CN107661101A - A kind of pain recognition methods, device and electronic equipment - Google Patents
A kind of pain recognition methods, device and electronic equipment Download PDFInfo
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Abstract
The application is related to biomedical engineering technology field, more particularly to a kind of pain recognition methods, device and electronic equipment.The pain recognition methods includes：The surface electromyogram signal of detection position when subject does curvature movement is gathered by signal acquisition module；Wherein, the subject includes healthy person and pain patients respectively；The contraction of muscle pattern of healthy person and pain patients detection position is analyzed respectively according to the surface electromyogram signal, obtains the otherness feature of healthy person and the surface electromyogram signal of pain patients detection position；Pain patients are identified according to the otherness feature of the surface electromyogram signal.The contraction of muscle pattern of healthy person and pain patients painful area is analyzed according to surface electromyogram signal by the application, obtain the otherness feature of healthy person and pain patients surface electromyogram signal, pain patients are identified according to otherness feature, so as to realize easy and effective, inexpensive, noninvasive radiationless, real-time pain identification.
Description
Technical Field
The present application relates to the field of biomedical engineering technologies, and in particular, to a pain recognition method and apparatus, and an electronic device.
Background
Lumbago is a general term for diseases of lumbosacral bones, nerves and other soft tissues which can cause pain in the back and the waist, and not only can the life and the working quality be affected, but also serious patients can cause disability, and the lumbago has the characteristics of high morbidity, high disability rate, high escape rate and low diagnosis and treatment rate at present and becomes a disease which seriously affects the health of human beings. One reason why low back pain is important is poor lumbar stability. When the stability of the spine is attacked, motor neurons activate proper muscles to protect, recover or avoid the unstable phenomenon of the spine, strain or pain is caused by heavy load contraction of waist muscles for a long time, and the early symptoms of lumbago patients tend to be serious, such as muscle strength reduction, muscle injury, muscle atrophy and the like.
At present, the diagnostic imaging of lumbago in clinic mainly comprises X-ray, CT, MRI and the like, wherein X-ray radiation harms human bodies and the tissue imaging definition is not high; the CT has low definition and resolution on the soft tissue image, and the CT radiation is harmful to the human body; MRI examination is expensive, increases the economic burden on the patient, and has long scanning time, which is difficult for the patient to tolerate. Thus, a low cost, non-radiative, real-time pain recognition device facilitates the identification of clinical low back pain patients.
The lumbago caused by various reasons is causally related to the instability of the lumbar vertebra caused by the reduction of the contraction capacity of the muscles of the lumbar vertebra to different degrees, so the detection of the local muscle state of the lumbar vertebra is one of means for preventing the lumbago. Researchers at home and abroad initially insert the needle electrode into muscle to detect electromyogram, the interference is small, the positioning performance is good, and the myogram is easy to identify.
Long-term research shows that the surface electromyographic signals can also acquire the movement information of muscles, and the surface electromyographic signals are a noninvasive detection method for recording bioelectricity signals issued during the neuromuscular activity through electrode plates stuck on the surfaces of the muscles, are simple to operate and easy to accept, so the surface electromyographic signals are widely applied to the fields of clinical medicine, biomedical engineering, artificial limb bionics, pattern recognition, sports and the like. However, the application of surface electromyographic signals is still in a stage of development compared to needle electrode detection. The method is closely related to the basic research of the neuromuscular system, the physiological research of the neuromuscular system provides a solid foundation for the application of the electromyographic signals, and the detection of the surface electromyographic signals provides a better detection method for the research of the neuromuscular system. During the exercise of the human body, the muscles contract to release electric signals. The contraction muscle is a function of an engine in human body movement to provide power for the human body movement, and plays a very important role in the movement, so that muscle research is an attractive and challenging field. The surface electromyogram signal is used as one of the human body movement muscle discharge physiological signals, and the operation is simple, and the real-time performance and the bionic performance are good. Patent CN201410277780.4 adopts human body support frame, myoelectricity collection and analysis system and force sensor to test the waist strength, endurance, etc. to reflect the waist muscle status, this system can evaluate the waist muscle status of lumbago patients, but fails to find the difference of muscle characteristics between healthy people and lumbago patients, and does not have identification module to identify normal and abnormal status, so it cannot distinguish healthy people from lumbago patients, and there is a certain limitation.
