Purpose This paper focuses on developing and testing three versions of interactive bike (iBikE) interfaces for remote monitoring and control of cycling exercise sessions to promote upper and lower limb rehabilitation. Methods Two versions of the system, which consisted of a portable bike and a tablet PC, were designed to communicate through either Bluetooth low energy (BLE) or Wi-Fi interfaces for real-time monitoring of exercise progress by both the users and their clinical team. The third version of the iBikE system consisted of a motorized bike and a tablet PC. It utilized conventional Bluetooth to implement remote control of the motorized bike’s speed during an exercise session as well as to provide real-time visualization of the exercise progress. We developed three customized tablet PC apps with similar user interfaces but different communication protocols for all the platforms to provide a graphical representation of exercise progress. The same microcontroller unit (MCU), ESP-32, was used in all the systems. Results Each system was tested in 1-minute exercise sessions at various speeds. To evaluate the accuracy of the measured data, in addition to reading speed values from the iBikE app, the cycling speed of the bikes was measured continuously using a tachometer. The mean differences of averaged RPMs for both data sets were calculated. The calculated values were 0.38 ± 0.03, 0.25 ± 0.27, and 6.7 ± 3.3 for the BLE system, the Wi-Fi system, and the conventional Bluetooth system, respectively. Conclusion All interfaces provided sufficient accuracy for use in telerehabilitation.

This study aimed to build machine learning (ML) algorithms for the automated classification of cycling exercise exertion levels using data from wearable devices. The best predictive features were selected using the minimum redundancy maximum relevance algorithm (mRMR). Top selected features were then used to build and assess the accuracy of five ML classifiers to predict the level of exertion. The Naïve Bayes showed the best F1 score of 79%. The proposed approach may be used for real-time monitoring of exercise exertion.

The real-time revolutions per minute (RPM) data, ECG signal, pulse rate, and oxygen saturation levels were collected during 16-minute cycling exercises. In parallel, ratings of perceived exertion (RPE) were collected each minute from the study participants. A 2-minute moving window, with one minute shift, was applied to each 16-minute exercise session to divide it into a total of fifteen 2-minute windows. Based on the self-reported RPE, each exercise window was labeled as “high exertion” or “low exertion” classes. The heart rate variability (HRV) characteristics in time and frequency domains were extracted from the collected ECG signals for each window. In addition, collected oxygen saturation levels, pulse rate, and RPMs were averaged for each window. The best predictive features were then selected using the minimum redundancy maximum relevance (mRMR) algorithm. Top selected features were then used to assess the accuracy of five ML classifiers to predict the level of exertion. The Naïve Bayes model demonstrated the best performance with an accuracy of 80% and an F1 score of 79%.