The Gender Effects of Heart Rate Variability Response during Short-Term Exercise using Stair Stepper from Statistical Analysis

Rosli, Nor Aziyatul Izni Mohd; Rahman, Mohd Azizi Abdul; Malarvili, M. B.; Komeda, Takashi; Mazlan, Saiful Amri; Zamzuri, Hairi

doi:10.11591/ijeecs.v2.i2.pp359-366

Cited by 3 publications

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“…This gender-dependent variation has been well recognized to be due to smaller heart size, lower hemoglobin levels, and less muscle mass in women (Belza and Warms, 2004). Furthermore, the difference in test duration may be related to intensity of LF component observed in males and females, as males tend to express sympathetic predominance, while females tend to manifest vagal predominance parasympathetic (Perini et al, 2000;Rosli et al, 2016). Heart rate variability is commonly used as a noninvasive tool to monitor cardiac autonomic activity, and HRV responses to an exercise test may provide useful information about the autonomic reaction to the physical and psychological stress (Michael et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Immersive Virtual Reality Influences Physiologic Responses to Submaximal Exercise: A Randomized, Crossover Trial

et al. 2021

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Objectives: This cross-sectional, randomly assigned study aimed to assess the influence of immersive virtual reality (VR) on exercise tolerance expressed as the duration of a submaximal exercise test (ET) on a cycle ergometer.Methods: The study enrolled 70 healthy volunteers aged 22–25years. Each participant performed an ET with and without VR. Time- and frequency-domain heart rate variability (HRV) parameters were analyzed for the first 3min (T1), the last 3min (T2), and the time at which the shorter of the two tests terminated (Tiso). In the time domain, a SD of R–R intervals (SDNN) and a root mean square of successive R–R interval differences (RMSSD) in milliseconds were computed. The following spectral components were considered: low frequency (LF), high frequency (HF), total power (TP), and LF/HF ratio. The study was registered in ClinicalTrials.gov (NCT04197024).Results: Compared to standard ET, tests in immersive VR lasted significantly longer (694 vs. 591s, p<0.00001) and were associated with lower HR response across the range of corresponding exercise levels, averaging 5–8 beats/min. In the multiple regression analysis, the ET duration was positively determined by male sex, immersion in VR, and negatively determined by HRT1 and RMSSDT1.Conclusion: Exercising in VR is associated with lower HR which allowed subjects to exercise for a longer time before reaching target heart rate (HR). In addition, the increase in exercise duration was found to be related to an adjustment in autonomic nervous activity at a given work rate favoring parasympathetic predominance.

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Section: Discussionmentioning

confidence: 99%

Immersive Virtual Reality Influences Physiologic Responses to Submaximal Exercise: A Randomized, Crossover Trial

et al. 2021

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The Fusion of HRV and EMG Signals for Automatic Gender Recognition during Stepping Exercise

et al. 2017

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In this paper, a new gender recognition approach in accordance with the fusion of features extracted from electromyogram (EMG) and heart rate variability (HRV) during stepping activity using a stair stepper device is proposed. The fusion of EMG and HRV is investigated based on feature fusion approach. The feature fusion is carried out by chaining the feature vector extracted from the EMG and HRV signals.A proposed approach comprises of a sequence of processing steps which are preprocessing, feature extraction, feature selection and the feature fusion. The results demonstrated that the fusion approach had enhanced the performance of gender recognition compared to solely on EMG or HRV for the gender recognition.

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Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

et al. 2017

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Gender recognition is trivial for a physiotherapist, but it is considered a challenge for computers. The electromyography (EMG) and heart rate variability (HRV) were utilized in this work for gender recognition during exercise using a stepper. The relevant features were extracted and selected. The selected features were then fused to automatically predict gender recognition. However, the feature selection for gender classification became a challenge to ensure better accuracy. Thus, in this paper, a feature selection approach based on both the performance and the diversity between the two features from the rank-score characteristic (RSC) function in a combinatorial fusion approach (CFA) (Hsu et al.) was employed. Then, the features from the selected feature sets were fused using a CFA. The results were then compared with other fusion techniques such as naive bayes (NB), decision tree (J48), k-nearest neighbor (KNN) and support vector machine (SVM). Besides, the results were also compared with previous researches in gender recognition. The experimental results showed that the CFA was efficient and effective for feature selection. The fusion method was also able to improve the accuracy of the gender recognition rate. The CFA provides much better gender classification results which is 94.51% compared to Barani's work (90.34%), Nazarloo's work (92.50%), and other classifiers.

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The Gender Effects of Heart Rate Variability Response during Short-Term Exercise using Stair Stepper from Statistical Analysis

Abstract: This study is aimed to explore the heart rate variability (HRV)

Cited by 3 publications

References 25 publications

Immersive Virtual Reality Influences Physiologic Responses to Submaximal Exercise: A Randomized, Crossover Trial

Immersive Virtual Reality Influences Physiologic Responses to Submaximal Exercise: A Randomized, Crossover Trial

The Fusion of HRV and EMG Signals for Automatic Gender Recognition during Stepping Exercise

Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

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