Disclosure of Invention
The application provides a pain recognition method, a pain recognition device and electronic equipment, and aims to solve at least one of the technical problems in the prior art to a certain extent.
In order to solve the above problems, the present application provides the following technical solutions:
a method of pain recognition, comprising:
step a: the method comprises the following steps of collecting surface electromyographic signals of a detection part when a subject makes a flexion movement through a signal collection module; wherein the subjects comprise healthy and painful patients, respectively;
step b: respectively carrying out comparative analysis on muscle contraction modes of the detection parts of the healthy person and the pain patient according to the surface electromyographic signals to obtain the difference characteristics of the surface electromyographic signals of the detection parts of the healthy person and the pain patient;
step c: and identifying the pain patient according to the difference characteristics of the myoelectric signals on the surface of the detection part.
The technical scheme adopted by the embodiment of the application further comprises the following steps: in the step a, the acquiring, by the signal acquisition module, the surface electromyogram signal of the detection site when the subject performs the flexion motion further includes: the disposable physiotherapy electrode piece is pasted on the detection part of a testee along the muscle fiber direction, and the signal acquisition module is connected with the disposable physiotherapy electrode piece.
The technical scheme adopted by the embodiment of the application further comprises the following steps: in the step a, the acquiring, by the signal acquisition module, the surface electromyogram signal of the detection site when the subject performs the flexion motion further includes: the signal acquisition module comprises a transmitting end and a receiving end, the transmitting end is connected with the disposable physiotherapy electrode slice, when a testee does flexion motion, the transmitting end is used for acquiring muscle analog signals of the testee, and the muscle analog signals are transmitted to the receiving end; and the receiving end performs analog-to-digital conversion on the muscle analog signal, and converts the muscle analog signal into a one-dimensional random voltage signal to obtain a surface electromyographic signal.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the step a and the step b further comprise the following steps: and preprocessing the surface electromyographic signals.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the preprocessing of the surface electromyogram signal specifically comprises:
step a 1: performing filtering processing on the surface myoelectric signal by adopting a band-pass filter;
step a 2: carrying out power frequency denoising processing on the filtered surface electromyographic signals by adopting a band elimination filter;
step a 3: and carrying out standardization processing on the power frequency denoised surface electromyographic signals by adopting a maximum value normalization algorithm.
Another technical scheme adopted by the embodiment of the application is as follows: a pain recognition device, comprising:
the signal acquisition module: the device is used for collecting surface electromyographic signals of a detection part when a subject makes flexion movement; wherein the subjects comprise healthy and painful patients, respectively;
a feature extraction module: the muscle contraction modes of the detection parts of the healthy person and the pain patient are respectively compared and analyzed according to the surface electromyographic signals, and the difference characteristics of the surface electromyographic signals of the detection parts of the healthy person and the pain patient are obtained;
a pain recognition module: the method is used for identifying the pain patient according to the difference characteristics of the surface electromyographic signals of the detection parts of the healthy person and the pain patient.
The technical scheme that this application embodiment adopted still includes disposable physiotherapy electrode piece, disposable physiotherapy electrode piece is pasted in the detection site of experimenter along muscle fibre direction, signal acquisition module and disposable physiotherapy electrode piece are connected.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the signal acquisition module comprises a transmitting end and a receiving end;
the transmitting end is connected with the disposable physiotherapy electrode slice, and is used for collecting muscle analog signals of a testee and transmitting the muscle analog signals to the receiving end when the testee makes flexion movement;
the receiving end is used for carrying out analog-to-digital conversion on the muscle analog signals, converting the muscle analog signals into one-dimensional random voltage signals and obtaining surface electromyographic signals.
