The booming wearable market and recent advances in material science has led to the rapid development of the various wearable sensors, actuators, and devices that can be worn, embedded in fabric, accessorized, or tattooed directly onto the skin. Wearable actuators, a subcategory of wearable technology, have attracted enormous interest from researchers in various disciplines and many wearable actuators and devices have been developed in the past few decades to assist and improve people’s everyday lives. In this paper, we review the actuation mechanisms, structures, applications, and limitations of recently developed wearable actuators including pneumatic and hydraulic actuators, shape memory alloys and polymers, thermal and hygroscopic materials, dielectric elastomers, ionic and conducting polymers, piezoelectric actuators, electromagnetic actuators, liquid crystal elastomers, etc. Examples of recent applications such as wearable soft robots, haptic devices, and personal thermal regulation textiles are highlighted. Finally, we point out the current bottleneck and suggest the prospective future research directions for wearable actuators.
To improve the accuracy of automatic defect classification, a novel algorithm has been developed based on the principal component analysis (PCA) and support vector machine (SVM) methods. The original defect data are transformed to principal component space by the PCA algorithm and then the optimal dataset is selected. Then, the SVM is used for defect classification. For estimating the actual classification accuracy of the proposed method in a concrete system, the bootstrap method is introduced. The experimental result demonstrates that the accuracy of the new method is 90.75%, which promotes the evaluation accuracy by 3.24% and 4.93% compared with the SVM and MLP-ANN, respectively. Furthermore, the new method takes less computing time than the MLP-ANN method.
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