The Effects of Static Stretching Versus Dynamic Stretching on Lower Extremity Joint Range of Motion, Static Balance, and Dynamic Balance

The purpose of this study was to examine the effects of static stretching (SS) versus dynamic stretching (SS) on lower extremity joint range of motion (ROM), static balance, and dynamic balance. Fifteen active subjects with tight hamstring and calf muscles participated. Hip flexion and knee extension ROM angle was measured using a fluid inclinometer. A closed-chain method of measuring ankle dorsiflexion ROM was used. Static balance was assessed in single-leg stance on a force plate using the time-to-boundary (TTB) measurement. The Star Excursion Balance Test (SEBT) was used to assess dynamic balance in three directions. These measurements were assessed before and after each of three interventions: DS, SS or warm-up alone (CN). The dependent variables included ROM measures (hip flexion, knee extension, and ankle dorsiflexion), SEBT measures (anterior (ANT), posterior-medial (PM), posterior-lateral (PL)), and TTB mean in anterior-posterior (AP) and medial-lateral (ML). Repeated measures ANOVA were used to analyze the data. There was a significant main effect (p \u3c 0.05) for time. Repeated measures ANOVA showed that knee extension ROM, hip flexion ROM, ankle dorsiflexion ROM, the SEBT (ANT, PM, PL) significantly (P\u3c0.05) increased regardless of what intervention (SS, DS, CN) was performed. There were no significant differences (p\u3e0.05) for the TTB (ML, AP) and there were also no significant interaction (p\u3e0.05) between interventions (SS, DS, CN) and time. The less stiff muscles and more slack connective tissue around the joints following stretching might attribute to the increased joint ROM. The enhanced ability to maintain dynamic balance after an increased flexibility might be due to a desensitized stretch reflex. A less responsive stretch reflex could suppress the postural deviations, enhance the proprioceptive input, and thus make it easier to establish equilibrium. Another contributor might be elevated muscle and body temperature, which enhance nerve conduction velocity. The sensory systems might play a dominant role in regulating the static postural control. Additional research is needed to more clearly understand the relationship between altered ROM, balance and stretching

Detecting objects using Rolling Convolution and Recurrent Neural Network

Abstract—At present, most of the existing target detection algorithms use the method of region proposal to search for the target in the image. The most effective regional proposal method usually requires thousands of target prediction areas to achieve high recall rate.This lowers the detection efficiency. Even though recent region proposal network approach have yielded good results by using hundreds of proposals, it still faces the challenge when applied to small objects and precise locations. This is mainly because these approaches use coarse feature. Therefore, we propose a new method for extracting more efficient global features and multi-scale features to provide target detection performance. Given that feature maps under continuous convolution lose the resolution required to detect small objects when obtaining deeper semantic information; hence, we use rolling convolution (RC) to maintain the high resolution of low-level feature maps to explore objects in greater detail, even if there is no structure dedicated to combining the features of multiple convolutional layers. Furthermore, we use a recurrent neural network of multiple gated recurrent units (GRUs) at the top of the convolutional layer to highlight useful global context locations for assisting in the detection of objects. Through experiments in the benchmark data set, our proposed method achieved 78.2% mAP in PASCAL VOC 2007 and 72.3% mAP in PASCAL VOC 2012 dataset. It has been verified through many experiments that this method has reached a more advanced level of detection

Piecewise-Smooth Support Vector Machine for Classification

Support vector machine (SVM) has been applied very successfully in a variety of classification systems. We attempt to solve the primal programming problems of SVM by converting them into smooth unconstrained minimization problems. In this paper, a new twice continuously differentiable piecewise-smooth function is proposed to approximate the plus function, and it issues a piecewise-smooth support vector machine (PWSSVM). The novel method can efficiently handle large-scale and high dimensional problems. The theoretical analysis demonstrates its advantages in efficiency and precision over other smooth functions. PWSSVM is solved using the fast Newton-Armijo algorithm. Experimental results are given to show the training speed and classification performance of our approach