CN104665828A - System and method based on electromyographic signal controlling remote controller - Google Patents

Abstract

The invention provides a system and method for controlling a remote controller based on electromyographic signals. The method includes: S1, collecting the myoelectric signal at the muscle of the forearm; S2, filtering the collected myoelectric signal; S3, calculating the integral myoelectric value according to the filtered myoelectric signal; S4, using the obtained The integrated myoelectric value is compared with the preset threshold value to obtain a myoelectric signal output command; S5. Perform remote control output setting according to the myoelectric signal output command. The present invention detects the myoelectric signal of wrist movement by collecting electrodes on the arm, and then performs signal processing and recognition on various wrist postures through signal processing, calculation and judgment, so as to simply control basic daily household appliances.

Description

System and method for controlling remote controller based on myoelectric signal

technical field

The present invention relates to signal processing, in particular to a system and method for controlling a remote controller based on myoelectric signals.

Background technique

Surface-Electromyography SEMG (Surface-Electromyography) characterizes the original information of muscle activity in a certain area. By detecting the EMG signals of different parts of the human body, it can comprehensively respond to the state of joint extension and flexion and muscle movement. According to different acquisition methods, EMG detection electrodes can be divided into surface electrodes and needle electrodes. Wherein, although the needle-shaped electrode can collect deeper myoelectric signals, it has trauma to the human body, which greatly restricts its practical application; the surface-mounted electrode is widely used, although it is noninvasive to the human body, it is also Various noises are introduced, such as motion artifact noise, power frequency noise, etc., and the use of surface-mounted electrodes to collect surface EMG signals needs to overcome problems such as weak surface EMG signals, easy interference, and low direct recognition. Therefore, the detected EMG signal needs to rely largely on the methods of signal processing and signal analysis to extract the identified signal and identify the motion states of different limbs. In recent years, there have been many advances in EMG research at home and abroad, such as: using EMG signals to control robotic hands, prosthetic control based on EMG signals, and gesture recognition based on EMG signals. SEMG performs feature extraction, and finally completes pattern recognition, and controls the machine equipment.

Contents of the invention

The technical problem solved by the present invention is to provide a system and method for controlling a remote controller based on electromyographic signals. The electromyographic signals of wrist movements are detected by collecting electrodes on the arm, and then after signal processing, calculation and judgment, various wrist postures can be detected. Signal processing and recognition for simple control of basic everyday household appliances.

In order to solve the above technical problems, the present invention provides a system for controlling remote controllers based on myoelectric signals, including a myoelectric signal acquisition module, a signal processing module, a signal calculation module, a signal judgment module, a signal output module, and a control module above the control module;

The electromyographic signal acquisition module includes a surface-mounted electrode, and the surface-mounted electrode is used for collecting electromyographic signals;

The signal processing module is used to preprocess the electromyography signal;

The signal calculation module is used to calculate the integral myoelectric value according to the preprocessed myoelectric signal;

The signal judging module compares the obtained integrated myoelectric value with a preset threshold value to obtain an electromyographic signal output command;

The signal output module is used for setting the remote control output according to the myoelectric signal output command.

Preferably, the surface-mounted electrode has a preamplifier, and the surface-mounted electrode is placed on the flexor carpi radialis, flexor carpi ulnaris, flexor hallucis longus, flexor digitorum deep and extensor digitorum of the forearm muscles place.

Preferably, the mutual spacing of the surface-mounted electrodes is 20mm, and the shielded wires are used to output to the signal processing module.

Preferably, the signal processing module includes an active bandpass filter and a power frequency notch filter, wherein the passband frequency of the active bandpass filter is 20Hz-500Hz, and the power frequency notch filter is based on LMS Algorithm settings.

Preferably, the formula for obtaining the integral myoelectric value is: Wherein, N is the data length of the integrated EMG value, and _xi is each small part after the whole data is divided into N parts.

In order to solve the above technical problems, the present invention also provides a method for controlling a remote controller based on myoelectric signals, comprising the following steps:

S1, collect the myoelectric signal at the forearm muscle;

S2. Filtering and processing the collected EMG signal;

S3. Calculate the integral myoelectric value according to the filtered myoelectric signal;

S4. Comparing the obtained integral myoelectric value with a preset threshold value to obtain an electromyographic signal output command;

S5. Perform remote control output setting according to the myoelectric signal output command.

