Gesture Recognition Through sEMG with Wearable Device Based on Deep Learning

Shen, Shiyu; Gu, Kang; Chen, Xinrong; Lv, Caixia; Wang, Ruchuan

doi:10.1007/s11036-020-01590-8

Cited by 25 publications

(14 citation statements)

References 16 publications

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“…As a result, great effort is being put towards incorporating sensors and microprocessors into various devices to create 'Smart Devices'. These devices size range from large (smart cars [1,2], smart homes [3][4][5]) to minuscule (smart watches [6,7] and other wearable devices [8][9][10]). Consequently, data are being generated and collected at an ever-growing rate, with projections of continual growth.…”

Section: Motivationmentioning

confidence: 99%

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Hasan

Al-Ramini

Abdel‐Rahman

et al. 2020

Sensors

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This work presents an approach to delay-based reservoir computing (RC) at the sensor level without input modulation. It employs a time-multiplexed bias to maintain transience while utilizing either an electrical signal or an environmental signal (such as acceleration) as an unmodulated input signal. The proposed approach enables RC carried out by sufficiently nonlinear sensory elements, as we demonstrate using a single electrostatically actuated microelectromechanical system (MEMS) device. The MEMS sensor can perform colocalized sensing and computing with fewer electronics than traditional RC elements at the RC input (such as analog-to-digital and digital-to-analog converters). The performance of the MEMS RC is evaluated experimentally using a simple classification task, in which the MEMS device differentiates between the profiles of two signal waveforms. The signal waveforms are chosen to be either electrical waveforms or acceleration waveforms. The classification accuracy of the presented MEMS RC scheme is found to be over 99%. Furthermore, the scheme is found to enable flexible virtual node probing rates, allowing for up to 4× slower probing rates, which relaxes the requirements on the system for reservoir signal sampling. Finally, our experiments show a noise-resistance capability for our MEMS RC scheme.

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Section: Motivationmentioning

confidence: 99%

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Hasan

Al-Ramini

Abdel‐Rahman

et al. 2020

Sensors

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show abstract

“…It was found that the RF model presented the most consistent results. More robust models introduced in the deep learning (DL) [17]- [19] era can identify and incorporate patterns from processed data resulting in an increasingly complex system, often presenting a better performance in terms of correlation and accuracy compared to classic ML models [7], [20] at the cost of requiring greater computational power and potentially sacrificing real-time performance.…”

Section: Introductionmentioning

confidence: 99%

On EMG Based Dexterous Robotic Telemanipulation: Assessing Machine Learning Techniques, Feature Extraction Methods, and Shared Control Schemes

et al. 2022

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Electromyography (EMG) signals are commonly used for the development of Muscle Machine Interfaces. EMG-based solutions provide intuitive and often hand-free control in a wide range of applications that range from the decoding of human intention in classification tasks to the continuous decoding of human motion employing regression models. In this work, we compare various machine learning and feature extraction methods for the creation of EMG based control frameworks for dexterous robotic telemanipulation. Various models are needed that can decode dexterous, in-hand manipulation motions and perform hand gesture classification in real-time. Three different machine learning methods and eight different time-domain features were evaluated and compared. The performance of the models was evaluated in terms of accuracy and time required to predict a data sample. The model that presented the best performance and prediction time trade-off was used for executing in real-time a telemanipulation task with the New Dexterity Autonomous Robotic Assistance (ARoA) platform (a humanoid robot). Various experiments have been conducted to experimentally validate the efficiency of the proposed methods. The robotic system is shown to successfully complete a series of tasks autonomously as well as to efficiently execute tasks in a shared control manner.

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“…A CNN can effectively predict human hand movements and improve the efficiency of online prediction thanks to its capacity to process complex and high-dimensional data. Shen et al [ 25 ] designed an sEMG-based gesture classification model using a CNN to avoid omission of important features and to improve the recognition accuracy. Yang et al [ 26 ] proposed a CNN-based framework to identify grasping types for understanding the static scene and the fineness of human movements.…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Hand Grasping Prediction and Control via Postural Synergy Basis Extraction

Liu

Yin

et al. 2022

Sensors

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The prediction of hand grasping and control of a robotic manipulator for hand activity training is of great significance to assist stroke patients to recover their biomechanical functions. However, the human hand and the figure joints have multiple degrees of freedom; therefore, it is complex to process and analyze all the collected data in hand modeling. To simplify the description of grasping activities, it is necessary to extract and decompose the principal components of hand actions. In this paper, the relationships among hand grasping actions are explored by extracting the postural synergy basis of hand motions, aiming to simplify hand grasping actions and reduce the data dimensions for robot control. A convolutional neural network (CNN)-based hand activity prediction method is proposed, which utilizes motion data to estimate hand grasping actions. The prediction results were then used to control a stimulated robotic model according to the extracted postural synergy basis. The prediction accuracy of the proposed method for the selected hand motions could reach up to 94% and the robotic model could be operated naturally based on patient’s movement intention, so as to complete grasping tasks and achieve active rehabilitation.

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Gesture Recognition Through sEMG with Wearable Device Based on Deep Learning

Cited by 25 publications

References 16 publications

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Colocalized Sensing and Intelligent Computing in Micro-Sensors

On EMG Based Dexterous Robotic Telemanipulation: Assessing Machine Learning Techniques, Feature Extraction Methods, and Shared Control Schemes

CNN-Based Hand Grasping Prediction and Control via Postural Synergy Basis Extraction

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