Effect of Muscle Fatigue on Surface Electromyography-Based Hand Grasp Force Estimation

Evaluation of fatigue and fatigability in people with Duchenne muscular dystrophy using a dynamic arm support - a pilot study

BACKGROUND

Due to progressive muscle wasting and weakness in patients with Duchenne Muscular Dystrophy (DMD), physical fatigability increases, upper extremity function reduces, which negatively impacts quality of life. Assistive technology such as dynamic arm supports (DAS) may help reduce this fatigability. This study aims to assess whether the novel Yumen 'EXone' DAS can reduce upper extremity fatigue and fatigability in DMD patients and healthy controls (HC), both with and without the DAS. Additionally, longitudinal changes in DMD patients were evaluated.

METHODS

Five DMD patients from the Yumen Bionics pioneer program and five HCs participated. Two submaximal tests simulating drinking and reaching were performed for two minutes, each with and without DAS. DMD participants completed these tests twice, at baseline (T0) and after 6-9 months (T1), while HCs completed them once. Physical fatigability was measured by the number of repetitions and changes in surface electromyography (sEMG) amplitude. Subjective fatigue was assessed using the Borg Scale (6-20) Rate of Perceived Exertion (RPE).

RESULTS

DMD participants generally performed more repetitions with the DAS than without. HCs showed similar or increased repetitions with the DAS. Assessing fatigability with sEMG was difficult due to the compensatory mechanisms used for the tests. Subjective fatigue scores on the Borg Scale were lower with the DAS for both DMD patients and HCs.

CONCLUSION

The Yumen 'EXone' DAS effectively reduces both fatigue and fatigability in DMD patients and healthy controls. Despite the methodological shortcomings, this research is one of the first studies investigating the impact of DAS on fatigue and fatigability.

Recovery of pinch force sense after short-term fatigue

The aim of this study was to identify the exact origin of force sense and identify whether it arises centrally or peripherally. The present study was designed to analyze the effects of short-term fatigue on pinch force sense and the duration of these effects. During the fatigue protocol, twenty (10 men and 10 women; Mage = 22.0 years old) young Chinese participants were asked to squeeze maximally until the pinch grip force decreased to 50% of its maximal due to fatigue. Participants were instructed to produce the target force (10% of maximal voluntary isometric contraction) using the same hand before and after fatigue (immediately, 10, 30, 60, 180, 300 s). The results showed significantly higher absolute error immediately after fatigue (1.22 ± 1.06 N) than before fatigue (0.68 ± 0.34 N), and 60 s (0.76 ± 0.69 N), 180 s (0.67 ± 0.42 N), and 300 s (0.75 ± 0.37 N) after fatigue (all P < 0.05) but with no effect on the variable error (P > 0.05). It was also revealed that there was a significant overestimate of the constant error values before (0.32 ± 0.61 N) and immediately after fatigue (0.80 ± 1.38 N, all P < 0.05), while no significant overestimation or underestimation exceeded 300 s after fatigue (P > 0.05). Our study results revealed that short-term fatigue resulted in a significant decrease in force sense accuracy, but it did not affect force sense consistently; however, force sense accuracy recovered to a certain extent within 10 s and 30 s, whereas it recovered fully within 60 s, and force sense directivity improvement exceeded 300 s after fatigue. The present study shows that the sense of tension (peripherally) is also an important factor affecting force sense. Our study supports the view that the periphery is part of the origin of force sense.

Effects of Muscle Fatigue and Recovery on the Neuromuscular Network after an Intermittent Handgrip Fatigue Task: Spectral Analysis of Electroencephalography and Electromyography Signals

Mechanisms underlying exercise-induced muscle fatigue and recovery are dependent on peripheral changes at the muscle level and improper control of motoneurons by the central nervous system. In this study, we analyzed the effects of muscle fatigue and recovery on the neuromuscular network through the spectral analysis of electroencephalography (EEG) and electromyography (EMG) signals. A total of 20 healthy right-handed volunteers performed an intermittent handgrip fatigue task. In the prefatigue, postfatigue, and postrecovery states, the participants contracted a handgrip dynamometer with sustained 30% maximal voluntary contractions (MVCs); EEG and EMG data were recorded. A considerable decrease was noted in EMG median frequency in the postfatigue state compared with the findings in other states. Furthermore, the EEG power spectral density of the right primary cortex exhibited a prominent increase in the gamma band. Muscle fatigue led to increases in the beta and gamma bands of contralateral and ipsilateral corticomuscular coherence, respectively. Moreover, a decrease was noted in corticocortical coherence between the bilateral primary motor cortices after muscle fatigue. EMG median frequency may serve as an indicator of muscle fatigue and recovery. Coherence analysis revealed that fatigue reduced the functional synchronization among bilateral motor areas but increased that between the cortex and muscle.

Workload Assessment of Tractor Operations with Ergonomic Transducers and Machine Learning Techniques