The technical scheme adopted by the embodiment of the application further comprises a signal preprocessing module, and the signal preprocessing module is used for preprocessing the surface myoelectric signals.
The technical scheme adopted by the embodiment of the application further comprises the following steps: the signal preprocessing module specifically comprises:
a filtering unit: the device is used for filtering the surface myoelectric signal by adopting a band-pass filter;
a power frequency denoising unit: the band elimination filter is used for carrying out power frequency denoising processing on the filtered surface electromyographic signals;
a normalization unit: the method is used for carrying out standardization processing on the power frequency denoised surface electromyographic signals by adopting a maximum value normalization algorithm.
The embodiment of the application adopts another technical scheme that: an electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the one processor to cause the at least one processor to perform the following operations of the pain recognition method described above:
the method comprises the following steps of collecting surface electromyographic signals of a detection part when a subject makes a flexion movement through a signal collection module; wherein the subjects comprise healthy and painful patients, respectively;
respectively carrying out comparative analysis on muscle contraction modes of the detection parts of the healthy person and the pain patient according to the surface electromyographic signals to obtain the difference characteristics of the surface electromyographic signals of the detection parts of the healthy person and the pain patient;
and identifying the pain patient according to the difference characteristics of the myoelectric signals on the surface of the detection part.
Compared with the prior art, the embodiment of the application has the advantages that: according to the pain recognition method, the pain recognition device and the electronic equipment, the surface electromyographic signals of the pain parts of the healthy person and the pain patient are respectively collected, the muscle contraction modes of the pain parts of the healthy person and the pain patient are contrastively analyzed according to the surface electromyographic signals, the difference characteristics of the surface electromyographic signals of the healthy person and the pain patient are obtained, and the pain patient is recognized according to the difference characteristics; the pain recognition method and the pain recognition device can realize simple, effective, low-cost, noninvasive, non-radiative and real-time pain recognition, and provide theoretical basis for later clinical diagnosis technology.
Drawings
Fig. 1 is a flow chart of a pain recognition method according to a first embodiment of the present application;
FIG. 2 is a flow chart of a pain recognition method according to a second embodiment of the present application;
FIG. 3 is a flexion motion posture diagram;
FIG. 4 is a schematic diagram of the change of the characteristic of the lumbar vertebra local muscle electromyographic signal; wherein, fig. 4a is a lumbar muscle electromyogram signal characteristic diagram of a healthy person, and fig. 4b is a lumbar local muscle electromyogram signal characteristic diagram of a patient with lumbago;
FIG. 5 is a ROC graph of determining lumbago from the buckling relaxation phenomenon according to the embodiment of the present application;
fig. 6 is a schematic structural diagram of a pain recognition apparatus according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a hardware device of a pain recognition method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Please refer to fig. 1, which is a flowchart illustrating a pain recognition method according to a first embodiment of the present application. The pain recognition method of the first embodiment of the present application includes the steps of:
step 100: the method comprises the following steps of collecting surface electromyographic signals of a detection part when a subject makes a flexion movement through a signal collection module;
in step 100, the subjects include healthy and painful patients, respectively.
Step 110: respectively carrying out comparative analysis on muscle contraction modes of detection parts of a healthy person and a painful patient according to the surface electromyographic signals to obtain difference characteristics of the surface electromyographic signals of the detection parts of the healthy person and the painful patient;
step 120: and identifying the pain patient according to the difference characteristics of the myoelectric signals on the surface of the detected part.
