Abstract
In this study, we designed and developed an intelligent exercise guidance system based on smart clothing. The system comprised smart clothing for electrocardiogram (ECG) signal acquisition and heart rate (HR) monitoring, an exercise control application program, and a cloud server. Music beats were used to guide the exercise routine. The use of an empirical mode decomposition (EMD)-based ECG signal denoising algorithm and a quadratic polynomial regression model (QPRM) of HR and running cadence (running steps per minute guided by music beats) were proposed for the system. Five types of experiments were conducted: Experiments I and II, R-peak detection; Experiment III, preset QPRMs; Experiment IV, degree of completion of exercises; and Experiment V, comparison of preset and trained QPRMs. The average accuracy and sensitivity of the EMD-based R-peak detection method were respectively 99.8% and 94.87% for ECG data from the MIT-BIH Arrhythmia Database and 96.46% and 98.75% for ECG data collected from university students during the walking exercise. The coefficient of determination and the mean absolute percentage error (MAPE) of the QPRMs were respectively 97.21% and 3.12% for increasing HR and 98.09% and 2.06% for decreasing HR. The average degrees of completion for warmup, training, and cooldown exercise stages were 97.05%, 91.91%, and 98.32%, respectively. The MAPEs of the preset and trained QPRMs were respectively 6.37% and 3.84% for increasing HR and 5.25% and 3.57% for decreasing HR. The experimental results demonstrated the effectiveness of the proposed system in exercise guidance.
Highlights
Cardiovascular disease (CVD) is the leading cause of death among patients with noncommunicable diseases [1]
Experiment I aimed to evaluate the performance of the proposed empirical mode decomposition (EMD)-based R-peak detection method by using full records from the MIT-BIH Arrhythmia Database [32, 33]
Experiment II aimed to evaluate the accuracy of R-peak detection for various running speeds with and without EMD-based ECG denoising
Summary
Cardiovascular disease (CVD) is the leading cause of death among patients with noncommunicable diseases [1]. The risk factors for CVD include a lack of physical exercise, poor nutrition, family history of genetic diseases, smoking, hypertension, and diabetes [2]. Among these risk factors, lack of physical exercise, which can increase the risk of Exercise guidance is a crucial component of an effective exercise routine. Astaras et al [11] and Kokonozi et al [12] presented prototypes of a wearable dry electrode device designed for exercise guidance and real-time monitoring of patients with CVD. Balsalobre-Fernandez et al [13] introduced a wearable band for measuring movement velocity
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