Multi-channel ultrathin flexible stretchable myoelectricity sensor and preparation method thereof

Abstract

The invention provides a multi-channel ultrathin flexible stretchable myoelectricity sensor and a preparation method thereof, and relates to the technical field of biological electrodes, the sensor comprises a flexible substrate, a processor and a plurality of sensing units; the sensing units are uniformly arranged on the flexible substrate; each sensing unit is used for acquiring a plurality of electromyographic signals and a plurality of muscle impedance signals within a set time period; the processor is used for performing feature extraction on each electromyographic signal to obtain a plurality of time domain features and a plurality of frequency domain features; the processor is used for performing feature extraction on each muscle impedance signal to obtain a plurality of impedance amplitude features; carrying out splicing fusion to obtain a fusion feature vector; and the processor obtains gesture classification based on the fused feature vector in combination with the classifier. According to the method, the problems of low recognition rate and poor robustness caused by unstable signals, single information dimension and signal asynchronization are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bioelectrode, in particular to a multi-channel ultrathin flexible stretchable electromyography sensor and a preparation method thereof. BACKGROUND

[0002] Bioelectric signals include electromyography signals, electrocardiogram signals, electroencephalogram signals and the like, wherein electromyography signals are often used in the fields of muscle fatigue evaluation and virtual reality. As a tool for collecting electromyography signals, electromyography electrodes are divided into single-channel electrodes and array-type electromyography electrode patches composed of multiple channels. Compared with single electrodes, array-type electrode patches can obtain electromyography signals of multiple channels covering muscle groups and extract more information about muscle group activation, coordination and the like, and thus are widely studied. Among them, flexible and stretchable electromyography array-type electrodes become an important development direction of electromyography array-type electrode patches due to their good skin adhesion, anti-interference ability and other characteristics.

[0003] Existing electromyography electrodes are rigid or thick flexible circuits, which have poor skin adhesion, are prone to displacement and noise during movement, and have insufficient signal stability. Only collecting electromyography signals, electromyography signals are easily disturbed by muscle fatigue, skin condition and the like, and lack of synchronous monitoring of muscle physical state (such as tension and morphological changes), resulting in incomplete description of gesture intention. SUMMARY

[0004] The present application aims to provide a multi-channel ultrathin flexible stretchable electromyography sensor and a preparation method thereof, and solve the problems of low recognition rate and poor robustness caused by unstable signals, single information dimension and asynchronous signals.

[0005] A multi-channel ultrathin flexible stretchable electromyography sensor comprises a flexible substrate, a processor and a plurality of sensing units. Each of the sensing units is uniformly arranged on the flexible substrate, and each of the sensing units is connected to the processor. Each of the sensing units is configured to obtain a plurality of initial electromyography signals and a plurality of initial muscle impedance signals within a set time period. The processor is configured to pre-process each of the initial electromyography signals to obtain a plurality of processed electromyography signals, and extract features from each of the processed electromyography signals to obtain a plurality of time domain features and a plurality of frequency domain features. The processor is configured to pre-process each of the initial muscle impedance signals to obtain a plurality of processed muscle impedance signals, and extract features from each of the processed muscle impedance signals to obtain a plurality of impedance amplitude features. The processor is configured to splice and fuse each of the time domain features, each of the frequency domain features and each of the impedance amplitude features to obtain a fused feature vector. The processor obtains gesture classification in combination with a classifier based on the fusion feature vector.

[0006] Preferably, each of the sensing units is configured to acquire a plurality of initial electromyography signals and a plurality of initial muscle impedance signals within a set time period. The processor sends a synchronization signal to control the sensing units to synchronously collect signals to obtain the initial electromyography signals and the initial muscle impedance signals, and the initial electromyography signals and the initial muscle impedance signals are time-aligned.

[0007] Preferably, the number of the sensing units is 4. Two of the sensing units are configured to acquire the initial electromyography signals, and the other two sensing units are configured to acquire the initial muscle impedance signals.

[0008] Preferably, the four sensing units are arranged in a V shape on the flexible substrate, and the four sensing units sequentially acquire the initial electromyography signals, the initial muscle impedance signals, the initial electromyography signals, and the initial muscle impedance signals in a clockwise direction.

[0009] Preferably, the flexible substrate comprises a glass substrate and polydimethylsiloxane (PDMS). The PDMS is spin-coated on the glass substrate.

[0010] Preferably, the classifier is any one of an artificial neural network, K-nearest neighbor, support vector machine, and linear discriminant analysis.

