Stimuli-responsive hydrogel strain sensors that synergize the advantages of both hydrogel and smart functional materials have attracted increasing interest from material design to emerging applications in health monitors and human–machine interfaces.

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An automatic system is developed for internal cracks detection in magnetic tiles based on the impact acoustics, using wavelet packet transform (WPT), principal component analysis (PCA) and hidden Markov model (HMM). In this system, the detecting device is considered as core part to collect and analyse the impact sounds. The original impact sounds are first decomposed up to six levels based on WPT to extract the features. PCA is then performed for dimension reduction and clustering analysis. By adopting the features extracted based on WPT and optimised by PCA as inputs, an HHM classifier is developed for automatic inspection. The results of classification show that the accuracy rate is 100%, demonstrating that the system has significant potential in detecting magnetic tile internal cracks.