Dynamic Elbow Flexion Force Estimation Through a Muscle Twitch Model and sEMG in a Fatigue Condition

High-Density EMG Grip Force Estimation During Muscle Fatigue via Domain Adaptation

Myoelectric interfaces hold promise for enabling intuitive and natural control of prostheses and exoskeletons. Muscle fatigue, whether due to prolonged use or heavy weight loads, can alter the distribution of electromyographic (EMG) signals, leading to inconsistencies compared to non-fatigued conditions. This presents significant challenges for accurately decoding user intentions. We thus propose a novel estimation method based on domain adaptation to improve grip force estimation accuracy during muscle fatigue. Specifically, the proposed method integrates an adversarial training strategy and an end-to-end deep learning model to align EMG feature distributions across non-fatigue and fatigue states. A baseline model, whose structure was identical to the force estimation network of the proposed method, was used for comparison. Eight subjects performed non-fatigue and fatigue gripping tasks, and grip force estimations were compared with dynamometer gold standard measurements. Results demonstrate that root mean square error (RMSE) of the proposed model was 51.9% lower than that of the baseline model during muscle fatigue. The proposed method leverages labeled data in the non-fatigue domain and employs adversarial objectives to ensure the learned features are applicable to both domains, which eliminates the need to pause to collect force label data in the fatigue domain, expediting and simplifying the calibration process. This study has the potential to improve the ability of myoelectric interfaces during muscle fatigue to enable users to intuitively retrieve and grip objects over extended periods, ultimately improving independence and quality of life.

A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs

Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG-force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle's coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 ± 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 ± 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN ± SD [min, max]). The proposed method was not significantly different from the recorded force signal (p-value = 0.610); that was not the case for the other tested models. The proposed method significantly outperformed the other methods (adj. p-value < 0.05). The average running time of each 250 ms signal of the training and testing of the proposed method was 25.7 ± 4.0 [22.3, 40.8] and 11.0 ± 2.9 [4.7, 17.8] in microseconds for the entire dataset. The proposed convex model is thus a promising method for estimating the force from the joints of the upper and lower limbs, with applications in load sharing, robotics, rehabilitation, and prosthesis control for the upper and lower limbs.

An Improved EMG-Driven Neuromusculoskeletal Model for Elbow Joint Muscle Torque Estimation

The accurate measurement of human joint torque is one of the research hotspots in the field of biomechanics. However, due to the complexity of human structure and muscle coordination in the process of movement, it is difficult to measure the torque of human joints in vivo directly. Based on the traditional elbow double-muscle musculoskeletal model, an improved elbow neuromusculoskeletal model is proposed to predict elbow muscle torque in this paper. The number of muscles in the improved model is more complete, and the geometric model is more in line with the physiological structure of the elbow. The simulation results show that the prediction results of the model are more accurate than those of the traditional double-muscle model. Compared with the elbow muscle torque simulated by OpenSim software, the Pearson correlation coefficient of the two shows a very strong correlation. One-way analysis of variance (ANOVA) showed no significant difference, indicating that the improved elbow neuromusculoskeletal model established in this paper can well predict elbow muscle torque.