Please refer to fig. 2, which is a flowchart illustrating a pain recognition method according to a second embodiment of the present application. The pain recognition method of the second embodiment of the present application includes the steps of:
step 200: sticking the disposable physiotherapy electrode piece to the detection part of a testee along the muscle fiber direction;
in step 200, the disposable physiotherapy electrode slice is used for recording bioelectricity signals of a detection part when the neuromuscular activity of a subject is detected; the subjects include a certain number of healthy people and pain patients (in the embodiment of the present application, the number of the subjects is 60, and may be specifically set according to practical applications), and the myoelectric signals are affected by age, fat, and other factors, so the age, height, weight, and other indicators of the subjects should be matched with each other. The application can be applied to pain recognition of various parts such as waist, back, legs and the like, and the detection part (namely the pasting part of the disposable physiotherapy electrode piece) is adjusted according to the pain recognition part. In the embodiment of the present application, for example, the disposable physiotherapy electrode pad is attached to the lumbar erector spinae muscle or multifidus muscle (ES/MF) of each subject, and before the disposable physiotherapy electrode pad is attached, the detection site of each subject needs to be sterilized (wiped with 75% alcohol).
Step 210: enabling the subject to do flexion movement according to the set flexion movement posture;
in step 210, for example, the lumbar pain recognition is performed, and the flexion movement posture is set according to the pain recognition portion as shown in fig. 3.
Step 220: collecting surface electromyographic signals of a detection part of a subject in the process of flexion motion through a signal collection module;
in step 220, the signal acquisition module includes a transmitting end and a receiving end, and the mode of the signal acquisition module for acquiring the surface electromyogram signal specifically is as follows: the electrode plate of the transmitting end is connected with the disposable physiotherapy electrode plate pasted on the body of a testee, in the movement process of the testee, the transmitting end collects muscle analog signals of the testee through the two connected electrode plates and transmits the muscle analog signals to the receiving end, and the receiving end performs analog-to-digital conversion on the muscle analog signals and converts the muscle analog signals into one-dimensional random voltage signals to obtain surface electromyographic signals. In the embodiment of the present application, the transmitting end is a bipac transmitting module, and the receiving end is an MP150 receiving device, which may be other types of physiotherapy equipment.
Step 230: preprocessing the collected surface electromyographic signals to obtain standard surface electromyographic signals;
in step 230, the pre-processing the surface myoelectric signal specifically includes:
step 231: performing filtering processing on the surface myoelectric signal by adopting a band-pass filter;
in step 231, for the identification of the waist pain, since the upper half of the human body is affected by the heartbeat, the main frequency band of the electrocardiosignal is 0.25 to 35Hz, and certain interference is generated on the bioelectricity signal generated by the muscle movement, so that the collected surface electromyography signal needs to be filtered by 35 to 500 Hz; in the embodiment of the present application, the band pass filter is a chebyshev band pass filter, and may specifically be another type of filter.
Step 232: carrying out power frequency denoising processing on the filtered surface electromyographic signals by adopting a band elimination filter;
in step 232, because 50Hz power frequency interference generated by the chinese universal voltage 220v has a large influence on the surface myoelectric signal, the surface myoelectric signal needs to be subjected to power frequency denoising processing; in the embodiment of the present application, the band-stop filter is a chebyshev band-stop filter, and may specifically be another type of filter.
Step 233: and standardizing the power frequency denoised surface electromyographic signals by adopting a maximum normalization algorithm to obtain standardized surface electromyographic signals.
In step 233, in order to ensure that the surface electromyographic signals of the subjects can be compared, a maximum normalization algorithm is required to normalize the surface electromyographic signals collected by all the subjects each time, so as to obtain normalized surface electromyographic signals.
