CN109864740B - Surface electromyogram signal acquisition sensor and equipment in motion state - Google Patents
Surface electromyogram signal acquisition sensor and equipment in motion state Download PDFInfo
- Publication number
- CN109864740B CN109864740B CN201811595198.7A CN201811595198A CN109864740B CN 109864740 B CN109864740 B CN 109864740B CN 201811595198 A CN201811595198 A CN 201811595198A CN 109864740 B CN109864740 B CN 109864740B
- Authority
- CN
- China
- Prior art keywords
- electrode
- data
- acceleration
- electromyographic
- electrodes
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The application provides surface electromyogram signal acquisition sensor and equipment of motion state, wherein, this sensor includes: a housing; the first electrode, the second electrode and the third electrode are embedded on the shell and are higher than the shell; a cavity is arranged between the electrodes, wherein the cavity is in an arc-shaped open area; and the double-sided adhesive tape is adhered to the shell and is used for being adhered to the skin of the target object, and the surface electromyographic signal acquisition sensor is fixed on the skin of the target object by exhausting air in the cavity. Through setting up the cavity and being higher than the electrode of shell for paste the use of sensor on skin and crowd the gas in the cavity, can so that the sensor can paste skin tightly, avoid the electrode to break away from skin, promoted surface electromyographic signal collector's steadiness and data accuracy.
Description
Technical Field
The application belongs to the technical field of psychology and human factors engineering, and particularly relates to a surface electromyographic signal acquisition sensor and equipment for a motion state.
Background
Currently, there are many types of muscle electrical signal measuring sensors on the market, generally classified into: invasive and non-invasive. The invasive electrode is not feasible for repeated tests in the scientific research field because it can cause loss to the skin, most of the non-invasive electrode measurements are performed in a static state or under the condition of small motion amplitude, once the subject has large motion, the measured electromyographic signal error is large because the measured signal is mixed with many motion deficits or electrical signal deficits, such as: the violation of electrocardio signals, the violation of skin electric signals caused by motion, and the like. Most of the sensors for measuring electrical signals on the market are based on the pasted electrode pads, which means that the cables connecting the electrode pads will also bring noise to the whole measuring system due to the movement of the cables, and the noise is unpredictable, and the noise is fatal to the EMG (surface electromyogram) signals.
In addition, some dry electrode schemes are available in the market, and due to the fact that the electrodes are fixed in the dry electrode schemes, if factors such as the distance between the electrodes and the material of the electrodes are not properly arranged, acquired data can be distorted. The electrode fixing method is characterized in that the electrode fixing method can be a sticking type or a binding type, a tested person can still relatively accurately measure signals under a static condition, but if the tested person moves, the signals can be lost, because the tested person moves, due to improper electrode fixing treatment, particularly a dry electrode and a binding belt mode, a state that one electrode is separated from the skin can occur instantaneously, and because the measurement is carried out according to the differential voltage between the electrodes, once one electrode is separated from the skin, the electrode state of the point is zero potential, the differential signal value calculated by the hardware part is wrong, and the data collected by the hardware part is meaningless for collecting the moving myoelectricity.
That is, for the conventional electromyographic signal acquisition, the problem of fatal signal interference caused by strenuous exercise cannot be solved, because in the exercise state, filtering is lacked, and analysis of correlation between the exercise signal and the electromyographic signal is lacked, which makes the noise ratio of the obtained signal larger, and causes great difficulty in analysis, extraction and filtering of data. In addition, the electromyographic sensor in the prior art is basically in an electrode plate sticking type, so that not only is the constraint of a cable and the noise of the cable brought, but also the cost is greatly improved due to the fact that the electrode plate is disposable and is repeatedly tested. In addition, the wearing problem in the prior art can cause that a certain electrode is separated from the skin instantly when the tested object moves, and the separation of the electrode from the skin can cause the error of MCU calculation data, thereby causing the error of the signal.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The application aims to provide a surface electromyogram signal acquisition sensor and equipment in a motion state, so that the problem that an electrode is separated from the skin when a user wears the sensor and equipment is solved.
The application provides a motion state's surface electromyogram signal acquisition sensor and equipment is realized like this:
a surface electromyographic signal acquisition sensor of a state of motion, comprising:
a housing;
the first electrode, the second electrode and the third electrode are embedded on the shell and are higher than the shell;
a cavity is arranged between the electrodes, wherein the cavity is in an arc-shaped open area;
and the double-sided adhesive tape is adhered to the shell and is used for being adhered to the skin of the target object, and the surface electromyographic signal acquisition sensor is fixed on the skin of the target object by exhausting air in the cavity.
In one embodiment, the first electrode is a positive input electrode, the second electrode is a negative input electrode, and the third electrode is a reference electrode; the first electrode, the second electrode and the third electrode are differentially input electrodes.
In one embodiment, the first electrode, the second electrode and the third electrode have a width of 5mm, a length of 10mm, and a thickness of 1mm, which is 0.3mm above the housing.
In one embodiment, the first electrode, the second electrode and the third electrode are arranged side by side with a distance of 10mm-20mm, and the direction of the ray formed by connecting the first electrode, the second electrode and the third electrode is along the trend of the muscle group.
In one embodiment, the first, second and third electrodes are 304 stainless steel with an outer layer coated with silver chloride.
In one embodiment, further comprising: a fourth electrode and a fifth electrode, wherein:
the first electrode, the third electrode, the fourth electrode and the fifth electrode are positioned at four vertexes of a square, and the second electrode is positioned at the center point of the square.
In one embodiment, the second electrode is 3.5mm from the first, third, fourth and fifth electrodes, which are 1mm above the housing.
A surface electromyographic signal acquisition apparatus of a state of motion, comprising:
the surface electromyogram signal acquisition sensor;
the electromyographic signal preprocessing device is used for preprocessing the electromyographic signals collected by the electrodes to obtain preprocessed electromyographic signals;
the acceleration sensor is used for acquiring acceleration data;
the central processing unit is used for processing the preprocessed electromyographic signals and the acceleration data to determine irrelevant data;
and the filter is used for filtering out irrelevant data.
In one embodiment, the pretreatment apparatus comprises, in order: the device comprises a voltage follower, a first amplifier, a first filter, a second amplifier, a second filter and an analog-to-digital converter.
In one embodiment, the first filter is a double-T50 HZ filter, and the second filter is a bessel filter.
According to the surface electromyographic signal acquisition sensor and the equipment in the motion state, the shell, the first electrode, the second electrode and the third electrode are arranged, embedded on the shell and higher than the shell; a cavity is arranged between the electrodes, wherein the cavity is in an arc-shaped open area; and the double-sided adhesive tape is adhered to the shell and is used for being adhered to the skin of the target object, and the surface electromyographic signal acquisition sensor is fixed on the skin of the target object by exhausting air in the cavity. Through setting up the cavity and being higher than the electrode of shell for paste the use of sensor on skin and crowd the gas in the cavity, can so that the sensor can paste skin tightly, avoid the electrode to break away from skin, promoted surface electromyographic signal collector's steadiness and data accuracy.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.
