Ad-hoc wireless device pairing enables impromptu interactions in smart spaces, such as resource sharing and remote control. The pairing experience is mainly determined by the device association process, during which users express their pairing intentions between the advertising device and the scanning device. Currently, most wireless devices are associated by selecting the advertiser's name from a list displayed on the scanner's screen, which becomes less efficient and often misplaced as the number of wireless devices increases. In this paper, we propose Tap-to-Pair, a spontaneous device association mechanism that initiates pairing from advertising devices without hardware or firmware modifications. Tapping an area near the advertising device's antenna can change its signal strength. Users can then associate two devices by synchronizing taps on the advertising device with the blinking pattern displayed by the scanning device. By leveraging the wireless transceiver for sensing, Tap-to-Pair does not require additional resources from advertising devices and needs only a binary display (e.g. LED) on scanning devices. We conducted a user study to test users' synchronous tapping ability and demonstrated that Tap-to-Pair can reliably detect users' taps. We ran simulations to optimize parameters for the synchronization recognition algorithm and provide pattern design guidelines. We used a second user study to evaluate the on-chip performance of Tap-to-Pair. The results show that Tap-to-Pair can achieve an overall successful pairing rate of 93.7% with three scanning devices at different distances.

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.

Modern information querying systems are progressively incorporating multimodal inputs like vision and audio. However, the integration of gaze --- a modality deeply linked to user intent and increasingly accessible via gaze-tracking wearables --- remains underexplored. This paper introduces a novel gaze-facilitated information querying paradigm, named G-VOILA, which synergizes users' gaze, visual field, and voice-based natural language queries to facilitate a more intuitive querying process. In a user-enactment study involving 21 participants in 3 daily scenarios (p = 21, scene = 3), we revealed the ambiguity in users' query language and a gaze-voice coordination pattern in users' natural query behaviors with G-VOILA. Based on the quantitative and qualitative findings, we developed a design framework for the G-VOILA paradigm, which effectively integrates the gaze data with the in-situ querying context. Then we implemented a G-VOILA proof-of-concept using cutting-edge deep learning techniques. A follow-up user study (p = 16, scene = 2) demonstrates its effectiveness by achieving both higher objective score and subjective score, compared to a baseline without gaze data. We further conducted interviews and provided insights for future gaze-facilitated information querying systems.