Step 240: respectively carrying out comparative analysis on muscle contraction modes of a healthy person and a painful patient according to the standardized surface electromyographic signals to obtain the difference characteristics of the surface electromyographic signals of the healthy person and the painful patient;
in step 240, taking the lumbar pain recognition as an example, by analyzing the characteristics of the lumbar muscle electromyographic signals, it is found that the lumbar muscle electromyographic signals of the healthy person and the lumbago patient are significantly different, as shown in fig. 4, which is a schematic diagram of the characteristic change of the lumbar local muscle electromyographic signal; fig. 4a is a schematic diagram of the electromyographic signal characteristics of lumbar muscles of a healthy person, and fig. 4b is a schematic diagram of the electromyographic signal characteristics of lumbar muscles of a patient with lumbago. As can be seen from fig. 4a, when the healthy person starts to bend forward, the local muscle contraction of the lumbar vertebra increases, as the forward bending angle of the trunk increases, the posterior fascia ligament is tensed, the muscle activity decreases, and when the trunk bends fully forward, the spinal stabilization is maintained by the tendon ligament and the joints, and the muscle activity decreases to the minimum; muscle activity increases as the trunk returns from flexion to erection, and this relaxed state of flexion is caused by the inhibition of muscle contraction and a decrease in stretch-sensing, a protective muscle contraction. As can be seen from fig. 4b, the lumbago patient has limited forward bending of the trunk due to the pain caused by the pathological changes of the tissues, pain-avoidance response, and the like, and the local muscles of the waist are continuously contracted to maintain the stable posture, so that the muscle activity is gradually weakened in the process of bending the trunk back to the upright state. Thus, the muscle contraction pattern of the healthy person and the pain patient is in the muscle flexion relaxation state (the healthy person has the muscle flexion relaxation phenomenon, and the pain patient does not have the muscle flexion relaxation phenomenon), the muscle movement control strategy (under the condition of sudden load change of the trunk, a healthy person senses the impending load change in advance, so that the rapid response time of the local lumbar stabilizing muscles can be obviously accelerated, the rapid response intensity of the local lumbar stabilizing muscles can be reduced, the lumbar stabilizing muscles contract before the whole trunk stabilizing muscles and the motor muscle groups, and the obvious trunk muscle recruitment and central movement control strategy from inside to outside is shown).
Step 250: pain patients are identified based on the differential characteristics.
In step 250, the contraction modes of local muscles of healthy people and pain patients are different (), and the pain patients are subjected to low-cost, noninvasive, non-radiative and real-time pain identification through the difference characteristics, so that a theoretical basis is provided for a later clinical diagnosis technology.
In the present application, the muscle flexion relaxation state is taken as an example of the difference characteristic, and by performing a lumbar muscle test on 60 subjects, when the lumbar muscle flexion relaxation phenomenon occurs, the subject is determined to be a healthy subject, and when the lumbar muscle flexion relaxation phenomenon does not occur, the subject is determined to be a lumbago patient, specifically, as shown in fig. 5, an ROC (receiver operating characteristic curve) graph for determining lumbago for the flexion relaxation phenomenon of the embodiment of the present application is shown. The identification accuracy of the lumbago patients is 82% through testing, so the application can be used for clinically identifying the pain of the patients.
Please refer to fig. 6, which is a schematic structural diagram of a pain recognition device according to an embodiment of the present application. The pain recognition device of the embodiment of the application comprises a disposable physiotherapy electrode piece, a signal acquisition module, a signal preprocessing module, a feature extraction module and a pain recognition module. Specifically, the functions of the respective modules are as follows:
the disposable physiotherapy electrode piece is stuck to the detection part of a testee along the muscle fiber direction; for recording bioelectrical signals at the detection site during neuromuscular activity in the subject; the subjects include a certain number of healthy persons and pain patients, and the age, height, weight, and other indicators of the subjects should be matched with each other because the electromyographic signals are affected by age, fat, and other factors. In the embodiment of the application, for example, the waist pain recognition is performed, the disposable physiotherapy electrode sheet is respectively attached to the lumbar erector spinae muscle or multifidus muscle (ES/MF) of each subject, and before the disposable physiotherapy electrode sheet is attached, the detection part of each subject needs to be disinfected.
The signal acquisition module comprises a transmitting end and a receiving end; wherein:
the electrode plate of the transmitting end is connected with the disposable physiotherapy electrode plate pasted on the body of a testee, when the testee carries out flexion movement according to a set flexion movement posture, the transmitting end collects muscle analog signals of the testee through the two connected electrode plates and transmits the muscle analog signals to the receiving end;
the receiving end is used for carrying out analog-to-digital conversion on the muscle analog signals, converting the muscle analog signals into one-dimensional random voltage signals and obtaining surface electromyographic signals. In the embodiment of the present application, the transmitting end is a bipac transmitting module, and the receiving end is an MP150 receiving device, which may be other types of physiotherapy equipment.