Preferably, in step S1, surface-mounted electrodes with a pre-amplification function are used to place five different positions of the forearm muscles, namely flexor carpi radialis, flexor carpi ulnaris, flexor hallucis longus, flexor digitorum muscle as well.

Preferably, in step S2, an active band-pass filter and a power frequency notch filter are used to filter the collected electromyographic signals, wherein the passband frequency of the active band-pass filter is 20 Hz-500 Hz , the power frequency notch filter is set based on the LMS algorithm.

Preferably, in step S3, the formula for obtaining the integral myoelectric value is:

Wherein, N is the data length of the integrated EMG value, and _xi is each small part after the whole data is divided into N parts.

Preferably, in step S4, the electromyographic signal output command is obtained to complete the binary code recognition of the action.

The present invention proposes a system and method for controlling a remote controller based on myoelectric signals. The operator accurately collects electrodes through the arm, detects the myoelectric signal data of wrist movement, and then performs signal processing on four different wrist postures through a circuit processing unit. and identification. After analyzing and processing the myoelectric signals of different postures of the wrist, namely wrist extension, wrist flexion, wrist rotation, and wrist rotation, etc., control four different buttons on the remote control, and match different transmitting unit modules to control different electronic equipment to operate. The present invention relies on the myoelectric signal platform and uses a brand-new remote control without other limb movements, which is convenient for some disabled people to complete the simple remote control operation of daily household appliances, relying on the reasonable placement of detection electrodes and the use of acquisition electrodes with pre-amplification functions , which greatly improves the accuracy and recognizability of the collected signals, and uses real-time signal processing methods to significantly improve the control rate and accuracy.

Description of drawings

Fig. 1 is the schematic diagram of the system of the present invention based on electromyography signal control remote controller;

Fig. 2 is the circuit diagram of active bandpass filter among the present invention;

Fig. 3 is the schematic diagram of the adaptive filter based on LMS algorithm among the present invention;

FIG. 4 is a flow chart of the method for controlling the remote controller based on the electromyographic signal of the present invention.

Fig. 5 is a flow chart of remote controller design in the present invention;

6 is a schematic circuit diagram of a signal processing module in the present invention;

Fig. 7 is a schematic diagram of the mid-infrared module of the present invention.

Detailed ways

The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

Please refer to FIG. 1 , the present invention discloses a system 100 for controlling a remote controller based on myoelectric signals, including a myoelectric signal acquisition module 20, a signal processing module 30, a signal calculation module 40, a signal judgment module 50, and a signal output module 60, And a control module 70 that controls the above modules.

The electromyographic signal collection module 20 includes surface-attached electrodes for collecting electromyographic signals. The surface-mounted electrode has a preamplifier placed at the position of the flexor carpi radialis, flexor carpi ulnaris, flexor hallucis longus, flexor digitorum profundus, and extensor digitorum of the forearm muscles. The distance between the surface-mounted electrodes is 20 mm, and the shielded wires are used to output to the signal processing module 30 . In the present invention, the surface-mounted electrode is accurately placed on the forearm muscle, which improves the detection accuracy and signal-to-noise ratio from the source of collection. Further, the surface-mounted electrode has a preamplifier electrode, which can reduce the number of wires and signal processing modules. 30, reducing environmental noise interference.

The signal processing module 30 is configured to preprocess the electromyography signal. The signal processing module 30 includes an active band-pass filter and a power frequency notch filter, wherein, please refer to FIG. 2 , the passband frequency of the active band-pass filter is 20Hz-500Hz, which is implemented in the form of a hardware circuit , the resistance values of resistors R1 and R2 are 40KΩ and 2.5KΩ respectively, the values of capacitors C1 and C2 are both 0.1μf, and the operational amplifier is OPA227; the power frequency trap is set based on the LMS algorithm, and the adaptive working of the LMS algorithm Please refer to Figure 3 for the principle of the frequency notch filter.

The signal calculation module 40 is configured to calculate the integral myoelectric value according to the preprocessed myoelectric signal. The formula for obtaining the integral myoelectric value is: Wherein, N is the data length of the integrated EMG value, and _xi is each small part after the whole data is divided into N parts.

