Prediction of lower limb discomfort of elderly drivers based on key dimensions in the leg space

Significant difference exists in spatial fitness and perception between elderly and non-elderly drivers. However, due to dynamic and real-time changes in human subjective feelings and joint movements, two-dimensional human body templates and human-machine simulation software are not enough to obtain necessary space parameters. In this study, seven key dimensions of the legroom were measured thrice and averaged, in the situation that the seat and posture are comfortable. Such anthropometric data can reflect dynamic perception that may change due to personal emotions and environmental influences. Extreme learning machine (ELM) was adopted to build a prediction model of leg space discomfort degree, and the influence of the activation function and the number of hidden layer neurons on the prediction accuracy of the model were analyzed. In addition, a multiple linear regression (MLR) model was established with the discomfort score as the dependent variable and the seven key dimensions as the independent variables. The results indicated that the ELM model could effectively predict elderly drivers’ discomfort degree (MSE = 0.182, MRE = 9.364, R² = 0.869) by learning the dimensions of the seven key positions. The MLR model (R² = 0.861) did not perform as well as ELM. However, the regression coefficients could reflect the degree to which each dimension affects the discomfort degree of leg space for elderly drivers. The conclusions could function in elderly-oriented in-vehicle space arrangement and driving risk assessment of elderly people.