Background: Increase age is one of the main factors influencing spatiotemporal and pressure parameters. Adolescence and young adulthood are unique periods in the life span that present opportunities and challenges in improving health. Meantime, this age span involve significant growth and development. But the previous studies seldom examine the adolescents gait characteristics and pay attention to the plantar pressure changes between this age span. Purpose: Given the complex variation of these transitional ages and their implications to the foot care, the primary aim of this study was to investigate plantar pressure differences between teenager girls and young female adults during walking using an EMED pressure plate (Novel, Germany). Method: Nineteen young female adults (YFA) age at 24.7 (±2.21) and nineteen teenager girls (TG) age at 18.25 (±1.23) were participated this study. Data collections including peak pressure, contact area, pressure time integral were performed with an EMED pressure plate. The measurement protocol was barefoot walking across the platform along a 10m long straight trail. Pressures were evaluated for seven plantar areas on the foot according to the anatomical structure. Result and Conclusion:The result showed that walking speed was similar in both teenager girls and young female adults. The teenager girls shown higher peak pressure in the first metatarsal (FM), fourth and fifth metatarsals (FAFM), middle foot (MF) and rear-foot (RF) areas while the YFA shown larger pressure time integral at big toe (BT) and other toes (OT). Contact area was lower in the YFA for the several foot regions compared to the TG BT, OT, FAFM and MF. Greater pressure time integrals of the FM, FAFM and MF were found in the TG compared to the YFA, while the YFA shown larger pressure time integral at BT, OT and RF. The TG shown greatest peak pressure and pressure time integral in the FM while the YFA shown biggest peak pressure and pressure time integral in the BT. This normative data will provide a basis to assess pediatric pathologic foot deformities more accurately and to distinguish dynamic foot deformities from anatomic foot deformities.

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With the development of unmanned aerial vehicle (UAV) control technology, one of the recent trends in this research domain is to utilize UAVs to perform non-contact transmission line inspection. The RGB camera mounted on UAVs collects large numbers of images during the transmission line inspection, but most of them contain no critical components of transmission lines. Hence, it is a momentous task to adopt image classification algorithms to distinguish key images from all aerial images. In this work, we propose a novel classification method to remove redundant data and retain informative images. A novel transmission line scene dataset, namely TLS_dataset, is built to evaluate the classification performance of networks. Then, we propose a novel convolutional neural network (CNN), namely TL-Net, to classify transmission line scenes. In comparison to other typical deep learning networks, TL-Nets gain better classification accuracy and less memory consumption. The experimental results show that TL-Net101 gains 99.68% test accuracy on the TLS_dataset.