The signal judging module 50 is configured to compare the obtained integral myoelectric value with a preset threshold value to obtain a myoelectric signal output command, and the myoelectric output command adopts a binary code. The present invention does not need complex algorithms, but only needs a threshold level detection circuit to detect the level and output a binary code.

The signal output module 60 is used for setting the remote control output according to the myoelectric signal output command. In the present invention, wrist movements can be set, corresponding signals can be obtained according to wrist movements, and can be recognized as binary codes, and different red-haired transmission signals can be carried by binary codes to complete remote control commands.

Please refer to FIG. 4, the present invention provides a method for controlling a remote controller based on myoelectric signals, including the following steps:

S1, collect the myoelectric signal at the forearm muscle;

In step S1, the surface-mounted electrodes with pre-amplification function were placed on five different positions of the forearm muscles, namely flexor carpi radialis, flexor carpi ulnaris, flexor hallucis longus, flexor digitorum profundus, and digitorum position of the extensor muscles.

S2. Filtering and processing the collected EMG signal;

In step S2, an active band-pass filter and a power frequency notch filter are used to filter the collected electromyographic signals, wherein the passband frequency of the active band-pass filter is 20Hz-500Hz, and the The power frequency notch filter is set based on the LMS algorithm.

S3. Calculate the integral myoelectric value according to the filtered myoelectric signal;

In step S3, the formula for obtaining the integral myoelectric value is: Wherein, N is the data length of the integrated EMG value, and _xi is each small part after the whole data is divided into N parts.

S4. Comparing the obtained integral myoelectric value with a preset threshold value to obtain an electromyographic signal output command;

In step S4, the obtained integral myoelectric value is compared with a preset threshold value and converted into a binary number. Among them, presetting means that the signal collected for the first time has been stored as a threshold value in the internal register of the processing unit. In this embodiment, it mainly performs signal processing and recognition on four different wrist postures, that is, extending the wrist, flexing the wrist, Internally rotated wrist and externally rotated wrist. Because of the wrist posture, there is no need to use complex algorithms to analyze the similarity of waveforms. Only the threshold comparator is used. If the signal amplitude detected by each channel is greater than the threshold value, the corresponding channel will output a binary signal 1, otherwise, a binary signal 0 will be output. Further, the binary value encoding of each channel under the four gestures is used as a judgment for different wrist movements.

S5. Perform remote control output setting according to the myoelectric signal output command.

According to step S5, please refer to FIG. 5, which is a design process of a remote controller based on SEMG. The remote controller is equipped with different infrared emission modules. In one embodiment, it can conveniently enable operations such as TV sets or air conditioners. Four functions of the remote control: such as volume adjustment, channel switching, wind speed adjustment, temperature adjustment, etc. After the EMG signal is collected and compared, the output value of each channel is input to the chip STM32. For details, please refer to Figure 6. Choose the STM32L103 series with 100-pin package, which comes with 12-bit high-precision analog-to-digital conversion, and is fully sufficient to support multi-channel input signal. The processing capability of STM32L is superior, and it is fully capable of digital processing capability and required MCU processing capability. The 12 and 13 pins of the STM32 are connected to an external 8M high-frequency crystal oscillator and R62, C68, and C69. The 23-26 feet and 29-32 feet are used as the analog-to-digital conversion input terminals of the signal acquisition, the 14th terminal is connected with the resistor R57, R58 and the capacitor C70 form a reset key. Pin 94 is connected to resistor R59, pin 37 is connected to resistor R60 and grounded, which are respectively BOOT ports. 6, 59, 75, 100, 28, 11 pins are connected to the power supply. 10, 27, 49, 74, and 99 are all grounded, and the sampling frequency and baud rate are selected by the program. After analog-to-digital conversion, the analog signal is stored in the DMA register, and pin 81 is output to the infrared transmitter module, and the infrared transmitter module completes the signal transmission to the corresponding device.

The infrared transmitter module is composed of single-chip microcomputer MSP430F415, please refer to Figure 7, if the signal is output from the STM32 to the infrared transmitter module MSP430F415, after initial experiments, different wrist signal recognition and control commands are shown in Table 1. Different commands have been stored in the registers of MSP430, corresponding to different commands are judged and infrared signals are emitted to complete the remote control of the corresponding equipment.

