Muscle- and Mode-Specific Responses of the Forearm Flexors to Fatiguing, Concentric Muscle Actions

Biceps Brachii and Brachioradialis Excitation in Biceps Curl Exercise: Different Handgrips, Different Synergy

The current study analyzed the excitation of biceps brachii, brachioradialis, and anterior deltoid during bilateral biceps curl performed with different handgrips. Ten competitive bodybuilders performed bilateral biceps curl in non-exhaustive 6-rep sets using 8-RM with the forearm in supinated, pronated, and neutral positions. The ascending and descending phase of each variation was separately analyzed using the normalized root mean square collected using surface electromyography. During the ascending phase, (i) biceps brachii excitation was greater with the supinated compared to the pronated [+19(7)%, ES: 2.60] and neutral handgrip [+12(9)%, ES: 1.24], (ii) the brachioradialis showed greater excitation with the supinated compared to the pronated [+5(4)%, ES: 1.01] and neutral handgrip [+6(5)%, ES: 1.10], (iii) the anterior deltoid excitation was greater with the pronated and neutral handgrip compared to the supinated condition [+6(3)% and +9(2)%, ES: 2.07 and 3.18, respectively]. During the descending phase, the anterior deltoid showed greater excitation in the pronated compared to the supinated handgrip [+5(4)%, ES: 1.02]. Changing the handgrips when performing biceps curl induces specific variations in biceps brachii and brachioradialis excitation and requires different anterior deltoid interventions for stabilizing the humeral head. Practitioners should consider including different handgrips in the biceps curl routine to vary the neural and mechanical stimuli.

Neuromuscular and mean force changes during a fatiguing bout of exercise with and without blood flow restriction

Surface electromyography (EMG) and mean force can be used to identify motor unit excitation and fatigue. Low-load resistance training with blood flow restriction (LL+BFR) may result in earlier fatigue and maximal muscle fibre recruitment compared to low-load resistance training (LL). The purpose of this investigation was to examine EMG and force responses during LL versus LL+BFR. Thirteen males (mean ± standard deviation = 24±4 years) completed a bout (1×30) of leg extension muscle actions at 30% of their 1 repetition maximum LL and LL+BFR while force, EMG amplitude, and EMG mean power frequency (EMG MPF) were recorded. EMG amplitude increased (74.2%) and EMG MPF decreased (22.6%) similarly during both conditions. There was no significant difference in mean force during the first 3 repetitions between LL+BFR (477.3±132.3 N) and LL (524.3±235.1 N) conditions, but mean force was lower during the last 3 repetitions for LL+BFR (459.7±179.3 N) compared to LL (605.4±276.4 N). The results of the present study indicated that a fatiguing bout of leg extension muscle actions performed LL and LL+BFR elicited similar neuromuscular responses. There was a significant difference in mean force during the last 3 repetitions (LL>LL+BFR) that may have been due to differences in the time spent near peak force.

Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time

The surface Electromyography (sEMG) signal contains information about movement intention generated by the human brain, and it is the most intuitive and common solution to control robots, orthotics, prosthetics and rehabilitation equipment. In recent years, gesture decoding based on sEMG signals has received a lot of research attention. In this paper, the effects of muscle fatigue, forearm angle and acquisition time on the accuracy of gesture decoding were researched. Taking 11 static gestures as samples, four specific muscles (i.e., superficial flexor digitorum (SFD), flexor carpi ulnaris (FCU), extensor carpi radialis longus (ECRL) and finger extensor (FE)) were selected to sample sEMG signals. Root Mean Square (RMS), Waveform Length (WL), Zero Crossing (ZC) and Slope Sign Change (SSC) were chosen as signal eigenvalues; Linear Discriminant Analysis (LDA) and Probabilistic Neural Network (PNN) were used to construct classification models, and finally, the decoding accuracies of the classification models were obtained under different influencing elements. The experimental results showed that the decoding accuracy of the classification model decreased by an average of 7%, 10%, and 13% considering muscle fatigue, forearm angle and acquisition time, respectively. Furthermore, the acquisition time had the biggest impact on decoding accuracy, with a maximum reduction of nearly 20%.

Electrode Size and Placement for Surface EMG Bipolar Detection from the Brachioradialis Muscle: A Scoping Review

The brachioradialis muscle (BRD) is one of the main elbow flexors and is often assessed by surface electromyography (sEMG) in physiology, clinical, sports, ergonomics, and bioengineering applications. The reliability of the sEMG measurement strongly relies on the characteristics of the detection system used, because of possible crosstalk from the surrounding forearm muscles. We conducted a scoping review of the main databases to explore available guidelines of electrode placement on BRD and to map the electrode configurations used and authors' awareness on the issues of crosstalk. One hundred and thirty-four studies were included in the review. The crosstalk was mentioned in 29 studies, although two studies only were specifically designed to assess it. One hundred and six studies (79%) did not even address the issue by generically placing the sensors above BRD, usually choosing large disposable ECG electrodes. The analysis of the literature highlights a general lack of awareness on the issues of crosstalk and the need for adequate training in the sEMG field. Three guidelines were found, whose recommendations have been compared and summarized to promote reliability in further studies. In particular, it is crucial to use miniaturized electrodes placed on a specific area over the muscle, especially when BRD activity is recorded for clinical applications.

