RageSense introduces a novel system for detecting and regulating player frustration during mobile gaming. Instead of relying on coarse emotion labels, RageSense estimates users’ valence and arousal levels in real time using near-ultrasonic acoustic sensing. By analyzing facial muscle movements via built-in smartphone speakers and microphones, our approach enables emotion sensing without requiring cameras or wearables, constituting a more unobtrusive, environment-resilient, and privacy-friendly approach than traditional emotion recognition. To transform detection into action, we integrate a large language model (LLM) that generates empathetic, context-aware interventions based on gameplay screenshots, behavioral signals, and emotional trajectories. These interventions are delivered in real time, tailored to the user’s emotional state, and designed to mitigate rage while enhancing player well-being. In a 53-participant field study, our system improved emotional state immediately after triggers and was preferred over random or template-based messages. To our knowledge, this is the first demonstration of near-ultrasonic, on-phone valence-arousal regression during mobile gameplay that directly drives real-time, context-aware interventions.

Hand pose tracking is essential for advancing applications in human-computer interaction. Current approaches, such as vision-based systems and wearable devices, face limitations in portability, usability, and practicality. We present a novel wearable system that reconstructs 3D hand pose and estimates per-finger forces using a minimal ring-watch sensor setup. A ring worn on the finger integrates an inertial measurement unit (IMU) to capture finger motion, while a smartwatch-based single-channel electromyography (EMG) sensor on the wrist detects muscle activations. By leveraging the complementary strengths of motion sensing and muscle signals, our approach achieves accurate hand pose tracking and grip force estimation in a compact wearable form factor. We develop a dual-branch transformer network that fuses IMU and EMG data with cross-modal attention to predict finger joint positions and forces simultaneously. A custom loss function imposes kinematic constraints for smooth force variation and realistic force saturation. Evaluation with 20 participants performing daily object interaction gestures demonstrates an average Mean Per Joint Position Error (MPJPE) of 0.57 cm and a fingertip force estimation (RMSE: 0.213, r=0.76). We showcase our system in a real-time Unity application, enabling virtual hand interactions that respond to user-applied forces. This minimal, force-aware tracking system has broad implications for VR/AR, assistive prosthetics, and ergonomic monitoring.

Data deduplication has been long studied to achieve data reduction. However, deploying deduplication on encryption enabled mobile systems might consume much memory footprint and computation time for hash calculations. Moreover, frequent file updates on deduplicated files could badly degrade the deduplication efficacy due to the increased file-system metadata penalty. Considering the characteristics of mobile devices, an efficient data deduplication method is proposed in this paper. First, it separates the hash calculation into foreground and background stages. The background stage calculates the hash values of potentially duplicate files while the foreground stage quickly hashes the file which is being written using a lightweight hash algorithm. Second, a dual-level node structure is proposed to improve the file update efficacy for deduplicated files, saving more storage space against the file re-splitting. Besides, we implement a superlink call to make deduplication process compatible with file-based encryption. These methods are combined to realize a transparent file deduplication (TFDedup) approach, which eliminates redundant data and reduces the associated cost of file update. Experimental results show that TFDedup succeeds to lower the space consumption when serving file updates by 55.6% and accelerate the deduplication process by 50.3%.