FIG. 1 is a bottom view of an electrode provided herein;
FIG. 2 is a side view of an electrode provided herein;
FIG. 3 is a schematic view of a needle electrode provided herein;
FIG. 4 is a schematic diagram of a data processing apparatus provided herein;
FIG. 5 is a flow chart of a data processing method provided herein;
fig. 6 is a flowchart of a surface electromyography signal processing method provided by the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Considering the existing muscle electrical signal measuring sensor, the test result is inaccurate under the condition that the testee moves. Therefore, in this example, an acceleration sensor is added on the basis of the muscle electrical signal measurement sensor, and the data acquired by the acceleration sensor and the data acquired by the myoelectricity are subjected to equidistant data segmentation on the time axis, that is, equal-duration data acquisition packets, which may also be referred to as sample number and window. Then, the signal characteristics of the acquired equidistant data are extracted to obtain relevant dimension data information (high dimension characteristic vector) of the electromyographic data and the acceleration, the root mean square amplitude of the acceleration, the sample entropy of the acceleration, and the relevance or consistency of the electromyographic signal and the acceleration signal.
Extracting parameters of non-dynamic physics and relevant dimension related data from the electromyographic signals, and then calculating and measuring the percentage of repeated structures in the time sequence; then, the correlation dimension is calculated again to measure the complexity of the time series. Two parameters are extracted from the acceleration: root Mean Square (RMS) magnitude and sample entropy. Where RMS is used to represent the complexity of measuring motion amplitude and SampEn (sample entropy) during motion. SampEn (sample entropy) is a nonlinear dynamical method that measures the negative natural logarithm of conditional probability. The subject in motion has a higher RMS and a lower SampEn (sample entropy) value than the subject at rest.
Then, a correlation between the electromyogram data and the acceleration data is calculated based on the above data, and the measurement of the correlation is mainly used to measure the similarity in the power spectrum thereof. The power spectrum can be estimated using Welch's averaged periodogram, for example, it can be calculated using windows with lengths of 2048ms and overlaps of more than 80%, and then the coherent region above 0.99 is used for further analysis, filtering out the incoherent dimensional data.
For dry electrodes, upgrades are also made in this example, including: the requirement for inter-electrode distance, and signal acquisition of small muscle masses also envisages dry electrodes, for example, five-pin non-invasive dry electrodes can be used to acquire data. The method is characterized in that the method is generally used for collecting large muscle signals by adopting electrodes of 5mm x 10mm, the spacing between the electrodes is controlled within 10mm-20mm, and the electrodes adopt silver chloride coatings to ensure low impedance to collect the signals. In addition, when the electrode is worn, in order to get rid of the constraint of the cable, the problems that the electrode moves relative to the skin and the electrode is separated from the skin for a short time in the moving process are solved. In this example, an open cavity is provided between the electrode and the cavity, wherein the cavity has an arc-shaped open area, specifically, the sensor can be adhered to the skin by using a customized double-sided adhesive tape, the electrode is adhered to the skin due to the protrusion of the electrode, then the cavity has a little space with the skin, after the electrode is adhered to the skin by using the double-sided adhesive tape, the air in the cavity is pressed downwards, and the sensor can be firmly fixed to the skin by using the air pressure principle. Due to the air pressure of the cavity, the electrode cannot move relative to the skin, and the phenomenon that the electrode is separated from the skin cannot occur. In addition, the reference electrode is upgraded, so that the position of the reference electrode does not need to be considered when the sensor is pasted, and the sensor only needs to be pasted along the direction of the muscle.
The above-mentioned solution is described below with reference to a specific embodiment, however, it should be noted that the specific embodiment is only for better describing the present application and is not to be construed as a limitation of the present application.
The myoelectric signals are collected in the motion state, and the purpose of collecting the myoelectric signals can be achieved by collecting the electric signals of a human body. For example, the electrical signal can be collected by a front-end silver chloride dry electrode, then the electrical signal is subjected to first differential amplification after passing through a signal follower, filtering is performed by using an anti-aliasing filter (e.g., a band-pass 1-500Hz filter), then power frequency interference is filtered, then the EMG original signal is subjected to second-stage differential amplification, the common mode rejection ratio CMRR is greater than 130dB, the total gain is 1000, and the noise is less than 1 μ V, finally, the small signal is subjected to filtering processing by a bessel filter, then, the collected signal is transmitted to an ADC for analog-to-digital conversion, and then the signal is sent to an MCU (including a DSP core, hereinafter referred to as MCU) for processing, which is a part of an analog circuit. Meanwhile, in the digital circuit part, the MCU acquires the value of the acceleration in real time, the acceleration can adopt a chip of ST company, the measuring range can reach +/-24 g, the acquired value of the acceleration is transmitted to the MCU, and the range of the noise is judged according to the acquired analog electric signal and the value of the acceleration. Specifically, the frequency domain information of the acquired acceleration signal can be obtained by performing fourier transform FFT on the acceleration data after the time-interval data, and then the EMG filtering can be performed in a targeted manner within the time-interval data range of the EMG according to the frequency noise range given by the frequency domain information. And finally, calculating related dimension data information by integrating the electromyogram data and the acceleration data, calculating the correlation between the acceleration and the electromyogram data by calculating an RMS root mean square value and a SampEn sample entropy value, calculating a power spectrum according to a Welch periodogram spectrum method, performing window sampling calculation, comparing the power spectrum or the frequency spectrum, filtering irrelevant data between the acceleration and the electromyogram data, and after filtering, sending the data to an upper computer for previewing the data through 2.4GHz wireless after data packaging.
Specifically, the method may include: the electrode device part and the data acquisition and processing method comprise two parts:
1) an electrode device portion:
the electrodes may include two types: the measuring electrodes of the large muscle group and the measuring electrodes of the small muscle group are used, wherein the large muscle group is generally pectoral muscle, abdominal muscle, lumbar muscle, biceps muscle, triceps muscle and the like, and the small muscle group is generally superficial flexor, deep flexor, cervical muscle and the like.
