2 Full Papers accepted @ CHI 2026 🎉
Our lab got two papers accepted at the CHI conference on Human Factors in Computing Systems, which is the leading international conference on Human-Computer Interaction.
Paper 1
🠶 It Shouldn’t Be This Difficult: Researcher Perspectives on Diversity and Inclusion in Usable Privacy and Security Research
Autors: Priyasha Chatterjee, Smirity Kaushik, Karola Marky, and Yixin Zou
🠶 Link to the paper: Paper
🠶 Abstract: While recent usable privacy and security (UPS) research has made progress in moving beyond “the average user,” a systematic account of how UPS researchers navigate diversity and inclusion in their work remains lacking. Through 20 in-depth semi-structured interviews with experienced researchers, we examine how and why they recruit diverse, underserved populations in their work, as well as the challenges they face in doing so, including conceptual difficulties in defining who is underserved, limited access to target populations, and inflexible peer review and publishing norms. Participants also reflected on their own positionality when planning and conducting studies, often expressing uncertainty about how to account for and articulate their positionality. We identify strategies researchers use to overcome challenges and highlight areas where collective action from the research community and institutions is needed to foster greater inclusion in UPS research practices.
Paper 2 (with Honourable Mention Award 🏆)
🠶 Sensing Your Vocals: Exploring the Activity of Vocal Cord Muscles for Pitch Assessment Using Electromyography and Ultrasonography
Autors: Kanyu Chen, Rebecca Panskus, Erwin Wu, Yichen Peng, Daichi Saito, Emiko Kamiyama, Ruiteng Li, Chen-Chieh Liao, Karola Marky, Kato Akira, Hideki Koike, and Kai Kunze
🠶 Link to the paper: Paper
🠶 Abstract: Vocal training is difficult because the muscles that control pitch, resonance, and phonation are internal and invisible to learners. This paper investigates how Electromyography (EMG) and ultrasonic imaging (UI) can make these muscles observable for training purposes. We report three studies. First, we analyze the EMG and UI data from 16 singers (beginners, experienced & professionals), revealing differences among three vocal groups of the muscle control proficiency. Second, we use the collected data to create a system that visualizes an expert’s muscle activity as reference. This system is tested in a user study with 12 novices, showing that EMG highlighted muscle activation nuances, while UI provided insights into vocal cord length and dynamics. Third, to compare our approach to traditional methods (audio analysis and coach instructions), we conducted a focus group study with 15 experienced singers. Our results suggest that EMG is promising for improving vocal skill development and enhancing feedback systems. We conclude the paper with a detailed comparison of the analyzed modalities (EMG, UI and traditional methods), resulting in recommendations to improve vocal muscle training systems.
