Synergistic Myoelectrical Activities of Forearm Muscles Improving Robust Recognition of Multi-Fingered Gestures

Luo, Xiuying; Wu, Xiaoying; Chen, Lin; Yun, Zhao; Zhang, Li; Li, Guanglin; Hou, Wensheng

doi:10.3390/s19030610

Cited by 24 publications

(13 citation statements)

References 50 publications

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“…The signal of sEMG is an electrical signal generated by the potentials generated through the contraction of many fiber bundles in the muscles, which are superimposed in time and space, such as in Figure 6. The signal has strong volatility and instability [17]. In Figure 7, we can see that the fluctuations of sEMG signals in different sign languages are different.…”

Section: Databasementioning

confidence: 99%

An Recognition–Verification Mechanism for Real-Time Chinese Sign Language Recognition Based on Multi-Information Fusion

Wang

Zhao

Zhou

et al. 2019

Sensors

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For online sign language recognition (SLR) based on inertial measurement unit (IMU) and a surface electromyography (sEMG) sensor, achieving high-accuracy is a major challenge. The traditional method for that is the segmentation–recognition mechanism, which has two key challenges: (1) it is difficult to design a highly robust segmentation method for online data with inconspicuous segmentation information; and (2) the diversity of input data will increase the burden of the classification. The recognition–verification mechanism was proposed to improve the performance of online SLR. In the recognition stage, we used sliding windows to pull the data, and applied a convolutional neural network (CNN) to classify the sign language signal. In the verification stage, the confidence was evaluated by the Siamese network to judge the correctness of the classification results. The accuracy and rapidity of the classification model were discussed for 86 categories of Chinese sign language. In the experiments for online SLR, the superiority of the recognition–verification mechanism compared to the segmentation–recognition mechanism was verified.

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Section: Databasementioning

confidence: 99%

An Recognition–Verification Mechanism for Real-Time Chinese Sign Language Recognition Based on Multi-Information Fusion

Wang

Zhao

Zhou

et al. 2019

Sensors

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“…Recognizing multiple gestures is a challenge as the recognition accuracy decreases with the increase in the number of gestures. Table 1 summarizes examples of previous literature attempting to recognize several gestures [10,[14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Int J Elec and Compmentioning

confidence: 99%

Recognition of additional myo armband gestures for myoelectric prosthetic applications

Hussain

Al-Khazzar

Raheema

2020

IJECE

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Myoelectric prostheses are a viable solution for people with amputations. The challenge in implementing a usable myoelectric prosthesis lies in accurately recognizing different hand gestures. The current myoelectric devices usually implement very few hand gestures. In order to approximate a real hand functionality, a myoelectric prosthesis should implement a large number of hand and ﬁnger gestures. However, increasing number of gestures can lead to a decrease in recognition accuracy. In this work a Myo arm band device is used to recognize fourteen gestures (ﬁve build in gestures of Myo armband in addition to nine new gestures). The data in this research is collected from three body-able subjects for a period of 7 seconds per gesture. The proposed method uses a pattern recognition technique based on Multi-Layer Perceptron Neural Network (MLPNN). The results show an average accuracy of 90.5% in recognizing the proposed fourteen gestures.

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“…Wearable sensors include electromyography (EMG), touch sensors, strain gauges, flex sensors, inertial sensors, and ultrasonic sensors [8]. Among wearable sensors, wearable inertial sensors may be the most widely employed for human-motion recognition [9,10]. In general, inertial sensors refer to sensor systems consisting of accelerometers and gyroscopes, and magnetometers.…”

Section: Introductionmentioning

confidence: 99%

Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction

Han

Yoon

2019

Sensors

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Human hand gestures are a widely accepted form of real-time input for devices providing a human-machine interface. However, hand gestures have limitations in terms of effectively conveying the complexity and diversity of human intentions. This study attempted to address these limitations by proposing a multi-modal input device, based on the observation that each application program requires different user intentions (and demanding functions) and the machine already acknowledges the running application. When the running application changes, the same gesture now offers a new function required in the new application, and thus, we can greatly reduce the number and complexity of required hand gestures. As a simple wearable sensor, we employ one miniature wireless three-axis gyroscope, the data of which are processed by correlation analysis with normalized covariance for continuous gesture recognition. Recognition accuracy is improved by considering both gesture patterns and signal strength and by incorporating a learning mode. In our system, six unit hand gestures successfully provide most functions offered by multiple input devices. The characteristics of our approach are automatically adjusted by acknowledging the application programs or learning user preferences. In three application programs, the approach shows good accuracy (90–96%), which is very promising in terms of designing a unified solution. Furthermore, the accuracy reaches 100% as the users become more familiar with the system.

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Synergistic Myoelectrical Activities of Forearm Muscles Improving Robust Recognition of Multi-Fingered Gestures

Cited by 24 publications

References 50 publications

An Recognition–Verification Mechanism for Real-Time Chinese Sign Language Recognition Based on Multi-Information Fusion

An Recognition–Verification Mechanism for Real-Time Chinese Sign Language Recognition Based on Multi-Information Fusion

Recognition of additional myo armband gestures for myoelectric prosthetic applications

Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction

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