Deep learning-based forecasting of temperature and humidity in footwear using multi-sensor data

Maintaining thermal comfort and regulating humidity in footwear is critical, particularly in environments where fluctuations in temperature and moisture impact user experience. Correct estimation of these parameters is crucial in selecting suitable material, thereby improving the efficiency and comfort of the footwear. This study proposes a comparison of the deep learning models (recurrent neural networks, long short-term memory (LSTM), bidirectional LSTM, DeepAR, and hybrid model (Cay, Vassiliadis et al.)) for the forecasting of temperature and humidity inside various types of footwear based on the multi-sensor data. A foot model that mimicked the actual temperature and perspiration process of the human foot was used, and sensor readings were taken from various points of the footwear. Performance of deep learning models was evaluated using four key metrics: mean absolute error, root mean squared error, explained variance score, and mean absolute percentage error. The findings indicate that the hybrid model outperforms the other models and achieves the highest predictive accuracy across all tested footwear conditions. This research contributes to the development of artificial intelligence-based predictive modeling for footwear climate control, offering a robust approach for determining thermophysiological comfort in wearable technologies and smart-manufacturing applications.