Relationship between grasping force and features of single-channel intramuscular EMG signals

Kamavuako, Ernest Nlandu; Farina, Dario; Yoshida, Ken; Jensen, Winnie

doi:10.1016/j.jneumeth.2009.09.006

Cited by 69 publications

(53 citation statements)

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“…To achieve certain fidelity-of-torque estimation, the relationship between EMG and joint torque has been assumed to be either linear (Hoozemans and Van Dieën 2005;Inman et al 1952) or nonlinear (Herzog et al 1998;Liu et al 1999). Several studies have also compared the difference in performance between linear and nonlinear models during a single movement (Kamavuako et al 2009(Kamavuako et al , 2012b and simultaneous degrees of freedom (DoF) (Jiang et al 2009). So far, linear and nonlinear methods have shown comparable performance during single movement tasks (Kamavuako et al 2012b); however, for estimating torque in several DoF, nonlinear estimators have shown greater performance (Jiang et al 2009).…”

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confidence: 99%

“…The amplitude of the surface EMG signal may be used to determine the speed or strength of activation of a prosthesis (e.g., DMC Plus; Ottobock, Minneapolis, MN). Intramuscular EMG is being investigated as a potential replacement for surface EMG in advanced devices that require simultaneous or more targeted control of movements (Farrell and Weir 2008;Hargrove et al 2007;Kamavuako et al 2009Kamavuako et al , 2012a, because intramuscular EMG signals are less affected by crosstalk and may provide more selective recordings. Intramuscular EMG also has the potential advantage of avoiding variation experienced in surface EMG due to changes in skin-electrode impedance or electrode shift in a socket.…”

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confidence: 99%

“…With intramuscular recordings, one can study MU recruitment and firing pattern (with higher force then surface EMG), both of which give information on CNS motor control and its disturbances (Trontelj et al 2005). The relationship between intramuscular EMG and muscle force has been demonstrated in a few studies (Inman et al 1952;Kamavuako et al 2009;Onishi et al 2000). Moreover, it has been shown that during single movements, features of selective intramuscular recordings correlate with the grasping force to a similar degree as features of the surface EMG recordings.…”

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confidence: 99%

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Wrist torque estimation during simultaneous and continuously changing movements: surface vs. untargeted intramuscular EMG

Kamavuako

Scheme

Englehart

2013

Journal of Neurophysiology

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Kamavuako EN, Scheme EJ, Englehart KB. Wrist torque estimation during simultaneous and continuously changing movements: surface vs. untargeted intramuscular EMG. J Neurophysiol 109: 2658-2665, 2013. First published March 20, 2013 doi:10.1152/jn.00086.2013In this paper, the predictive capability of surface and untargeted intramuscular electromyography (EMG) was compared with respect to wrist-joint torque to quantify which type of measurement better represents joint torque during multiple degrees-of-freedom (DoF) movements for possible application in prosthetic control. Ten able-bodied subjects participated in the study. Surface and intramuscular EMG was recorded concurrently from the right forearm. The subjects were instructed to track continuous contraction profiles using single and combined DoF in two trials. The association between torque and EMG was assessed using an artificial neural network. Results showed a significant difference between the two types of EMG (P Ͻ 0.007) for all performance metrics: coefficient of determination (R 2 ), Pearson correlation coefficient (PCC), and root mean square error (RMSE). The performance of surface EMG (R 2 ϭ 0.93 Ϯ 0.03; PCC ϭ 0.98 Ϯ 0.01; RMSE ϭ 8.7 Ϯ 2.1%) was found to be superior compared with intramuscular EMG (R 2 ϭ 0.80 Ϯ 0.07; PCC ϭ 0.93 Ϯ 0.03; RMSE ϭ 14.5 Ϯ 2.9%). The higher values of PCC compared with R 2 indicate that both methods are able to track the torque profile well but have some trouble (particularly intramuscular EMG) in estimating the exact amplitude. The possible cause for the difference, thus the low performance of intramuscular EMG, may be attributed to the very high selectivity of the recordings used in this study. simultaneous movement; intramuscular EMG; surface electromyography; dynamic movement; torque and force estimation

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Wrist torque estimation during simultaneous and continuously changing movements: surface vs. untargeted intramuscular EMG

Kamavuako

Scheme

Englehart

2013

Journal of Neurophysiology

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“…A circumference armband should be able to roughly estimate MVC, by automatically measuring the circumference of the forearm, this is viable as there is a correlation between muscle size and muscle force [3], and muscle force and MVC [11]. From the previous statement we can logical assume muscle size should be able to predict MVC.…”

Section: Introductionmentioning

confidence: 99%

Using forearm circumference for automatic threshold calibration for simple EMG control

Cannan

2013

2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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Abstract-Training and calibration is normally required for the users of Electromyographic (EMG) control systems, which can require a lot of time and expertise. It is necessary to reduce training and calibration so that EMG control can be deployed in real-world applications, with minimal hassle. Based on our previous work, this paper investigates a novel method of using an automatic circumference measuring device to determine forearm circumference, in order to estimate maximum voluntary contraction (MVC) via linear regression. MVC is commonly used for threshold adjustment, hence automatic estimation can be used to auto-calibrate EMG thresholds. A new load cell based automatic measuring circumference device is demonstrated, and an experiment is performed which confirms the potential of using forearm circumference for estimating MVC. Experimental results confirm the feasibility of the concept, and the performance of the developed device.

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“…Although the linear relationship has been found in many previous works (e.g. Kamavuako, Farina, Yoshida, & Jensen, 2009;Woods & Bigland-Ritchie, 1983), several researchers have found a non-linear relationship between force generated and EMG muscle activity (e.g. Lawrence & De Luca, 1983;Woods & Bigland-Ritchie, 1983).…”

Section: Figure 1 Three Main Modules Of Mcis: the Surface Emg Acquismentioning

confidence: 88%

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface

Phinyomark

Quaine

Laurillau

Advances in Medical Technologies and Clinical Practice

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Muscle-computer interfaces (MCIs) based on surface electromyography (EMG) pattern recognition have been developed based on two consecutive components: feature extraction and classification algorithms. Many features and classifiers are proposed and evaluated, which yield the high classification accuracy and the high number of discriminated motions under a single-session experimental condition. However, there are many limitations to use MCIs in the real-world contexts, such as the robustness over time, noise, or low-level EMG activities. Although the selection of the suitable robust features can solve such problems, EMG pattern recognition has to design and train for a particular individual user to reach high accuracy. Due to different body compositions across users, a feasibility to use anthropometric variables to calibrate EMG recognition system automatically/semi-automatically is proposed. This chapter presents the relationships between robust features extracted from actions associated with surface EMG signals and twelve related anthropometric variables. The strong and significant associations presented in this chapter could benefit a further design of the MCIs based on EMG pattern recognition.

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Relationship between grasping force and features of single-channel intramuscular EMG signals

Cited by 69 publications

References 26 publications

Wrist torque estimation during simultaneous and continuously changing movements: surface vs. untargeted intramuscular EMG

Wrist torque estimation during simultaneous and continuously changing movements: surface vs. untargeted intramuscular EMG

Using forearm circumference for automatic threshold calibration for simple EMG control

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface

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