As shown in fig. 1, it is a bottom view of the electrodes, wherein 1,2, 3 are electrodes, each having a width of 5mm, a length of 10mm, and a thickness of 1 mm. The distance between the electrodes may be set to 10mm to 20 mm. Specifically, the electrode can be made of 304 stainless steel, and the outer layer is processed by silver chloride to reduce the transmission impedance of signals. An embedded process can be adopted between the electrode and the shell, as shown in fig. 2, which is a side view of the electrode, wherein 1,2, 3 are electrodes, the size is the same as above, the electrode can be embedded on the shell and is 0.3mm higher than the shell, in fig. 2, 5 is the shell of the sensor, and 4 is a cavity formed between the shell and the electrode. The sensor can be a wireless transmission sensor, so that a cable is not needed, signal acquisition does not need the cable, and acquisition can be completed directly through 3 dry electrodes integrated on the shell. The signals of the electrodes can be divided into: the device comprises a positive input electrode, a negative input electrode and a reference electrode, wherein any one of the electrodes 1,2 and 3 can be the reference electrode, and the direction of a ray formed by vertical connecting lines of the three electrodes is consistent with the direction of the trend of a muscle group when the device is pasted.
In order to avoid the problem of data error caused by electrode separation in the movement process, all electrodes are input in a differential mode, data signal calculation errors can be caused at the moment when any one electrode falls off, if the electrode falls off for a long time, a real electromyographic EMG signal cannot be obtained for a long time, if the electrode continuously falls off and is attached continuously, data calculated by a differential operational amplifier is in error, the obtained signal is a bad signal, and therefore data cannot be effectively analyzed and modeled. In order to solve these problems, the sensor is attached by means of a double-sided adhesive tape without leaving a tape, and the double-sided adhesive tape leaks out of the electrode portion when the sensor is attached. When the sensor is pasted, one surface of the double-sided adhesive tape can be attached according to the outline of the bottom of the sensor, then the other surface of the double-sided adhesive tape is torn off, the direction of rays formed by connecting the three electrodes of the sensor is along the trend of muscle groups, the electrodes are firstly contacted with the skin, then the sensor is pressed towards the inner side of the skin until the double-sided adhesive tape of the cavity part at the bottom of the sensor is completely adhered to the skin so as to remove air between the cavity and the skin, and the skin is felt to be slightly embraced by the cavity ring to have the force of pulling up the muscle. Because the air among the cavity, the electrode and the skin is exhausted, the relative motion between the electrode and the skin can be prevented under the action of pressure, so that the situation that the electrode is separated from the skin instantly is avoided, and the aim of accurately acquiring data is fulfilled.
As shown in fig. 3, which is a needle electrode, 1,2, 3, 4, 5 are electrode contacts, and although they are needle electrodes, they do not pierce the skin, and belong to the category of non-invasive electrodes. The electrode belongs to a miniature electrode, the diameter of each electrode contact is 0.9mm, and the protruding height of the electrode contact and the outer shell is 1 mm. The distance between the electrodes 1, 3, 4 and 5 and the central electrode 2 is 3.5mm, needle electrodes can be attached through adhesive double-sided adhesive, one side of the double-sided adhesive is firstly attached according to the electrode holes to leak out of the electrode holes, then the other layer is removed, the electrodes are attached to small muscle groups, then the electrodes are firstly contacted with the skin and then pressed in the direction vertical to the muscle, so that the adhesive is fully contacted with the surface of the muscle, the muscle is sensed to have upward lifting force, and a slight prickling feeling indicates that the needle electrodes are correctly attached. Compared with the dry electrode with the 3 electrodes, the needle electrode with the five electrode contacts does not need to consider the direction of the electrode, and only the circle center electrode is directly pasted on the muscle group.
2) The data acquisition and processing part comprises:
myoelectricity in a motion state takes leg muscles as an example, and after the sensors are well adhered, the switches are turned on to acquire data. Taking three electrodes as an example, as shown in fig. 4, the input ends of the electrodes respectively pass through a voltage follower, so as to ensure that the followers can well isolate interference caused by a preceding stage; after passing through the signal follower, carrying out 20 times of differential amplification on the signal for the first time, wherein the amplitude of the obtained signal is 20 times of that of the original signal; then, a double T-shaped 50HZ filter is connected to inhibit the influence of power frequency interference on the circuit; then, using an anti-aliasing filter Butterworth to process signals of the band-pass filter of 1-500 Hz; the amplifier used here has a common mode rejection ratio CMRR of more than 130dB in order to get a better signal.
Then, the total gain of the signals after the two-stage amplification is 1000, the noise is less than 1 μ V, and finally, the small signals are filtered through a Bessel filter, wherein the sampling rate can be more than 2048 Hz. Then, after passing through a 24-bit ADC, it is connected to the MCU for data processing. Meanwhile, the MCU also collects acceleration data, and the acceleration data is three-dimensional information data. So far, after the ADC conversion, discrete digital data of the EMG can be obtained, and data information of the acceleration can also be obtained.
After acquiring the acquired data, the MCU may perform discretization processing (after analog-to-digital conversion) on the data, and then perform data equal-time-distance counting, specifically, perform equal-time-distance counting according to 2048 points, where the frequency of acquiring the electromyographic signal data is the same as the frequency of acquiring the acceleration data, both the frequencies are 2048Hz, and determine the counter count of the point where the acquisition is started without packet loss, and the starting point of the equal-time-distance counting is the same counter count, and then count 2048 counters backwards, where the equal-time-distance counting is completed. However, it should be noted that the MCU does not terminate the acquisition of the electromyographic signal data and the acceleration data while performing the data acquisition, the ADC for acquiring the electromyographic signal data and the SPI for acquiring the acceleration data may be acquired in the DMA mode, and the acquired data may be respectively put into the corresponding memories to wait for the MCU to perform the data acquisition at equal time intervals, and then used for the analysis and filtering. The acquisition is always performed, not only once, but also at equal time intervals. However, differently, as the acquisition time goes on, the count value of the starting counter of the equidistant data acquisition also goes backward, for example, 2048 points can be acquired after the acquisition is started, for example, the count value of the counting started by the MCU is 2, then the count value of the ending counter is 2049, the data is stored in the corresponding memory, the second equidistant data acquisition is performed after the data analysis, the data analysis requires time, the starting count of the counting of the second time is uncertain, possibly, the count value of the counting is 5, then the count value of the ending counter should be 2052, and so on, there are infinite data acquisition. In order to ensure the correctness of the result, the data acquired at the n-th time and the data acquired at the n-1-th time have to be ensured to have the overlap of more than 80 percent.
After the time-interval acquisition is completed, the acceleration signal is analyzed as shown in fig. 5. The purpose of analyzing the acceleration signal is to determine whether the subject is in motion. According to the indication of the acceleration signal (the value of the acceleration signal can be directly read from the chip without additional calculation), the data of the three axes XYZ are basically unchanged and slightly changed, the synthesis direction of one axis or the three axes is vertical downwards, the acceleration value approaches to 9.8, the tested object is in a resting state or a small action state, and only 0-500Hz band-pass filtering and 50Hz notching are needed to obtain better EMG data. If the value of the read acceleration signal changes irregularly, the three-axis acceleration synthesis direction changes irregularly, and the value of the acceleration changes continuously, which indicates that the tested object is in a motion state at the moment. At the moment, the acceleration data needs to be judged according to the data of the equal time intervals, signals are converted into frequency domain information through Fourier transform FFT (fast Fourier transform), the action frequency information f at the moment is obtained, and then the data of the equal time intervals containing the frequency information f is filtered according to the frequency information f, so that the purpose of filtering is achieved. The basic filtering part is completed so far.
