연구 분야

Research

Intelligent Signal Processing

Multi-channel 1D Biomedical Signal Processing is a subfield of digital signal processing that jointly analyzes one-dimensional time-series bio-signals simultaneously acquired from spatially distributed sensors using mathematical and statistical models. While single-channel processing primarily focuses on temporal variations in frequency and amplitude, multi-channel processing incorporates spatial correlation to model underlying spatio-temporal dynamics. Representative bio-signals in this domain include High-Density surface electromyography (HD-sEMG), electroencephalography (EEG), electrocardiography (ECG), and multi-site photoplethysmography (PPG/rPPG).

We develop non-invasive sensing pipelines to capture cardiovascular dynamics and neuromuscular intent with high fidelity. For vital monitoring, we utilize camera-based remote photoplethysmography (rPPG) to extract facial blood volume pulses, robustly estimating heart rate, HRV, and respiration while actively suppressing motion and lighting artifacts.
To decode motor intent, we process surface electromyography (sEMG) signals by measuring muscle action potentials across electrode arrays. By filtering volume conduction noise and cross-talk, our framework resolves fine motor commands and joint torque in real time, serving as an intuitive, low-latency control stream for next-generation XR interfaces and assistive prosthetics.

We develop intelligent clinical decision-support systems focused on objective diagnosis and quantitative grading in wound care—specifically targeting pressure injuries (bedsores), burns, and diabetic foot ulcers (DFU). Grounded in our proprietary, large-scale standardized clinical datasets, our diagnostic frameworks integrate multi-spectral feature extraction with morphological tissue segmentation to evaluate wound progression, tissue viability, and healing trajectories.
To overcome critical clinical bottlenecks—such as rare pathological cases, class imbalances, and privacy-constrained medical data collection—we research advanced Generative AI architectures (e.g., conditional diffusion models and generative adversarial frameworks). By synthesizing physiologically consistent, high-fidelity synthetic wound lesions and counterfactual progression patterns, we establish robust data augmentation pipelines that significantly enhance model generalizability and diagnostic fairness in real-world clinical environments.

We investigate Edge-Cloud Collaborative Intelligence architectures that optimize LLM inference by partitioning autoregressive decoding between client browsers and cloud servers. Leveraging client-side WebGPU acceleration, the system executes initial transformer layers locally with zero installation, preserving user privacy and local KV caches while offloading computation-heavy layers to the cloud.
To minimize token generation latency, we employ activation compression and adaptive layer-partitioning strategies. By quantizing intermediate boundary states, the framework substantially reduces per-token network payloads without sacrificing perplexity, ensuring scalable, low-latency token generation across heterogeneous client devices.

We develop model compression and hardware-aware optimization frameworks to deploy deep learning onto resource-constrained embedded systems. By applying Post-Training Quantization (PTQ), Quantization-Aware Training (QAT), and structured pruning, we convert complex networks into low-bit formats (INT8/INT4) with minimal accuracy degradation.
To maximize throughput and energy efficiency, we co-design these models for target Neural Processing Units (NPUs) and micro-accelerators. Through operator fusion, on-chip memory scheduling, and specialized edge compilation toolchains, we eliminate hardware memory bottlenecks and achieve ultra-low-latency on-device inference for robotics, wearables, and edge vision.

We develop edge-centric AIoT systems that unify on-device vision with IoT sensor networks for low-latency inference and autonomous field control. Deployed across smart farms and event venues, our hierarchical Edge Hubs process environmental telemetry and video streams locally, ensuring uninterrupted operations while performing real-time analytics like crop morphometry, yield forecasting, and crowd hazard detection.
Building on this infrastructure, we design machine learning pipelines for closed-loop actuation and proactive decision-making. By correlating multimodal sensor data with visual analytics, the system triggers automated interventions—such as microclimate regulation in agricultural facilities or crowd routing and emergency alerts at venue sites—monitored seamlessly through a synchronized cloud-edge hybrid dashboard.

We develop an Extended Reality (XR) rehabilitation platform for upper- and lower-limb amputees to facilitate pre-prosthetic training prior to wearing physical robotic prostheses. By processing surface electromyography (sEMG) signals through noise-filtering and signal-stabilization pipelines, the system decodes fine-grained motor intent and estimates kinematics in real time to actuate virtual limbs in an immersive Mixed Reality (MR) environment. This closed-loop visual-proprioceptive feedback framework mitigates muscle atrophy, enhances residual-limb neuromotor control, and alleviates phantom limb pain (PLP), while supporting flexible deployment across both clinical and non-face-to-face telerehabilitation settings.

We develop an AI-driven facial analytics and interactive XR feedback framework that extracts dense 3D facial geometry (including 478 ocular and facial landmarks) from single RGB video streams without depth camera dependencies. Through multi-stage geometric registration (scale normalization, global alignment, and point-to-plane ICP), the system resolves head-motion drift to perform rigorous static and dynamic bilateral symmetry evaluations, while simultaneously decoding affective expressions and facial identities. Seamlessly bridging clinical motion analysis with immersive environments, these kinematic and emotional dynamics drive real-time virtual avatar mapping and closed-loop visual biofeedback, establishing an embodied interaction pipeline for digital therapeutics, facial paralysis rehabilitation, and social XR.

We specialize in high-precision 3D pose estimation by fusing stereo vision and RGB-D depth sensor pipelines, reconstructing physically accurate metric coordinates (X, Y, Z) across multiple anatomical scales: full-body kinematics, fine-grained hand articulations, and dense facial landmarks. By effectively mitigating monocular depth ambiguity and self-occlusion, our framework translates spatial biomechanics into quantifiable clinical indicators, including spatiotemporal gait abnormality analysis for dementia screening and millimeter-level symmetry evaluation for facial palsy grading. Seamlessly integrated into 3D spatial simulation environments, these metric kinematic streams drive real-time digital twin avatars, virtual biomechanical modeling, and interactive clinical simulations, enabling clinicians and patients to analyze abnormal motor trajectories, predict recovery progressions, and evaluate therapeutic interventions with high fidelity.