The clothing comfort sensation is a combination of complex components, including psychological and physiological responses. General linear analysis is not always sufficient for the evaluation of the clothing comfort sensation. The current study sought to predict the clothing comfort sensation of wearing an undershirt using an artificial neural network (ANN). We constructed ANN models with psychological sensation data and physiological response data as inputs, including electrocardiogram and thermo-physiological indicators, and the clothing comfort sensation as the output. For the input layer of the model, three conditions were used: the psychological response data only, the physiological response data only, and both the psychological and physiological data. The number of hidden layers in the models ranged from one to three, and the number of units in each hidden layer was changed when fixed values of 30, 60, and 90 were used, or according to the number of data points in the input conditions. The results revealed that, among the three conditions, the accuracy rate was higher when both psychological and physiological response data were used as input. The prediction results exhibited an accuracy rate of up to 85% for unknown test data. The results suggest that the method of evaluating the state of clothing comfort sensation when wearing an undershirt using psychophysiological response measurement was effective and that neural networks are useful for predicting the clothing comfort sensation.

服装的舒适感是复杂成分的组合，包括心理和生理反应。一般的线性分析并不总是足以评估服装的舒适感。目前的研究试图使用人工神经网络 (ANN) 来预测穿着汗衫的服装舒适感。我们以心理感觉数据和生理反应数据为输入，包括心电图和热生理指标，以服装舒适感为输出，构建了人工神经网络模型。对于模型的输入层，使用了三个条件：仅心理反应数据、仅生理反应数据以及心理和生理数据。模型中隐藏层的数量从 1 到 3 不等，当使用固定值30、60和90时，或根据输入条件中的数据点数，改变每个隐藏层的单元数。结果表明，在三种情况中，心理和生理反应数据均作为输入的准确率较高。预测结果对未知测试数据的准确率高达 85%。结果表明，使用心理生理反应测量评估穿着汗衫时服装舒适感状态的方法是有效的，并且神经网络可用于预测服装舒适感。在三个条件中，心理和生理反应数据同时作为输入的准确率更高。预测结果对未知测试数据的准确率高达 85%。结果表明，使用心理生理反应测量评估穿着汗衫时服装舒适感状态的方法是有效的，并且神经网络可用于预测服装舒适感。在三个条件中，心理和生理反应数据同时作为输入的准确率更高。预测结果对未知测试数据的准确率高达 85%。结果表明，使用心理生理反应测量评估穿着汗衫时服装舒适感状态的方法是有效的，并且神经网络可用于预测服装舒适感。