The heterogeneous and ubiquitous input demands in smart spaces call for an input device that can enable rich and spontaneous interactions. We propose ThermalRing, a thermal imaging smart ring using low-resolution thermal camera for identity-anonymous, illumination-invariant, and power-efficient sensing of both dynamic and static gestures. We also design ThermalTag, thin and passive thermal imageable tags that reflect the heat from the human hand. ThermalTag can be easily made and applied onto everyday objects by users. We develop sensing techniques for three typical input demands: drawing gestures for device pairing, click and slide gestures for device control, and tag scan gestures for quick access. The study results show that ThermalRing can recognize nine drawing gestures with an overall accuracy of 90.9%, detect click gestures with an accuracy of 94.9%, and identify among six ThermalTags with an overall accuracy of 95.0%. Finally, we show the versatility and potential of ThermalRing through various applications.

The Qwerty soft keyboard is widely used on mobile devices. However, keyboards often consume a large portion of the touchscreen space, occluding the application view on the smartphone and requiring a separate input interface on the smartwatch. Such space consumption can affect the user experience of accessing information and the overall performance of text input. In order to free up the screen real estate, this article explores the concept of Sparse Keyboard and proposes two new ways of presenting the Qwerty soft keyboard. The idea is to use users’ spatial memory and the reference effect of spatial landmarks on the graphical interface. Our final design K3-SGK displays only three keys while L5-EYOCN displays only five line segments instead of the entire Qwerty layout. To achieve this, we employ a user-centered computational design method: first study the reference effect of a single landmark key (line segment) from empirical data, then make assumptions to generalize the effect to multiple landmarks, and finally optimize the best designs. To make the text entry function more complete, we also design and implement gestural interactions for editing operations and non-alphabetical characters’ input. User evaluation shows that participants can quickly learn how to type with K3-SGK and L5-EYOCN . After five 15-phrase typing sessions, participants achieve 88.1%--92.8% of the full Qwerty keyboard in terms of words per minute on the smartphone and 98.4%--99.1% on the smartwatch. The differences on character and word error rate between our keyboard designs and the full Qwerty keyboard are not significant. The results of out-of-vocabulary words input are also promising. In addition, participants can quickly recall the typing skills and maintain the input performance even after a few days. User feedbacks in real application contexts show that with the low occlusion keyboard, users can acquire more information and perform less scrolling on the smartphone and achieve a higher input efficiency on the smartwatch with a more fluent input experience.

We propose VirtualGrasp, a novel gestural approach to retrieve virtual objects in virtual reality. Using VirtualGrasp, a user retrieves an object by performing a barehanded gesture as if grasping its physical counterpart. The object-gesture mapping under this metaphor is of high intuitiveness, which enables users to easily discover, remember the gestures to retrieve the objects. We conducted three user studies to demonstrate the feasibility and effectiveness of the approach. Progressively, we investigated the consensus of the object-gesture mapping across users, the expressivity of grasping gestures, and the learnability and performance of the approach. Results showed that users achieved high agreement on the mapping, with an average agreement score [35] of 0.68 (SD=0.27). Without exposure to the gestures, users successfully retrieved 76% objects with VirtualGrasp. A week after learning the mapping, they could recall the gestures for 93% objects.

We present Lip-Interact, an interaction technique that allows users to issue commands on their smartphone through silent speech. Lip-Interact repurposes the front camera to capture the user's mouth movements and recognize the issued commands with an end-to-end deep learning model. Our system supports 44 commands for accessing both system-level functionalities (launching apps, changing system settings, and handling pop-up windows) and application-level functionalities (integrated operations for two apps). We verify the feasibility of Lip-Interact with three user experiments: evaluating the recognition accuracy, comparing with touch on input efficiency, and comparing with voiced commands with regards to personal privacy and social norms. We demonstrate that Lip-Interact can help users access functionality efficiently in one step, enable one-handed input when the other hand is occupied, and assist touch to make interactions more fluent.

Touch interaction on smartwatches suffers from the awkwardness of having to use two hands and the "fat finger" problem. We present Float, a wrist-to-finger input approach that enables one-handed and touch-free target selection on smartwatches with high efficiency and precision using only commercially-available built-in sensors. With Float, a user tilts the wrist to point and performs an in-air finger tap to click. To realize Float, we first explore the appropriate motion space for wrist tilt and determine the clicking action (finger tap) through a user-elicitation study. We combine the photoplethysmogram (PPG) signal with accelerometer and gyroscope to detect finger taps with a recall of 97.9% and a false discovery rate of 0.4%. Experiments show that using just one hand, Float allows users to acquire targets with size ranging from 2mm to 10mm in less than 2s to 1s, meanwhile achieve much higher accuracy than direct touch in both stationary (>98.9%) and walking (>71.5%) contexts.

We present 1D Handwriting, a unistroke gesture technique enabling text entry on a one-dimensional interface. The challenge is to map two-dimensional handwriting to a reduced one-dimensional space, while achieving a balance between memorability and performance efficiency. After an iterative design, we finally derive a set of ambiguous two-length unistroke gestures, each mapping to 1-4 letters. To input words, we design a Bayesian algorithm that takes into account the probability of gestures and the language model. To input letters, we design a pause gesture allowing users to switch into letter selection mode seamlessly. Users studies show that 1D Handwriting significantly outperforms a selection-based technique (a variation of 1Line Keyboard) for both letter input (4.67 WPM vs. 4.20 WPM) and word input (9.72 WPM vs. 8.10 WPM). With extensive training, text entry rate can reach 19.6 WPM. Users' subjective feedback indicates 1D Handwriting is easy to learn and efficient to use. Moreover, it has several potential applications for other one-dimensional constrained interfaces.

Ten-finger freehand mid-air typing is a potential solution for post-desktop interaction. However, the absence of tactile feedback as well as the inability to accurately distinguish tapping finger or target keys exists as the major challenge for mid-air typing. In this paper, we present ATK, a novel interaction technique that enables freehand ten-finger typing in the air based on 3D hand tracking data. Our hypothesis is that expert typists are able to transfer their typing ability from physical keyboards to mid-air typing. We followed an iterative approach in designing ATK. We first empirically investigated users' mid-air typing behavior, and examined fingertip kinematics during tapping, correlated movement among fingers and 3D distribution of tapping endpoints. Based on the findings, we proposed a probabilistic tap detection algorithm, and augmented Goodman's input correction model to account for the ambiguity in distinguishing tapping finger. We finally evaluated the performance of ATK with a 4-block study. Participants typed 23.0 WPM with an uncorrected word-level error rate of 0.3% in the first block, and later achieved 29.2 WPM in the last block without sacrificing accuracy.

A novel STAP algorithm based on sparse recovery technique, called CS-STAP, were presented. Instead of using conventional maximum likelihood estimation of covariance matrix, our method utilizes the echo statistics on spatial-temporal plane, which is extracted from sample data of only ONE training range cell with Compressed Sensing techniques, to construct a new estimator of covariance matrix, and build the optimal detector based on it. Full description of CS-STAP is given. Numerical result on real data has provided the evidence for great potential of CS-STAP as a effective approach when clutter is non-stationary because it need much less training data compared with common STAP methods.