Comparison of EMG signal of the flexor hallucis longus recorded using surface and intramuscular electrodes during walking

Functional relationship between the foot intrinsic and extrinsic muscles in walking

The intrinsic and extrinsic muscles are considered to stabilize the foot and contribute to propulsion during walking. This study aimed to clarify the functional relationship between intrinsic and extrinsic muscles during walking. Thirteen healthy men participated in this study. The muscle activities of the intrinsic muscles (quadratus plantae and abductor hallucis), and the extrinsic muscles (flexor hallucis longus, flexor digitorum longus, and tibialis posterior) were measured using fine-wire and surface electromyography during walking. The muscle onset timing after foot contact was calculated and compared among muscles using the one-way ANOVA. The stance phase was divided into early and late braking, and early and late propulsion phases. Muscle activity among phases was compared using repeated-measures ANOVA. The onset time of the abductor hallucis was significantly earlier than those of the flexor digitorum longus and tibialis posterior. The quadratus plantae demonstrated significantly earlier onset than that of the tibialis posterior. In the late propulsion phase, the activity of extrinsic muscles decreased, whereas intrinsic muscles were continuously active. Early activation of the intrinsic muscles may stabilize the foot for efficient torque production by the extrinsic muscles. Furthermore, the intrinsic muscles may contribute to the final push-off after the deactivation of extrinsic muscles.

Plantar intrinsic foot muscle activation during functional exercises compared to isolated foot exercises in younger adults

BACKGROUND

Training the plantar intrinsic foot muscles (PIFMs) has the potential to benefit patients with lower extremity musculoskeletal conditions as well as the aged population. Isolated foot exercises, often standard in clinical practice, are difficult to perform, whereas functional exercises are much easier to accomplish. However, it is unclear whether functional exercises are comparable to isolated foot exercises in activating the PIFMs.

OBJECTIVE

This study aims to compare the activation of PIFMs between functional exercises versus isolated foot exercises.

METHODS

Using surface electromyography (EMG), muscle activation of three PIFMs was measured in four functional exercises (i.e. normal/unstable toe stance, toe walking, and hopping) versus a muscle-specific isolated foot exercise in 29 younger adults, resulting in 12 comparisons.

RESULTS

Functional exercises showed larger mean EMG amplitudes than the isolated foot exercises in 25% of the 12 comparisons, while there was no difference in the remaining 75%.

CONCLUSION

Functional exercises provoked comparable or even more activation of the PIFMs than isolated foot exercises. Given that functional exercises are easier to perform, this finding indicates the need to further investigate the effectiveness of functional exercises in physical therapy to improve muscle function and functional task performance in populations that suffer from PIFM weakness or dysfunction.

Electroencephalogram and surface electromyogram fusion-based precise detection of lower limb voluntary movement using convolution neural network-long short-term memory model

The electroencephalogram (EEG) and surface electromyogram (sEMG) fusion has been widely used in the detection of human movement intention for human–robot interaction, but the internal relationship of EEG and sEMG signals is not clear, so their fusion still has some shortcomings. A precise fusion method of EEG and sEMG using the CNN-LSTM model was investigated to detect lower limb voluntary movement in this study. At first, the EEG and sEMG signal processing of each stage was analyzed so that the response time difference between EEG and sEMG can be estimated to detect lower limb voluntary movement, and it can be calculated by the symbolic transfer entropy. Second, the data fusion and feature of EEG and sEMG were both used for obtaining a data matrix of the model, and a hybrid CNN-LSTM model was established for the EEG and sEMG-based decoding model of lower limb voluntary movement so that the estimated value of time difference was about 24 ∼ 26 ms, and the calculated value was between 25 and 45 ms. Finally, the offline experimental results showed that the accuracy of data fusion was significantly higher than feature fusion-based accuracy in 5-fold cross-validation, and the average accuracy of EEG and sEMG data fusion was more than 95%; the improved average accuracy for eliminating the response time difference between EEG and sEMG was about 0.7 ± 0.26% in data fusion. In the meantime, the online average accuracy of data fusion-based CNN-LSTM was more than 87% in all subjects. These results demonstrated that the time difference had an influence on the EEG and sEMG fusion to detect lower limb voluntary movement, and the proposed CNN-LSTM model can achieve high performance. This work provides a stable and reliable basis for human–robot interaction of the lower limb exoskeleton.

Brachialis Muscle Activity Can Be Measured With Surface Electromyography: A Comparative Study Using Surface and Fine-Wire Electrodes

Muscle activities of the elbow flexors, especially the brachialis muscle (BR), have been measured with intramuscular electromyography (EMG) using the fine-wire electrodes. It remains unclear whether BR activity can be assessed using surface EMG. The purpose of this study was to compare the EMG patterns of the BR activity recorded during elbow flexion using surface and fine-wire electrodes and to determine whether surface EMG can accurately measure the BR activity. Six healthy men were asked to perform two tasks—a maximum isometric voluntary contractions (MVICs) task and an isotonic elbow-flexion task without lifting any weight. The surface and intramuscular EMG were simultaneously recorded from the BR and the long and short heads of the biceps brachii muscle (BBLH and BBSH, respectively). The locations of the muscles were identified and marked under ultrasonographic guidance. The peak cross-correlation coefficients between the EMG signals during the MVICs task were calculated. For the isotonic elbow-flexion task, the EMG patterns for activities of each muscle were compared between the surface and the fine-wire electrodes. All cross-correlation coefficients between the surface EMG signals from the muscles were lower than 0.3. Furthermore, the EMG patterns of the BR activity were not significantly different between the surface and the fine-wire electrodes. The BR has different EMG pattern from the BBLH and the BBSH. The BR activity, conventionally measured with intramuscular EMG, can be accurately accessed with surface EMG during elbow flexion performed without lifting any weight, independent from the BBLH and BBSH activities.