Please find the full list of my publications on Google Scholar.
2026
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VibOmni: Towards Scalable Bone-conduction Speech Enhancement on Earables
Lixing He, Yunqi Guo, Haozheng Hou, and 1 more author
IEEE Transactions on Mobile Computing, 2026
Earables, such as True Wireless Stereo earphones and VR/AR headsets, are increasingly popular, yet their compact design poses challenges for robust voice-related applications like telecommunication and voice assistant interactions in noisy environments. Existing speech enhancement systems, reliant solely on omnidirectional microphones, struggle with ambient noise like competing speakers. To address these issues, we propose VibOmni, a lightweight, end-to-end multi-modal speech enhancement system for earables that leverages bone-conducted vibrations captured by widely available Inertial Measurement Units (IMUs). VibOmni integrates a two-branch encoder-decoder deep neural network to fuse audio and vibration features. To overcome the scarcity of paired audio-vibration datasets, we introduce a novel data augmentation technique that models Bone Conduction Functions (BCFs) from limited …
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Beyond Transactions: Sustaining Rural Mutual Aid through Mediated Reciprocity
Yunqi Guo, Lan Zeng, Lixing He, and 5 more authors
In Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems, 2026
Rural China faces rapid aging as out-migration weakens family support and formal services remain inaccessible. Community-based mutual aid emerges as a critical alternative, yet transaction-oriented models consistently fail. Through fieldwork in two contrasting villages involving 38 interviews and a participatory workshop, we investigate why quantifying care erodes the relational logic of rural mutual aid. We identify three dilemmas: weakening connections where depopulation makes needs invisible, suppressed help-seeking driven by dignity concerns and fear of unrepayable debt, and erosion of motivation as helping behaviors become socially invisible. Findings reveal that transaction-oriented platforms commodify care, creating “cold” exchanges that clash with “warm” relational norms. We propose a mediated reciprocity framework with three concepts: Relational Routing, Voice Mediation, and Symbolic …
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CoHear: Conversation Enhancement via Multi-earphone Collaboration
Lixing He, Yunqi Guo, Zhenyu Yan, and 1 more author
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2026
In crowded social settings like conferences, background noise, overlapping voices, and lively interactions often lead to "cocktail party deafness," hindering clear conversation. While modern earphones are a promising platform for speech enhancement, existing solutions are limited: they either operate on a single device, ignoring the multi-party nature of conversation, or rely on impractical assumptions like fixed conversation areas and pre-recorded audio. We present CoHear, a collaborative system that leverages a network of earphones to holistically model and enhance speech at the conversation level. CoHear bridges acoustic sensor networks with deep learning for target speech extraction through two key contributions: 1) a novel, conversation-driven network that dynamically forms groups based on user interaction, using verbal and non-verbal cues (primarily head orientation) for robust, infrastructure-free coordination; and 2) a bandwidth-efficient, robust target speech extraction model that effectively utilizes peer-relayed audio as conditioning signals, even under network constraints. CoHear is evaluated in both real-world experiments and simulations. Results show that the conversation network obtains more than 90% accuracy in group formation, improves speech quality by up to 8.8 dB over state-of-the-art baselines, and demonstrates real-time performance on a mobile device. In a user study with 20 participants, CoHear achieved higher scores than baseline with good usability.
2025
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MobiCom’25Best Paper Award
AquaScan: A Sonar-based Underwater Sensing System for Human Activity Monitoring
Haozheng Hou, Bowen Zheng, Sitong Cheng, and 6 more authors
In Proceedings of the 31st Annual International Conference on Mobile Computing and Networking, 2025
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A Model Context Protocol Server for Custom Sensor Tool Creation
Yunqi Guo, Guanyu Zhu, Kaiwei Liu, and 1 more author
In 3rd International Workshop on Networked AI Systems (NetAISys ’25), Jun 2025
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Assistive AR System for Enhancing Human-Human and Human-Environment Interactions
Yunqi Guo
In Proceedings of the 23rd Annual International Conference on Mobile Systems, Applications and Services - Rising Star Forum, 2025
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TaskSense: A Translation-like Approach for Tasking Heterogeneous Sensor Systems with LLMs
Kaiwei Liu, Bufang Yang, Lilin Xu, and 6 more authors
In Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems, May 2025
An increasing number of environments, such as smart homes and factories, are being equipped with multiple sensor systems to enable diverse intelligent applications. However, most existing sensor coordination systems require manually predefined rules, limiting their ability to handle flexible and complex tasks. While recent approaches leverage large language models (LLMs) to interact with external APIs, they struggle to fully understand the capabilities and data dependencies of practical sensor systems. This paper introduces TaskSense, a novel system that coordinates multiple sensor systems in response to users’ complex queries. TaskSense introduces a sensor language that automatically translates the capabilities and data dependencies of sensor systems into vocabularies and grammar rules that can be understood by LLMs. It then interprets user intentions into executable task plans for sensor systems using this sensor language in combination with LLMs. Meanwhile, TaskSense checks the solvability of user queries and verifies the correctness of task plan dependencies. To further enhance robustness, TaskSense incorporates a dynamic plan execution mechanism that adjusts plans based on real-time feedback from sensor data availability, data quality and execution results. TaskSense is deployed on real-world smart home systems, utilizing six popular LLMs. The system is evaluated across 4 scenarios involving 9 types of sensor systems, over 60 APIs, 170 tasks and 5 types of data modalities. Results show that TaskSense achieves up to 2x higher planning accuracy and a 75% increase in answer accuracy using the similar amount of tokens compared with baseline approaches.
