Mapping of EMG signal to hand grip force at varying wrist angles

Sidek, Shahrul Na’im; Mohideen, Ahmad Jazlan Haja

doi:10.1109/iecbes.2012.6498069

Cited by 23 publications

(12 citation statements)

References 16 publications

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“…This noise cannot be completely eliminated as all electronic equipment generates noise [36]. However, the severity of the noise can be reduced using high-quality electronic components.…”

Section: Electromyography Preprocessingmentioning

confidence: 99%

EMG Processing Based Measures of Fatigue Assessment during Manual Lifting

Shair

Ahmad

Marhaban

et al. 2017

BioMed Research International

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Manual lifting is one of the common practices used in the industries to transport or move objects to a desired place. Nowadays, even though mechanized equipment is widely available, manual lifting is still considered as an essential way to perform material handling task. Improper lifting strategies may contribute to musculoskeletal disorders (MSDs), where overexertion contributes as the highest factor. To overcome this problem, electromyography (EMG) signal is used to monitor the workers' muscle condition and to find maximum lifting load, lifting height and number of repetitions that the workers are able to handle before experiencing fatigue to avoid overexertion. Past researchers have introduced several EMG processing techniques and different EMG features that represent fatigue indices in time, frequency, and time-frequency domain. The impact of EMG processing based measures in fatigue assessment during manual lifting are reviewed in this paper. It is believed that this paper will greatly benefit researchers who need a bird's eye view of the biosignal processing which are currently available, thus determining the best possible techniques for lifting applications.

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“…This noise cannot be completely eliminated as all electronic equipment generates noise [36]. However, the severity of the noise can be reduced using high-quality electronic components.…”

Section: Electromyography Preprocessingmentioning

confidence: 99%

EMG Processing Based Measures of Fatigue Assessment during Manual Lifting

Shair

Ahmad

Marhaban

et al. 2017

BioMed Research International

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“…Electromyography (EMG) signals are formed by the potentials generated by electrically activated muscle cells, which are 30-100ms prior to body motions. So if it is compared with angle and force information, biological EMG signals are better because they can reflect upon the human motion intention in advance and is used as feedback also [2].…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Knee and hip joint of lower limb robotic rehabilitation device requires a certain set of joint kinematics and dynamics for each phase of rehabilitation. Dynamic parameters (damping, stiffness, inertia) associated with EMG are always changing and it is important that these parameters are captured from EMG to produce suitable control signal to generate desired knee joint kinematics and dynamics [2].…”

Section: Introductionmentioning

confidence: 99%

ANFIS to estimate damping coefficient from EMG to optimize the interaction force

Anwar

Al-Jumaily

2016

2016 International Conference on Microelectronics, Computing and Communications (MicroCom)

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Abstract-although Lower Limb Robotic Rehabilitation device exhibit a great prospect in the rehabilitation of impaired limb, yet it has not been widely applied to clinical rehabilitation. This is mostly due to the insufficient bidirectional information interaction between exoskeleton and patient. In the shared control at the interaction point, it is very important that the deficiency of impaired lower limb in sharing the knee joint dynamics (Capturing of the intended action of the patient) is extracted beforehand to estimate as to how much assistance the robotic exoskeleton would provide. The intended action data that can be extracted from EMG signal may include the intended posture, intended torque, intended knee joint angle, intended knee joint torque and impedance parameter. In this paper, an application of Adaptive Network Based Fuzzy Inference System (ANFIS) has been proposed for proprioceptive feedback on the status of the interaction force at the patient robotic exoskeleton interaction point. ANFIS has been used to model the relationship between input and output. Interaction forces, rate of change in surface electromyography (EMG) signal are two inputs to ANFIS model and impedance parameters damping coefficients (also stiffness) is output. Impedance control law has damping as one of the tuning parameter. The resultant total torque is calculated from this law. The proposed model is able to estimate damping and demonstrate decent accuracy in modulating the knee joint dynamics to minimize the interaction force at the Patient Exoskeleton interaction point.

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“…Surface EMG is widely used as a non-invasive method to map the relationship between electrical activity of the muscles and the generated muscle force. Therefore, the estimation of the hand force by using the sEMG signal is very essential in many applications such as design and control of a cybernetic prosthetics, sport medicine, medical rehabilitation, clinical diagnostics, kinesiology and biomechanics [3,4,5]. However, the number of muscles of interest, EMG needed channels, the number and location of surface electrodes and type of gestures can all affect the relationship between muscle force and muscle activity [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…The relation of EMG signal to hand grip force at varying wrist angles had been studied by Sidek et al [4]; in this study, the authors investigated the relationship between forearm EMG, hand grip force, and wrist angles recorded by surface electrodes located on FCR, FDS; and EDS muscles. MVC range was 2 to 30% and recorded with 90 o , 60 o and 120 o degrees of wrist angle.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Different Hand and Finger Grip Patterns using Surface Electromyography and Hand Dynamometry

Buniya

Al-Timemy

Aldoori

et al. 2020

alkej

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Recording an Electromyogram (EMG) signal is essential for diagnostic procedures like muscle health assessment and motor neurons control. The EMG signals have been used as a source of control for powered prosthetics to support people to accomplish their activities of daily living (ADLs). This work deals with studying different types of hand grips and finding their relationship with EMG activity. Five subjects carried out four functional movements (fine pinch, tripod grip and grip with the middle and thumb finger, as well as the power grip). Hand dynamometer has been used to record the EMG activity from three muscles namely; Flexor Carpi Radialis (FCR), Flexor Digitorum Superficialis (FDS), and Abductor Pollicis Brevis (ABP) with different levels of Maximum Voluntary Contraction (MVC) (10-100%). In order to analyze the collected EMG and force data, the mean absolute value of each trial is calculated followed by a calculation of the average of the 3 trials for each grip for each subject across the different MVC levels utilized in the study. Then, the mean and the standard deviation (SD) across all participants (3 males and 2 females) are calculated for FCR, FDS and APB muscles with multiple % MVC, i.e 10, 30, 50, 70 % MVC for each gesture. The results showed that APB muscle has the highest mean EMG activity across all grips, followed by FCR muscle. Furthermore, the grip with the thumb and middle fingers is the grip with the highest EMG activity for 10-70% MVC than the power grip. As for the 100% MVC, thumb and middle fingers grip achieved the highest EMG activity for APB muscle, while the power grip has the highest EMG activity for both FCR and FDS muscles.

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Mapping of EMG signal to hand grip force at varying wrist angles

Cited by 23 publications

References 16 publications

EMG Processing Based Measures of Fatigue Assessment during Manual Lifting

EMG Processing Based Measures of Fatigue Assessment during Manual Lifting

ANFIS to estimate damping coefficient from EMG to optimize the interaction force

Analysis of Different Hand and Finger Grip Patterns using Surface Electromyography and Hand Dynamometry

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