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.

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