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Indoor environments play a critical role in health, safety, and infection control. From healthcare facilities to shared public interiors, spatial configuration can either mitigate or exacerbate health risks. Our work investigates how airflow, visibility, circulation patterns, and zoning can reduce exposure to infectious diseases and support psychological comfort.
Indoor environments play a critical role in health, safety, and infection control. From healthcare facilities to shared public interiors, spatial configuration can either mitigate or exacerbate health risks. Our work investigates how airflow, visibility, circulation patterns, and zoning can reduce exposure to infectious diseases and support psychological comfort.
By analyzing spatial attributes and their relationship with user behavior, we develop design strategies that improve air quality, minimize contamination risk, and enable adaptive responses in times of public health crisis. We aim to create indoor environments that are not only functional but also resilient and healing.
By analyzing spatial attributes and their relationship with user behavior, we develop design strategies that improve air quality, minimize contamination risk, and enable adaptive responses in times of public health crisis. We aim to create indoor environments that are not only functional but also resilient and healing.
An Interpretable AI for Smart Homes: Identifying Fall Prevention Strategies for Older Adults Using Multimodal Deep Learning
Baek, J., Li, Y., Lim, L., & Chong, J. W. (2025). An Interpretable AI for Smart Homes: Identifying Fall Prevention Strategies for Older Adults Using Multimodal Deep Learning. IEEE Journal of Biomedical and Health Informatics.
Falls are a significant cause of mortality among older adults and are considered preventable emergencies. We developed an interpretation framework, including multi-modal predictive models that capture both static and time-series data, global feature importance analysis via a perturbation approach, permutation importance (PIMP), SHap-ley Additive exPlanations (SHAP) for time-series data ex-planation, and intervention of selected features to identify fall prevention strategies at home. Our predictive model utilizing the BiCrossNet architecture achieved a prediction accuracy of 98% when 12,540 data points were used. It was found that activity-related and indoor thermal environment features are important for predicting fall emergency occurrences (FEO).
Evaluating the impact of windows, artificial windows, and ceiling height on stress levels through subjective and objective measures
Baek, D., Kim, H., Wei, Q., Lee, S., & Lim, L. (2025). Evaluating the impact of windows, artificial windows, and ceiling height on stress levels through subjective and objective measures. Building and Environment, 113182.
This study examined how specific indoor design factors affect stress levels, focusing on four physical mock-up rooms: (1) a wall-only, (2) a window, (3) an artificial window, and (4) a higher ceiling height. When the stress levels in the four rooms were compared, the participants in the rooms with a window presented significantly lower stress levels than did those in the three other rooms, with a wall-only, an artificial window, and a higher ceiling height. Interestingly, the room with a higher ceiling height presented varying stress levels relative to other rooms. The stress levels at higher ceiling heights were significantly higher than those in the room with a window. However, compared with a wall-only room or an artificial window room, stress levels in a room with a higher ceiling height statistically insignificantly differed and fluctuated depending on the type of stress measurement.
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