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SocialMind: LLM-based Proactive AR Social Assistive System with Human-like Perception for In-situ Live Interactions
Bufang Yang*, Yunqi Guo*, Lilin Xu*, and 4 more authors
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2025
2024
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Sensor2Scene: Foundation Model-driven Interactive Realities
Yunqi Guo, Kaiyuan Hou, Zhenyu Yan, and 3 more authors
FMSys, 2024
Augmented Reality (AR) is acclaimed for its potential to bridge the physical and virtual worlds. Yet, current integration between these realms often lacks a deep understanding of the physical environment and the subsequent scene generation that reflects this understanding. This research introduces Sensor2Scene, a novel system framework designed to enhance user interactions with sensor data through AR. At its core, an AI agent leverages large language models (LLMs) to decode subtle information from sensor data, constructing detailed scene descriptions for visualization. To enable these scenes to be rendered in AR, we decompose the scene creation process into tasks of text-to-3D model generation and spatial composition, allowing new AR scenes to be sketched from the descriptions. We evaluated our framework using an LLM evaluator based on five metrics on various datasets to examine the correlation between sensor readings and corresponding visualizations, and demonstrated the system’s effectiveness with scenes generated from end-to-end. The results highlight the potential of LLMs to understand IoT sensor data. Furthermore, generative models can aid in transforming these interpretations into visual formats, thereby enhancing user interaction. This work not only displays the capabilities of Sensor2Scene but also lays a foundation for advancing AR with the goal of creating more immersive and contextually rich experiences.
2023
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Sign-to-911: Emergency Call Service for Sign Language Users with Assistive AR Glasses
Yunqi Guo, Jinghao Zhao, Boyan Ding, and 6 more authors
MobiCom, 2023
Sign-to-911 offers a compact mobile system solution to fast and runtime American Sign Language (ASL) and English translations. It is designated as 911 call services for ASL users with hearing disabilities upon emergencies. It enables bidirectional translations of ASL-to-English and English-to-ASL. The signer wears the AR glasses, runs Sign-to-911 on his/her smartphone and glasses, and interacts with a 911 operator. The design of Sign-to-911 departs from the popular deep learning based solution paradigm, and adopts simpler traditional AI/machine learning (ML) models. The key is to exploit ASL linguistic features to simplify the model structures and improve accuracy and speed. It further leverages recent component solutions from graphics, vision, natural language processing, and AI/ML. Our evaluation with six ASL signers and 911 call records has confirmed its viability.
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LDRP: Device-Centric Latency Diagnostic and Reduction for Cellular Networks without Root
Zhaowei Tan, Jinghao Zhao, Yuanjie Li, and 3 more authors
IEEE TMC, 2023
2021
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A Model Obfuscation Approach to IoT Security
Yunqi Guo, Zhaowei Tan, Kaiyuan Chen, and 2 more authors
IEEE CNS, 2021
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On Key Reinstallation Attacks over 4G LTE Control-Plane: Feasibility and Negative Impact
Muhammad Taqi Raza, Yunqi Guo, Songwu Lu, and 1 more author
ACM ACSAC, 2021
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SecureSIM: Rethinking Authentication and Access Control for SIM/eSIM
Jinghao Zhao, Boyan Ding, Yunqi Guo, and 2 more authors
ACM MobiCom, 2021
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Data-Plane Signaling in Cellular IoT: Attacks and Defense
Zhaowei Tan, Boyan Ding, Jinghao Zhao, and 2 more authors
ACM MobiCom, 2021
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Experience: a Five-Year Retrospective of MobileInsight
Yuanjie Li, Chunyi Peng, Zhehui Zhang, and 9 more authors
ACM MobiCom, 2021
2020
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Towards Model-Centric Security for IoT Systems
Yunqi Guo, Zhaowei Tan, and Songwu Lu
IEEE ICCCN, 2020