The rapid advancement of AI-generated content (AIGC) promises to transform various aspects of human life significantly. This work particularly focuses on the potential of AIGC to revolutionize image creation, such as photography and self-expression. We introduce ContextCam, a novel human-AI image co-creation system that integrates context awareness with mainstream AIGC technologies like Stable Diffusion. ContextCam provides user’s image creation process with inspiration by extracting relevant contextual data, and leverages Large Language Model-based (LLM) multi-agents to co-create images with the user. A study with 16 participants and 136 scenarios revealed that ContextCam was well-received, showcasing personalized and diverse outputs as well as interesting user behavior patterns. Participants provided positive feedback on their engagement and enjoyment when using ContextCam, and acknowledged its ability to inspire creativity.

Problematic smartphone use negatively affects physical and mental health. Despite the wide range of prior research, existing persuasive techniques are not flexible enough to provide dynamic persuasion content based on users’ physical contexts and mental states. We first conducted a Wizard-of-Oz study (N=12) and an interview study (N=10) to summarize the mental states behind problematic smartphone use: boredom, stress, and inertia. This informs our design of four persuasion strategies: understanding, comforting, evoking, and scaffolding habits. We leveraged large language models (LLMs) to enable the automatic and dynamic generation of effective persuasion content. We developed MindShift, a novel LLM-powered problematic smartphone use intervention technique. MindShift takes users’ in-the-moment app usage behaviors, physical contexts, mental states, goals & habits as input, and generates personalized and dynamic persuasive content with appropriate persuasion strategies. We conducted a 5-week field experiment (N=25) to compare MindShift with its simplified version (remove mental states) and baseline techniques (fixed reminder). The results show that MindShift improves intervention acceptance rates by 4.7-22.5% and reduces smartphone usage duration by 7.4-9.8%. Moreover, users have a significant drop in smartphone addiction scale scores and a rise in self-efficacy scale scores. Our study sheds light on the potential of leveraging LLMs for context-aware persuasion in other behavior change domains.

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.

Tracking fine-grained finger movements with IMUs for continuous 2D-cursor control poses significant challenges due to limited sensing capabilities. Our findings suggest that finger-motion patterns and the inherent structure of joints provide beneficial physical knowledge, which lead us to enhance motion perception accuracy by integrating physical priors into ML models. We propose MouseRing, a novel ring-shaped IMU device that enables continuous finger-sliding on unmodified physical surfaces like a touchpad. A motion dataset was created using infrared cameras, touchpads, and IMUs. We then identified several useful physical constraints, such as joint co-planarity, rigid constraints, and velocity consistency. These principles help refine the finger-tracking predictions from an RNN model. By incorporating touch state detection as a cursor movement switch, we achieved precise cursor control. In a Fitts’ Law study, MouseRing demonstrated input efficiency comparable to touchpads. In real-world applications, MouseRing ensured robust, efficient input and good usability across various surfaces and body postures.

We present DRG-Keyboard, a gesture keyboard enabled by dual IMU rings, allowing the user to swipe the thumb on the index fingertip to perform word gesture typing as if typing on a miniature QWERTY keyboard. With dual IMUs attached to the user's thumb and index finger, DRG-Keyboard can 1) measure the relative attitude while mapping it to the 2D fingertip coordinates and 2) detect the thumb's touch-down and touch-up events combining the relative attitude data and the synchronous frequency domain data, based on which a fingertip gesture keyboard can be implemented. To understand users typing behavior on the index fingertip with DRG-Keyboard, we collected and analyzed user data in two typing manners. Based on the statistics of the gesture data, we enhanced the elastic matching algorithm with rigid pruning and distance measurement transform. The user study showed DRG-Keyboard achieved an input speed of 12.9 WPM (68.3% of their gesture typing speed on the smartphone) for all participants. The appending study also demonstrated the superiority of DRG-Keyboard for better form factors and wider usage scenarios. To sum up, DRG-Keyboard not only achieves good text entry speed merely on a tiny fingertip input surface, but is also well accepted by the participants for the input subtleness, accuracy, good haptic feedback, and availability.

This paper presents LangAware, a collaborative approach for constructing personalized context for context-aware applications. The need for personalization arises due to significant variations in context between individuals based on scenarios, devices, and preferences. However, there is often a notable gap between humans and machines in the understanding of how contexts are constructed, as observed in trigger-action programming studies such as IFTTT. LangAware enables end-users to participate in establishing contextual rules in-situ using natural language. The system leverages large language models (LLMs) to semantically connect low-level sensor detectors to high-level contexts and provide understandable natural language feedback for effective user involvement. We conducted a user study with 16 participants in real-life settings, which revealed an average success rate of 87.50% for defining contextual rules in a variety of 12 campus scenarios, typically accomplished within just two modifications. Furthermore, users reported a better understanding of the machine’s capabilities by interacting with LangAware.