Individual system of self-monitoring of the daily motor activity of athletes

Nagovitsyn, Roman Sergeevich; Osipov, Aleksander; Loginov, Denis Vasilevich; Prikhodov, Dmitry Sergeevich; Vapaeva, Anna

doi:10.14198/jhse.2020.15.proc2.01

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Future scientific research must attempt to utilize objective measures (e.g., personal activity trackers, fitness apps, home electronic scales) to assess PA level and behaviour even if in-person data collection is not possible for the time being [17]. The implementation of daily monitoring of overall PA and sport/fitness PA of young individuals must stimulate students to increase their daily, weekly and monthly PA level [38].…”

Section: Discussionmentioning

confidence: 99%

The impact of the universities closure on physical activity and academic performance in physical education in university students during the COVID-19 pandemic

Osipov

Ratmanskaya

Zemba

et al. 2021

PES

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Background and Study Aim. Physical activity (PA) is regarded as a critical component of a healthy lifestyle and disease prevention. Different constraints placed upon social interaction and public spaces due to COVID-19 could decrease PA and academic performance in physical education (PE) in student population. The purpose of the research was to assess overall and sport/fitness PA level and academic performance in PE in a sample of university students pre- to post-cancellation of face-to-face PE classes due to the COVID-19 pandemic. Material and Methods. Participants (n=209) – university students (19-20 years old). The overall period of the research is September-December 2020. All participants (male–n=106 and female–n=103) were divided into approximately equal groups: humanities students (male–MG-1, female–FG-1), medicine students (male–MG-2, female–FG-2), technical students (male–MG-3, female–FG-3). The main research tools: modified student self-reported questionnaire (on the base of IPAQ), academic grading (scores for self-completed PE tasks). In surveys, all participants self-rated overall and sport/fitness PA level pre- to post-cancellation of face-to-face PE classes. The participants had to complete 32 special PE tasks, during the research period. PE teachers rated participants by a special grading scale. Results. For the male and female students’ groups there were no significant differences of overall PA, sport/fitness PA level and academic performance in PE pre-cancellation of face-to-face PE classes. All participants significantly (p≤0.05) decreased overall PA, sport/fitness PA level and academic performance in PE post-cancellation. There was a significant (p≤0.05) large amount of overall PA level in MG-2 students and sport/fitness PA level in MG-1 students’ post-cancellation of face-to-face PE classes. For the male students there were no significant differences of grading in PE pre- to post-cancellation of face-to-face PE classes between participants groups. There was a significant (p≤0.05) large amount of overall PA level post-cancellation of face-to-face PE classes in FG-2 students. There was a significantly (p≤0.05) lower amount of sport/fitness PA level post-cancellation of face-to-face PE classes in FG-3 students. There was a significant drop in mean grading scores in PE post-cancellation of face-to-face PE classes in students. FG-2 participants had significantly (p≤0.05) higher grading scores in PE post-cancellation of face-to-face PE classes. Conclusions. The universities closure and cancellation of face-to-face PE classes due to the COVID-19 pandemic decreased overall PA, sport/fitness PA level and academic performance in PE in all participants. It is important to note that the proportional decrease in overall PA for the male students was actually large, then decrease in overall PA for the female students was less. The findings highlight the need for active interventions of PA promotion targeted at the student population in the context of self-isolation measures during the COVID-19 pandemic.

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Section: Discussionmentioning

confidence: 99%

The impact of the universities closure on physical activity and academic performance in physical education in university students during the COVID-19 pandemic

Osipov

Ratmanskaya

Zemba

et al. 2021

PES

View full text Add to dashboard Cite

show abstract

“…Athletes need to continuously improve their competitive ability, which requires higher-intensity physical training to tap their potential and achieve better results. Only by arranging training plans reasonably can physical injuries caused by excessive training intensity be prevented [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

The Recognition Method of Athlete Exercise Intensity Based on ECG and PCG

Wang

Zhu

2022

Computational and Mathematical Methods in Medicine

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Athletes usually arrange their training plans and determine their training intensity according to the coach’s experience and simple physical indicators such as heart rate during exercise. However, the accuracy of this method is poor, and the training plan and exercise intensity arranged according to this method can easily cause physical damage, or the training cannot meet the actual needs. Therefore, in order to realize the reasonable arrangement and monitoring of athletes’ training, a method of human exercise intensity recognition based on ECG (electrocardiogram) and PCG (Phonocardiogram) is proposed. First, the ECG and PCG signals are fused into a two-dimensional image, and the dataset is marked and divided according to the different motion intensities. Then, the training set is trained with a CNN (convolutional neural network) to obtain the prediction model of the neural network. Finally, the neural network model is used to identify the ECG and PCG signals to judge the exercise intensity of the athlete, so as to adjust the training plan according to the exercise intensity. The recognition accuracy of the model on the dataset can reach 95.68%. Compared with the use of heart rate to detect the physical state during exercise, ECG records the total potential changes in the process of depolarization and repolarization of the heart, and PCG records the waveform of the beating sound of the heart, which contains richer feature information. Combined with the CNN method, the athlete’s exercise intensity prediction model constructed by extracting the features of the athlete’s ECG and PCG signals realizes the real-time monitoring of the athlete’s exercise intensity and has high accuracy and generalization ability.

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Individual system of self-monitoring of the daily motor activity of athletes

Cited by 2 publications

References 23 publications

The impact of the universities closure on physical activity and academic performance in physical education in university students during the COVID-19 pandemic

The impact of the universities closure on physical activity and academic performance in physical education in university students during the COVID-19 pandemic

The Recognition Method of Athlete Exercise Intensity Based on ECG and PCG

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