For complex irregular motion, the read acceleration values are scattered and irregular, and the XYZ-axis acceleration synthesized points are random and scattered and irregular. At this time, some complex operations are required to remove the motion noise.
Then, two parameters of the acceleration signal are taken, the root mean square amplitude RMS and the sample entropy SampEN. The root mean square value RMS is used for representing motion amplitude in a motion process, and the sample entropy SampEN is used for representing complexity. The sample entropy SampEN is a nonlinear dynamics method, and measures the negative natural logarithm of conditional probability, and two sequences in a time sequence with n points being similar are similar to a time sequence with n +1 points.
And then, calculating the correlation between the EMG and the acceleration data, obtaining related area data by calculating the power spectrum or the frequency spectrum of the EMG and the power spectrum or the frequency spectrum of the acceleration data, and filtering irrelevant data. Specifically, the estimation can be performed by a Welch average periodogram method, for example, the Welch average periodogram method can be performed on the basis of overlap of window length 2048 and data 80%, data with related area data higher than 0.99 is divided into related data areas, and other unrelated data is filtered out by filtering.
In the above calculation process of the EMG fractal dimension, the fractal dimension is a common method for measuring the complexity and irregularity of the signal. For the EMG signal, when the subject is in a calm state, the measured dimension information is less, and the obtained fractal dimension is smaller. Therefore, the electromyographic EMG signals are easy to analyze and filter under normal resting conditions. However, in the moving state, the measured dimension information is more, various kinds of noise are mixed in the real EMG signal data, and the calculated correlation dimension is larger, which means that the signal is more complex. Generally, clutter signals included in the EMG are many, such as an electrical signal of an electrocardiogram, noise of an amplifier, power frequency noise, power supply noise and the like. The fractal dimension of these signals is small, but the signal activity of EMG is complex, especially when moving.
The electromyographic signal has fractal dimension characteristics, is an energy spectrum S (k) corresponding to the signal, and has the following characteristics:
S(k)∝1/kT
where T is a positive real number.
Assuming that the raw data signal for the resulting EMG is divided into N different regions, the matrix of study components is Y, then,
Y＝[S(1),S(2),S(3),……S(N)]T
and (3) carrying out ascending arrangement on the separated source signals according to the size of the fractal dimension through a FASTICA algorithm, and recording as:
a(1),a(2),a(3),……a(N)
the fractal dimension sequences a (i) (i ═ 1,2, 3, … N) are the fractal dimensions of the equidistant signals.
The sequence of these fractal dimensions corresponds to the source signal:
S＝[S’(1),S’(2),S’(3),……S’(N)]。
setting a sequence noise signal of a fractal dimension separated from EMG original data as follows:
s1 ═ S '(1), S' (2), S '(3), … … S' (k) … … … … … … … … … … … … … … (equation 1)
In order to automatically recognize the noise in the unique source as much as possible into S1, k is set to satisfy the following condition:
k＝[N/2]+1
wherein [ N/2] represents that the maximum integer larger than N/2 is not satisfied, and the source signals of other fractal dimension sequences are as follows:
s2 ═ S '(k +1), S' (k +2), S '(k +3), … … S' (N) ] … … … … … … … … … … … (equation 2)
The root mean square value and the sample entropy of the acceleration sensor can be calculated as follows:
calculating the root mean square value of the current equal time distance data quantity according to a root mean square formula:
wherein, X is the value that the acceleration was gathered, and N is the point number, and wherein, N is 2048, and what root mean square value mainly embodied is amplitude information.
In the above calculation, the root mean square value of the equal time interval data volume is calculated according to the root mean square formula, and the root mean square value is only used for judging the magnitude and amplitude condition of the acceleration data and the magnitude range of the acceleration.
Sample entropy:
assume the raw data as: X-X1, X2, … …, xn, where the sample length is n-2048, the embedding dimension is m, and the vector capacity is r, then the m-vector can be:
XI＝[xi,xi+1,……xi+n]，i＝1,2…,n-m；
defining the distance between x (i) and x (j) as d [ x (i), and the maximum value of time corresponding to x (j) is:
d[x(i),x(j)]＝max[x(i+k)-x(j+k)]
wherein x (j) is the remaining vector except x (i), j is 1,2, … … n-m, j is not equal to i;
counting the number of d [ x (i), x (j) ] smaller than r and the ratio of the value to the total distance n-m-1, and recording as B (r):
bm (r) ═ 1/(n-m-1) { wherein d [ x (i), x (j) ] is less than the number of r };
averaging Bm (r): bm (r) is 1/n-m (Σ bm (r)), and the average value is 1 to n-m.
Repeating the above steps for dimension m +1, i.e. for the vector of m +1 points, yields Bm +1 (r).
Theoretically, the sample entropy of this time series is:
SampEn (m, r) ═ lim [ -ln { (Bm +1(r))/(Bm (r)) } ]. … … … … … … … … … … (equation 3)
The sample entropy formula described above represents the complexity of the acceleration data in the time dimension. In this example, a low-complexity sequence is generally considered to be 0.99 or less, and a high-complexity sequence is considered to be 0.99 or more. The sequence of sample entropies below 0.99 is filtered in a first way, i.e. by calculating the frequency value f of the acceleration, the frequency f in the electromyographic signal is filtered out. Sequences with sample entropies below 0.99 require an analysis of the complex sequence by band-in. Substituting the fractal dimension data in S2 in the above formula 1 into formula 3, performing sample entropy estimation on the dimensions of the respective frequencies in the frequency domain, for example: let m equal S' (K + 1); the capacity r is 2048 (illustrated here) and the sample entropy value is estimated. Then, for the sample with the entropy value lower than 0.99, the frequency is filtered according to the frequency of the data of the segment, the FFT conversion is carried out according to the acceleration value in the data capacity of the segment to obtain the frequency f of the segment, and then the frequency f corresponding to the EMG original data is filtered, so that the EMG data with the movement clutter filtered is obtained. If the sample entropy is larger than 0.99, the time series data of the data is considered to be complex, the motion data of the data is considered to be invalid, and the data of the frequency is deleted.
So far, the motion clutter of the EMG original signal is filtered through the acceleration.
