Instantaneous myoelectricity image based gesture identification method

A technology of gesture recognition and myoelectricity, applied to pattern recognition in signals, character and pattern recognition, instruments, etc.

Active Publication Date: 2016-05-25
ZHEJIANG UNIV
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  • Instantaneous myoelectricity image based gesture identification method
  • Instantaneous myoelectricity image based gesture identification method
  • Instantaneous myoelectricity image based gesture identification method

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Embodiment

[0051] The present invention recognizes gestures based on instantaneous electromyographic signals, according to figure 1 , mainly consists of two parts: the offline training part and the online recognition part.

[0052] The offline training part includes:

[0053] a.according to image 3 Attach electrodes to the tester.

[0054] b. collection figure 2 8 gestures in . Each gesture was collected 10 times, and each gesture lasted for 3 seconds, and there was a 7-second rest between two consecutive exertions.

[0055] c. Perform preprocessing on the instantaneous EMG signal collected by the array electrodes, and linearly transform the EMG signal in the range of ±2.5mV into the range from 0 to 255. The instantaneous myoelectric signal values ​​at each moment are arranged in a matrix according to the spatial positions of the array electrodes to form an instantaneous myoelectric image.

[0056] d. Use colored natural images to pre-train the 16-layer VGGNet network used to cla...

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Abstract

The invention discloses an instantaneous myoelectricity image based gesture identification method. During a training stage, firstly instantaneous myoelectricity signals acquired by array electrodes are preprocessed and arranged according to electrode positions to form an instantaneous myoelectricity image; and secondly an image classifier such as a deep convolutional neural network is trained by using the instantaneous myoelectricity image and a gesture tag corresponding to the instantaneous myoelectricity image to obtain network model parameters. During a test stage, firstly to-be-identified instantaneous myoelectricity signals acquired by the array electrodes are preprocessed and arranged according to the electrode positions to form the instantaneous myoelectricity image; and secondly the trained model parameters are substituted into the classifier to identify the gesture tag corresponding to the instantaneous myoelectricity signals. According to the instantaneous myoelectricity image based gesture identification method, a gesture can be quickly and accurately identified based on the instantaneous myoelectricity image and an image classification method. No literature for gesture identification by the instantaneous myoelectricity signals exists at home and abroad yet.

Description

technical field [0001] The invention belongs to the field of combining computer and biological signals, and specifically uses an image classifier represented by a deep convolutional neural network to recognize gestures corresponding to instantaneous myoelectric images extracted from instantaneous myoelectric signals. Background technique [0002] With the rapid development of new technologies such as computer vision, touch interaction, and perceptual computing, perceptual user interface (PUI) has become one of the research focuses in the field of human-computer interaction. Perceptual user interface is a highly interactive and multi-channel user interface based on the interaction between people and the real world. Its goal is to make human-computer interaction and human-real world interaction consistent. Reach the realm of intuitive and natural interaction. In order to enable computers to better judge and understand human interaction intentions, "integration of life, machin...

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Application Information

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IPC IPC(8): G06K9/00
CPCG06F2218/02G06F2218/12
Inventor 耿卫东杜宇李嘉俊卫文韬胡钰
Owner ZHEJIANG UNIV
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