opportunities. We first introduce FontCode, an information embedding technique for text documents. Given a text document with specific fonts, our method can embed user-specified information (e.g., URLs, meta data, etc) in the text by perturbing the glyphs of text characters while preserving the text content. The embedded information can later be retrieved using a smartphone in real time. Then, we present Vidgets, a family of mechanical widgets, specifically push buttons and rotary knobs that augment mobile devices with tangible user interfaces. When these widgets are attached to a mobile device and a user interacts with them, the nonlinear mechanical response of the widgets shifts the device slightly and quickly. Subsequently, this subtle motion can be detected by the Inertial Measurement Units (IMUs), which is commonly installed on mobile devices.
Next, we propose BackTrack, a trackpad placed on the back of a smartphone to track finegrained finger motions. Our system has a small form factor, with all the circuits encapsulated in a thin layer attached to a phone case. It can be used with any off-the-shelf smartphone, requiring no power supply or modification of the operating systems. BackTrack simply extends the finger tracking area of the front screen, without interrupting the use of the front screen.
Lastly, we demonstrate MoiréBoard, a new camera tracking method that leverages a seemingly irrelevant visual phenomenon, the moiré effect. Based on a systematic analysis of the moiré effect under camera projection, MoiréBoard requires no power nor camera calibration. It can easily be made at a low cost (e.g., through 3D printing) and ready to use with any stock mobile device with a camera. Its tracking algorithm is computationally efficient and can run at a high frame rate. It is not only simple to implement, but also tracks devices at a high accuracy, comparable to the state-of-the-art commercial VR tracking systems.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-6664-1x04 |
| Date | January 2021 |
| Creators | Xiao, Chang |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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