Study on the Recognition of Exercise Intensity and Fatigue on Runners Based on Subjective and Objective Information

PCG-based exercise fatigue detection method using multi-scale feature fusion model

Accurate detection of exercise fatigue based on physiological signals is vital for reasonable physical activity. Existing studies utilize widely Electrocardiogram (ECG) signals to achieve exercise monitoring. Nevertheless, ECG signals may be corrupted because of sweat or loose connection. As a non-invasive technique, Phonocardiogram (PCG) signals have a strong ability to reflect the Cardiovascular information, which is closely related to physical state. Therefore, a novel PCG-based detection method is proposed, where the feature fusion of deep learning features and linear features is the key technology of improving fatigue detection performance. Specifically, Short-Time Fourier Transform (STFT) is employed to convert 1D PCG signals into 2D images, and images are fed into the pre-trained convolutional neural network (VGG-16) for learning. Then, the fusion features are constructed by concatenating the VGG-16 output features and PCG linear features. Finally, the concatenated features are sent to Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA) to distinguish six levels of exercise fatigue. The experimental results of two datasets show that the best performance of the proposed method achieves 91.47% and 99.00% accuracy, 91.49% and 99.09% F1-score, 90.99% and 99.07% sensitivity, which has comparable performance to an ECG-based system which is as gold standard (94.32% accuracy, 94.33% F1-score, 94.52% sensitivity).

Prediction of instantaneous perceived effort during outdoor running using accelerometry and machine learning

The rate of perceived effort (RPE) is a subjective scale widely used for defining training loads. However, the subjective nature of the metric might lead to an inaccurate representation of the imposed metabolic/mechanical exercise demands. Therefore, this study aimed to predict the rate of perceived exertions during running using biomechanical parameters extracted from a commercially available running smartwatch. Forty-three recreational runners performed a simulated 5-km race on a track, providing their RPE from a Borg scale (6-20) every 400 m. Running distance, heart rate, foot contact time, cadence, stride length, and vertical oscillation were extracted from a running smartwatch (Garmin 735XT). Machine learning regression models were trained to predict the RPE at every 5 s of the 5-km race using subject-independent (leave-one-out), as well as a subject-dependent regression method. The subject-dependent method was tested using 5%, 10%, or 20% of the runner's data in the training set while using the remaining data for testing. The average root-mean-square error (RMSE) in predicting the RPE using the subject-independent method was 1.8 ± 0.8 RPE points (range 0.6-4.1; relative RMSE ~ 12 ± 6%) across the entire 5-km race. However, the error from subject-dependent models was reduced to 1.00 ± 0.31, 0.66 ± 0.20 and 0.45 ± 0.13 RPE points when using 5%, 10%, and 20% of data for training, respectively (average relative RMSE < 7%). All types of predictions underestimated the maximal RPE in ~ 1 RPE point. These results suggest that the data accessible from commercial smartwatches can be used to predict perceived exertion, opening new venues to improve training workload monitoring.

Boys-men mean-power-frequency differences in progressive exercise to exhaustion, confounded by variability and adiposity

BACKGROUND

Only scant research has compared children's mean power frequency (MPF) to adults', with a clear overview still lacking. A significant obstacle has been MPF's high variability, which this study aimed to overcome by elucidating the MPF characteristics distinguishing boys from men in progressive exhaustive exercise.

METHODS

Electromyographic (EMG) data of 20 men (23.5 ± 2.5yrs) and 17 boys (10.2 ± 1.0 yrs), who performed progressively exhausting, intermittent isometric knee extensions, were subjected to secondary MPF analysis. Participants' vastus lateralis MPF data series were transformed to third-order polynomial regressions and expressed as percentages of the peak polynomial MPF values (%MPFpeak). The resulting curves were compared at 5-% time-to-exhaustion (TTE) intervals, using repeated-measures ANOVA. Raw MPFpeak values were adiposity corrected to 0% fat and used to convert the %MPFpeak data back to absolute MPF values (Hz) for estimating muscle-level MPF.

