Predicting physiological parameters in fatiguing bicycling exercises using muscle activation timing

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.

Heart Rate Variability-Based Subjective Physical Fatigue Assessment

Accurate assessment of physical fatigue is crucial to preventing physical injury caused by excessive exercise, overtraining during daily exercise and professional sports training. However, as a subjective feeling of an individual, physical fatigue is difficult for others to objectively evaluate. Heart rate variability (HRV), which is derived from electrocardiograms (ECG) and controlled by the autonomic nervous system, has been demonstrated to be a promising indicator for physical fatigue estimation. In this paper, we propose a novel method for the automatic and objective classification of physical fatigue based on HRV. First, a total of 24 HRV features were calculated. Then, a feature selection method was proposed to remove useless features that have a low correlation with physical fatigue and redundant features that have a high correlation with the selected features. After feature selection, the best 11 features were selected and were finally used for physical fatigue classifying. Four machine learning algorithms were trained to classify fatigue using the selected features. The experimental results indicate that the model trained using the selected 11 features could classify physical fatigue with high accuracy. More importantly, these selected features could provide important information regarding the identification of physical fatigue.

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 Novel Method for Classification of Running Fatigue Using Change-Point Segmentation

Blood lactate accumulation is a crucial fatigue indicator during sports training. Previous studies have predicted cycling fatigue using surface-electromyography (sEMG) to non-invasively estimate lactate concentration in blood. This study used sEMG to predict muscle fatigue while running and proposes a novel method for the automatic classification of running fatigue based on sEMG. Data were acquired from 12 runners during an incremental treadmill running-test using sEMG sensors placed on the vastus-lateralis, vastus-medialis, biceps-femoris, semitendinosus, and gastrocnemius muscles of the right and left legs. Blood lactate samples of each runner were collected every two minutes during the test. A change-point segmentation algorithm labeled each sample with a class of fatigue level as (1) aerobic, (2) anaerobic, or (3) recovery. Three separate random forest models were trained to classify fatigue using 36 frequency, 51 time-domain, and 36 time-event sEMG features. The models were optimized using a forward sequential feature elimination algorithm. Results showed that the random forest trained using distributive power frequency of the sEMG signal of the vastus-lateralis muscle alone could classify fatigue with high accuracy. Importantly for this feature, group-mean ranks were significantly different (p < 0.01) between fatigue classes. Findings support using this model for monitoring fatigue levels during running.