Through the scheme of the example, the problem of wearing of the EMG sensor is solved, the situation that the electrode is separated from the skin instantly can not occur, the problem that electromyographic EMG signals are inaccurate to collect in the motion state is solved, the problem of real-time analysis of filtering of the electromyographic signals in the motion state is solved, and the signals are more accurate.
In the above example, an acceleration sensor is added on the basis of a muscle electrical signal measurement sensor, equidistant data segmentation is performed on the time axis through data collected by the acceleration sensor and data collected by myoelectricity, and then, signal characteristics are extracted from the obtained equidistant data, so as to obtain relevant dimensional data information (high dimensional characteristic vector) of the myoelectricity data and the acceleration, the root mean square amplitude of the acceleration, the sample entropy of the acceleration, and the relevance or consistency of the myoelectricity signal and the acceleration signal. The problem of the fatal signal interference that violent motion brought can not be solved to current collection flesh electrical signal through above-mentioned scheme, solved current flesh electrical sensor simultaneously and all be the electrode slice paste formula basically, not only brought the constraint of cable and the noise of cable, simultaneously, because the electrode slice is disposable, the test of relapse, cost improvement by a wide margin. In addition, the wearing problem in the prior art can cause the situation that a certain electrode is separated from the skin instantly when the tested object moves, and the situation that the electrode is separated from the skin can cause the error of MCU calculation data, thereby causing the error of the signal.
In this example, a surface electromyographic signal acquisition sensor of a motion state is provided, which may comprise the following components:
a housing;
the first electrode, the second electrode and the third electrode are embedded on the shell and are higher than the shell;
a cavity is arranged between the electrodes, wherein the cavity is in an arc-shaped open area;
and the double-sided adhesive tape is adhered to the shell and is used for being adhered to the skin of the target object, and the surface electromyographic signal acquisition sensor is fixed on the skin of the target object by exhausting air in the cavity.
Specifically, the first electrode may be a positive input electrode, the second electrode may be a negative input electrode, and the third electrode may be a reference electrode; the first electrode, the second electrode and the third electrode are differentially input electrodes.
In practical implementation, the width of the first electrode, the width of the second electrode, and the width of the third electrode may be 5mm, the length of the first electrode is 10mm, the thickness of the first electrode is 1mm, and the first electrode is 0.3mm higher than the housing. The first electrode, the second electrode and the third electrode are arranged in parallel, the distance between the first electrode, the second electrode and the third electrode is 10-20 mm, and the direction of a ray formed by connecting the first electrode, the second electrode and the third electrode is along the trend of a muscle group. The first electrode, the second electrode and the third electrode may be made of 304 stainless steel, and the outer layer is coated with silver chloride.
The sensor may also be a five-electrode sensor, as shown in fig. 3, that is, may further include: a fourth electrode 4 and a fifth electrode 5, wherein: the first electrode 1, the third electrode 3, the fourth electrode 4 and the fifth electrode 5 are positioned at four vertexes of a square, and the second electrode 2 is positioned at the center point of the square. The distance between the second electrode and the first electrode, the distance between the second electrode and the third electrode, the distance between the fourth electrode and the fifth electrode are 3.5mm, and the first electrode, the second electrode, the third electrode, the distance between the fourth electrode and the fifth electrode are 1mm higher than the shell. The needle type electrode can be attached through the adhesive sticker double-sided adhesive, one side of the double-sided adhesive is firstly attached according to the electrode hole, the electrode hole leaks, then the other layer is removed, the electrode is attached to the small muscle group, then the electrode is firstly contacted with the skin, and then the electrode is pressed in the direction vertical to the muscle, so that the adhesive sticker is fully contacted with the surface of the muscle, the muscle is sensed to have upward lifting force, and the needle type electrode is indicated to be correctly attached by sensing slight stabbing pain.
In this example, there is also provided a surface electromyogram signal acquisition device of a motion state, which may include: the surface electromyogram signal acquisition sensor; the electromyographic signal preprocessing device is used for preprocessing the electromyographic signals collected by the electrodes to obtain preprocessed electromyographic signals; the acceleration sensor is used for acquiring acceleration data; the central processing unit is used for processing the preprocessed electromyographic signals and the acceleration data to determine irrelevant data; and the filter is used for filtering out irrelevant data.
The pretreatment device may sequentially include: the device comprises a voltage follower, a first amplifier, a first filter, a second amplifier, a second filter and an analog-to-digital converter. Specifically, the first filter may be a double-T50 HZ trap, and the second filter may be a bessel-stack filter. For example, the input ends of the electrodes pass through a voltage follower respectively, so that the followers can be ensured to well isolate interference caused by a preceding stage; after passing through the signal follower, carrying out 20 times of differential amplification on the signal for the first time, wherein the amplitude of the obtained signal is 20 times of that of the original signal; then, a double T-shaped 50HZ filter is connected to inhibit the influence of power frequency interference on the circuit; then, using an anti-aliasing filter Butterworth to process signals of the band-pass filter of 1-500 Hz; the amplifier used here has a common mode rejection ratio CMRR of more than 130dB in order to get a better signal.
Based on the above surface electromyogram signal acquisition device, in this example, a surface electromyogram signal processing method in a motion state is provided, as shown in fig. 6, the method may include the following steps:
step 601: acquiring an electromyographic data signal and an acceleration data signal;
step 602: discretizing the electromyographic data signals and the acceleration data signals to obtain discretized electromyographic data and discretized acceleration data;
step 603: performing equal-time distance data acquisition on the discretized electromyographic data and the discretized acceleration data;
step 604: determining the correlation between the electromyographic data obtained by equal time distance data acquisition and acceleration data;
step 605: and filtering irrelevant data in the electromyographic data and the acceleration data obtained by the equal time distance data taking according to the relevance.
Specifically, the step 604 of determining the correlation between the electromyographic data obtained by the equal time interval access and the acceleration data may include:
s1: calculating fractal dimension information of electromyographic data obtained by taking data at equal time intervals;
s2: calculating to obtain scalar parameters for measuring the complexity of the time sequence according to the fractal dimension information;
s3: calculating the root mean square and sample entropy of acceleration data obtained by taking the number at equal time intervals;
s4: determining the complexity of the sample entropy according to the root-mean-square and the sample entropy;
s5: obtaining an electromyographic data power spectrum according to the scalar parameter;
s6: obtaining an acceleration data power spectrum according to the complexity of the sample entropy;
s7: and determining relevant area data of the electromyographic data power spectrum and the acceleration data power spectrum as the correlation between the electromyographic data and the acceleration data, wherein the data of the irrelevant area is used for filtering.
When the method is implemented, Welch average periodogram can be adopted to determine relevant area data of the electromyogram data power spectrum and the acceleration data power spectrum.
After irrelevant data in the electromyographic data and the acceleration data obtained by the equal-time-distance data acquisition are filtered, the filtered data can be packaged to obtain a packaged data packet; and sending the encapsulated data packet to an upper computer in a wireless mode.
