Motion comfort analysis of tight-fitting sportswear from multi-dimensions using intelligence systems

Abstract

Focusing on the “human–sport-clothing” system, this paper analyzed the influence of combinations of different tights and sport states on human body parts and overall body comfort from multiple dimensions. However, the motion state and some fabric parameters are non-numerical parameters, which could not be used for model analysis directly. In addition, there are too many numerical fabric parameters whose relationships are complicated, and it is difficult for general models to deal with these relationships, resulting in low accuracy of the comfort prediction model. Moreover, when using the artificial neural network to study comfort, it has some difficulties in expressing comfort and low prediction accuracy. To solve these problems, One-Hot was used to encode non-numerical parameters, and then intelligent algorithms were adopted to deal with these complex fabric parameters. Finally, a comfort prediction model was established in combination with an adaptive fuzzy reasoning system. The results showed that different fabric combinations and motion states had significant effects on local comfort (comfort of specific human body parts) and global comfort (whole body comfort). Moreover, the prediction model with non-numerical parameters has higher accuracy than the model without non-numerical parameters, which indicated that the prediction accuracy of the model had been improved after the introduction of One-Hot coding, so the non-numerical parameters cannot be ignored. The particle swarm optimization algorithm-cuckoo search algorithm-adaptive network-based fuzzy inference system hybrid model was superior to the particle swarm optimization algorithm-adaptive network-based fuzzy inference system and cuckoo search algorithm-adaptive network-based fuzzy inference system model in predicting local comfort and global comfort.