Graph attention spatio-temporal graph neural network based human–light interaction modeling for indoor lighting environments
- 1Academy of Art and Media Design, NanChang Institute of Science and Technology, NanChang, Jiangxi, China
Abstract
In indoor light environment design, the dynamic interaction between human behavior and lighting systems has an important impact on environmental perception results. However, existing methods are difficult to describe its spatial structure and temporal evolution characteristics at the same time. To this end, this paper constructs a human-light spatio-temporal relationship modeling method based on graph attention spatio-temporal graph neural network, which uniformly represents human body nodes and lamp nodes as spatio-temporal graph structures, and introduces heterogeneous graph attention and adaptive temporal attention mechanisms for joint modeling. The study conducted experimental verification on real indoor scene data for 7 consecutive days and a total of 302,400 time steps. The results show that the proposed model achieves MAE of 0.096 ± 0.010, MSE of 0.017 ± 0.004, RMSE of 0.130 ± 0.015, and R² of 0.943 ± 0.018 in the environment perception prediction task, which is significantly better than various comparison models. At the same time, the environmental perception results show stable and continuous evolution characteristics in the space and time dimensions. Research shows that modeling of human-light spatiotemporal relationships based on graph attention can effectively improve the accuracy and interpretability of indoor light environment perception, and provides important methodological support for intelligent light environment design and human factor-driven control.
Related
- PeTMiner: Mining plant endogenous target mimics via ternary interaction modeling with hierarchical attention and adaptive weighted loss
- Some temporal and geographic records of Abatus species (Echinoidea, Schizasteridae) in Antarctic and Subantarctic environments.
- YOLO-CBNet: A robust attention-enhanced detection framework for underwater fish recognition in aquaculture environments
- Behavioral signatures suggest distinct modes of suppressing irrelevant information during tactile temporal attention in human participants
- Overlapping perceptual benefits of temporal attention in aperiodic and periodic streams are mediated by delta phase alignment, but not alpha suppression, in sensory circuits
BibTeX
@article{gao2026graph,
title = {{Graph attention spatio-temporal graph neural network based human–light interaction modeling for indoor lighting environments}},
author = {Ting Gao and Lingling Sun},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0358774},
url = {https://doi.org/10.1371/journal.pone.0358774},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Graph attention spatio-temporal graph neural network based human–light interaction modeling for indoor lighting environments
AU - Ting Gao
AU - Lingling Sun
PY - 2026
DA - 2026/09/25
JO - PLOS One
DO - 10.1371/journal.pone.0358774
UR - https://doi.org/10.1371/journal.pone.0358774
AB - In indoor light environment design, the dynamic interaction between human behavior and lighting systems has an important impact on environmental perception results. However, existing methods are difficult to describe its spatial structure and temporal evolution characteristics at the same time. To this end, this paper constructs a human-light spatio-temporal relationship modeling method based on graph attention spatio-temporal graph neural network, which uniformly represents human body nodes and lamp nodes as spatio-temporal graph structures, and introduces heterogeneous graph attention and adaptive temporal attention mechanisms for joint modeling. The study conducted experimental verification on real indoor scene data for 7 consecutive days and a total of 302,400 time steps. The results show that the proposed model achieves MAE of 0.096 ± 0.010, MSE of 0.017 ± 0.004, RMSE of 0.130 ± 0.015, and R² of 0.943 ± 0.018 in the environment perception prediction task, which is significantly better than various comparison models. At the same time, the environmental perception results show stable and continuous evolution characteristics in the space and time dimensions. Research shows that modeling of human-light spatiotemporal relationships based on graph attention can effectively improve the accuracy and interpretability of indoor light environment perception, and provides important methodological support for intelligent light environment design and human factor-driven control.
SN - 1932-6203
ER -