Xiaoran "Van" Fan
Van is a Research Scientist at Google. He leads the Multimodal Sensing Foundation Research team. This team develops unified foundational models by integrating diverse inputs (like audio, radar, inertial, and video) to solve complex tasks in health, safety, home automation, and user identity. Van is an experimentalist and system builder. He is enthusiastic about real-world-experiment-driven research.
Authored Publications
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mmMUSE: An mmWave-based Motion-resilient Universal Speech Enhancement System
Chenming He
Yanyong Zhang
Kai Wang
Dequan Wang
Lingyu Wang
the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), ACM (2026) (to appear)
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Voice-based smart systems can greatly enhance user experiences by allowing higher-quality interactions through better voice perception. Speech enhancement can benefit such systems by isolating noise from speech. Recently, integrating millimeter-wave (mmWave) with audio for speech perception has gained increasing attention due to microphones' limitations in noisy environments. However, mmWave-based vocal extraction is severely affected by motion, which disperses vocal signals across ranges and introduces distortions. In this paper, we propose an mmWave-based motion-resilient universal speech enhancement system called mmMUSE, which fuses mmWave and audio signals. To mitigate motion interference, we develop a Doppler-based method for motion-robust vocal signal extraction. Moreover, by introducing the Vocal-Noise-Ratio metric to assess the prominence of vocal signals from mmWave, we achieve real-time voice activity detection that gains 3.81 dB of SISDR in noisy speeches. Additionally, we design a two-stage complex-valued network that includes an attention-based fusion network for cross-modal complementing and a time-frequency masking network for correcting amplitude and phase of speech to isolate noises.
Using mmWave and audio datasets from 46 participants, mmMUSE outperforms the state-of-the-art speech enhancement models, achieving an average SISDR improvement of 3.12 dB. Additionally, mmMUSE achieves SISDR improvements of 16.51 dB, 17.93 dB, 14.93 dB, and 18.95 dB in controlled environments involving intense noise, extensive motion, multiple speakers, and various obstructive materials, respectively. Finally, we evaluate mmMUSE in real-world scenarios including running, public spaces, and driving, maintaining a word error rate (WER) below 10%.
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Toward Sensor-In-the-Loop LLM Agent: Benchmarks and Implications
Zhiwei Ren
Junbo Li
Minjia Zhang
Di Wang
Longfei Shangguan
SenSys 2025 - The 23rd ACM Conference on Embedded Networked Sensor Systems (2025)
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This paper advocates for sensor-informed personal agents that can take advantage of sensor hints on wearables to enhance the personal agent's response. We demonstrate that such a sensor-in-the-loop design paradigm can be easily integrated into existing LLM agents by building a prototype named WellMax based on existing well-developed techniques such as structured prompt tuning and few-shot prompting. The head-to-head comparison with a non-sensor-informed agent across five use scenarios demonstrates that this sensor-in-the-loop design can effectively improve users' needs and their overall experience. The deep-dive into agents' replies and participants' feedback further reveals that sensor-in-the-loop agents not only provide more contextually relevant responses but also exhibit a greater understanding of user priorities and situational nuances. We further conduct two case studies to examine the potential pitfalls and distill key insights from this sensor-in-the-loop agent. We believe this work sets the stage for more intelligent, empathetic, and effective interactions in future AI-driven personal assistants.
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Ghost Points Matter: Far-Range Vehicle Detection with a Single mmWave Radar in Tunnel
Chengzhen Meng
Chenming He
Jianmin Ji
Yanyong Zhang
Haojie Ren
Dequan Wang
Rui Xia
MobiCom 2025: The 31th Annual International Conference On Mobile Computing And Networking, ACM
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Vehicle detection in tunnels is crucial for traffic monitoring and accident response, yet remains underexplored. In this paper, we develop mmTunnel, a millimeter-wave radar system that achieves far-range vehicle detection in tunnels. The main challenge here is coping with ghost points caused by multi-path reflections, which lead to severe localization errors and false alarms. Instead of merely removing ghost points, we propose correcting them to true vehicle positions by recovering their signal reflection paths, thus reserving more data points and improving detection performance, even in occlusion scenarios. However, recovering complex 3D reflection paths from limited 2D radar points is highly challenging. To address this problem, we develop a multi-path ray tracing algorithm that leverages the ground plane constraint and identifies the most probable reflection path based on signal path loss and spatial distance. We also introduce a curve-to-plane segmentation method to simplify tunnel surface modeling such that we can significantly reduce the computational delay and achieve real-time processing.
