Creating interactive papercrafts often involves the processes of craft making (e.g., folding, cutting, gluing, etc.) and fabricating the embedded functional circuitry. These two processes are usually separated in the current practice, potentially making the workflow laborious and affecting the in-paper circuit stability. To address the issue of the separated crafting and circuiting processes, we present LiqMetCraft, a toolkit for creating electronics-embedded papercrafts through an integrated process. The toolkit allows users to construct the craft structure by folding and cutting, and these crafting actions forms the circuit traces simultaneously. This is achieved by using liquid-metal-dyed paper-like fabric which partially becomes conductive due to the cutting/folding-induced pressure while the unpressed parts of the paper remain insulated. The LiqMetCraft toolkit consists of software interfaces for papercraft design and instructions, and hardware components, mainly the liquid-metal-dyed paper-like fabrics and other off-the-shelf components, for physical prototyping. Currently our toolkit supports the making of electronics-embedded paper cutting, origami, and 3D paper models. The user studies with 42 participants shows that the participants quickly learned the toolkit and found the integrated process of circuit assembly and shape formation to be engaging and inspiring.

Providing haptic feedback in virtual reality (VR) remains challenging, with active haptic systems often being bulky and expensive, while passive haptics typically require pre-fabricated objects or extensive real-world setup. We present ProXeek, an LLM-based multi-agent system that opportunistically leverages real-world objects as haptic proxies for VR experiences. During VR development, the designers could annotate the virtual interactables with emphasized haptic characteristics using our node-based editing interface. On the user side, end users capture environmental snapshots via commodity VR headset, and the system employs LLM-based multimodal reasoning and multi-objective optimization to match virtual objects with viable physical proxies. Our technical evaluation demonstrates that ProXeek effectively identifies viable proxies, confirming the effectiveness of the algorithmic constraint design in our matching pipeline. Our user study further reveals that the ProXeek-selected physical objects could deliver significantly higher haptic fidelity and enhance presence compared to 3D-printed replicas and visual-only interaction.

Abstract In this article, we propose a mobile edge computing (MEC)‐related system named PD‐Gait, which can measure gait parameters of Parkinson's disease patients in a contactless and privacy‐preserving manner. We utilize inaudible acoustic signals and band‐pass filters to achieve privacy data protection in the physical layer. The proposed framework can be easily deployed in the mobile end of MEC, and hence release the edge server in cybersecurity attacks fighting. The gait parameters include stride cycle time length and moving speed, and hence providing an objective basis for the doctors' judgment. PD‐Gait utilizes acoustic signals in bands from 16 to 23 kHz to achieve device‐free sensing, which would release both doctors and patients from the tedious wearing process and psychological burden caused by traditional wearable devices. To achieve robust measurement, we propose a novel acoustic ranging method to avoid “broken tones” and “uneven peak distribution” in the received data. The corresponding ranging accuracy is 0.1 m. We also propose auto‐focus micro‐Doppler features to extract robust stride cycle time length, and can achieve an accuracy of 0.052 s. We deployed PD‐Gait in a brain hospital and collected data from 8 patients. The total walked distance is over 330 m. From the overall trend, our results are highly correlated with the doctor's judgment.

This paper addresses the fundamental problem of identifying driver talking directions using a single smartphone, which can help drivers by warning distraction of having conversations with passengers in a vehicle and enable safety enhancement. The basic idea of our system is to perform talking status and direction identification using two microphones on a smartphone. We first use the sound recorded by the two microphones to identify whether the driver is talking or not. If yes, we then extract the so-called channel fingerprint from the speech signal and classify it into one of three typical driver talking directions, namely, front, right and back, using a trained model obtained in advance. The key novelty of our scheme is the proposition of channel fingerprint which leverages the heavy multipath effects in the harsh in-vehicle environment and cancels the variability of human voice, both of which combine to invalidate traditional TDoA, DoA and fingerprint based sound source localization approaches. We conducted extensive experiments using two kinds of phones and two vehicles for four phone placements in three representative scenarios, and collected 23 hours voice data from 20 participants. The results show that our system can achieve 95.0% classification accuracy on average.