RESULTS

No overall interaction or group effects could be shown between the %MPFpeak plots, but pairwise comparisons revealed significantly higher men's values at 50-70%TTE and lower at 100%TTE, i.e. boys' shallower MPF rise and decline. The adiposity-corrected boys' and men's composite MPF values peaked at 125.7 ± 2.5 and 166.0 ± 2.4 Hz, respectively (110.7 ± 1.7 and 122.5 ± 2.1 Hz, uncorrected), with a significant group effect (p < 0.05) and pairwise differences at all %TTE points.

CONCLUSIONS

The boys were lower than the men in both the observed and, more so, in the adiposity-corrected MPF values that presumably estimate muscle-level MPF. The boys' shallower MPF rise and decline conform to children's claimed type-II motor-unit activation and/or compositional deficits and their related known advantage in muscular endurance.

Effect of vitamin energy drinks on relieving exercise-induced fatigue in muscle group by ultrasonic bioimaging data analysis

OBJECTIVE

This work was aimed to analyze the effect of vitamin energy drink on muscle fatigue by surface electromyography (SEMG) and ultrasonic bioimaging (USBI).

METHODS

20 healthy men were selected to do increasing load fatigue test. Surface electromyographic signals and ultrasonic biological images were collected based on wavelet threshold function with improved thresholds. Time domain and frequency domain characteristic integrated electromyography (IEMG), root mean square amplitude (RMS), average power frequency (MPF), and surface and deep muscle morphological changes were analyzed. Hemoglobin concentration (HB), red blood cell number (RBC), mean volume of red blood cell (MCV), blood lactic acid (BLA), malondialdehyde (MDA), and phosphocreatine kinase (CK) were measured.

RESULTS

1) the Accuracy (94.10%), Sensitivity (94.43%), Specificity (93.75%), and Precision (94.07%) of the long and short-term memory (LSTM) specificity for muscle fatigue recognition were higher than those of other models. 2) Compared with the control group, the levels of BLA, MDA, and CK in the experimental group were decreased and HB levels were increased after exercise (P < 0.05). 3) IEMG and RMS of the experimental group were higher than those of the control group, and increased with time (P < 0.05). 4) The mean amplitude of the response signal decreased with time. Compared with the control group, the surface muscle thickness, deep muscle thickness, total muscle thickness, contrast, and homogeneity (HOM) decreased in the experimental group; while the angular second moment (ASM) and contrast increased, showing great differences (P < 0.05).

CONCLUSION

Surface electromyographic signal and ultrasonic biological image can be used as auxiliary monitoring techniques for muscle fatigue during exercise. Drinking vitamin energy drinks before exercise can relieve physical fatigue to a certain extent and promote the maintenance of muscle microstructure.

Effects of the Interactive Features of Virtual Partner on Individual Exercise Level and Exercise Perception

BACKGROUND

We designed an exercise system in which the user is accompanied by a virtual partner (VP) and tested bodyweight squat performance with different interactive VP features to explore the comprehensive impact of these VP features on the individual's exercise level (EL) and exercise perception.

METHODS

This experiment used three interactive features of VP, including body movement (BM), eye gaze (EG), and sports performance (SP), as independent variables, and the exercise level (EL), subjective exercise enjoyment, attitude toward the team formed with the VP, and local muscle fatigue degree of the exerciser as observational indicators. We designed a 2 (with or without VP's BM) × 2 (with or without VP's EG) × 2 (with or without VP's SP) within-participants factorial experiment. A total of 40 college students were invited to complete 320 groups of experiments.

