Human Facial Aggressive Detection System Based on Facial-Width-to-Height Ratio

Hasan, Tabreer T.; Issa, Abbas H.

doi:10.1088/1757-899x/978/1/012046

Cited by 2 publications

(1 citation statement)

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“…First stage, there are many methods to collect the electromyography signals from the muscles, first method by collecting data from the skin surface using different sensors such as Myo armband, which is considered a commercial way. The second method is collecting the data using a needle that is considered as a medical method and rarely used in research [6,7].…”

Section: Introductionmentioning

confidence: 99%

EMG Signals Classification of Wide Range Motion Signals for Prosthetic Hand Control

Mahmood¹

2021

IJIES

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The recent revolution in the biomedical field carried out the researchers to work on the prosthetic technique because it reflects the amputee's need. Therefore, the electromyography (EMG) signals generated by muscle contractions are used to implement the prosthetic human body parts. This paper presents a pattern recognition system based on two EMG data; the first EMG data represents the general body movements collected from biceps and triceps muscles for six different motions, i.e., bowing, clapping, handshaking, hugging, jumping, and running. The Root Mean Square, Difference Absolute Standard Deviation Value, and Principal Component Analysis extract the raw signal data features and enhance classification accuracy. The machine learning method is applied, i.e., Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) are used for data classification; the results show high accuracy reached 94.8% and 98.9%, respectively. Whereas, the second EMG data is selected to be more specific in hand movements, i.e., cylindrical, spherical, palmar, lateral, hook, and tip motions, because these significant motions are the first step implementing any prosthetic hand. Consequently, the mean, Standard Deviation Value, and Principal Component Analysis extract the raw signal feature. Meanwhile, the same algorithm used in the first data classification is also used to classify the second data because it shows high accuracy and good performance. SVM algorithm is used to classify the data and achieved high training accuracy, reaching 89%. The high training accuracy for different hand movements is considered an essential step toward implementing human prosthetic parts to help the people who suffer from an amputee.

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Section: Introductionmentioning

confidence: 99%