We have evaluated mmTunnel with comprehensive experiments. In two test tunnels, we conducted controlled experiments in various scenarios with cars and trucks. Our system achieves an average F1 score of 93.7% for vehicle detection while maintaining real-time processing. Even in the challenging occlusion scenarios, the F1 score remains above 91%. Moreover, we collected extensive data from a public tunnel with heavy traffic at times and show our method could achieve an F1 score of 91.5% in real-world traffic conditions.
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LeakyFeeder: In-Air Gesture Control Through Leaky Acoustic Waves
Yongjie Yang
Tao Chen
Zhenlin An
Shirui Cao
Shangguan Longfei
SenSys 2025 - The 23rd ACM Conference on Embedded Networked Sensor Systems (2025)
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We present LeekyFeeder, a mobile application that explores the acoustic signals leaked from headphones to reconstruct gesture motions around the ear for fine-grained gesture control. To achieve this goal, LeekyFeeder reuses the speaker and feed-forward microphones on active noise cancellation (ANC) headphones as a SONAR system, emitting an inaudible frequency-modulated continuous-wave (FMCW) signal to track gesture reflections over time. Since this single-receiver SONAR system is unable to differentiate reflection angles and further disentangle signal reflections from different gesture parts, we draw on principles of multi-modal learning to frame gesture motion reconstruction as a multi-modal translation task and propose a deep learning-based approach to fill the information gap between low-dimensional FMCW ranging readings and high-dimensional 3D hand movements. We implement LeekyFeeder on a pair of Google Pixel Buds and conduct experiments to examine the efficacy and robustness of LeekyFeeder in various conditions. Experiments based on six gesture types inspired by Apple Vision Pro demonstrate that LeekyFeeder achieves a PCK performance of 89% at 3cm across ten users, with an average MPJPE and MPJRPE error of 2.71cm and 1.88cm, respectively.
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See Through Vehicles: Fully Occluded Vehicle Detection with Millimeter Wave Radar
Chenming He
Chengzhen Meng
Chunwang He
Beibei Wang
Yubo Yan
Yanyong Zhang
MobiCom 2024: The 30th Annual International Conference On Mobile Computing And Networking
Preview abstract
A crucial task in autonomous driving is to continuously detect nearby vehicles. Problems thus arise when a vehicle is occluded and becomes “unseeable”, which may lead to accidents. In this study, we develop mmOVD, a system that can detect fully occluded vehicles by involving millimeter-wave radars to capture the ground-reflected signals passing beneath the blocking vehicle’s chassis. The foremost challenge here is coping with ghost points caused by frequent multi-path reflections, which highly resemble the true points. We devise a set of features that can efficiently distinguish the ghost points by exploiting the neighbor points’ spatial and velocity distributions. We also design a cumulative clustering algorithm to effectively aggregate the unstable ground reflected radar points over consecutive frames to derive the bounding boxes of the vehicles.
We have evaluated mmOVD in both controlled environments and real-world environments. In an underground garage and two campus roads, we conducted controlled experiments in 56 scenes with 8 vehicles, including a minibus and a motorcycle. Our system accurately detects occluded vehicles for the first time, with a 91.1% F1 score for occluded vehicle detection and a 100% success rate for occlusion event detection. More importantly, we drove 324km on crowded roads at a speed up to 70km per hour and show we could achieve an occlusion detection success rate of 92% and a low false alarm rate of 4% with only 10% of the training data in complex real-world environments.
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Leveraging Foundation Models for Zero-Shot IoT Sensing
Dinghao Xue
Tao Chen
Guohao Lan
Qun Song
ECAI 2024: The 27th European Conference on Artificial Intellegence (to appear)
Preview abstract
Zero-shot learning (ZSL) aims to classify data of unseen classes with the help of semantic information. Foundation models (FMs) trained on web-scale data have shown impressive ZSL capability in natural language processing and visual understanding. However, leveraging FMs' generalized knowledge for zero-shot Internet of Things (IoT) sensing using signals such as mmWave, IMU, and Wi-Fi has not been fully investigated. In this work, we align the IoT data embeddings with the semantic embeddings generated by an FM's text encoder for zero-shot IoT sensing. To utilize the physics principles governing the generation of IoT sensor signals to derive more effective prompts for semantic embedding extraction, we propose to use cross-attention to combine a learnable soft prompt that is optimized automatically on training data and an auxiliary hard prompt that encodes domain knowledge of the IoT sensing task. To address the problem of IoT embeddings biasing to seen classes due to the lack of unseen class data during training, we propose a data augmentation method to synthesize unseen class IoT data for fine-tuning IoT feature extractor and embedding projector. We evaluate our approach on multiple IoT sensing tasks. Experiment results show that our approach achieves superior open-set detection and generalized zero-shot learning performance compared with various baselines.