RESULTS

(1) Regarding EL, the main effects of BM and SP were significant (p < 0.001). The pairwise interaction effects of the three independent variables on EL were all significant (p < 0.05). (2) Regarding exercise perception, the main effects of BM (p < 0.001) and EG (p < 0.001) on subjective exercise enjoyment were significant. The main effect of BM on the attitude toward the sports team formed with the VP was significant (p < 0.001). The interaction effect of BM and SP on the attitude toward the sports team formed with the VP was significant (p < 0.001). (3) Regarding the degree of local muscle fatigue, the main effects of BM, EG, and SP and their interaction effects were not significant (p > 0.05).

CONCLUSION

BM and EG from the VP elevate EL and exercise perception during squat exercises, while the VP with SP inhibited the EL and harmed exercise perception. The conclusions of this study can provide references to guide the interactive design of VP-accompanied exercise systems.

Exercise fatigue diagnosis method based on short-time Fourier transform and convolutional neural network

Reasonable exercise is beneficial to human health. However, it is difficult for ordinary athletes to judge whether they are already in a state of fatigue that is not suitable for exercise. In this case, it is easy to cause physical damage or even life-threatening. Therefore, to health sports, protecting the human body in sports not be injured by unreasonable sports, this study proposes an exercise fatigue diagnosis method based on short-time Fourier transform (STFT) and convolutional neural network (CNN). The method analyzes and diagnoses the real-time electrocardiogram, and obtains whether the current exerciser has exercise fatigue according to the electrocardiogram. The algorithm first performs short-time Fourier transform on the electrocardiogram (ECG) signal to obtain the time spectrum of the signal, which is divided into training set and validation set. The training set is then fed into the convolutional neural network for learning, and the network parameters are adjusted. Finally, the trained convolutional neural network model is applied to the test set, and the recognition result of fatigue level is output. The validity and feasibility of the method are verified by the ECG experiment of exercise fatigue degree. The experimental recognition accuracy rate can reach 97.70%, which proves that the constructed sports fatigue diagnosis model has high diagnostic accuracy and is feasible for practical application.

Application of Surface Electromyography in Exercise Fatigue: A Review

Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.

The Effect of Music Tempo on Fatigue Perception at Different Exercise Intensities

Background: This study aimed to clarify the effect of music tempo on runners’ perception of fatigue at different exercise intensities and while listening to music of different tempos through running experiments. Methods: This study used a within-subject two-factor experimental design with music tempo (fast music, slow music, no music) and exercise intensity (high intensity, low intensity) as independent variables and the time to fatigue perception (TFP), the difference in heart rate (HR) and the difference in the median frequency (MF) of surface electromyography (sEMG) signals as observation indexes. Eighteen participants completed a total of 108 sets of running experiments. Results: (1) The main effect of music tempo on the TFP was significant (p < 0.001). (2) The main effect of exercise intensity on the TFP was significant (p < 0.001), and the main effect on the difference in HR was significant (p < 0.001). (3) The interaction effect of music tempo and exercise intensity on the TFP was significant (p < 0.05). Conclusions: Exercisers’ subjective perception of fatigue was affected by music tempo and the interaction between music tempo and exercise intensity, and exercisers’ objective fatigue perception was influenced mostly by exercise intensity. The findings of this study provide guidance for runners’ choice of music at different intensities of exercise. Whether it is low-intensity exercise or high-intensity exercise, listening to fast music while exercising can help runners perform better mentally and physically during their runs.

A Study on the Relationship between RPE and sEMG in Dynamic Contraction Based on the GPR Method

The rating of perceived exertion (RPE) and surface electromyography (sEMG) describe exercise intensity subjectively and objectively, while there has been a lack of research on the relationship between them during dynamic contractions to predict exercise intensity, comprehensively. The purpose of this study was to establish a model of the relationship between sEMG and RPE during dynamic exercises. Therefore, 20 healthy male subjects were organized to perform an incremental load test on a cycle ergometer, and the subjects’ RPEs (Borg Scale 6–20) were collected every minute. Additionally, the sEMGs of the subjects’ eight lower limb muscles were collected. The sEMG features based on time domain, frequency domain and time–frequency domain methods were extracted, and the relationship model was established using Gaussian process regression (GPR). The results show that the sEMG and RPE of the selected lower limb muscles are significantly correlated (p < 0.05) but that they have different monotonic correlation degrees. The model that was established with all three domain features displayed optimal performance and when the RPE was 13, the prediction error was the smallest. The study is significant for lower limb muscle training strategy and quantification of training intensity from both subjective and objective aspects, and lays a foundation for sEMG further applications in rehabilitation medicine and sports training.

