Measurement system to study the relationship between forearm EMG signals and wrist position at varied hand grip force

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

doi:10.1109/icobe.2012.6178999

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Al-Timemy et al investigated the force variation effect, where subjects were asked to perform 4 different grip patterns [19]. It must be noticed that previous studies used the commercially available force sensors and grip dynamometers were more conventional to measure grip force [20][21][22]. Few researchers have used grip dynamometers to measure grip force at the variable grip span capability [20,23].…”

Section: Introductionmentioning

confidence: 99%

EMG based classification for pick and place task

Khan¹,

Khan²,

Farooq³

2021

Biomed. Phys. Eng. Express

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The hand amputee is deprived of number of activities of daily living. To help the hand amputee, it is important to learn the pattern of muscles activity. There are several elements of tasks, which involve forearm along with the wrist and hand. The one very important task is pick and place activity performed by the hand. A pick and place action is a compilation of different finger motions for the grasping of objects at different force levels. This action may be better understood by learning the electromyography signals of forearm muscles. Electromyography is the technique to acquire electrical muscle activity that is used for the pattern recognition technique of assistive devices. Regarding this, the different classification characterizations of EMG signals involved in the pick and place action, subjected to variable grip span and weights were considered in this study. A low-level force measuring gripper, capable to bear the changes in weights and object spans was designed and developed to simulate the task. The grip span varied from 6 cm to 9 cm and the maximum weight used in this study was 750 gms. The pattern recognition classification methodology was performed for the differentiation of phases of the pick and place activity, grip force, and the angular deviation of metacarpal phalangeal (MCP) joint. The classifiers used in this study were decision tree (DT), support vector machines (SVM) and k-nearest neighbor (k-NN) based on the feature sets of the EMG signals. After analyses, it was found that k-NN performed best to classify different phases of the activity and relative deviation of MCP joint with an average classification accuracy of 82% and 91% respectively. However; the SVM performed best in classification of force with a particular feature set. The findings of the study would be helpful in designing the assistive devices for hand amputee.

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Section: Introductionmentioning

confidence: 99%

EMG based classification for pick and place task

Khan¹,

Khan²,

Farooq³

2021

Biomed. Phys. Eng. Express

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“…[49][50] (Dfrobot, Oymotion, China) with sensor dimensions of 22mm x 35 mm and weight of 36 grams. The two muscles produce considerable contractions when respondents perform hand open, hand close, pinch and curve movements.…”

mentioning

confidence: 99%

Implementation of Supervised Machine Learning on Embedded Raspberry Pi System to Recognize Hand Motion as Preliminary Study for Smart Prosthetic Hand

Triwiyanto,

Luthfiyah,

Caesarendra

et al. 2023

IJEEI

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EMG signals have random, non-linear, and non-stationary characteristics that require the selection of the suitable feature extraction and classifier for application to prosthetic hands based on EMG pattern recognition. This research aims to implement EMG pattern recognition on an embedded Raspberry Pi system to recognize hand motion as a preliminary study for a smart prosthetic hand. The contribution of this research is that the time domain feature extraction model and classifier machine can be implemented into the Raspberry Pi embedded system. In addition, the machine learning training and evaluation process is carried out online on the Raspberry Pi system. The online training process is carried out by integrating EMG data acquisition hardware devices, time domain features, classifiers, and motor control on embedded machine learning using Python programming. This study involved ten respondents in good health. EMG signals are collected at two lead flexor carpi radialis and extensor digitorum muscles. EMG signals are extracted using time domain features (TDF) mean absolute value (MAV), root mean square (RMS), variance (VAR) using a window length of 100 ms. Supervised machine learning decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN) are chosen because they have a simple algorithm structure and less computation. Finally, the TDF and classifier are embedded in the Raspberry Pi 3 Model B+ microcomputer. Experimental results show that the highest accuracy is obtained in the open class, 97.03%. Furthermore, the additional datasets show a significant difference in accuracy (p-value <0.05). Based on the evaluation results obtained, the embedded system can be implemented for prosthetic hands based on EMG pattern recognition.

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