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Exploring the Feasibility of Remote Cardiac Auscultation Using Earphones
Tao Chen
Yongjie Yang
Xiuzhen Guo
Jie Xiong
Shangguan Longfei
MobiCom 2024: The 30th Annual International Conference On Mobile Computing And Networking
Preview abstract
The elderly over 65 accounts for 80% of COVID deaths in the United States. In response to the pandemic, the federal, state governments, and commercial insurers are promoting video visits, through which the elderly can access specialists at home over the Internet, without the risk of COVID exposure. However, the current video visit practice barely relies on video observation and talking. The specialist could not assess the patient's health conditions by performing auscultations.
This paper tries to address this key missing component in video visits by proposing Asclepius, a hardware-software solution that turns the patient's earphones into a stethoscope, allowing the specialist to hear the patient's fine-grained heart sound (i.e., PCG signals) in video visits. To achieve this goal, we contribute a low-cost plug-in peripheral that repurposes the earphone's speaker into a microphone and uses it to capture the patient's minute PCG signals from her ear canal. As the PCG signals suffer from strong attenuation and multi-path effects when propagating from the heart to ear canals, we then propose efficient signal processing algorithms coupled with a data-driven approach to de-reverberate and further correct the amplitude and frequency distortion in raw PCG receptions. We implement Asclepius on a 2-layer PCB board and follow the IRB protocol to evaluate its performance with 30 volunteers. Our extensive experiments show that Asclepius can effectively recover Phonocardiogram (PCG) signals with different types of earphones. The feedback from cardiologists also confirms the efficacy and efficiency of our system. PCG signal samples and benchmark results can be found at an anonymous link https://asclepius-system.github.io/
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APG: Audioplethysmography for Cardiac Monitoring in Hearables
David Pearl
Rich Howard
Longfei Shangguan
Trausti Thormundsson
MobiCom 2023: The 29th Annual International Conference On Mobile Computing And Networking (MobiCom), Association for Computing Machinery (ACM) (to appear)
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This paper presents Audioplethysmography (APG), a novel cardiac monitoring modality for active noise cancellation (ANC) headphones. APG sends a low intensity ultrasound probing signal using an ANC headphone's speakers and receives the echoes via the on-board feedback microphones. We observed that, as the volume of ear canals slightly changes with blood vessel deformations, the heartbeats will modulate these ultrasound echoes. We built mathematical models to analyze the underlying physics and propose a multi-tone APG signal processing pipeline to derive the heart rate and heart rate variability in both constrained and unconstrained settings. APG enables robust monitoring of cardiac activities using mass-market ANC headphones in the presence of music playback and body motion such as running.
We conducted an eight-month field study with 153 participants to evaluate APG in various conditions. Our studies conform to the (Institutional Review Board) IRB policies from our company. The presented technology, experimental design, and results have been reviewed and further improved by feedback garnered from our internal Health Team, Product Team, User Experience (UX) Team and Legal team. Our results demonstrate that APG achieves consistently high HR (3.21% median error across 153 participants in all scenarios) and HRV (2.70% median error in interbeat interval, IBI) measurement accuracy. Our UX study further shows that APG is resilient to variation in: skin tone, sub-optimal seal conditions, and ear canal size.
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Design Earable Sensing Systems: Perspectives and Lessons Learned from Industry
Trausti Thormundsson
Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2023 ACM International Symposium on Wearable Computers, ACM (to appear)
Preview abstract
Earables computing is an emerging research community as the industry witnesses the soaring of True Wireless Stereo (TWS) Active Noise Canceling (ANC) earbuds in the past ten years. There is an increasing trend of newly initiated earable research spanning across mobile health, user-interfaces, speech processing, and context-awareness. Head-worn devices are anticipated to be the next generation Mobile Computing and Human-Computer Interaction (HCI) platform. In this paper, we share our design experiences and lessons learned in building hearable sensing systems from the industry perspective. We also give our takes on future directions of the earable research.
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