Hysteresis in the electromyography-force relationship: toward an optimal model for the estimation of force

Relationship between EMG features and force in orbicularis oris muscle

BACKGROUND

Lip incompetence resulting from mouth breathing is a common clinical manifestation, while there are no definite indicators of amplitude and intensity of muscle functional training in clinical practice, which leads to unsatisfactory training results.

OBJECTIVE

The aim was to quantify the relationship between electromyography (EMG) and force in orbicularis oris muscle, so that the indicators of muscle functional training can be evaluated using EMG signals, so as to improve the training effects.

METHODS

The EMG and the force signals of orbicularis oris muscle from 0% to 100% MVC within 5 s in twelve healthy subjects (six males and six females; age, 25 ± 2 years; mass, 60 ± 15 kg) were recorded simultaneously for three trials. Four EMG features consisting of RMS, WAMP, SampEn and FuzzyEn were analyzed. The regression analyses were performed using first-order and third-order polynomial model.

RESULTS

There were high correlations between the four EMG features and muscle force with the two models. The third-order model yielded a higher coefficient of determination (R2) than the linear model (p< 0.001) and the result of FuzzyEn (R2: 0.884 ± 0.059) was the highest in the four features.

CONCLUSION

The third-order model with FuzzyEn of EMG signals may be used to guide the muscle functional training.

Hammerstein-Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals

This paper develops a novel approach to characterise muscle force from electromyography (EMG) signals, which are the electric activities generated by muscles. Based on the nonlinear Hammerstein-Wiener model, the first part of this study outlines the estimation of different sub-models to mimic diverse force profiles. The second part fixes the appropriate sub-models of a multimodel library and computes the contribution of sub-models to estimate the desired force. Based on a pre-existing dataset, the obtained results show the effectiveness of the proposed approach to estimate muscle force from EMG signals with reasonable accuracy. The coefficient of determination ranges from 0.6568 to 0.9754 using the proposed method compared with a range of 0.5060 to 0.9329 using an artificial neural network (ANN), generating significantly different accuracy (p < 0.03). Results imply that the use of multimodel approach can improve the accuracy in proportional control of prostheses.

The effect of wrist posture on extrinsic finger muscle activity during single joint movements

Wrist posture impacts the muscle lengths and moment arms of the extrinsic finger muscles that cross the wrist. As a result, the electromyographic (EMG) activity associated with digit movement at different wrist postures must also change. We sought to quantify the posture-dependence of extrinsic finger muscle activity using bipolar fine-wire electrodes inserted into the extrinsic finger muscles of able-bodied subjects during unrestricted wrist and finger movements across the entire range of motion. EMG activity of all the recorded finger muscles were significantly different (p < 0.05, ANOVA) when performing the same digit movement in five different wrist postures. Depending on the wrist posture, EMG activity changed by up to 70% in individual finger muscles for the same movement, with the highest levels of activity observed in finger extensors when the wrist was extended. Similarly, finger flexors were most active when the wrist was flexed. For the finger flexors, EMG variations with wrist posture were most prominent for index finger muscles, while the EMG activity of all finger extensor muscles were modulated in a similar way across all digits. In addition to comprehensively quantifying the effect of wrist posture on extrinsic finger EMG activity in able-bodied subjects, these results may contribute to designing control algorithms for myoelectric prosthetic hands in the future.

Grip Force and 3D Push-Pull Force Estimation Based on sEMG and GRNN

The estimation of the grip force and the 3D push-pull force (push and pull force in the three dimension space) from the electromyogram (EMG) signal is of great importance in the dexterous control of the EMG prosthetic hand. In this paper, an action force estimation method which is based on the eight channels of the surface EMG (sEMG) and the Generalized Regression Neural Network (GRNN) is proposed to meet the requirements of the force control of the intelligent EMG prosthetic hand. Firstly, the experimental platform, the acquisition of the sEMG, the feature extraction of the sEMG and the construction of GRNN are described. Then, the multi-channels of the sEMG when the hand is moving are captured by the EMG sensors attached on eight different positions of the arm skin surface. Meanwhile, a grip force sensor and a three dimension force sensor are adopted to measure the output force of the human's hand. The characteristic matrix of the sEMG and the force signals are used to construct the GRNN. The mean absolute value and the root mean square of the estimation errors, the correlation coefficients between the actual force and the estimated force are employed to assess the accuracy of the estimation. Analysis of variance (ANOVA) is also employed to test the difference of the force estimation. The experiments are implemented to verify the effectiveness of the proposed estimation method and the results show that the output force of the human's hand can be correctly estimated by using sEMG and GRNN method.