When performing equal-time-distance fetching on the discretized electromyographic data and the discretized acceleration data, performing equal-time-distance fetching according to 2048 points, wherein starting points of the discretized electromyographic data and the discretized acceleration data are counted by the same counter; and the data overlapping rate of the current equal-time distance access and the last equal-time distance access can be controlled to be more than 80%.
According to the surface electromyographic signal acquisition sensor and the equipment in the motion state, the shell, the first electrode, the second electrode and the third electrode are arranged, embedded on the shell and higher than the shell; a cavity is arranged between the electrodes, wherein the cavity is in an arc-shaped open area; and the double-sided adhesive tape is adhered to the shell and is used for being adhered to the skin of the target object, and the surface electromyographic signal acquisition sensor is fixed on the skin of the target object by exhausting air in the cavity. Through setting up the cavity and being higher than the electrode of shell for paste the use of sensor on skin and crowd the gas in the cavity, can so that the sensor can paste skin tightly, avoid the electrode to break away from skin, promoted surface electromyographic signal collector's steadiness and data accuracy.
Although the present application provides method steps as described in an embodiment or flowchart, additional or fewer steps may be included based on conventional or non-inventive efforts. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.
The apparatuses or modules illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. The functionality of the modules may be implemented in the same one or more software and/or hardware implementations of the present application. Of course, a module that implements a certain function may be implemented by a plurality of sub-modules or sub-units in combination.
The methods, apparatus or modules described herein may be implemented in computer readable program code to a controller implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, Application Specific Integrated Circuits (ASICs), programmable logic controllers and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
Some of the modules in the apparatus described herein may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary hardware. Based on such understanding, the technical solutions of the present application may be embodied in the form of software products or in the implementation process of data migration, which essentially or partially contributes to the prior art. The computer software product may be stored in a storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to perform the methods described in the various embodiments or portions of the embodiments of the present application.
The embodiments in the present specification are described in a progressive manner, and the same or similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. All or portions of the present application are operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, mobile communication terminals, multiprocessor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
While the present application has been described with examples, those of ordinary skill in the art will appreciate that there are numerous variations and permutations of the present application without departing from the spirit of the application, and it is intended that the appended claims encompass such variations and permutations without departing from the spirit of the application.
Claims (9)
1. A surface electromyogram signal acquisition device of a motion state, comprising:
a surface electromyogram signal acquisition sensor in a motion state;
the electromyographic signal preprocessing device is used for preprocessing the electromyographic signals collected by the electrodes to obtain preprocessed electromyographic signals;
the acceleration sensor is used for acquiring acceleration data;
the central processing unit is used for processing the preprocessed electromyographic signals and the acceleration data, and determining the correlation between the acceleration and the electromyographic data so as to determine irrelevant data;
a filter for filtering out uncorrelated data;
wherein, the surface electromyogram signal acquisition sensor of motion state includes:
a housing;
the first electrode, the second electrode and the third electrode are embedded on the shell and are higher than the shell;
an open cavity is arranged between the electrodes, wherein the cavity is in an arc-shaped open area;
the double-sided adhesive tape is pasted on the shell, is used for pasting on the skin of a target object, and enables the surface electromyographic signal acquisition sensor to be fixed on the skin of the target object by exhausting air in the cavity;
the determining of the correlation of the acceleration with the electromyogram data includes:
calculating fractal dimension information of the obtained electromyographic data;
calculating to obtain scalar parameters for measuring the complexity of the time sequence according to the fractal dimension information, and obtaining an electromyographic data power spectrum according to the scalar parameters;
calculating the root mean square and sample entropy of acceleration data obtained by taking the number at equal time intervals;
determining the complexity of the sample entropy according to the root mean square and the sample entropy, and obtaining an acceleration data power spectrum according to the complexity of the sample entropy;
and determining relevant area data of the electromyographic data power spectrum and the acceleration data power spectrum as the relevance of the electromyographic data and the acceleration data.
2. The apparatus of claim 1, wherein the first electrode is a positive input electrode, the second electrode is a negative input electrode, and the third electrode is a reference electrode; the first electrode, the second electrode and the third electrode are differentially input electrodes.
3. The apparatus of claim 1, wherein the first, second, and third electrodes are 5mm wide, 10mm long, 1mm thick, and 0.3mm above the housing.
4. The apparatus of claim 1, wherein the first electrode, the second electrode and the third electrode are arranged in parallel with a distance of 10mm to 20mm, and a direction of a ray formed by connecting the first electrode, the second electrode and the third electrode is along a trend of a muscle group.
5. The apparatus of claim 1, wherein the first, second, and third electrodes are 304 stainless steel with an outer layer coated with silver chloride.
6. The apparatus of claim 1, further comprising: a fourth electrode and a fifth electrode, wherein:
the first electrode, the third electrode, the fourth electrode and the fifth electrode are positioned at four vertexes of a square, and the second electrode is positioned at the center point of the square.
7. The apparatus of claim 6, wherein the second electrode is 3.5mm from the first, third, fourth, and fifth electrodes, which are 1mm above the housing.
8. The apparatus according to any one of claims 1 to 7, wherein the pre-treatment means comprises, in order: the device comprises a voltage follower, a first amplifier, a first filter, a second amplifier, a second filter and an analog-to-digital converter.
