Key motion characteristics of side-step movements in hip-hop dance and their effect on the evaluation by judges

INFLUENCE OF SQUARE DANCING ON MOTOR FUNCTION OF MIDDLE-AGED AND ELDERLY WOMEN

ABSTRACT Introduction The quality of life of middle-aged and elderly women is affected by the physiological effects of aging on the locomotor system; moderate aerobic exercise is one of the practices that can delay these deleterious effects. Square dancing has functional characteristics of aerobic exercise, but there are still no studies on its effects on motor function in middle-aged and elderly women who practice it regularly. Objective Explore the long-duration square dance exercises’ effect on motor function in middle-aged and elderly women. Methods 45 middle-aged and elderly women, divided into experimental and control groups, participated. The experimental group (n=25) performed square dancing exercises of 90 minutes four times a week for six months. Indicators of physical function, vital capacities, and motor function indices were collected. Results After exercise, improved grip strength of the middle-aged women in the square dance group and the 1-minute sessions was observed; in particular, the mean value of the selection response reduced from 516.20±83.87 before exercise to 440.28±58.07, a very significant difference. Conclusion Long-term square dance exercise has a particular effect on improving the cardiopulmonary function of middle-aged and elderly women and significantly improved motor function. Evidence Level II; Therapeutic Studies - Investigating the result.

Analysis of Main Movement Characteristics of Hip Hop Dance Based on Deep Learning of Dance Movements

In order to explore the main action characteristics of hip hop dance, a deep learning recognition system based on dance action is proposed. The network is based on convolution, pooling, and full connection calculation in a convolutional neural network (CNN). On the one hand, the pixel information in the video frame is extracted as the network input feature in the spatial domain. On the other hand, in the time domain, in order to better represent the change characteristics of video actions, optical flow information is introduced, and the optical flow vector change of pixels in DT time is calculated by the pyramid algorithm (LK) as the time domain convolution feature. In order to evaluate the performance of the network, this article takes the recognition of dance movements as an example to test the application of the algorithm. The test dataset contains 101 fully identified dance movements. The test results show that the proposed algorithm is 10.90% higher than F1 of inception V3, and the recognition accuracy is 10.85% and 5.27% higher than that of inception V3 and 3D-CNN networks, respectively. For the problems and difficulties brought by single-mode video action recognition, a multimodal action recognition method is introduced to achieve better results based on a large number of training data. Different depth networks have different characteristics. CNN network pays more attention to the relationship between local information, so it is suitable for image recognition and detection tasks. The RNN network is expanded in the time dimension, so it is suitable for the modal information related to similar videos. Therefore, based on multimodal information and a depth neural network, a depth feature extraction and fusion method for multimodal information is designed. Different methods of feature extraction and fusion are tried in the experiment, and the experimental results are analyzed. It proves that the deep learning and recognition of dance movement can effectively explore the main movement characteristics of hip hop dance.