Dynamic muscular workload assessments of tractor operators are rarely studied or documented, which is critical to improving their performance efficiency and safety. A study was conducted to assess and model dynamic load on muscles, physiological variations, and discomfort of the tractor operators arriving from the repeated clutch and brake operations using wearable non-invasive ergonomic transducers and data-run techniques. Nineteen licensed tractor operators operated three different tractor types of varying power ranges at three operating speeds (4-5 km/h), and on two common operating surfaces (tarmacadam and farm roads). During these operations, ergonomic transducers were utilized to capture the load on foot muscles (gastrocnemius right [GR] and soleus right [SR] for brake operation and gastrocnemius left [GL], and soleus left [SL] for clutch operation) using electromyography (EMG). Forces exerted by the feet during brake and clutch operations were measured using a custom-developed foot transducer. During the process, heart rate (HR) and oxygen consumption rates (OCR) were also measured using HR monitor and K4b2 systems, and energy expenditure rate (EER) was determined using empirical equation. Post-tractor operation cycle, an overall discomfort rating (ODR) for that operation was manually recorded on a 10-point psychophysical scale. EMG-based maximum volumetric contraction (%MVC) measurements revealed higher strain on GR (%MVC = 43%), GL (%MVC = 38%), and SR (%MVC = 41%) muscles which in normal conditions should be below 30%. The clutch and brake actuation forces were recorded in the ranges of 90-312 N and 105-332 N, respectively and were significantly affected by the operating speed, tractor type, and operating surface (p < 0.05). EERs of the operators were measured in the moderate-heavy to heavy ranges (9-24 kJ/min) during the course of trials, suggesting the need to refine existing clutch and brake system designs. Average operator ODR responses indicated 7.8% operations in light, 48.5% in light-moderate, 25.2% in moderate, 10.7% in moderate-high, and 4.9% operations in high discomfort categories. When evaluated for the possibility of minimizing the number of transducers for physical workload assessment, EER showed moderate-high correlations with the EMG signals (r_GR = 0.78, r_GL = 0.75, r_SR = 0.68, r_SL = 0.66). Similarly, actuation forces had higher correlations with EMG signals for all the selected muscles (r = 0.70-0.87), suggesting the use of simpler transducers for effective operator workload assessment. As a means to minimize subjectivity in ODR responses, machine learning algorithms, including K-nearest neighbor (KNN), random forest classifier (RFC), and support vector machine (SVM), predicted the ODR using body mass index (BMI), HR, EER, and EMG at high accuracies of 87-97%, with RFC being the most accurate. Such high-throughput and data-run ergonomic evaluations can be instrumental in reconsidering workplace designs and better fits for end-users in terms of agricultural tractors and machinery systems.

Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography

Many researchers have used machine learning models to control artificial hands, walking aids, assistance suits, etc., using the biological signal of electromyography (EMG). The use of such devices requires high classification accuracy. One method for improving the classification performance of machine learning models is normalization, such as z-score. However, normalization is not used in most EMG-based motion prediction studies because of the need for calibration and fluctuation of reference value for calibration (cannot re-use). Therefore, in this study, we proposed a normalization method that combines sliding-window and z-score normalization that can be implemented in real-time processing without need for calibration. The effectiveness of this normalization method was confirmed by conducting a single-joint movement experiment of the elbow and predicting its rest, flexion, and extension movements from the EMG signal. The proposed method achieved 77.7% accuracy, an improvement of 21.5% compared to the non-normalization (56.2%). Furthermore, when using a model trained by other people’s data for application without calibration, the proposed method achieved 63.1% accuracy, an improvement of 8.8% compared to the z-score (54.4%). These results showed the effectiveness of the simple and easy-to-implement method, and that the classification performance of the machine learning model could be improved.

Proposed Fatigue Index for the Objective Detection of Muscle Fatigue Using Surface Electromyography and a Double-Step Binary Classifier

The objective detection of muscle fatigue reports the moment at which a muscle fails to sustain the required force. Such a detection prevents any further injury to the muscle following fatigue. However, the objective detection of muscle fatigue still requires further investigation. This paper presents an algorithm that employs a new fatigue index for the objective detection of muscle fatigue using a double-step binary classifier. The proposed algorithm involves analyzing the acquired sEMG signals in both the time and frequency domains in a double-step investigation. The first step involves calculating the value of the integrated EMG (IEMG) to determine the continuous contraction of the muscle being investigated. It was found that the IEMG value continued to increase with prolonged muscle contraction and progressive fatigue. The second step involves differentiating between the high-frequency components (HFC) and low-frequency components (LFC) of the EMG, and calculating the fatigue index. Basically, the segmented EMG signal was filtered by two band-pass filters separately to produce two sub-signals, namely, a high-frequency sub-signal (HFSS) and a low-frequency sub-signal (LFSS). Then, the instantaneous mean amplitude (IMA) was calculated for the two sub-signals. The proposed algorithm indicates that the IMA of the HFSS tends to decrease during muscle fatigue, while the IMA of the LFSS tends to increase. The fatigue index represents the difference between the IMA values of the LFSS and HFSS, respectively. Muscle fatigue was found to be present and was objectively detected when the value of the proposed fatigue index was equal to or greater than zero. The proposed algorithm was tested on 75 EMG signals that were extracted from 75 middle deltoid muscles. The results show that the proposed algorithm had an accuracy of 94.66% in distinguishing between conditions of muscle fatigue and non-fatigue.

Prediction of Long-Term Elbow Flexion Force Intervals Based on the Informer Model and Electromyography

Accurate and long-term prediction of elbow flexion force can be used to recognize the intended movement and help wearable power-assisted robots to improve control performance. Our study aimed to find a proper relationship between electromyography and flexion force. However, the existing methods must incorporate biomechanical models to produce accurate and timely predictions of flexion force. Elbow flexion force is largely determined by the contractile properties of muscles, and the relationship between flexion force and the motor function of muscles has to be thoroughly analyzed. Therefore, based on the investigation on the contributions of different muscles to the flexion force, original electromyography signals were decomposed into non-linear and non-stationary parts. We selected the mean absolute value (MAV) of the non-linear part and the variance of the non-stationary part as inputs for an Informer prediction model that does not require detailed a priori knowledge of biomechanical models and is optimized for processing time sequences. Finally, a long-term flexion force probability interval is proposed. The proposed framework performs well in predicting long-term flexion force and outperforms other state-of-the-art models when compared to experimental results.