Fatigue effect on cross-talk in mechanomyography signals of extensor and flexor forearm muscles during maximal voluntary isometric contractions

OBJECTIVE

This paper presents the analyses of the fatigue effect on the cross-talk in mechanomyography (MMG) signals of extensor and flexor forearm muscles during pre- and post-fatigue maximum voluntary isometric contraction (MVIC).

METHODS

Twenty male participants performed repetitive submaximal (60% MVIC) grip muscle contractions to induce muscle fatigue and the results were analyzed during the pre- and post-fatigue MVIC. MMG signals were recorded on the extensor digitorum (ED), extensor carpi radialis longus (ECRL), flexor digitorum superficialis (FDS) and flexor carpi radialis (FCR) muscles. The cross-correlation coefficient was used to quantify the cross-talk values in forearm muscle pairs (MP1, MP2, MP3, MP4, MP5 and MP6). In addition, the MMG RMS and MMG MPF were calculated to determine force production and muscle fatigue level, respectively.

RESULTS

The fatigue effect significantly increased the cross-talk values in forearm muscle pairs except for MP2 and MP6. While the MMG RMS and MMG MPF significantly decreased (p<0.05) based on the examination of the mean differences from pre- and post-fatigue MVIC.

CONCLUSION

The presented results can be used as a reference for further investigation of cross-talk on the fatigue assessment of extensor and flexor muscles' mechanic.

Sex differences for fatigue-induced changes in muscle blood flow, but not eccentric peak torque or neuromuscular responses

OBJECTIVES

The purpose of this study was to examine the effects of exercise intensity and strength on sex-related differences in eccentric peak torque (PT), muscle blood flow, and neuromuscular responses following fatiguing, submaximal forearm flexion eccentric protocols.

METHODS

Thirty-six subjects were stratified by sex and strength into 4 equal groups and randomly performed fatiguing eccentric, isokinetic (180°·s^-1), forearm flexion protocols at 40% or 80% of eccentric PT. Eccentric PT, muscle blood flow, and neuromuscular responses were measured prior to (pretest), immediately (posttest), and 5-min after (5-min recovery) performing the fatiguing protocols.

RESULTS

There was no sex-, intensity-, or strength-related difference in the magnitude of decrease in eccentric PT at posttest (80.0% of pretest) or the magnitude of recovery at 5-min (87.8% of pretest). Muscle blood flow increased similarly for men (139.8% of pretest) and women (178.7% of pretest) at posttest, but the magnitude of recovery was greater for the women (62.9%) than the men (41.4%). The neuromuscular responses were not affected by sex-, intensity-, or strength-related differences.

CONCLUSIONS

These findings indicated that there were few sex-related differences in eccentric PT, muscle blood flow, and neuromuscular responses as a result of the fatiguing eccentric protocols performed at a high or low intensity of exercise.

Associations between lower-limb muscle activation and knee flexion in post-stroke individuals: A study on the stance-to-swing phases of gait

Reduced knee flexion is a leading feature of post-stroke gait, but the causes have not been well understood. The purpose of this study was to investigate the relationship between the knee flexion and the lower-limb muscle activation within the stance-to-swing phases of gait cycle in the post-stroke hemiplegic patients. Ten stroke patients and 10 age- and gender-matched healthy subjects participated in the experiment. The lower-limb kinematic signals and the surface electromyography (sEMG) signals of the left and right rectus femoris (RF), biceps femoris (BF) and lateral gastrocnemius (GS) were recorded during walking. The angle range (AR) of knee flexion, the root mean square (RMS) and the mean frequency (MNF) of sEMG signals were calculated from the terminal stance (TSt) to the initial swing (ISw) phases of gait cycle. Stroke patients showed lower bilateral AR of knee flexion and lower RMS of GS on the paretic side, but higher MNF of RF on the non-paretic side compared with the controls. Within the stroke patients, significant differences were found between their paretic and non-paretic limbs in the AR of knee flexion, as well as in the RMS and MNF of GS (p < 0.05). Regression analysis showed that the RMS of BF, MNF of BF and MNF of GS explained 82.1% of variations in AR of knee flexion on paretic side (r² = 0.821). But the RMS and MNF of all the muscles (including the RF, GS and BF) could explain 65.6% of AR of knee flexion variations on the non-paretic side (r² = 0.656), and 45.2% of variations for the healthy subjects (r² = 0.452). The reduced knee flexion during gait was associated with altered magnitude and frequency of muscle contractions and with simplified muscle synergy in the post-stroke hemiplegic patients. Identifying the muscles that are responsible for knee stiffness may facilitate improvement of rehabilitation strategy for post-stroke gait.