The signal preprocessing module: the system is used for preprocessing the collected surface electromyographic signals to obtain standard surface electromyographic signals; specifically, the signal preprocessing module comprises a filtering unit, a power frequency denoising unit and a standardization unit;
a filtering unit: the device is used for filtering the surface myoelectric signal by adopting a band-pass filter; for the identification of the waist pain, because the upper half of the human body is influenced by the heart beating, the main frequency band of electrocardiosignals is 0.25-35Hz, and certain interference is generated on bioelectricity signals generated by muscle movement, so that 35-500Hz filtering processing needs to be carried out on the collected surface electromyography signals; in the embodiment of the present application, the bandpass filter used by the filtering unit is a chebyshev bandpass filter, and may specifically be another type of filter.
A power frequency denoising unit: the band elimination filter is used for carrying out power frequency denoising processing on the filtered surface electromyographic signals; because 50Hz power frequency interference generated by the Chinese universal voltage 220v has great influence on the surface myoelectric signals, the surface myoelectric signals need to be subjected to power frequency denoising treatment; in the embodiment of the present application, the band-stop filter adopted by the power frequency denoising unit is a chebyshev band-stop filter, and may be specifically other types of filters.
A normalization unit: the device is used for carrying out standardization processing on the power frequency denoised surface electromyographic signals by adopting a maximum value normalization algorithm to obtain standardized surface electromyographic signals; in order to ensure that the surface electromyographic signals of all the subjects can be compared, a maximum normalization algorithm is adopted to normalize the surface electromyographic signals acquired by all the subjects each time, so as to obtain normalized surface electromyographic signals.
A feature extraction module: the muscle contraction mode analysis module is used for respectively carrying out comparative analysis on muscle contraction modes of a healthy person and a pain patient according to the standardized surface electromyogram signals to obtain the difference characteristics of the surface electromyogram signals of the healthy person and the pain patient; taking the waist pain identification as an example, by analyzing the characteristics of the waist muscle electromyographic signals, it is found that the waist muscle electromyographic signals of the healthy person and the lumbago patient show significant difference, as shown in fig. 4, a schematic diagram of the characteristic change of the lumbar vertebra local muscle electromyographic signals is shown; fig. 4a is a schematic diagram of the electromyographic signal characteristics of lumbar muscles of a healthy person, and fig. 4b is a schematic diagram of the electromyographic signal characteristics of lumbar muscles of a patient with lumbago. As can be seen from fig. 4a, when the healthy person starts to bend forward, the local muscle contraction of the lumbar vertebra increases, as the forward bending angle of the trunk increases, the posterior fascia ligament is tensed, the muscle activity decreases, and when the trunk bends fully forward, the spinal stabilization is maintained by the tendon ligament and the joints, and the muscle activity decreases to the minimum; muscle activity increases as the trunk returns from flexion to erection, and this relaxed state of flexion is caused by the inhibition of muscle contraction and a decrease in stretch-sensing, a protective muscle contraction. As can be seen from fig. 4b, the lumbago patient has limited forward bending of the trunk due to the pain caused by the pathological changes of the tissues, pain-avoidance response, and the like, and the local muscles of the waist are continuously contracted to maintain the stable posture, so that the muscle activity is gradually weakened in the process of bending the trunk back to the upright state. Thus, the muscle contraction pattern of the healthy person and the pain patient is in the muscle flexion relaxation state (the healthy person has the muscle flexion relaxation phenomenon, and the pain patient does not have the muscle flexion relaxation phenomenon), the muscle movement control strategy (under the condition of sudden load change of the trunk, a healthy person senses the impending load change in advance, so that the rapid response time of the local lumbar stabilizing muscles can be obviously accelerated, the rapid response intensity of the local lumbar stabilizing muscles can be reduced, the lumbar stabilizing muscles contract before the whole trunk stabilizing muscles and the motor muscle groups, and the obvious trunk muscle recruitment and central movement control strategy from inside to outside is shown).
