Biosignal-based transferable attention Bi-ConvGRU deep network for hand-gesture recognition towards online upper-limb prosthesis control

Xie, Baao; Meng, James; Li, Baihua; Harland, Andy R.

doi:10.1016/j.cmpb.2022.106999

Cited by 20 publications

(4 citation statements)

References 25 publications

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“…Xie et al [16] developed an advanced neural network model, Bi-ConvGRU, to recognize hand gestures from EMG signals, allowing detailed measurement of muscle activity. This model was evaluated by considering 18 hand gestures from the Ninapro dataset performed by both amputee and non-amputee individuals.…”

Section: Related Workmentioning

confidence: 99%

Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization

Aviles,

Alvarez-Alvarado,

Robles-Ocampo

et al. 2024

Bioengineering

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Accurate classification of electromyographic (EMG) signals is vital in biomedical applications. This study evaluates different architectures of recurrent neural networks for the classification of EMG signals associated with five movements of the right upper extremity. A Butterworth filter was implemented for signal preprocessing, followed by segmentation into 250 ms windows, with an overlap of 190 ms. The resulting dataset was divided into training, validation, and testing subsets. The Grey Wolf Optimization algorithm was applied to the gated recurrent unit (GRU), long short-term memory (LSTM) architectures, and bidirectional recurrent neural networks. In parallel, a performance comparison with support vector machines (SVMs) was performed. The results obtained in the first experimental phase revealed that all the RNN networks evaluated reached a 100% accuracy, standing above the 93% achieved by the SVM. Regarding classification speed, LSTM ranked as the fastest architecture, recording a time of 0.12 ms, followed by GRU with 0.134 ms. Bidirectional recurrent neural networks showed a response time of 0.2 ms, while SVM had the longest time at 2.7 ms. In the second experimental phase, a slight decrease in the accuracy of the RNN models was observed, standing at 98.46% for LSTM, 96.38% for GRU, and 97.63% for the bidirectional network. The findings of this study highlight the effectiveness and speed of recurrent neural networks in the EMG signal classification task.

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Section: Related Workmentioning

confidence: 99%

Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization

Aviles,

Alvarez-Alvarado,

Robles-Ocampo

et al. 2024

Bioengineering

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show abstract

“…Following some surgeries and diseases, physical rehabilitation therapy is frequently required, and using multimodal measurement systems makes it possible to track the development more precisely [196]. J. Monge et al [197] presented an intelligent physical rehabilitation system that used augmented reality, EMG, ECG, and IMU sensors, remote health status monitoring, and supported rehabilitation to engage patients.…”

Section: E Rehabilitation and Prosthesis Controlmentioning

confidence: 99%

Surface Electromyography and Artificial Intelligence for Human Activity Recognition—A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation

Rani,

Hashmi,

Gupta

2023

IEEE Access

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Human activity recognition (HAR) has become increasingly popular in recent years due to its potential to meet the growing needs of various industries. Electromyography (EMG) is essential in various clinical and biological settings. It is a metric that helps doctors diagnose conditions that affect muscle activation patterns and monitor patients' progress in rehabilitation. Despite its widespread Application, existing methods for recording and interpreting EMG data need more signal detection and robust categorization. Recent material science and Artificial Intelligence (AI) developments have significantly improved EMG detection. With an increasingly elderly patient population, HAR is increasingly used to monitor patients' Activities of Daily Living (ADLs) in healthcare settings. It is also being used in security settings to identify suspect behavior, and Surface EMG (sEMG) is a potential non-invasive treatment for HAR since it monitors muscle contractions during exercise. sEMG and AI have revolutionized HAR systems in recent years. Sophisticated methods are required to recognize, break down, manufacture, and classify the EMG signals obtained by muscles. This review summarizes the various research papers based on HAR with EMG. AI has made tremendous contributions to biomedical signals classification. The different approaches of preprocessing, feature extraction, Reduction techniques, Deep Learning (DL) and Machine Learning (ML) based classification methods of EMG signals are then briefly explained. We focused on latest ML/DL methods used in HAR, Hardware involved in HAR with EMG and EMG based Application .We also discovered open issues and future research direction that may point to new lines of inquiry for ongoing research toward EMG-based detection.

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“…Кроме того, распознавание жестов находит применение при осуществлении человекомашинного взаимодействия [8][9][10][11][12], а также в биомедицине, например, для управления протезами конечностей на основе нервных импульсов предплечья [13]. Дополнительно, для повышения точности работы алгоритмов распознавания жестов применяют специализированное оборудование, такое как перчатки для распознавания жестов [14], другие носимые на руках датчики (например, акселерометр, гироскоп и магнитометр [15] или приборы, регистрирующие биосигналы тела человека.…”

Section: анализ существующих технологийunclassified

Recurrent Neural Network for Recognition of Gestures of the Russian Language, Taking into Account the Language Dialect of the Siberian Region

Zhukovskaya

Pyataeva

2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

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Sign recognition is an important task, in particular for the communication of the deaf and hard of hearing population with people who do not know sign language. Russian sign language is poorly studied, Russian sign language of the Siberian region has significant differences from others within the Russian language group. There is no generally accepted data set for Russian Sign Language. The paper presents a gesture recognition algorithm based on video data. The gesture recognition algorithm is based on the identification of key features of the hands and posture of a person. Gestures were classified using the LSTM recurrent neural network. To train and test the results of gesture recognition, we independently developed a data set consisting of 10 sign words. The selection of words for the data set was made among the most popular words of the Russian language, as well as taking into account the maximum difference in the pronunciation of gestures of the language dialect of the Siberian region. The implementation of the gesture recognition algorithm was carried out using Keras neural network design and deep learning technologies, the OpenCV computer vision library, the MediaPipe machine learning framework, and other auxiliary libraries. Experimental studies conducted on 300 video sequences confirm the effectiveness of the proposed algorithm.

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Biosignal-based transferable attention Bi-ConvGRU deep network for hand-gesture recognition towards online upper-limb prosthesis control

Cited by 20 publications

References 25 publications

Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization

Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization

Surface Electromyography and Artificial Intelligence for Human Activity Recognition—A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation

Recurrent Neural Network for Recognition of Gestures of the Russian Language, Taking into Account the Language Dialect of the Siberian Region

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