Ergonomic chair parameter optimization based on user prototype recognition and improved genetic algorithm

Purpose This paper aims to develop a data-driven ergonomic chair design framework that improves multi-parameter chair design for user groups with heterogeneous anthropometric characteristics. Design/methodology/approach Large-scale anthropometric data from the Anthropometric Survey of US Army Personnel (ANSUR) II database are clustered using K-means to identify representative user prototypes. Prototype-specific ideal seat parameters are then de-rived according to ergonomic principles. To solve the resulting multi-prototype optimization problem, an improved genetic algorithm (IC2GA) is proposed by integrating a clearing mechanism and a crowding replacement strategy, enabling joint optimization of multiple chair parameters while preserving solution diversity. Findings The proposed method achieved a mean comfort score of 74.00 ± 0.02 across multiple independent repeated experiments, and showed a computational efficiency of 18.89 ± 8.41 s. Ablation analysis shows that introducing prototype identification increased the comfort score from 64.96 ± 0.49 to 73.46 ± 0.06, indicating that modelling user diversity was the primary reason for the performance improvement. Prototype matching analysis further demonstrates that the mean own-other difference is 21.38, confirming that continuous improvement at the individual level is stable. Research limitations/implications This assessment is primarily based on simulation studies and focuses on adult anthropometric data, static sitting postures, and a limited set of geometric parameters. Therefore, further validation through physical experiments and a broader sample population is still required. Originality/value This study integrates user prototype recognition, prototype-specific parameter mapping, and an improved clearing-crowding genetic algorithm into a unified framework for the parametric design of ergonomic chairs. It provides both a refined multi-prototype modelling strategy and an optimization method that maintains diversity across different body types.