[0011] The application also provides a preparation method of the above-mentioned multi-channel ultrathin flexible stretchable electromyography sensor. The AgNFs film is prepared based on magnetron sputtering. The initial glass substrate is soaked in a soap water solution, then washed with clean water, soaked in a hydrochloric acid solution, and washed again to obtain the glass substrate. The PDMS is spin-coated on the glass substrate, and the flexible substrate is obtained after drying. The flexible substrate is placed in clean water, the AgNFs film is placed on the water surface and flattened. The flexible substrate is used to pick up the AgNFs film at a set angle, and the AgNFs film is dried along a set direction, and then the initial sensor is obtained after drying and baking. The AgNFs film on the initial sensor is etched to obtain the sensing units.

[0012] Preferably, the concentration of the hydrochloric acid solution is 1%-2%.

[0013] Preferably, the AgNFs film on the initial sensor is etched to obtain each of the sensing units. Mark a set electrode pattern on the initial sensor, and etch the AgNFs film according to the set electrode pattern to obtain each of the sensing units.

[0014] Effects of the present application are as follows: The multi-channel ultrathin flexible stretchable electromyography sensor collects electromyography signals and muscle impedance signals, the electromyography signals reflect nerve driving, and the muscle impedance signals reflect physical form changes of muscles, and the two provide a more comprehensive and more robust gesture "fingerprint", effectively resisting the defect that a single signal is susceptible to interference.

[0015] The multi-channel ultrathin flexible stretchable electromyography sensor has a flexible stretchable structure that seamlessly fits the skin, and motion artifacts are suppressed; synchronous acquisition ensures consistency of multi-modal signals in the time domain, laying a foundation for accurate fusion. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a structural schematic diagram of the multi-channel ultrathin flexible stretchable electromyography sensor of the present application.

[0017] In the figure: 1, flexible substrate; 2, sensing unit. DETAILED DESCRIPTION

[0018] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings.

[0019] Figure 1 is a structural schematic diagram of the multi-channel ultrathin flexible stretchable electromyography sensor of the present application. As shown in the figure, Figure 1 the present application provides a multi-channel ultrathin flexible stretchable electromyography sensor, which comprises: a flexible substrate 1, a processor, and a plurality of sensing units 2.

[0020] Each sensing unit 2 is uniformly arranged on the flexible substrate 1; each sensing unit 2 is connected to the processor. Preferably, the flexible substrate 1 comprises a glass substrate and (Polydimethylsiloxane, abbreviated as PDMS) polydimethylsiloxane; the PDMS is spin-coated on the glass substrate.

[0021] Each sensing unit 2 is used to obtain a plurality of initial electromyography signals and a plurality of initial muscle impedance signals within a set time period. Specifically, the processor sends a synchronization signal to control each sensing unit 2 to synchronously collect signals to obtain each initial electromyography signal and each initial muscle impedance signal, and each initial electromyography signal and each initial muscle impedance signal are time-aligned.

[0022] Preferably, the number of sensing units 2 is 4.

[0023] 2 sensing units 2 are used to obtain initial electromyography signals; 2 sensing units 2 are used to obtain initial muscle impedance signals.

[0024] 4 sensing units 2 are arranged in a V shape on the flexible substrate 1, and in a clockwise direction, the 4 sensing units 2 obtain initial electromyography signals, initial muscle impedance signals, initial electromyography signals, and initial muscle impedance signals in turn.

[0025] The processor is used for pre-processing each initial electromyography signal to obtain a plurality of processed electromyography signals, and extracting features from each processed electromyography signal to obtain a plurality of time domain features and a plurality of frequency domain features.

[0026] The processor is used for pre-processing each initial muscle impedance signal to obtain a plurality of processed muscle impedance signals, and extracting features from each processed muscle impedance signal to obtain a plurality of impedance amplitude features.

[0027] Specifically, the pre-processing includes filtering and rectification. Further, the filtering is selected as a band-pass filter to remove motion artifacts and power frequency interference.

[0028] The processor splices and fuses each time domain feature, each frequency domain feature, and each impedance amplitude feature to obtain a fusion feature vector. The fusion feature vector is a high-dimensional, information-complementary feature vector.

[0029] The processor obtains gesture classification based on the fusion feature vector in combination with a classifier.

[0030] In the embodiment, the classifier is any one of an artificial neural network, K-nearest neighbor, support vector machine, and linear discriminant analysis. By comparing the performance of different classifiers, the optimal model is selected to realize high-precision gesture classification.

[0031] The application also provides a preparation method of a multi-channel ultrathin flexible stretchable electromyography sensor, and the preparation method comprises the following steps: An AgNFs film is prepared based on magnetron sputtering.

