Emerging smart textiles have enriched a variety of wearable technologies, including fiber optic technology. Optic fibers are widely applied in communication, sensing, and healthcare, and smart textiles enable fiber optic technology to be worn close to soft and curved human body parts for personalized functions. This review briefly introduces wearable fiber optic applications with various functions, including fashion and esthetics, vital signal monitoring, and disease treatment. The main working principles of side emission, wavelength modulation, and intensity modulation are summarized. In addition, textile fabrication techniques, including weaving and knitting, are discussed and illustrated as combination methods of embedding fiber optic technology into textile fabric. In conclusion, the combination of optical fibers and textiles has drawn considerable interest and developed rapidly. This work provides an overview of textile-based wearable fiber optic technology and discusses potential textile fabrication techniques for further improvement of wearable fiber optic applications.

In the research of breast morphology, numerous breast features are measured, whereas only a few parameters are adopted for classification. Therefore, how to extract the key variables from the multi-dimensional features in a rational way is an issue that is focused upon. This study aimed to reduce the complexity of the dimensionality reduction for further improving the objectivity and interpretability of the selected breast features. Since the random forest (RF) algorithm can quantify the feature importance during training, the method was adopted to determine the optimal breast features for classification and recognition in this paper. Firstly, the anthropometric data of 360 females from northwestern China aged from 19 to 27 years were measured by non-contact three-dimensional body scanning technology and the contact manual measurement method. Then, the k-means clustering was applied to categorize breast shapes, and the RF algorithm was utilized to quantify and rank the importance of 25 breast features. Finally, to verify the availability of the RF algorithm on breast feature selection, the t-distributed stochastic neighbor embedding method was adopted to visualize the distribution of breast shape clusters into two dimensions. Meanwhile, four neural networks were determined to recognize the breast morphology. The results demonstrate that fewer breast features can effectively increase the accuracy of breast shape classification and recognition. The best performance of breast shape classification and recognition is obtained when the number of breast features is 13. In this case, the average Hamming loss of four neural networks is the smallest (0.1136). Interestingly, the bust circumference and the horizontal curve of breasts across the bust points are found to be the most important of the 25 breast features in this paper. The importance of the breast curve features is higher than that of the breast cross-sectional features, while the breast positioning features have the lowest importance. Meanwhile, the RF algorithm is verified to be more effective than traditional dimensionality reduction methods, such as principal component analysis, hierarchical clustering, and recursive feature elimination. The approach developed in this paper can be generalized to the dimensionality reduction of other body morphology.

In this work, the deformation of bilateral breasts was investigated with an established hybrid model to predict the nipple movement specifically for senior women during yoga exercise. A motion capture system was used to collect the displacement of 10 markers on the breasts from 11 senior women (average age of 62) during yoga practice and then the data were analyzed by integrating the absolute grey relation analysis (AGRA) and extreme learning machine (ELM). The right and left breasts had the maximum motion amplitude in the horizontal direction but they were respectively featured with contraction and extension during yoga practice. AGRA showed that the nipple motion was highly associated with the vertical region above the nipple for the left breast but the parallel region along with the nipple for the right breast. The ELM model is able to predict the nipple movement within tolerable error (∼0.0037). This study lays a foundation for a better understanding of ageing breast kinematics during yoga poses with limited practical experiments. Besides, the accurate and efficient results can be used not only for yoga pose instruction but also for ergonomic sports bra design.

The control of breast motions is a critical indicator to evaluate the comfort and function of sports bras. If the breast motions can be predicted based on the gait parameters detected by wearable sensors, it will more economical and convenient to evaluate the bras. Thirteen unmarried Chinese females with a breast cup of 75B were recruited in this study to investigate the regularity of breast motions and the relevance between breast motions and gaits during running exercises. The breast motion indicator is the distance alteration of breast regions. The gaits were described by the rotation angles of the hip, knee, ankle joints, and the foot height off the ground. Firstly, the Mann-Whitney U test and the Kruskal-Wallis H test were utilized to analyze the motion diversity among the eight breast regions. Then, the gray correlation analysis was applied to explore the relevance between breast motions and gaits. Finally, the back-propagation neural network, the genetic algorithm, and the particle swarm optimization algorithm were utilized to construct the prediction models for breast motions based on gait parameters. The results demonstrate that the same breast regions on the bilateral breasts and the different breast regions on the ipsilateral breasts present a significant motion diversity. There is a moderate correlation between breast motions and gait parameters, and the back-propagation neural network optimized by the particle swarm optimization algorithm performs better in breast motion prediction, which has a coefficient of determination of 84.58% and a mean absolute error of 0.2108.

Finite-element simulation of garment represents a fundamental technology for digital design and virtual fitting; however, conventional methods are hindered by challenges such as complex modeling, difficulties in parameter acquisition, and low computational efficiency. Artificial intelligence (AI) technologies, including deep learning, physics-informed neural networks, graph neural networks, and large language models, offer novel avenues to address these limitations. This paper reviews essential AI technologies for fabric parameter identification and surrogate model construction, discusses the intelligent computer-aided engineering mechanism driven by large language models, and establishes a closed-loop intelligent simulation framework. This framework facilitates a fully automated finite-element simulation workflow, significantly reducing the modeling threshold while improving efficiency and accuracy, thus offering a new, efficient, and cost-effective paradigm for the digital transformation of the apparel industry. Finally, the paper summarizes current challenges in data management, physical consistency, and multiscale coupling, and anticipates future developments, including AI agents and digital twin garment.