Classification of Myoelectric Signal using Spectrogram Based Window Selection

Too, Jingwei; Abdullah, Abdul Rahim; Saad, Norhashimah Mohd; Ali, Nursabillilah Mohd; Zawawi, Tengku Nor Shuhada Tengku

doi:10.30880/ijie.2019.11.04.021

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…While artificial neural networks have shown promising results in EMG signal classification [ 21 , 50 ], other machine learning algorithms, such as Support Vector Machine [ 51 , 52 ], K Nearest Neighbors [ 53 , 54 ], and Multilayer Perceptron [ 55 ], have also been successfully employed in this domain [ 56 ]. The choice of algorithm often depends on the specific requirements and characteristics of the classification task.…”

Section: Background and Related Workmentioning

confidence: 99%

A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network

Avilés-Mendoza,

Gaibor-León,

Asanza

et al. 2023

Biomimetics

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About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.

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Section: Background and Related Workmentioning

confidence: 99%

A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network

Avilés-Mendoza,

Gaibor-León,

Asanza

et al. 2023

Biomimetics

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“…Several studies showed the usefulness of TD, especially in terms of its quickness, simplicity, and lack of any necessary transformation. [12]. The main disadvantage of the TD is that the features are produced by the signal's stationary nature.…”

Section: Step2 : Feature Extractionmentioning

confidence: 99%

Recognition of Upper Limb Movements Based on Hybrid EEG and EMG Signals for Human-Robot Interaction

2023

IJCCCE

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Upper limb amputation is a condition that severely limits the amputee’s movement. Patients who have lost the use of one or more of their upper extremities have difficulty performing activities of daily living. To help improve the control of upper limb prosthesis with pattern recognition, non-invasive approaches (EEG and EMG signals) is proposed in this paper and are integrated with machine learning techniques to recognize the upper-limb motions of subjects. EMG and EEG signals are combined, and five features are utilized to classify seven hand movements such as (wrist flexion (WF), outward part of the wrist (WE), hand open (HO), hand close (HC), pronation (PRO), supination (SUP), and rest (RST)). Experiments demonstrate that using mean absolute value (MAV), waveform length (WL), Wilson Amplitude (WAMP), Sine Slope Changes (SSC), and Cardinality features of the proposed algorithm achieves a classification accuracy of 89.6% when classifying seven distinct types of hand and wrist movement. Index Terms— Human Robot Interaction, Bio-signals Analysis, LDA classifier.

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“…Discrete Wavelet Transform (DWT) is one of the tools for analyzing and processing signals and images, allowing for efficient representation of information at different scales and frequencies. It has found applications in various fields, including vision and audio processing, data compression, and feature extraction [10]. For the identification process, the Neural network algorithm is used.…”

Section: Introductionmentioning

confidence: 99%

Discrete Wavelet and Neural Network Algorithm for Real-time Identification Finger Movement

Risandrya,

Sinaga,

Pamungkas

2024

Proceedings of the 6th International Conference on Applied Engineering, ICAE 2023, 7 November 2023, Batam, Riau Islands, Indone

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Electromyography (EMG) Signals from human muscles can be utilized in various fields. One of them is in the control field. Some control systems use a person's finger movements. So, an algorithm is needed to recognize hand movement patterns. This paper examines systems' capabilities using neural network algorithms with discrete wavelet transforms. The signal is obtained from the EMG signal generated by the surface EMG sensor. This sensor is issued on the user's upper arm. This study used three healthy subjects with five-finger movements. This system is able to recognize patterns of finger movements, about 79.79%.

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Classification of Myoelectric Signal using Spectrogram Based Window Selection

Cited by 4 publications

References 23 publications

A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network

A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network

Recognition of Upper Limb Movements Based on Hybrid EEG and EMG Signals for Human-Robot Interaction

Discrete Wavelet and Neural Network Algorithm for Real-time Identification Finger Movement

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