A pain recognition module: for identifying pain patients based on the differential characteristics. The contraction modes of local muscles of healthy people and pain patients are different, and the pain patients are subjected to low-cost, noninvasive, non-radiative and real-time pain identification through the difference characteristics.
Fig. 7 is a schematic structural diagram of a hardware device of a pain recognition method according to an embodiment of the present invention, and as shown in fig. 7, the device includes one or more processors and a memory. Taking a processor as an example, the apparatus may further include: an input device and an output device.
The processor, memory, input devices, and output devices may be connected by a bus or other means, as exemplified by the bus connection in fig. 7.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor executes various functional applications and data processing of the electronic device, i.e., implements the processing method of the above-described method embodiment, by executing the non-transitory software program, instructions and modules stored in the memory.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data and the like. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processing device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device may receive input numeric or character information and generate a signal input. The output device may include a display device such as a display screen.
The one or more modules are stored in the memory and, when executed by the one or more processors, perform the following for any of the above method embodiments:
the method comprises the following steps of collecting surface electromyographic signals of a detection part when a subject makes a flexion movement through a signal collection module; wherein the subjects comprise healthy and painful patients, respectively;
respectively carrying out comparative analysis on muscle contraction modes of the detection parts of the healthy person and the pain patient according to the surface electromyographic signals to obtain the difference characteristics of the surface electromyographic signals of the detection parts of the healthy person and the pain patient;
and identifying the pain patient according to the difference characteristics of the myoelectric signals on the surface of the detection part.
The product can execute the method provided by the embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
An embodiment of the present invention provides a non-transitory (non-volatile) computer storage medium storing computer-executable instructions that may perform the following operations:
the method comprises the following steps of collecting surface electromyographic signals of a detection part when a subject makes a flexion movement through a signal collection module; wherein the subjects comprise healthy and painful patients, respectively;
respectively carrying out comparative analysis on muscle contraction modes of the detection parts of the healthy person and the pain patient according to the surface electromyographic signals to obtain the difference characteristics of the surface electromyographic signals of the detection parts of the healthy person and the pain patient;
and identifying the pain patient according to the difference characteristics of the myoelectric signals on the surface of the detection part.
An embodiment of the present invention provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform the following:
the method comprises the following steps of collecting surface electromyographic signals of a detection part when a subject makes a flexion movement through a signal collection module; wherein the subjects comprise healthy and painful patients, respectively;
respectively carrying out comparative analysis on muscle contraction modes of the detection parts of the healthy person and the pain patient according to the surface electromyographic signals to obtain the difference characteristics of the surface electromyographic signals of the detection parts of the healthy person and the pain patient;
and identifying the pain patient according to the difference characteristics of the myoelectric signals on the surface of the detection part.
According to the pain recognition method, the pain recognition device and the electronic equipment, the surface electromyographic signals of the pain parts of the healthy person and the pain patient are respectively collected, the muscle contraction modes of the pain parts of the healthy person and the pain patient are contrastively analyzed according to the surface electromyographic signals, the difference characteristics of the surface electromyographic signals of the healthy person and the pain patient are obtained, and the pain patient is recognized according to the difference characteristics; the pain recognition method and the pain recognition device can realize simple, effective, low-cost, noninvasive, non-radiative and real-time pain recognition, and provide theoretical basis for later clinical diagnosis technology.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (11)
1. A method of pain recognition, comprising:
step a: the method comprises the following steps of collecting surface electromyographic signals of a detection part when a subject makes a flexion movement through a signal collection module; wherein the subjects comprise healthy and painful patients, respectively;
step b: respectively carrying out comparative analysis on muscle contraction modes of the detection parts of the healthy person and the pain patient according to the surface electromyographic signals to obtain the difference characteristics of the surface electromyographic signals of the detection parts of the healthy person and the pain patient;
step c: and identifying the pain patient according to the difference characteristics of the myoelectric signals on the surface of the detection part.
