arXiv subjects

Lan Sun

Publications and source records attributed to Lan Sun.

6 recordsLinked to original sources

arXiv2026

IMU-HOI: A Symbiotic Framework for Coherent Human-Object Interaction and Motion Capture via Contact-Conscious Inertial Fusion

Capturing full-body human motion with object interactions is crucial for AR/VR and robotics applications, yet it remains challenging for conventional vision-based methods due to occlusions and constrained capture volumes. Inertial measurement units (IMUs) offer a compelling alternative without line-of-sight requirements, but existing IMU-based motion capture assumes an isolated human and ignores object contacts and dynamics. To bridge this gap, we present IMU-HOI, a novel framework that jointly recovers full-body human pose and 6-DoF object trajectory from sparse IMUs on the body and object, explicitly modeling human-object interaction. Our approach first infers probabilistic hand-object contacts directly from IMU streams and uses them as a high-level signal to route between kinematic and inertial reasoning. These contact cues drive a three-stage fusion pipeline that refines human pose and root translation, and fuses hand-based forward kinematics with object-IMU integration for object motion, yielding coherent, drift-resilient trajectories for both human and object. Experiments on challenging human-object interaction scenarios demonstrate substantial accuracy gains over prior inertial motion capture methods. Moreover, IMU-HOI can be plugged into existing sparse-IMU mocap backbones with minimal changes, effectively extending the scope of purely inertial motion capture from isolated humans to full human-object interaction and joint motion estimation.

cs.CV↗

arXiv2025

RadarLLM: Empowering Large Language Models to Understand Human Motion from Millimeter-Wave Point Cloud Sequence

Millimeter-wave radar offers a privacy-preserving and environment-robust alternative to vision-based sensing, enabling human motion analysis in challenging conditions such as low light, occlusions, rain, or smoke. However, its sparse point clouds pose significant challenges for semantic understanding. We present RadarLLM, the first framework that leverages large language models (LLMs) for human motion understanding from radar signals. RadarLLM introduces two key innovations: (1) a motion-guided radar tokenizer based on our Aggregate VQ-VAE architecture, integrating deformable body templates and masked trajectory modeling to convert spatial-temporal radar sequences into compact semantic tokens; and (2) a radar-aware language model that establishes cross-modal alignment between radar and text in a shared embedding space. To overcome the scarcity of paired radar-text data, we generate a realistic radar-text dataset from motion-text datasets with a physics-aware synthesis pipeline. Extensive experiments on both synthetic and real-world benchmarks show that RadarLLM achieves state-of-the-art performance, enabling robust and interpretable motion understanding under privacy and visibility constraints, even in adverse environments. This paper has been accepted for presentation at AAAI 2026. This is an extended version with supplementary materials.

cs.LG↗

arXiv2025

Suite-IN++: A FlexiWear BodyNet Integrating Global and Local Motion Features from Apple Suite for Robust Inertial Navigation

The proliferation of wearable technology has established multi-device ecosystems comprising smartphones, smartwatches, and headphones as critical enablers for ubiquitous pedestrian localization. However, traditional pedestrian dead reckoning (PDR) struggles with diverse motion modes, while data-driven methods, despite improving accuracy, often lack robustness due to their reliance on a single-device setup. Therefore, a promising solution is to fully leverage existing wearable devices to form a flexiwear bodynet for robust and accurate pedestrian localization. This paper presents Suite-IN++, a deep learning framework for flexiwear bodynet-based pedestrian localization. Suite-IN++ integrates motion data from wearable devices on different body parts, using contrastive learning to separate global and local motion features. It fuses global features based on the data reliability of each device to capture overall motion trends and employs an attention mechanism to uncover cross-device correlations in local features, extracting motion details helpful for accurate localization. To evaluate our method, we construct a real-life flexiwear bodynet dataset, incorporating Apple Suite (iPhone, Apple Watch, and AirPods) across diverse walking modes and device configurations. Experimental results demonstrate that Suite-IN++ achieves superior localization accuracy and robustness, significantly outperforming state-of-the-art models in real-life pedestrian tracking scenarios.

cs.CV↗

arXiv2025

mmDEAR: mmWave Point Cloud Density Enhancement for Accurate Human Body Reconstruction

Millimeter-wave (mmWave) radar offers robust sensing capabilities in diverse environments, making it a highly promising solution for human body reconstruction due to its privacy-friendly and non-intrusive nature. However, the significant sparsity of mmWave point clouds limits the estimation accuracy. To overcome this challenge, we propose a two-stage deep learning framework that enhances mmWave point clouds and improves human body reconstruction accuracy. Our method includes a mmWave point cloud enhancement module that densifies the raw data by leveraging temporal features and a multi-stage completion network, followed by a 2D-3D fusion module that extracts both 2D and 3D motion features to refine SMPL parameters. The mmWave point cloud enhancement module learns the detailed shape and posture information from 2D human masks in single-view images. However, image-based supervision is involved only during the training phase, and the inference relies solely on sparse point clouds to maintain privacy. Experiments on multiple datasets demonstrate that our approach outperforms state-of-the-art methods, with the enhanced point clouds further improving performance when integrated into existing models.

cs.CV↗

arXiv2024

Suite-IN: Aggregating Motion Features from Apple Suite for Robust Inertial Navigation

With the rapid development of wearable technology, devices like smartphones, smartwatches, and headphones equipped with IMUs have become essential for applications such as pedestrian positioning. However, traditional pedestrian dead reckoning (PDR) methods struggle with diverse motion patterns, while recent data-driven approaches, though improving accuracy, often lack robustness due to reliance on a single device.In our work, we attempt to enhance the positioning performance using the low-cost commodity IMUs embedded in the wearable devices. We propose a multi-device deep learning framework named Suite-IN, aggregating motion data from Apple Suite for inertial navigation. Motion data captured by sensors on different body parts contains both local and global motion information, making it essential to reduce the negative effects of localized movements and extract global motion representations from multiple devices.

cs.LG↗

arXiv2016

Decreased aneurysmal subarachnoid hemorrhage incidence rate in elderly population than in middle aged population: a retrospective analysis of 8,144 cases in Mainland China

Purpose: Rupture of an intracranial aneurysm is the most common cause of subarachnoid haemorrhage (SAH), which is a life-threatening acute cerebrovascular event that typically affects working-age people. This study aims to investigate the aneurysmal SAH incidence rate in elderly population than in middle aged population in China. Materials and methods: Aneurysmal SAH cases were collected retrospectively from the archives of 21 hospitals in Mainland China. All the cases collected were from September 2016 and backward consecutively for a period of time up to 8 years. SAH was initially diagnosed by brain computed tomography, and CT angiography (CTA) or digital subtraction angiography (DSA) was followed and SAH was confirmed to be due to cerebral aneurysm. When for cases multiple bleeding occurred, the age of the first SAH was used in this study. The toltal incidence from all hospital at each age were summed together for females and males; then adjusted by the total population number at each age for females and males. The total population data was from the 2010 population census of the People's Republic of China. Results: In total there were 8,144 cases, with 4,861 females and 3,283 males. Our analysis shows for both females and males the relative aneurysmal SAH rate started to decrease after around 65 years old. The males the relative aneurysmal SAH rate might have started to decrease after around 55 years old. Conclusion: In contrast to previous reports, our data demonstrated a decreased aneurysmal subarachnoid hemorrhage incidence rate in elderly population than in middle aged population. Our data therefore support the hypothesis that aneurysms do not grow progressively once they form but probably either rupture or stabilize and that very elderly patients are at a reduced risk of rupture compared with atients who are younger with the same-sized aneurysms.

q-bio.TO↗