
 Basketball players' visual and neurological characteristics may affect their sports performance. In this paper, 20 basketball players and 20 non-athletes received a motion vision test and a neurological efficiency test. The experimental stimulus was to determine whether there was a ball on the picture. The relevant visual data were obtained by an eye tracker. The brain area activity data were obtained by functional magnetic resonance imaging (fMRI). The data were processed and analyzed. The results showed that the reaction time of group A (basketball players) was 526.78 ± 75.36 ms, and the correct rate was 94.12 ± 3.45%, both of which were better than group B (non-athletes). The fixation duration and fixation frequency of group A were 204.77 ± 40.23 ms and 1.67 ± 0.41 times, suggesting good fixation stability, and group A activated fewer brain areas than group B. The experimental results verify that basketball players have better target capture ability and higher neural efficiency while consuming fewer neural resources.

Effects of Motion Vision and Neural Efficiency On Target Capture in Basketball Players

Cascaded Tapered Optical Fiber Vocs Sensors with Surface Modified Zif-8

—Computer technology's maturity has enabled intelligent and interactive sports training. Jumping rope test in secondary school faces difficulties due to bulky testing equipment and inefficient data measurement. An ALSTM-LSTM model based on visual human posture estimation is proposed for motion system analysis. Joint pose features are fused through LSTM, and the attention mechanism assigns weights to feature sequences to achieve motion recognition, considering the data's multidimensional and hierarchical nature. The model’s precision value is 95.83. Its average accuracy is much higher than LSTM, ML-KNN, and RSN models. Additionally, the model has 95.2% accuracy in localizing jump rope stance movements with low data consumption. The model can improve the accuracy of the analysis of the jump rope sport’s posture based on the characteristics of human movement, and inspire new technical tools for teaching instruction.

http://thesai.org/Downloads/Volume14No5/Paper_96-Analysis_and_System_Construction_of_ALSTM_LSTM_Model.pdf

Analysis and System Construction of ALSTM-LSTM Model-based Sports Jumping Rope Movement

EMG signal acquisition is mostly used in medical research. However, it has not been applied in athletes’ sports state recognition and body state detection, and there are few related studies at present. In order to promote the application of EMG signal acquisition in sports, this study combined with the actual needs of athletes to construct an EMG signal acquisition system that can collect athletes’ motion status. At the same time, in order to improve the effect of EMG signal acquisition, a wavelet packet principal component analysis model is proposed. In addition, in order to ensure the recognition efficiency of athletes’ motion state, this paper uses linear discriminant analysis method as the motion recognition assistant algorithm. Finally, this paper judges the performance of this research model by setting up comparative experiments. The research shows that the wavelet packet principal component analysis model performance is significantly better than the traditional algorithm, and the recognition rate for some subtle motions is also high. In addition, this study provides a theoretical reference for the application of EMG signals in the sports industry.

Research on athlete skipping surface electromyography and energy consumption based on principal component analysis of wavelet packet

Fiber-optic sensors are an indispensable element of modern sensing technologies by virtue of their low cost, excellent electromagnetic immunity, and remote sensing capability. Optical Vernier effect is widely used to enhance sensitivity of fiber-optic sensors but requires bulky and complex cascaded interferometers. Here we propose and experimentally demonstrate an ultracompact (∼2 mm by ∼2 mm) Vernier-effect-improved sensor by only using a single microfiber-knot resonator. With the Vernier effect achieved by controlling the optical beating with the spectral ripple of a super light emitting diode (SLED), we show ∼20x sensitivity enhancement for quantitative temperature monitoring. Our sensor creates a new practical method to realize Vernier effect in fiber-optic sensors and beyond.

Yanan Yu

Papers

Research on athlete skipping surface electromyography and energy consumption based on principal component analysis of wavelet packet

Ultracompact Vernier-effect-improved sensor by a single microfiber-knot resonator.

A novel displacement demodulation method of a laser interferometer for primary vibration calibration

A study on finite amplitude standing waves in stepped acoustic resonator