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Hyunsoo Kim

Assistant Professor

Dankook University

South Korea

Research Interests

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About

Hyunsoo Kim is an Assistant Professor at Dankook University in South Korea. His research areas include ambient detection in construction environments, worker safety monitoring using advanced tracking frameworks, and the evaluation of worker fatigue in exoskeleton-assisted tasks. He has contributed significantly to the synthesis of construction datasets for machine learning and the development of thermal comfort models utilizing wearable bio sensors.

Articles (18)

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2026

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2021

Collaborators (2)

Gaang Lee

Assistant Professor

University of Alberta

CANADA

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Taehoon Kim

Professor

Seoul National University

SOUTH KOREA

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Hyunsoo Kim

Research Interests

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Articles (18)

Comparative analysis of forecasting construction costs via automated machine learning approach and inclusion of economic index

The Design of a Fireproofing Spray Robot Using Quality Function Deployment

Hybrid continuous construction worker pose estimation under occlusion and missing data using ST-GCN and Kalman filtering

Relationship-Based Ambient Detection for Concrete Pouring Verification: Improving Detection Accuracy in Complex Construction Environments

Irregular Openings Identification at Construction Sites Based on Few-Shot Learning

YOLOv8-SAMURAI: A Hybrid Tracking Framework for Ladder Worker Safety Monitoring in Occlusion Scenarios

Evaluation of Worker Fatigue During Exoskeleton-Assisted Tasks with Varying Intensities

Occlusion-Aware Worker Detection in Masonry Work: Performance Evaluation of YOLOv8 and SAMURAI

Performance Analysis of Wearable Robotic Exoskeleton in Construction Tasks: Productivity and Motion Stability Assessment

Time-Series Image-Based Automated Monitoring Framework for Visible Facilities: Focusing on Installation and Retention Period

The Influence of Fatigue, Recovery, and Environmental Factors on the Body Stability of Construction Workers

Interpretable general thermal comfort model based on physiological data from wearable bio sensors: Light Gradient Boosting Machine (LightGBM) and SHapley Additive exPlanations (SHAP)

Massive-Scale construction dataset synthesis through Stable Diffusion for Machine learning training

Productivity Measurement through IMU-Based Detailed Activity Recognition Using Machine Learning: A Case Study of Masonry Work

Determination of workers' compliance to safety regulations using a spatio-temporal graph convolution network

Feasibility of DRNN for Identifying Built Environment Barriers to Walkability Using Wearable Sensor Data from Pedestrians’ Gait

Two-Step k-means Clustering Based Information Entropy for Detecting Environmental Barriers Using Wearable Sensor

The Feasibility of Information-Entropy-Based Behavioral Analysis for Detecting Environmental Barriers

Collaborators (2)

Gaang Lee

Taehoon Kim

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