Lactobacillus fermentum CQPC08 Attenuates Exercise-Induced Fatigue in Mice Through Its Antioxidant Effects and Effective Intervention of Galactooligosaccharide

Aim

The purpose of this study is to study the antioxidant effect of Lactobacillus fermentum CQPC08 (CQPC08) on exercise-induced fatigue, and the beneficial intervention of GOS on CQPC08.

Methods

We use the treadmill to establish a fatigue model caused by exercise, and perform drug treatment after exercise. We tested the exhaustive exercise time of mice; investigated the changes of mice body weight, liver index, histopathology, serum biochemical indicators and mRNA expression levels of oxidative and inflammation-related genes; and assessed the potential fatigue inhibitory effect of CQPC08, and the anti-oxidation effect of the combination of GOS and CQPC08.

Results

The results suggest that CQPC08 and combination with GOS reduces fatigue-induced oxidative damage of the liver, and it decreases blood urea nitrogen (BUN), lactic acid (LA), glutamic-oxaloacetic transaminase (GOT), glutamic-pyruvic transaminase (GPT), malonaldehyde (MDA), inducible nitric oxide synthase (iNOS), tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6 in serum. Higher levels of serum catalase (CAT), glutathione peroxidase (GSH-Px) and superoxide dismutase (SOD) were found. Treatment with the CQPC08 and combination with GOS correlates with lower relative mRNA expression levels of neuronal NOS (nNOS), iNOS, and TNF-α, and with higher mRNA expression levels of catalase and copper/zinc (Cu/Zn) and manganese (Mn) SOD enzymes in the liver and muscles.

Conclusion

These results suggest that CQPC08 can resolve exercise-induced fatigue by improving antioxidant ability in mice, and the combination of GOS and CQPC08 enhances this ability of CQPC08.

Heart Rate Changes Before, During, and After Treadmill Walking Exercise in Normal Dogs

In dogs, changes in heart rate (HR) can reflect conditioning, fear, anticipation, and pain; however, these are not routinely assessed in veterinary rehabilitation patients. Knowing the expected HR changes during rehabilitation exercises can guide protocols and can optimize post-operative therapy. The primary objectives of the study were to assess HR in dogs undergoing treadmill exercise (TE) during the walk and to compare the three collection techniques of HR, namely, auscultation, a HR monitor (HR MONITOR), and a Holter monitor (HOLTER). We hypothesized that the HR would increase by 20% during TE, that HR taken after TE would not be the same as the HR during TE, and that all methods of measurement would have good agreement. HR was recorded in all methods simultaneously, in eight adult healthy large breed dogs during rest (REST), immediately before TE (PRE), during TE (WALK), and 15 and 60 s after TE (POST-15, POST-60). Statistical analyses included Spearman and Pearson correlations, Bland-Altman analyses, and a repeated measures ANOVA with Sidak's post-hoc test (significant at value of p < 0.05). Increased HR was reflected in TE during WALK, and elevations in HR during WALK were not reflected in POST timepoints. Auscultation was also not possible during WALK. Significant moderate-to-strong correlations existed among all monitoring options at each of the timepoints (rho range = 0.5-0.9, p < 0.05). There were no correlations between peak HR and age or weight. The main limitation of this study is that only healthy and large breed dogs were used. Both monitors captured the increase in HR during exercise and could guide TE regimens to minimize patient risk of injury and to maximize training effectiveness.