Frequency Shifts in Muscle Activation during Static Strength Elements on the Rings before and after an Eccentric Training Intervention in Male Gymnasts

During ring performance in men’s gymnastics, static strength elements require a high level of maximal muscular strength. The aim of the study was to analyze the effect of a four-week eccentric–isokinetic training intervention in the frequency spectra of the wavelet-transformed electromyogram (EMG) during the two static strength elements, the swallow and support scale, in different time intervals during the performance. The gymnasts performed an instrumented movement analysis on the rings, once before the intervention and twice after. For both elements, the results showed a lower congruence in the correlation of the frequency spectra between the first and the last 0.5 s interval than between the first and second 0.5 s intervals, which was indicated by a shift toward the predominant frequency around the wavelet with a center frequency of 62 Hz (Wavelet W10). Furthermore, in both elements, there was a significant increase in the congruence of the frequency spectra after the intervention between the first and second 0.5 s intervals, but not between the first and last ones. In conclusion, the EMG wavelet spectra presented changes corresponding to the performance gain with the eccentric training intervention, and showed the frequency shift toward a predominant frequency due to acute muscular fatigue

Specific Eccentric–Isokinetic Cluster Training Improves Static Strength Elements on Rings for Elite Gymnasts

In gymnastics, coaches are constantly searching for efficient training methods in order to improve the athletes’ performance. Therefore, in this study we aimed to investigate the effects of a novel, four-week, gymnastic-specific, eccentric–isokinetic (0.1 m/s) cluster training on a computer-controlled training device on the improvement of two static strength elements on rings (swallow and support scale). Nine elite male gymnasts participated in this study. Outcome parameters were maximum strength and strength endurance in maintaining the static position of both elements. After four weeks of training, specific maximum strength increased significantly (swallow: +4.1%; d = 0.85; p = 0.01; support scale: +3.6%; d = 2.47; p = 0.0002) and strength endurance tended to improve (swallow: +104.8%; d = 0.60; p = 0.07; support scale: +26.8%; d = 0.27; p = 0.19). Our results demonstrate that top athletes can considerably improve ring-specific strength and strength endurance in only four weeks. We assumed that the high specificity but also the unfamiliar stimulus of slow eccentric movements with very long times under maximal muscle tension led to these improvements. We suggest to use this type of training periodically and during phases in which the technical training load is low

Specific eccentric–isokinetic cluster training improves static strength elements on rings for elite gymnasts

In gymnastics, coaches are constantly searching for efficient training methods in order to improve the athletes’ performance. Therefore, in this study we aimed to investigate the effects of a novel, four-week, gymnastic-specific, eccentric–isokinetic (0.1 m/s) cluster training on a computer-controlled training device on the improvement of two static strength elements on rings (swallow and support scale). Nine elite male gymnasts participated in this study. Outcome parameters were maximum strength and strength endurance in maintaining the static position of both elements. After four weeks of training, specific maximum strength increased significantly (swallow: +4.1%; d = 0.85; p = 0.01; support scale: +3.6%; d = 2.47; p = 0.0002) and strength endurance tended to improve (swallow: +104.8%; d = 0.60; p = 0.07; support scale: +26.8%; d = 0.27; p = 0.19). Our results demonstrate that top athletes can considerably improve ring-specific strength and strength endurance in only four weeks. We assumed that the high specificity but also the unfamiliar stimulus of slow eccentric movements with very long times under maximal muscle tension led to these improvements. We suggest to use this type of training periodically and during phases in which the technical training load is lo

Recovering Localized Adversarial Attacks

Göpfert JP, Wersing H, Hammer B. Recovering Localized Adversarial Attacks. In: Tetko IV, Kůrková V, Karpov P, Theis F, eds. Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2019: 302-311.Deep convolutional neural networks have achieved great successes over recent years, particularly in the domain of computer vision. They are fast, convenient, and – thanks to mature frameworks – relatively easy to implement and deploy. However, their reasoning is hidden inside a black box, in spite of a number of proposed approaches that try to provide human-understandable explanations for the predictions of neural networks. It is still a matter of debate which of these explainers are best suited for which situations, and how to quantitatively evaluate and compare them [1]. In this contribution, we focus on the capabilities of explainers for convolutional deep neural networks in an extreme situation: a setting in which humans and networks fundamentally disagree. Deep neural networks are susceptible to adversarial attacks that deliberately modify input samples to mislead a neural network’s classification, without affecting how a human observer interprets the input. Our goal with this contribution is to evaluate explainers by investigating whether they can identify adversarially attacked regions of an image. In particular, we quantitatively and qualitatively investigate the capability of three popular explainers of classifications – classic salience, guided backpropagation, and LIME – with respect to their ability to identify regions of attack as the explanatory regions for the (incorrect) prediction in representative examples from image classification. We find that LIME outperforms the other explainers