Estimation of Resistance Training RPE using Inertial Sensors and Electromyography

This is experimental HTML to improve accessibility. We invite you to report rendering errors.
Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off.
Learn more about this project and help improve conversions.

Accurate estimation of rating of perceived exertion (RPE) can enhance resistance training through personalized feedback and injury prevention. This study investigates the application of machine learning models to estimate RPE during single-arm dumbbell bicep curls, using data from wearable inertial and electromyography (EMG) sensors. A custom dataset of 69 sets and over 1000 repetitions was collected, with statistical features extracted for model training. Among the models evaluated, a random forest classifier achieved the highest performance, with 41.4% exact accuracy and 85.9%
±
1
RPE accuracy. While the inclusion of EMG data slightly improved model accuracy over inertial sensors alone, its utility may have been limited by factors such as data quality and placement sensitivity. Feature analysis highlighted eccentric repetition time as the strongest RPE predictor. The results demonstrate the feasibility of wearable-sensor-based RPE estimation and identify key challenges for improving model generalizability.

Over the last decade, science-supported approaches to strength training optimization have advanced considerably [1], which, in turn, has increased the demand for research on resistance training. In parallel, the awareness and application of machine learning (ML) and artificial intelligence in sports have expanded significantly, with demonstrated use cases including exercise classification, rehabilitation monitoring, and performance assessment [2]. The convergence of these two areas presents a valuable research opportunity. In this study, we investigate ML methods for using inertial measurements to estimate the rating of perceived exertion (RPE), while also examining the potential role of electromyography (EMG) data during the training phase.

RPE is a key measure in resistance training, quantifying the perceived intensity of exercise. The term “perceived” is central, as it implies inherent uncertainty and subjectivity. Several scales exist to measure RPE, such as the Borg Scale (6–20) [3]; however, one of the most intuitive and widely used scales is the Borg CR10 scale [3]. Defined by Gunnar Borg in 1982, this scale ranges from 1 to 10, where 1 indicates no exertion and 10 represents absolute failure. Generally, values above 6 are recognized as representing difficult exercise [4]. Given its relevance and widespread adoption in resistance training, this paper uses the Borg CR10 scale.

Intensity is widely regarded as a key factor influencing muscular hypertrophy [5], defined as an increase in the cross-sectional area of muscle [6]. In bodybuilding, training intensity is critical for maximizing muscle growth. However, intensity is equally important in strength-focused training, such as powerlifting, as higher intensities promote neural adaptations, improvements in the rate of force development, and strength gains. A detailed understanding of intensity is therefore essential for designing effective training programs that optimize both hypertrophy and strength outcomes. If trainees misunderstand the scale, they could be at risk of either poor training results, or significant and lasting injury [7]. An estimation system mitigates these risks by removing a degree of ambiguity surrounding the measure. This is particularly important in the current digital era, where personal training is increasingly delivered online [8]. In such settings, the ability to remotely monitor and regulate effort has become critical, making an automated system for accurately estimating RPE especially valuable.

A range of studies have investigated the use of ML for estimating RPE. However, most have focused on cardiovascular exercise rather than resistance training. For example, Carey et al. explored exertion estimation in Australian football players using wearable accelerometers, GPS receivers, and heart rate monitors [9]. Whilst numerous studies have investigated exertion estimation using inertial or physiological sensors, research specifically focused on estimating RPE directly from EMG signals, particularly using machine learning and wearable EMG systems, remains limited. This gap is, for example, evident in the PERSIST dataset [10], which integrates inertial sensors, heart rate monitors, and electrocardiography sensors for resistance training, but does not include EMG data.

In this study, wearable inertial measurement units (IMUs) and surface electromyography (SEMG) sensors are employed to capture movement and muscle activity during resistance training. These sensors were selected due to their non-invasive nature, ease of use, and, in the case of IMUs, their ubiquity in modern wearable devices, making them well-suited for real-world and long-term applications. A novel EMG- and IMU-based dataset of resistance training repetitions is presented, and multiple ML models are evaluated for RPE estimation. EMG data is used only during the training phase to generate labels and inform feature selection. Specifically, extracted EMG features are used to encode labels via dimensionality reduction techniques, and models are trained to estimate these labels using IMU data. During testing, only IMU data is provided as input, reflecting real-world deployment conditions where EMG data is typically not available. Our contributions are threefold:

The first investigation of using EMG signals during training for IMU-based RPE estimation is provided, along with benchmarking of multiple ML models for this task;