9. The apparatus of claim 8, wherein the first filter is a double-T50 HZ filter and the second filter is a Bezier filter.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811595198.7A CN109864740B (en) | 2018-12-25 | 2018-12-25 | Surface electromyogram signal acquisition sensor and equipment in motion state |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811595198.7A CN109864740B (en) | 2018-12-25 | 2018-12-25 | Surface electromyogram signal acquisition sensor and equipment in motion state |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109864740A CN109864740A (en) | 2019-06-11 |
| CN109864740B true CN109864740B (en) | 2022-02-01 |
Family
ID=66917091
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811595198.7A Active CN109864740B (en) | 2018-12-25 | 2018-12-25 | Surface electromyogram signal acquisition sensor and equipment in motion state |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109864740B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110755071A (en) * | 2019-10-23 | 2020-02-07 | 西安交通大学 | Split type wireless electromyographic signal sensor |
| CN114403904B (en) * | 2021-12-31 | 2023-01-13 | 北京津发科技股份有限公司 | Device for determining muscle state based on electromyographic signals and muscle blood oxygen saturation |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN85101834A (en) * | 1985-04-01 | 1987-01-10 | 艾斯特勒科技公司 | Fix and stable electrode with vacuum |
| WO1993016633A1 (en) * | 1992-02-20 | 1993-09-02 | Humanteknik Ab | A device for securing an object to a surface by vacuum |
| CN201578241U (en) * | 2010-01-18 | 2010-09-15 | 段峰 | Surface electromyogram signal filter |
| CN102440773A (en) * | 2010-10-14 | 2012-05-09 | 张晓光 | Electromyography signal acquisition device |
| CN105078404A (en) * | 2015-09-02 | 2015-11-25 | 北京津发科技股份有限公司 | Fully automatic eye movement tracking distance measuring calibration instrument based on laser algorithm and use method of calibration instrument |
| CN105597298A (en) * | 2016-04-05 | 2016-05-25 | 哈尔滨工业大学 | Fitness effect evaluation system based on electromyographic signal and body movement detection |
| CN105636518A (en) * | 2013-09-12 | 2016-06-01 | 社会医疗法人苏西厚生会松波医学研究所 | Body fluid collection device and examination device |
| CN206729887U (en) * | 2016-12-12 | 2017-12-12 | 天津职业技术师范大学 | A kind of sensor of measurement surface electromyographic signal |
Family Cites Families (49)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6816744B2 (en) * | 2001-05-29 | 2004-11-09 | Reproductive Health Technologies, Inc. | Device and system for remote for in-clinic trans-abdominal/vaginal/cervical acquisition, and detection, analysis, and communication of maternal uterine and maternal and fetal cardiac and fetal brain activity from electrical signals |
| US6937899B2 (en) * | 2001-08-30 | 2005-08-30 | Medtronic, Inc. | Ischemia detection |
| JP3660330B2 (en) * | 2002-08-13 | 2005-06-15 | 独立行政法人科学技術振興機構 | Impedance measurement device and motor learning support device |
| GB0306629D0 (en) * | 2003-03-22 | 2003-04-30 | Qinetiq Ltd | Monitoring electrical muscular activity |
| AU2005247045B2 (en) * | 2004-05-25 | 2011-01-27 | Andrew J Ronchi | Apparatus and method for monitoring strain and/or load applied to a mammal |
| CN100500086C (en) * | 2005-11-26 | 2009-06-17 | 安徽君诚体育器材有限公司 | Electromyographic signal collection and action form synchronization method |
| US7539532B2 (en) * | 2006-05-12 | 2009-05-26 | Bao Tran | Cuffless blood pressure monitoring appliance |
| JP5005331B2 (en) * | 2006-12-19 | 2012-08-22 | 富士重工業株式会社 | Muscle force sensor |
| US8285381B2 (en) * | 2008-07-02 | 2012-10-09 | Niveus Medical, Inc. | Systems and methods for automated muscle stimulation |
| AU2010282150B2 (en) * | 2009-08-14 | 2016-03-31 | David Burton | Anaesthesia and consciousness depth monitoring system |
| WO2012003451A2 (en) * | 2010-07-01 | 2012-01-05 | Stimdesigns Llc | Universal closed-loop electrical stimulation system |
| US10499829B2 (en) * | 2010-11-01 | 2019-12-10 | G-Tech Medical, Inc. | Wearable wireless patches containing electrode pair arrays for gastrointestinal electrodiagnostics |
| CN102178524B (en) * | 2011-05-23 | 2012-09-05 | 天津大学 | Synchronization likehood-based electroencephalograph and electromyography synergistic analyzing method |
| CN102512157B (en) * | 2011-12-15 | 2014-08-20 | 重庆大学 | Dynamic electrocardiogram T wave alternate quantitative analysis method based on models |
| ITMI20120494A1 (en) * | 2012-03-27 | 2013-09-28 | B10Nix S R L | APPARATUS AND METHOD FOR THE ACQUISITION AND ANALYSIS OF A MUSCULAR ACTIVITY |
| CN102657527B (en) * | 2012-05-15 | 2014-03-12 | 北京大学 | Electromyographic signal acquisition equipment and identification method for identifying motion of ankle joint in real time |
| CN102722643A (en) * | 2012-05-28 | 2012-10-10 | 天津大学 | Multi-information merging movement mode identification method used for control of artificial lower surface electromyogram limb |
| CN102930284B (en) * | 2012-09-13 | 2015-06-17 | 杭州电子科技大学 | Surface electromyogram signal pattern recognition method based on empirical mode decomposition and fractal |
| CN103054575B (en) * | 2013-01-15 | 2016-03-23 | 中国医学科学院生物医学工程研究所 | Based on the method for myoelectricity research muscle fatigue static after electricity irritation effect |
| CN103190905B (en) * | 2013-04-01 | 2014-12-03 | 武汉理工大学 | Multi-channel surface electromyography signal collection system based on wireless fidelity (Wi-Fi) and processing method thereof |
| CN103294199B (en) * | 2013-06-09 | 2017-09-12 | 华东理工大学 | A kind of unvoiced information identifying system based on face's muscle signals |
| CN103617411B (en) * | 2013-10-17 | 2016-09-07 | 杭州电子科技大学 | Electromyographic signal recognition methods based on complexity and fractal dimension and fractal length |
| CN103584855B (en) * | 2013-10-24 | 2015-06-10 | 燕山大学 | Electroencephalogram and electromyogram synchronous acquisition and information transfer characteristic analysis method |
| US20170156640A1 (en) * | 2013-10-24 | 2017-06-08 | Ryan ROBUCCI | System and method for detection and measurement of body part movement using capacitive sensors and inertial sensing systems |
| CN103584872B (en) * | 2013-10-29 | 2015-03-25 | 燕山大学 | Psychological stress assessment method based on multi-physiological-parameter integration |
| CA2948526A1 (en) * | 2014-06-05 | 2015-12-30 | University Of Florida Research Foundation, Inc. | Functional electrical stimulation cycling device for people with impaired mobility |
| EP3185750B1 (en) * | 2014-08-25 | 2020-04-08 | Drägerwerk AG & Co. KGaA | Rejecting noise in a signal |
| CA2880623A1 (en) * | 2015-02-02 | 2016-08-02 | Jian Chen | Emg diagnostic method for traumatic brain injury |
| CN204537060U (en) * | 2015-04-23 | 2015-08-05 | 宁波市美灵思医疗科技有限公司 | A kind of human-computer interaction device based on myoelectricity stream and multi-sensor cooperation effect |
