Machine Learning Approaches for Exercise Exertion Level Classification Using Data from Wearable Physiologic Monitors.

This research aimed to develop a model for real-time prediction of aerobic exercise exertion levels. ECG signals were registered during 16-minute cycling exercises. Perceived ratings of exertion (RPE) were collected each minute from the study participants. Based on the reported RPE, each consecutive minute of the exercise was assigned to the "high exertion" or "low exertion" class. The characteristics of heart rate variability (HRV) in time and frequency domains were used as predictive features. The top ten ranked predictive features were selected using the minimum redundancy maximum relevance (mRMR) algorithm. The support vector machine demonstrated the highest accuracy with an F1 score of 82%.

Exercise Exertion Levels Prediction Based on Real-Time Wearable Physiological Signal Monitoring.

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%.

Automated Classification of Exercise Exertion Levels Based on Real-Time Wearable Physiological Signal Monitoring.

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.

Development and Evaluation of Wireless Interfaces to Monitor and Control Cycling Exercise During Home Telerehabilitation.

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.

Exercise Exertion Level Prediction Using Data from Wearable Physiologic Monitors.

This study aims to develop machine learning (ML) algorithms to predict exercise exertion levels using physiological parameters collected from wearable devices. Real-time ECG, oxygen saturation, pulse rate, and revolutions per minute (RPM) data were collected at three intensity levels during a 16-minute cycling exercise. Parallel to this, throughout each exercise session, the study subjects' ratings of perceived exertion (RPE) were gathered once per minute. Each 16-minute exercise session was divided into a total of eight 2-minute windows. Each exercise window was labeled as "high exertion," or "low exertion" classes based on the self-reported RPEs. For each window, the gathered ECG data were used to derive the heart rate variability (HRV) features in the temporal and frequency domains. Additionally, each window's averaged RPMs, heart rate, and oxygen saturation levels were calculated to form all the predictive features. The minimum redundancy maximum relevance algorithm was used to choose the best predictive features. Top selected features were then used to assess the accuracy of ten ML classifiers to predict the next window's exertion level. The k-nearest neighbors (KNN) model showed the highest accuracy of 85.7% and the highest F1 score of 83%. An ensemble model showed the highest area under the curve (AUC) of 0.92. The suggested method can be used to automatically track perceived exercise exertion in real-time.

Telemonitoring of Home-Based Biking Exercise: Assessment of Wireless Interfaces.

BACKGROUND: Telerehabiliation has been shown to have great potential in expanding access to rehabilitation services, enhancing patients' quality of life, and improving clinical outcomes. Stationary biking exercise can serve as an effective aerobic component of home-based physical rehabilitation programs. Remote monitoring of biking exercise provides necessary safeguards to ensure exercise adherence and safety in patients' homes. The scalability of the current remote monitoring of biking exercise solutions is impeded by the high cost that limits patient access to these services, especially among older adults with chronic health conditions. OBJECTIVE: The aim of this project was to design and test two low-cost wireless interfaces for the telemonitoring of home-based biking exercise. METHODS: We designed an interactive biking system (iBikE) that comprises a tablet PC and a low-cost bike. Two wireless interfaces to monitor the revolutions per minute (RPM) were built and tested. The first version of the iBikE system uses Bluetooth Low Energy (BLE) to send information from the iBikE to the PC tablet, and the second version uses a Wi-Fi network for communication. Both systems provide patients and their clinical teams the capability to monitor exercise progress in real time using a simple graphical representation. The bike can be used for upper or lower limb rehabilitation. We developed two tablet applications with the same graphical user interfaces between the application and the bike sensors but with different communication protocols (BLE and Wi-Fi). For testing purposes, healthy adults were asked to use an arm bike for three separate subsessions (1 minute each at a slow, medium, and fast pace) with a 1-minute resting gap. While collecting speed values from the iBikE application, we used a tachometer to continuously measure the speed of the bikes during each subsession. Collected data were later used to assess the accuracy of the measured data from the iBikE system. RESULTS: Collected RPM data in each subsession (slow, medium, and fast) from the iBikE and tachometer were further divided into 4 categories, including RPM in every 10-second bin (6 bins), RPM in every 20-second bin (3 bins), RPM in every 30-second bin (2 bins), and RPM in each 1-minute subsession (60 seconds, 1 bin). For each bin, the mean difference (iBikE and tachometer) was then calculated and averaged for all bins in each subsession. We saw a decreasing trend in the mean RPM difference from the 10-second to the 1-minute measurement. For the 10-second measurements during the slow and fast cycling, the mean discrepancy between the wireless interface and tachometer was 0.67 (SD 0.24) and 1.22 (SD 0.67) for the BLE iBike, and 0.66 (SD 0.48) and 0.87 (SD 0.91) for the Wi-Fi iBike system, respectively. For the 1-minute measurements during the slow and fast cycling, the mean discrepancy between the wireless interface and tachometer was 0.32 (SD 0.26) and 0.66 (SD 0.83) for the BLE iBike, and 0.21 (SD 0.21) and 0.47 (SD 0.52) for the Wi-Fi iBike system, respectively. CONCLUSIONS: We concluded that a low-cost wireless interface provides the necessary accuracy for the telemonitoring of home-based biking exercise.