Training prediction and athlete heart rate measurement based on multi-channel PPG signal and SVM algorithm

Tang, Lei; Cai, Zhu; Luo, Hui

doi:10.3233/jifs-189571

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…(Table 2) From Table 2, it can be seen that the maximum strength index of both groups has been improved to a certain extent. 6 The bench press, power clean, clean, and jerk index values of the athletes in the experimental group were significantly different from those before the experiment by T-test (P<0.01). The index value of the half-squat was significantly different from before the experiment (P<0.05).…”

Section: Resultsmentioning

confidence: 76%

Impacts of Aerobic Exercise on the Physical Fitness of Shot Put Athletes

Xiao-juan

Song

Liu

2023

Rev Bras Med Esporte

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Introduction: Shot put is a competitive sport that combines speed, strength, and explosiveness. Training the skill with aerobic exercise has attracted the attention of many coaches and scholars on competitive sports training. Shot put is a fast-powered activity, and its shooting speed is the determining factor for sports performance. It is believed that aerobic exercise can improve the ability of its practitioners. Objective: To analyze the impacts of aerobic exercise on the physical fitness of shot put athletes. Methods: 18 shot put athletes were randomly selected as volunteers for research that consisted of a random division between experimental and control groups. The experimental group received a protocol involving aerobic training, while the control group received regular training before, during, and after the experiment. The maximal strength and explosive power of the two groups of athletes were tested before and after the intervention, and mathematical-statistical methods were used to process the collected data. Results: The maximum strength index of both groups was improved; however, the improvement in the experimental group was statistically relevant(P<0.05). The values of the pressure index, power, and balance of the athletes from the experimental group were statistically altered to those before the experiment (P<0.01). Conclusion: The implementation of aerobic exercise in the routine of shot put athletes can effectively improve the performance in the sport and the special abilities of its practitioners. The training method analyzed was also shown to improve specific performance in the shot put athletes. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

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Section: Resultsmentioning

confidence: 76%

Impacts of Aerobic Exercise on the Physical Fitness of Shot Put Athletes

Xiao-juan

Song

Liu

2023

Rev Bras Med Esporte

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“…Consequently, it reduces the interference of external factors with the help of the de-noising algorithm of multi-channel spectral matrix decomposition. The results of controlled experiments verified the validity of the model and the accuracy of the measurement [ 12 ]. Wang and Gao designed a wearable sensor device to collect real-time data from athletes based on the Internet of Things system.…”

Section: Related Workmentioning

confidence: 76%

Optimization of track and field training methods based on SSA-BP and its effect on athletes' explosive power

Jing,

Wang,

Suo

2024

Heliyon

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“…Manas et al [ 18 ] developed an intelligent, noninvasive wearable physiological parameter monitoring device that uses several different sensors to monitor human body temperature and heart rate and uses wireless networks to track human health status. Lei et al [ 19 ] used support vector machines and multichannel PCG signals to build a model for measuring athletes' heart rate detection. Sujadevi et al used a 3-layer CNN to detect PCG anomalies, and the resulting model achieved 80% accuracy.…”

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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Training prediction and athlete heart rate measurement based on multi-channel PPG signal and SVM algorithm

Cited by 10 publications

References 14 publications

Impacts of Aerobic Exercise on the Physical Fitness of Shot Put Athletes

Impacts of Aerobic Exercise on the Physical Fitness of Shot Put Athletes

Optimization of track and field training methods based on SSA-BP and its effect on athletes' explosive power

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

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