| CN106155300A (en) * | 2015-04-23 | 2016-11-23 | 宁波市美灵思医疗科技有限公司 | A kind of based on myoelectricity stream and the human-computer interaction device of multi-sensor cooperation effect and using method |
| CN105445022B (en) * | 2015-11-17 | 2017-11-14 | 中国矿业大学 | A kind of planetary gear method for diagnosing faults based on dual-tree complex wavelet transform entropy Fusion Features |
| CN105550636B (en) * | 2015-12-04 | 2019-03-01 | 中国电子科技集团公司第三研究所 | A kind of method and device of target type discrimination |
| WO2017137435A1 (en) * | 2016-02-08 | 2017-08-17 | Koninklijke Philips N.V. | Device, system and method for skin detection |
| CN105640542B (en) * | 2016-02-22 | 2018-12-14 | 绍兴柯桥巨鹏差别纤维有限公司 | A kind of dedicated ECG monitor of pediatric nursing |
| CN107280667A (en) * | 2016-03-31 | 2017-10-24 | 北京航空航天大学 | A kind of wireless surface myoelectricity acquisition system with acceleration transducer |
| CN105771087A (en) * | 2016-04-06 | 2016-07-20 | 上海乃欣电子科技有限公司 | Rehabilitation training system based on music and myoelectricity feedback simulating |
| CN106109174B (en) * | 2016-07-14 | 2018-06-08 | 燕山大学 | A kind of healing robot control method based on myoelectric feedback impedance self-adaptive |
| JP6896967B2 (en) * | 2016-08-02 | 2021-06-30 | グンゼ株式会社 | Human body motion detection wear |
| CN106156524B (en) * | 2016-07-29 | 2018-08-28 | 东北大学 | A kind of online gait planning method of Intelligent lower limb power assisting device |
| CN106371610B (en) * | 2016-09-23 | 2020-06-09 | 重庆金瓯科技发展有限责任公司 | Electroencephalogram signal-based driving fatigue detection method |
| CN106482937B (en) * | 2016-09-30 | 2019-08-27 | 南方电网科学研究院有限责任公司 | A kind of monitoring method of mechanical state of high-voltage circuit breaker |
| JP2018075147A (en) * | 2016-11-08 | 2018-05-17 | ＭｅｄＶｉｇｉｌａｎｃｅ株式会社 | Input system and measuring apparatus |
| CN106691474A (en) * | 2016-11-25 | 2017-05-24 | 中原电子技术研究所（中国电子科技集团公司第二十七研究所） | Brain electrical signal and physiological signal fused fatigue detection system |
| CN108227905A (en) * | 2016-12-21 | 2018-06-29 | 上海健康医学院 | A kind of Gamecontrol system based on surface electromyogram signal |
| CN107456217A (en) * | 2017-09-04 | 2017-12-12 | 东北大学 | Human body physiological state detection intelligent helmet and system based on Multi-sensor Fusion |
| CN107822629B (en) * | 2017-09-11 | 2020-09-11 | 上海傲意信息科技有限公司 | Method for detecting myoelectric axes on surfaces of limbs |
| CN107788991A (en) * | 2017-10-26 | 2018-03-13 | 复旦大学 | Wearable lower limb rehabilitation assessment system |
| CN108261197A (en) * | 2018-03-19 | 2018-07-10 | 上海理工大学 | Upper limb healing evaluation system and method based on surface myoelectric and motion module |
| CN108814597B (en) * | 2018-04-16 | 2021-09-07 | 深圳睿瀚医疗科技有限公司 | Rehabilitation training evaluation method and system based on interaction of motion information and myoelectricity |
-
2018
- 2018-12-25 CN CN201811595198.7A patent/CN109864740B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN85101834A (en) * | 1985-04-01 | 1987-01-10 | 艾斯特勒科技公司 | Fix and stable electrode with vacuum |
| WO1993016633A1 (en) * | 1992-02-20 | 1993-09-02 | Humanteknik Ab | A device for securing an object to a surface by vacuum |
| CN201578241U (en) * | 2010-01-18 | 2010-09-15 | 段峰 | Surface electromyogram signal filter |
| CN102440773A (en) * | 2010-10-14 | 2012-05-09 | 张晓光 | Electromyography signal acquisition device |
| CN105636518A (en) * | 2013-09-12 | 2016-06-01 | 社会医疗法人苏西厚生会松波医学研究所 | Body fluid collection device and examination device |
| CN105078404A (en) * | 2015-09-02 | 2015-11-25 | 北京津发科技股份有限公司 | Fully automatic eye movement tracking distance measuring calibration instrument based on laser algorithm and use method of calibration instrument |
| CN105597298A (en) * | 2016-04-05 | 2016-05-25 | 哈尔滨工业大学 | Fitness effect evaluation system based on electromyographic signal and body movement detection |
| CN206729887U (en) * | 2016-12-12 | 2017-12-12 | 天津职业技术师范大学 | A kind of sensor of measurement surface electromyographic signal |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109864740A (en) | 2019-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hou et al. | A real-time QRS detection method based on phase portraits and box-scoring calculation | |
| CN109846481B (en) | Surface electromyogram signal processing method and device, data processing device and storage medium | |
| Taji et al. | Impact of skin–electrode interface on electrocardiogram measurements using conductive textile electrodes | |
| CN110338786B (en) | Epileptic discharge identification and classification method, system, device and medium | |
| US9402579B2 (en) | Real-time assessment of absolute muscle effort during open and closed chain activities | |
| CN106725488A (en) | A kind of wireless field density breathing detection method, device and breathing detection instrument | |
| CN103919548B (en) | Detecting device and detecting equipment for swallowing muscle disorders | |
| Andrade et al. | The application of the Hilbert spectrum to the analysis of electromyographic signals | |
| CN107822629B (en) | Method for detecting myoelectric axes on surfaces of limbs | |
| CN107530015B (en) | Vital sign analysis method and system | |
| Rocha et al. | Weighted-cumulated S-EMG muscle fatigue estimator | |
| Das et al. | A flexible touch sensor based on conductive elastomer for biopotential monitoring applications | |
| CN109864740B (en) | Surface electromyogram signal acquisition sensor and equipment in motion state | |
| Abdelouahad et al. | Time and frequency parameters of sEMG signal—Force relationship | |
| Chou et al. | Pulse rate variability estimation method based on sliding window iterative DFT and Hilbert transform | |
| CN107320097A (en) | The method and apparatus that muscular fatigue feature is extracted using electromyographic signal marginal spectrum entropy | |
| CN106108885B (en) | Method and instrument for evaluating body motion state and electrocardio interference | |
| CN109864731B (en) | Pulse measuring method and device, terminal equipment and readable storage medium | |
| US20170245804A1 (en) | Brain activity measurement device, program, and method | |
| Plesnik et al. | ECG signal acquisition and analysis for telemonitoring | |
| Amorim et al. | Application of surface electromyography in the dynamics of human movement | |
| CN114550932A (en) | Sleep apnea risk assessment method, device, equipment and storage medium | |
| CN110946562B (en) | Physiological electric signal measurement and analysis method and system based on Micro bit microprocessor | |
| Thongpanja et al. | The effects of the force of contraction and elbow joint angle on mean and median frequency analysis for muscle fatigue evaluation | |
| CN114271830A (en) | Electrocardiosignal detection method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |