Model-based Fusion of Surface Electromyography with Kinematic and Kinetic Measurements for Monitoring of Muscle Fatigue

Abstract

This study proposes a novel method for monitoring muscle fatigue using muscle-specific dynamic models which relate joint time-frequency signatures extracted from the relevant electromyogram (EMG) signals with the corresponding estimated muscle forces. Muscle forces were estimated using physics-driven musculoskeletal models which incorporate muscle lengths and contraction velocities estimated from the available kinematic and kinetic measurements. For any specific individual, such a muscle-specific dynamic model is trained using EMG and movement data collected in the early stages of an exercise, i.e., during the least-fatigued behavior. As the exercise or physical activity of that individual progresses and fatigue develops, residuals yielded by that model when approximating the newly arrived data shift and change because of the fatigue-induced changes in the underlying dynamics. In this paper, we propose quantitative evaluation of those changes via the concept of a muscle-specific Freshness Index (FI) which at any given time expresses overlaps between the distribution of that muscle’s model residuals observed on the most recently collected data and the distribution of modeling residuals observed during non-fatigued behavior. The newly proposed method was evaluated using data collected during a repetitive sawing motion experiment with 12 healthy participants. The performance of the FI as a fatigue metric was compared with the performance of the instantaneous frequency of the relevant EMG signals, which is a more traditional and widely used metric of muscle fatigue. It was found that the FI reflected the progression of muscle fatigue with desirable properties of stronger monotonic trends and smaller noise levels compared to the traditional, instantaneous frequency-based metrics.

System-based Monitoring of Muscular Fatigue in Lower-Extremity Movement

“…Rather than directly measuring muscle forces, which is unfeasible in our dynamic use case, this paper follows Ou et. al (2022) and pursues indirect estimation of muscle generated forces using first-principle physics and available external measurements.…”

“…With the sEMG features from time-frequency analysis and the muscle parameters and forces from OpenSim, we have the inputs and outputs necessary for the model. Following Ou et al (2022), a Growing Structure Multiple Model System (GSMMS) was utilized to model the relations between inputs and outputs of each muscle. These divide-and-conquer type models have local model tractability and interpretability, while at the same time being capable of modeling the welldocumented nonlinearities in muscle dynamics (Millard et al, 2013) to within an arbitrary accuracy.…”

“…As the participant becomes more fatigued, the muscle dynamics change, thus altering the distribution of predicted muscle force errors and causing the similarity index to decrease towards zero. Following Ou et al (2022), this metric will be referred to as the System-based Freshness Index (SFI).…”

“…As the exercise progressed, these distributions described using the GMM framework are updated and compared with those observed during the training process. Following Ou et al (2022), the resulting fatigue metrics will be referred to as the EMG-based Freshness Index (EFI). A flowchart of the System-based Freshness Index is shown in Figure 2.…”

“…Zhang, Lockhart, and Soangra (2013) utilized support vector machines to classify the participant's gait cycle as "normal" or fatigued. Finally, Ou, Gates, Johnson, and Djurdjanovic (2022) captured the ever-changing muscle dynamics during a cyclic upper extremity task by building a Growing Structure Multiple Model System (GSMMS) during the participants' "fresh" state and monitoring the participants' fatigue via the model's predicted muscle force error. Their findings showed a significantly less noisy fatigue metric relative to traditional sEMG-based methods allowing for analysis of individual muscle recovery and fatigue.…”