[0032] The initial glass substrate is soaked in a soap water solution, then washed with clean water, soaked in a hydrochloric acid solution, and washed again to obtain a glass substrate. Preferably, the concentration of the hydrochloric acid solution is 1%-2%.

[0033] PDMS is spin-coated on the glass substrate, and after spinning, the glue is dried to obtain a flexible substrate.

[0034] The flexible substrate is placed in clean water, and the AgNFs film is placed on the water surface and flattened.

[0035] The AgNFs film is lifted at a set angle using a flexible substrate and dried along a set direction. After drying, it is then baked to obtain the initial sensor.

[0036] The AgNFs thin film on the initial sensor is etched to obtain each sensing unit. Specifically, a set electrode pattern is marked on the initial sensor, and the AgNFs thin film is etched according to the set electrode pattern to obtain each sensing unit.

[0037] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A multi-channel ultrathin flexible stretchable electromyography sensor, characterized in that, It includes: Flexible substrate, processor and several sensing units; Each of the aforementioned sensing units is uniformly disposed on the flexible substrate; Each of the aforementioned sensing units is connected to the processor; Each of the aforementioned sensing units is used to acquire several initial electromyographic signals and several initial muscle impedance signals within a set time period; The processor is used to preprocess each of the initial electromyographic signals to obtain several processed electromyographic signals, and to extract features from each of the processed electromyographic signals to obtain several time-domain features and several frequency-domain features. The processor is used to preprocess each of the initial muscle impedance signals to obtain several processed muscle impedance signals, and to extract features from each of the processed muscle impedance signals to obtain several impedance amplitude features. The processor concatenates and fuses each of the time-domain features, each of the frequency-domain features, and each of the impedance amplitude features to obtain a fused feature vector. The processor, based on the fused feature vector and combined with a classifier, obtains a gesture classification.

2. The multi-channel ultrathin flexible stretchable electromyography sensor according to claim 1, characterized in that, Each of the aforementioned sensing units is used to acquire several initial electromyographic signals and several initial muscle impedance signals within a set time period, specifically: The processor sends a synchronization signal to control each of the sensing units to synchronously acquire signals, thereby obtaining each of the initial electromyographic signals and each of the initial muscle impedance signals, and the initial electromyographic signals and each of the initial muscle impedance signals are time-aligned.

3. The multi-channel ultrathin flexible stretchable electromyography sensor according to claim 1, characterized in that, The number of sensing units is 4; Two of the aforementioned sensing units are used to acquire the initial electromyographic signal; two of the aforementioned sensing units are used to acquire the initial muscle impedance signal.

4. The multi-channel ultrathin flexible stretchable electromyography sensor according to claim 3, characterized in that, The four sensing units are arranged in a U-shape on the flexible substrate. In a clockwise direction, the four sensing units sequentially acquire the initial electromyographic signal, the initial muscle impedance signal, the initial electromyographic signal, and the initial muscle impedance signal.

5. The multi-channel ultrathin flexible stretchable electromyography sensor according to claim 1, characterized in that, The flexible substrate includes a glass substrate and polydimethylsiloxane (PDMS); The PDMS is spin-coated onto the glass substrate.

6. The multi-channel ultrathin flexible stretchable electromyography sensor according to claim 1, characterized in that, The classifier can be any one of artificial neural networks, K-nearest neighbors, support vector machines, and linear discriminant analysis.

7. A method for fabricating a multi-channel ultrathin flexible stretchable electromyography sensor according to any one of claims 1-6, characterized in that, Preparation methods include: AgNFs thin films were prepared by magnetron sputtering; The initial glass substrate was soaked in a soapy water solution and then rinsed with water. After rinsing, it was soaked in a hydrochloric acid solution and rinsed again to obtain the glass substrate. PDMS is spin-coated onto the glass substrate, and after spin coating and drying, a flexible substrate is obtained. The flexible substrate was placed in clean water, and the AgNFs film was placed on the water surface and flattened. The AgNFs film is lifted at a set angle using a flexible substrate and blown dry in a set direction. After blowing dry, it is dried to obtain the initial sensor. The AgNFs thin film on the initial sensor is etched to obtain each sensing unit.

8. The method for fabricating a multi-channel ultrathin flexible stretchable electromyography sensor according to claim 7, characterized in that, The concentration of the hydrochloric acid solution is 1%-2%.

9. The method for fabricating a multi-channel ultrathin flexible stretchable electromyography sensor according to claim 7, characterized in that, The etching of the AgNFs thin film on the initial sensor to obtain each sensing unit specifically involves: A set electrode pattern is marked on the initial sensor, and the AgNFs thin film is etched according to the set electrode pattern to obtain each of the sensing units.

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