2. The pain recognition method according to claim 1, wherein in the step a, the collecting surface electromyography signals of the detected part when the subject performs the flexion movement by the signal collection module further comprises: the disposable physiotherapy electrode piece is pasted on the detection part of a testee along the muscle fiber direction, and the signal acquisition module is connected with the disposable physiotherapy electrode piece.
3. The pain recognition method according to claim 2, wherein in the step a, the collecting surface electromyography signals of the detected part when the subject performs the flexion movement by the signal collection module further comprises: the signal acquisition module comprises a transmitting end and a receiving end, the transmitting end is connected with the disposable physiotherapy electrode slice, when a testee does flexion motion, the transmitting end is used for acquiring muscle analog signals of the testee, and the muscle analog signals are transmitted to the receiving end; and the receiving end performs analog-to-digital conversion on the muscle analog signal, and converts the muscle analog signal into a one-dimensional random voltage signal to obtain a surface electromyographic signal.
4. The method for pain recognition according to any one of claims 1 to 3, further comprising, between step a and step b: and preprocessing the surface electromyographic signals.
5. The pain recognition method of claim 4, wherein the pre-processing of the surface electromyography signals specifically comprises:
step a 1: performing filtering processing on the surface myoelectric signal by adopting a band-pass filter;
step a 2: carrying out power frequency denoising processing on the filtered surface electromyographic signals by adopting a band elimination filter;
step a 3: and carrying out standardization processing on the power frequency denoised surface electromyographic signals by adopting a maximum value normalization algorithm.
6. A pain recognition device, comprising:
the signal acquisition module: the device is used for collecting surface electromyographic signals of a detection part when a subject makes flexion movement; wherein the subjects comprise healthy and painful patients, respectively;
a feature extraction module: the muscle contraction modes of the detection parts of the healthy person and the pain patient are respectively compared and analyzed according to the surface electromyographic signals, and the difference characteristics of the surface electromyographic signals of the detection parts of the healthy person and the pain patient are obtained;
a pain recognition module: the method is used for identifying the pain patient according to the difference characteristics of the surface electromyographic signals of the detection parts of the healthy person and the pain patient.
7. The pain recognition device of claim 6, further comprising a disposable physiotherapy electrode sheet, wherein the disposable physiotherapy electrode sheet is adhered to the detection site of the subject along the muscle fiber direction, and the signal acquisition module is connected to the disposable physiotherapy electrode sheet.
8. The pain recognition device of claim 7, wherein the signal acquisition module comprises a transmitting end and a receiving end;
the transmitting end is connected with the disposable physiotherapy electrode slice, and is used for collecting muscle analog signals of a testee and transmitting the muscle analog signals to the receiving end when the testee makes flexion movement;
the receiving end is used for carrying out analog-to-digital conversion on the muscle analog signals, converting the muscle analog signals into one-dimensional random voltage signals and obtaining surface electromyographic signals.
9. The pain recognition device of any one of claims 6 to 8, further comprising a signal pre-processing module for pre-processing the surface myoelectric signal.
10. The pain recognition device of claim 9, wherein the signal preprocessing module comprises:
a filtering unit: the device is used for filtering the surface myoelectric signal by adopting a band-pass filter;
a power frequency denoising unit: the band elimination filter is used for carrying out power frequency denoising processing on the filtered surface electromyographic signals;
a normalization unit: the method is used for carrying out standardization processing on the power frequency denoised surface electromyographic signals by adopting a maximum value normalization algorithm.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the following operations of the pain recognition method of any one of claims 1 to 5 above:
the method comprises the following steps of collecting surface electromyographic signals of a detection part when a subject makes a flexion movement through a signal collection module; wherein the subjects comprise healthy and painful patients, respectively;
respectively carrying out comparative analysis on muscle contraction modes of the detection parts of the healthy person and the pain patient according to the surface electromyographic signals to obtain the difference characteristics of the surface electromyographic signals of the detection parts of the healthy person and the pain patient;
and identifying the pain patient according to the difference characteristics of the myoelectric signals on the surface of the detection part.
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