Table 1 Wrist signal recognition and control commands

The present invention proposes a system and method for controlling a remote controller based on myoelectric signals. The operator accurately collects electrodes through the arm, detects the myoelectric signal data of wrist movement, and then performs signal processing on four different wrist postures through a circuit processing unit. and identification. After analyzing and processing the myoelectric signals of different postures of the wrist, such as wrist extension, wrist flexion, internal rotation, and external rotation, etc., control the four different buttons of the remote control, and match different transmitting unit modules to control different electronic equipment to operate. The present invention relies on the myoelectric signal platform and uses a brand-new remote control without other limb movements, which is convenient for some disabled people to complete simple remote control operations of daily household appliances, relying on the reasonable placement of detection electrodes and the use of acquisition electrodes with pre-amplification functions , which greatly improves the accuracy and recognizability of the collected signals, and uses real-time signal processing methods to significantly improve the control rate and accuracy.

It can be understood that those skilled in the art can make various other corresponding changes and deformations according to the technical concept of the present invention, and all these changes and deformations should belong to the protection scope of the claims of the present invention.

Claims (10)

1. A system based on myoelectric signal control remote controller, characterized in that, comprising a myoelectric signal acquisition module, a signal processing module, a signal calculation module, a signal judgment module, a signal output module, and a control module controlling the above modules; The electromyographic signal acquisition module includes a surface-mounted electrode, and the surface-mounted electrode is used for collecting electromyographic signals; The signal processing module is used to preprocess the electromyography signal; The signal calculation module is used to calculate the integral myoelectric value according to the preprocessed myoelectric signal; The signal judging module compares the obtained integrated myoelectric value with a preset threshold value to obtain an electromyographic signal output command; The signal output module is used for setting the remote control output according to the myoelectric signal output command. 2. The system of controlling the remote controller based on myoelectric signal according to claim 1 is characterized in that: the surface-attached electrode has a preamplifier, and the surface-attached electrode is placed on the flexor carpi radialis and the ulnar side of the forearm muscle At the location of the wrist flexors, flexor hallucis longus, flexor digitorum profundus, and digitorum extensors. 3. The system for controlling the remote controller based on myoelectric signals according to claim 2, characterized in that: the distance between the surface-mounted electrodes is 20 mm, and the shielded wires are used to output to the signal processing module. 4. The system for controlling a remote controller based on myoelectric signals according to claim 1, wherein the signal processing module includes an active band-pass filter and a power frequency notch filter, wherein the active band-pass filter The passband frequency of the filter is 20Hz-500Hz, and the power frequency notch filter is set based on the LMS algorithm. 5. the system based on myoelectric signal control remote controller according to claim 1, is characterized in that: the obtaining formula of described integral myoelectric value is: Wherein, N is the data length of the integrated EMG value, and _xi is each small part after the whole data is divided into N parts. 6. A method for controlling a remote controller based on myoelectric signals, comprising the following steps: S1, collect the myoelectric signal at the forearm muscle; S2. Filtering and processing the collected EMG signal; S3. Calculate the integral myoelectric value according to the filtered myoelectric signal; S4. Comparing the obtained integral myoelectric value with a preset threshold value to obtain an electromyographic signal output command; S5. Perform remote control output setting according to the myoelectric signal output command. 7. The method for controlling a remote controller based on myoelectric signals according to claim 6, wherein, in step S1, the surface-attached electrodes with pre-amplification function are used to be placed on five different positions of the forearm muscles, respectively radial Flexor carpi lateralis, flexor carpi ulnaris, flexor hallucis longus, flexor digitorum profundus, and extensor digitorum. 8. The method for controlling a remote controller based on an electromyographic signal according to claim 6, wherein in step S2, an active band-pass filter and a power frequency notch filter are used to filter the collected electromyographic signal , wherein, the passband frequency of the active bandpass filter is 20Hz-500Hz, and the power frequency notch filter is set based on the LMS algorithm. 9. the method for controlling remote controller based on myoelectric signal according to claim 6, is characterized in that, in step S3, the obtaining formula of described integral myoelectric value is: Wherein, N is the data length of the integrated EMG value, and _xi is each small part after the whole data is divided into N parts. 10. The method for controlling the remote controller based on the myoelectric signal according to claim 6, characterized in that in step S4, obtaining the myoelectric signal output command is to complete the binary code recognition of the action.