Real-time hand pose estimation has a wide range of applications spanning gaming, robotics, and human-computer interaction. In this paper, we introduce EITPose, a wrist-worn, continuous 3D hand pose estimation approach that uses eight electrodes positioned around the forearm to model its interior impedance distribution during pose articulation. Unlike wrist-worn systems relying on cameras, EITPose has a slim profile (12 mm thick sensing strap) and is power-efficient (consuming only 0.3 W of power), making it an excellent candidate for integration into consumer electronic devices. In a user study involving 22 participants, EITPose achieves with a within-session mean per joint positional error of 11.06 mm. Its camera-free design prioritizes user privacy, yet it maintains cross-session and cross-user accuracy levels comparable to camera-based wrist-worn systems, thus making EITPose a promising technology for practical hand pose estimation.

Enabling students to dynamically transition between individual and collaborative learning activities has great potential to support better learning. We explore how technology can support teachers in orchestrating dynamic transitions during class. Working with five teachers and 199 students over 22 class sessions, we conducted classroom-based prototyping of a co-orchestration technology ecosystem that supports the dynamic pairing of students working with intelligent tutoring systems. Using mixed-methods data analysis, we study the resulting observed classroom dynamics, and how teachers and students perceived and experienced dynamic transitions as supported by our technology. We discover a potential tension between teachers’ and students’ preferred level of control: students prefer a degree of control over the dynamic transitions that teachers are hesitant to grant. Our study reveals design implications and challenges for future human-AI co-orchestration in classroom use, bringing us closer to realizing the vision of highly-personalized smart classrooms that address the unique needs of each student.

This paper presents a wideband millimeter-wave low-noise amplifier (LNA) in a 65-nm CMOS process. A triple-coupled transformer-based input matching technique is proposed to enhance the input power matching bandwidth. In addition, by leveraging a transformer between the two-stage common source (CS) amplifiers, the noise figure (NF) is reduced while the transconductance of the second stage is improved to extend the gain bandwidth. The proposed LNA is designed to achieve an ultra-wide 10-dB return loss bandwidth from 12.4 to 46.9 GHz. The effective input matching bandwidth is limited by the 3-dB gain bandwidth, which is from 12.9 to 38.6 GHz. It achieves a peak gain of 11.9 dB and a minimum NF of 3.2 dB. The third-order input intercept point (IIP3) is −0.45 dBm at 30 GHz. The proposed LNA occupies a core area of 0.18 mm2and consumes only 5.1 mW.

This study introduces a novel proxemic-aware augmented reality (AR) system to mitigate information overload in AR-enabled human-robot interaction (HRI). The system leverages human-robot proxemics to automatically adjust what and how much visual content needs to be presented. Therefore, the operator can perceive the relevant data through AR interfaces without being overwhelmed by excessive information exposure. We propose a task-specific model for evaluating human-robot proxemic (HRP), where the system can identify HRP levels based on raw features, such as distance and orientation. Based on HRP levels, we design a set of visual elements for presenting robots’ information at various levels of detail. To demonstrate the functionality of the system, we present a series of proof-of-concept applications showing that our system can assist the operator in a wide range of HRI tasks. The user study proves that the proxemic-aware AR system can reduce mental loading, increase visual clarity, and improve interaction efficiency in HRI.