Research

Here are my research projects and academic work. Want to know more? Email me.

DOI: 10.54941/ahfe1005685

Effects of Daylight Intensity on Emotion Regulation in a Virtual Healing Space

With increasing social pressure, the youth population in colleges and universities generally faces increasingly severe negative emotional problems, which not only affect academic performance but also significantly reduce the quality of daily life. In response to this challenge, numerous educational institutions have established psychological healing rooms to provide emotional support and stress management services. However, existing psychological healing spaces often fail to achieve the expected healing effects and affect student engagement due to a lack of sufficient privacy and interactivity. To address this issue, this study proposes a virtual psychological healing space design based on the audiovisual fusion effect, aiming to improve students’ emotional regulation and mental health management by enhancing privacy and interactivity. The design prototype incorporates four natural environments: water and sky, woodland and sky, grassland and sky, and elemental uniform distribution environments. Each environment is paired with customized Lofi music to enhance the healing effect through a multi-sensory experience. Additionally, by utilizing Unreal Engine’s dynamic sky and weather simulation system for natural light, real-time transitions between four lighting environments (early morning, midday, dusk, and night) were achieved in virtual reality to explore the specific effects of light intensity on emotion regulation. This study surveyed 30 college student participants to collect the subjective effects of environmental lighting on emotions in a virtual healing space through semi-structured interviews. This study demonstrates that varying lighting conditions have a significant impact on participants’ emotional states. It underscores the critical necessity of customizing lighting to accommodate the specific needs of users in the design of virtual healing spaces, thereby minimizing the influence of inter-individual differences on therapeutic outcomes. The results of this study provide an empirical basis for designing virtual healing spaces and are expected to offer a theoretical reference and practical basis for implementing personalized healing strategies in virtual environments

Under Review

A Multi-Objective Optimization Method for UI Interfaces Integrating NSGA-II and Simulated Annealing

Traditional user interface layout design heavily relies on designers' subjective judgment and lacks effective quantitative assessment methods. This study aims to overcome this shortcoming. Therefore, this paper proposes a multi-objective optimization (MOO) system designed for the low-fidelity prototyping phase to enable faster production of high-quality interface layouts. A hybrid algorithm (SNSGA-II) is designed in the system, which combines the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the simulated annealing (SA) algorithm, along with a scoring system including both aesthetics and usability. During the high-temperature phase, the SA algorithm is responsible for global exploration, while the genetic algorithm is introduced during the low-temperature phase for refined optimization, combined with an improved sigmoid function to dynamically balance the weights of each evaluation dimension. The experimental results show that this system achieved significant optimization effects across three UI design tasks with varying complexity levels. Compared with traditional NSGA-II algorithm, the SNSGA-II algorithm demonstrated approximately 50% improvement in convergence speed. Evaluation by 20 professional UI designers showed that system-optimized designs exhibited consistent improvements across all complexity levels: overall design quality enhanced by 3.0%, 6.7%, and 6.5% for simple, moderate, and complex tasks respectively; aesthetic scores correspondingly increased by 5.6%, 5.7%, and 5.2%; learnability indicators improved by 4.1%, 6.1%, and 7.2% respectively. This study confirmed the effectiveness of this approach in balancing aesthetics and usability, demonstrating its algorithmic performance and practical optimization potential.

Post-Disaster Temporary Settlement Navigation: A Comparative Study Using VR and Space Syntax

In recent years, natural disasters and armed conflicts have occurred with increasing frequency, severely disrupting everyday life. These events produce large numbers of displaced refugees, creating significant challenges for the planning and design of post-disaster temporary settlements. One crucial issue is how to use spatial layout to facilitate efficient movement and resource acquisition within these settlements. This study identifies four prototypical spatial configurations commonly found in temporary settlements—fishbone, organic growth, grid, and bagua—and constructs corresponding 3D virtual environments. To simulate the navigation behaviors of disaster victims seeking supplies and gathering within settlements, 36 university students were recruited to perform navigation tasks in VR across the four layouts. Their task completion time and walking trajectories were recorded.

Using space syntax analysis, we quantified the intelligibility of each layout and extracted three types of optimal paths between task nodes: the shortest metric path, the fewest-turns path, and the minimum-angle-change path. This study focuses on two core questions:
(1) Whether differences in spatial intelligibility across layouts significantly affect navigation efficiency (i.e., reaction time).
(2) How strongly each of the three optimal path types influences participants’ route choices.

Statistical results show that settlements with higher spatial intelligibility yield significantly shorter reaction times, while gender and prior spatial-training experience have no significant effect. Overlap analysis between participants’ actual trajectories and the three optimal path types revealed the following ranking (from highest to lowest similarity): shortest metric path, minimum-angle-change path, and fewest-turns path.

Through the combined use of space syntax and VR-based comparative experiments, this study reveals the mechanisms by which settlement layout shapes spatial navigation behavior in post-disaster contexts, providing valuable guidance for the future planning and design of temporary disaster relief settlements.

Development of an Immersive IMU-Based Gamified Virtual Reality System for ACL Postoperative Knee Rehabilitation

Postoperative rehabilitation following anterior cruciate ligament reconstruction remains limited by insufficient real time feedback, inconsistent adherence, and the absence of individualized sensor driven evaluation. Current home based rehabilitation practices rarely capture the neuromechanical factors that underlie functional recovery, including proprioceptive deficits, joint loading asymmetries, and altered movement strategies. This research proposes the development of an immersive rehabilitation framework that integrates inertial measurement unit based motion capture with virtual reality environments to investigate how real time digital representations of movement can enhance sensorimotor learning. By combining multi sensor IMU data with a VR based digital twin of lower limb motion, the system will provide multimodal biofeedback that adapts to the user’s performance in real time. The project aims to examine how immersive feedback influences proprioceptive recalibration, movement symmetry, and patient motivation, and to develop computational models capable of characterizing individual recovery trajectories. The expected contribution is a validated sensor driven rehabilitation paradigm that advances theoretical understanding of motor learning in postoperative populations while informing the design of next generation interactive rehabilitation technologies.

Balancing Initiative in Embodied Navigation: Comparing Conventional, Automatic, and Proactive AI Guidance in a VR Kitchen Task

This study investigates how three AI assistance strategies—Conventional (user-initiated), Automatic (time-based), and Proactive (behavior-triggered)—shape wayfinding performance and user experience in a VR kitchen task. Using behavioral logs, trajectories, and event data from three participants across all conditions, we observe consistent patterns: the Conventional assistant leads to prolonged hesitation and delayed help-seeking; the Automatic assistant reduces disorientation but disrupts natural exploration through frequent, time-driven prompts; and the Proactive assistant intervenes only when behavioral cues indicate genuine confusion, providing timely support without interrupting ongoing activity. These results offer preliminary evidence that Proactive assistance achieves the best balance between efficiency and experiential quality—improving navigation performance while preserving user agency and reducing cognitive load. We discuss design implications for embodied AI guidance, including respecting bodily cues, aligning intervention timing with user rhythms, and avoiding over-automation in XR navigation systems.