Welding usually involves high temperatures, toxic solvents, or conditions not compatible with human bodies, which severely limit the fusion of electronics and human tissues. To achieve direct welding of electronics on human skin, the intrinsically sticky conductors that can simultaneously achieve metal‐grade electrical conductivity (≈41 7000 S m⁻¹), hydrogel‐grade stretchability (>900% strain), and self‐adhesiveness (1.8 N cm⁻¹) are reported. The sticky conductors composed of gallium indium alloy and acrylate polymer adhesives have a surface‐enriched structure, which can form instant mechanical and electrical connections with different surfaces through gentle pressure without involving conditions that may damage human tissues. Based on the sticky conductors, the in situ welding of electronics on the skin is realized. To demonstrate the feasibility of in situ welding, electronic tattoos are achieved for movement monitoring. Intrinsically sticky electrodes that can resist drying and simultaneously deform with the skin for electrophysiological measurement are also developed.

Large and repeated impacts on the heel during running are among the primary reasons behind runners’ injuries. Reducing plantar pressure can be conducive to reducing running injury and improving running performance and is primarily achieved by modifying the design parameters of running shoes. This study examines the effect of design parameters of running shoes (i.e., heel-cup, insole material, midsole material, and insole thickness) on landing peak plantar pressure and determines the combination of different parameters that optimize cushion effects by employing the Taguchi method. We developed the foot–shoe finite element (FE) model through reverse engineering. Model assembly with different design parameters was generated in accordance with the Taguchi method orthogonal table. The effectiveness of the model was verified using the static standing model in Ansys. The significance and contribution of different design parameters, and the optimal design to reduce plantar pressure during landing, were determined using the Taguchi method. In the descending order of percentage contribution was a conforming heel-cup (53.18%), insole material (25.89%), midsole material (7.81%), and insole thickness (2.69%). The more conforming heel-cup (p < 0.001) and softer insole (p = 0.001) reduced the heel pressure during landing impact. The optimal design of running shoe in this study was achieved with a latex insole, a 6 mm insole thickness, an Asker C-45 hardness midsole, and a 100% conforming heel-cup. The conforming heel-cup and the insole material significantly affected the peak plantar pressure during heel landing. The implementation of a custom conforming heel-cup is imperative for relieving high plantar pressure for long-distance heel-strike runners.

To diagnose pneumoconiosis using a computer-aided diagnosis system based on digital chest radiographs. Lung fields were first extracted by combining the traditional Otsu-threshold method with a morphological reconstruction on digital radiographs (DRs), and then subdivided into six non-overlapping regions (region (a-f)). Twenty-two wavelet-based energy texture features were calculated exclusively from each region and selected using a decision tree algorithm. A support vector machine (SVM) with a linear kernel was trained using samples with texture features to classify an individual region of a healthy subject or a pneumoconiosis patient. The final classification results were obtained by integrating these individual classifiers with the weighted voting method. All models were developed on a dataset of 85 healthy controls and 40 stage I or II pneumoconiosis patients and validated by using the bootstrap resampling with replacement method. The areas under receiver operating characteristic curves (AUCs) of regions (c) and (f) were 0.688 and 0.563, which were worse than those of the other four regions. Region (c) and (f) were both excluded from the individual classifiers that were going to be assembled further. When built on the selected texture features, each individual SVM showed a higher diagnostic performance for the training set and the test set. The classification performance after an ensemble was 0.997 and 0.961 of the AUC value for the training and test sets, respectively. The final results were 0.974 ± 0.018 for AUC value and 0.929 ± 0.018 for accuracy. The integrated SVM model built on the selected feature set showed the highest diagnostic performance among all individual SVM models. The model has good potential in diagnosing pneumoconiosis based on digital chest radiographs.