Ergonomic Fuzzy Evaluation of Firefighting Operation Motion

The firefighting operation motion has an important impact on the safety and comfort of firefighting operation. As a judgment criterion of the firefighting efficiency, the comfort level is hard to judge in that it is completely decided by human feeling, so the comprehensive fuzzy evaluation is utilized for evaluation of comfort level. In this paper, firstly the factor and judgment set of firefighting operation comfort level are determined, and the fuzzy weight evaluation is obtained by questionnaires and analytic hierarchy process. Secondly, the joint angles of some particular motions are determined by motion capture equipment, the moment is obtained by ergonomic engineering software, and then the comprehensive comfort evaluation on firefighting operation motion is completed. Finally, the objective evaluation system of firefighting operation comfort is established. 1. Introduction Firefighting grows more and more difficult owing to many types of fire accidents nowadays. The operation efficiency becomes the important factor on the firefighting according to statistics of fire accidents. Therefore, the rationality and reliability of firefighting operation directly affect the rescue work. Motion analysis can be used to optimize and standardize human operation motion by means of detecting and tracking human operation. The motion analysis of firefighting is based on the collection, classification, and evaluation of particular rescue motion of fire men and the research results will be the fundamentals for firefighting product design and fire man training optimization. The motion analysis can be classified into two methods: visual motion observation method and image motion observation method [1]. The ergonomics analysis of firefighting operation and fire men mainly focuses on firefighting training, fire extinguisher, and fire man uniforms. Jiao et al. applied BP neural network to firefighting training evaluation. The comprehensive evaluation on the basic information, training methods, training contents, training management, and achievement of the tested groups are discussed [2]. Based on SAQ+B (Scenario Animation Question and Browse), Chen and Li proposed an evaluation method for simulation of firefighting training. The training process is first divided into several units, and then the training problems are abstracted. Through experts grading, integrating calculation method of AHP by Matlab, the firefighting training evaluation is divided into two parts of knowledge acquisition and capability test. Therefore the quantitative estimation for the training subject