Biomechanics of Rotational Movement in Off-Ice Figure Skating Jumps: Applications to Training

Abstract

Introduction The aim of this study was to characterize kinematic parameters of the rotational body movement during the office figure skating multi-revolution jumping exercise and to propose applications to training. Material and Methods Six elite Polish female junior singles figure skaters were examined performing single and their respective maximum total rotation vertical jumps in off-ice conditions. Measurements of the ground reaction force were performed with a KISTLER (500 Hz) piezoelectric sensor platform with BioWare Software, synchronized with the cinematographic analysis using the Vicon T-Series motion capture system. Results The analysis revealed positive linear relations between selected jump parameters of the pre-take-off phase and after the landing. Two moments of countering torque were also noticed: the first in the landing phase, and the second before reaching the stabilized position. Conclusions Applications of the findings for off-ice training were discussed.

Jumping Action Recognition for Figure Skating Video in IoT Using Improved Deep Reinforcement Learning

“…When the number of experiments reaches 60, the true positive rate of different algorithms reaches the maximum value. The true positive rate of algorithms in the paper is 0.98, which is 0.23, 0.26, 0.24, 0.32, 0.29 higher than the algorithms in BRMFSJ [10], POSL [1], AHDM [16], ARMHJ [22] and HARM [13], respectively, It shows that compared with the experimental comparison algorithm, the true positive rate of the algorithm in this paper is higher, which proves that the recognition result of this algorithm is more accurate.…”

“…According to the data in Table 3, we can see that with the increase of the number of experiments, the intersection and merging ratio of different algorithms shows a fluctuating trend. The maximum IoU of the algorithm in the paper is 0.97, which is 0.26, 0.27, 0.24, 0.29, 0.3 higher than that of the algorithms in BRMFSJ [10], POSL [1], AHDM [16], ARMHJ [22] and HARM [13], respectively. This shows that the IoU of the algorithm in the paper is higher than that of the algorithm in experiment comparison, it shows that the action recognition result of this algorithm is closer to the reality.…”

“…After the implementation of the confidence fusion scheme, the final jumping action recognition result from figure skating videos is obtained. [10], POSL [1], AHDM [16], ARMHJ [22] and HARM [13] are used for jumping action recognition, and then the true positive rate is calculated according to the action recognition results.…”

“…The features are input to improve the deep reinforcement learning network model, and the jump action recognition results are obtained. At the same time, the algorithms in BRMFSJ [10], POSL [1], AHDM [16], ARMHJ [22] and HARM [13] are applied to perform action recognition on UCF 101 dataset. According to the recognition accuracy and recall of different recognition algorithms, the PR curve image is drawn, and then the area under the curve is calculated by 11 point interpolation method to obtain the AP value.…”

“…Thus, it is difficult to accurately obtain the jumping action recognition results from the figure skating video by using the traditional action recognition algorithm [8]. Mazurkiewicz [10] analyzed the data of single person figure skater's single person and maximum total rotation vertical jump under the condition of de-icing. The piezoelectric sensor platform and Bio-Ware software were used to calculate the ground reaction force.…”