Re-evaluation of EMG-torque relation in chronic stroke using linear electrode array EMG recordings

The objective was to re-evaluate the controversial reports of EMG-torque relation between impaired and non-impaired sides using linear electrode array EMG recordings. Ten subjects with chronic stroke performed a series of submaximal isometric elbow flexion tasks. A 20-channel linear array was used to record surface EMG of the biceps brachii muscles from both impaired and non-impaired sides. M-wave recordings for bilateral biceps brachii muscles were also made. Distribution of the slope of the EMG-torque relations for the individual channels showed a quasi-symmetrical "M" shaped pattern. The lowest value corresponded to the innervation zone (IZ) location. The highest value from the slope curve for each side was selected for comparison to minimize the effect of electrode placement and IZ asymmetry. The slope was greater on the impaired side in 4 of 10 subjects. There were a weak correlation between slope ratio and strength ratio and a moderate to high correlation between slope ratio and M-wave ratio between two sides. These findings suggest that the EMG-torque relations are likely mediated and influenced by multiple factors. Our findings emphasize the importance of electrode placement and suggest the primary role of peripheral adaptive changes in the EMG-torque relations in chronic stroke.

Effects of contraction path and velocity on the coordination of hand muscles during a three-digit force production task

Though many studies indicated that the behavior of single muscle was different between contraction and relaxation, the effect of contraction history profile on multiple muscles has not been investigated. In this study, we analyzed the influence of contraction history on the coordination patterns of hand muscles during a three-digit force production task. The effects of the contraction and relaxation paths with two contraction velocities (5% and 10% maximum voluntary contraction per second) were investigated. The results showed that the force-independent characteristic of muscle coordination patterns still held regardless of the contraction history profiles. In addition, the effect of contraction path was more significant than that of velocity. The study provides a potential way to overcome the impact of contraction disturbance for improving the robustness of the human-machine interface (HMI) based on electromyographic (EMG) pattern recognition.

Extra-torque of human tibialis anterior during electrical stimulation with linearly varying frequency and amplitude trains

This work aimed to characterise the whole human muscle input/output law during electrical stimulation with triangular varying frequency and amplitude trains through combined analysis of torque, mechanomyogram (MMG) and electromyogram (EMG). The tibialis anterior (TA) of ten subjects (age 23-35 years) was investigated during static contraction obtained through neuromuscular electrical stimulation. After potentiation, TA underwent two 15s stimulation patterns: (a) frequency triangle (FT): 2 > 35 > 2 Hz at Vmax (amplitude providing full motor unit recruitment); (b) amplitude triangle (AT): Vmin > Vmax > Vmin (Vmin providing TA least mechanical response) at 35 Hz. 2 > 35 Hz or Vmin > Vmax as well as 35 > 2 Hz or Vmax > Vmin were defined as up-going ramp (UGR) and down-going ramp (DGR), respectively. TA torque, MMG and EMG were detected by a load cell, an optical laser distance sensor and a probe with two silver bar electrodes, respectively. For both FT and AT, only the two mechanical signals resulted always larger in DGR than in UGR, during AT extra-torque and extra-MMG were present even in the first 1/3 of the amplitude range where EMG data presented no significant differences between DGR and UGR. Our data suggest that extra-torque and extra-displacement are evident for both FT and AT, being mainly attributed to an intrinsic muscle property.

Active and Progressive Exoskeleton Rehabilitation Using Multisource Information Fusion From EMG and Force-Position EPP

Although exoskeletons have received enormous attention and have been widely used in gait training and walking assistance in recent years, few reports addressed their application during early poststroke rehabilitation. This paper presents a healthcare technology for active and progressive early rehabilitation using multisource information fusion from surface electromyography and force-position extended physiological proprioception. The active-compliance control based on interaction force between patient and exoskeleton is applied to accelerate the recovery of the neuromuscular function, whereby progressive treatment through timely evaluation contributes to an effective and appropriate physical rehabilitation. Moreover, a clinic-oriented rehabilitation system, wherein a lower extremity exoskeleton with active compliance is mounted on a standing bed, is designed to ensure comfortable and secure rehabilitation according to the structure and control requirements. Preliminary experiments and clinical trial demonstrate valuable information on the feasibility, safety, and effectiveness of the progressive exoskeleton-assisted training.

Simultaneous and proportional force estimation in multiple degrees of freedom from intramuscular EMG

This letter investigates simultaneous and proportional estimation of force in 2 degree-of-freedoms (DoFs) from intramuscular electromyography (EMG). Intramuscular EMG signals from three able-bodied subjects were recorded along with isometric forces in multiple DoF from the right arm. The association between five EMG features and force profiles was modeled using an artificial neural network. Correlation coefficients between the measured and the estimated forces were 0.85 ± 0.056 and 0.88 ± 0.05 without and with post processing, respectively. The results showed that force can be estimated in 2 DoFs with high accuracy and that the degree of performance depended on the force function (task) to be estimated.