CN111387978A - Method, device, equipment and medium for detecting action section of surface electromyogram signal - Google Patents
Method, device, equipment and medium for detecting action section of surface electromyogram signal Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/389—Electromyography [EMG]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
Abstract
A method, a device, equipment and a medium for detecting an action section of a surface electromyogram signal are provided, wherein the method comprises the following steps: collecting an initial surface electromyographic signal; preprocessing the initial surface electromyographic signals to obtain surface electromyographic signals; performing conversion operation on the surface myoelectric signal to obtain a conversion signal; correcting the converted signal based on a baseline threshold to obtain a corrected signal; carrying out equidistant integration processing on the correction signal through a kernel function to obtain a judgment signal; and when the judging signal is larger than the preset threshold value, determining that the surface electromyographic signal corresponding to the judging signal is in the action section. According to the embodiment of the application, the collected initial surface electromyogram signals are preprocessed, converted and baseline corrected, then the correction signals are processed by utilizing the kernel function to obtain the judgment signals, the fluctuation of the surface electromyogram signals caused by muscle tension is weakened, the misjudgment of the activity section caused by the muscle tension is reduced, the calculated amount is reduced, the detection time delay is shortened, and the accuracy and precision of detection are improved.
Description
Technical Field
The application relates to the field of wearable equipment, in particular to a method, a device, equipment and a medium for detecting an action section of a surface electromyogram signal.
Background
The surface electromyogram signal is generally divided into a rest potential section and an action potential section, the detection of the action section of the surface electromyogram signal refers to determining the starting position and the ending position of the action potential section, and the accurate and effective distinction of the rest potential section and the action potential section is one of the important steps of electromyogram signal gesture recognition, and the detection methods of the commonly used action potential sections include the following steps:
(1) the moving average method comprises the steps of firstly obtaining the average value of the surface electromyogram signals, then obtaining the norm of the average value, carrying out moving average processing on the instantaneous energy of the surface electromyogram signals by adopting a window function, judging the obtained value and a proper threshold value, and considering that the signals larger than the threshold value are action potential sections and the signals smaller than the threshold value are rest potential sections;
(2) and (3) detecting standard deviation and absolute mean: establishing a single or double threshold value for judgment by using the standard deviation and the absolute mean value of the surface electromyogram signal;
(3) wavelet transformation method: calculating the maximum value output by a group of matched filters under different scales by utilizing continuous wavelet transform decomposition, and judging the starting position and the ending position of the action section by comparing the value with a threshold value;
(4) a statistical criterion decision method: a model-based method for detecting motion segments, the onset of which adapts to the measured signal as a sudden change in the time-varying parameters of a statistical process model, and the accuracy of the algorithm can be assessed by means of the statistical model.
In the implementation process, the calculation amount of the moving average method is large, corresponding delay is generated by adopting a sliding window and the running time of the algorithm, and the detection precision is low when the noise amount of the surface electromyogram signal is large; in the standard deviation and absolute mean detection method, the detection precision is low when the data volume of the sliding window is small, and delay is generated when the data volume of the sliding window is large; the wavelet transform method has large calculation amount and low accuracy; the calculation amount of the statistical criterion decision method is large.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
In order to solve the technical problems, the application provides a method, a device, equipment and a medium for detecting an action section of a surface electromyogram signal, which can reduce the calculation amount, shorten the delay, reduce the misjudgment of an action section caused by muscle tension, and improve the detection precision and accuracy.
In a first aspect, the present application provides a method for detecting an action segment of a surface electromyogram signal, the method comprising the steps of:
collecting an initial surface electromyographic signal;
preprocessing the initial surface electromyographic signal to obtain a surface electromyographic signal;
performing conversion operation on the surface electromyographic signal to obtain a conversion signal;
correcting the converted signal based on a baseline threshold to obtain a corrected signal;
carrying out equidistant integration processing on the correction signal through a kernel function to obtain a judgment signal;
and when the judging signal is larger than a preset threshold value, determining that the surface electromyographic signal corresponding to the judging signal is in an action segment.
With reference to the first aspect, in a first possible implementation manner of the first aspect, the baseline threshold is determined according to the following formula:
thr＝mean{MAV1,MAV2,MAV3,...,MAVm}+A；
where thr is the baseline threshold, MAViThe maximum value of signals in a sliding window in the resting state data of the surface electromyogram signals is shown, i is a positive integer between 1 and k, m is the number of the sliding windows, and A is a constant.
With reference to the first aspect, in a second possible implementation manner of the first aspect, the performing equidistant integration processing on the correction signal through a kernel function to obtain a decision signal includes:
initializing the kernel function;
introducing the correction signals into kernel functions one by one, and updating the kernel functions after introducing one correction signal;
and calculating the unit equidistant integral of the kernel function based on a trapezoidal method to obtain a judgment signal corresponding to the correction signal.
With reference to the first aspect, in a third possible implementation manner of the first aspect, the determining that the surface electromyography signal corresponding to the determination signal is in an action segment when the determination signal is greater than a preset threshold includes:
if one or more previous judging signals are smaller than or equal to a preset threshold value, when the judging signals are switched to be larger than the preset threshold value, determining that the surface electromyographic signals currently corresponding to the judging signals are the initial positions of the action sections;
and if the previous one or more judgment signals are larger than or equal to a preset threshold value, when the judgment signals are switched to be smaller than the preset threshold value, determining that the surface electromyographic signals currently corresponding to the judgment signals are the termination positions of the action sections.
With reference to the first aspect, in a fourth possible implementation manner of the first aspect, the time series of the initial surface electromyography signal is represented by the following formula:
{s1，s2，s3，...，si，...，sn}；
the converted signal is expressed by the following formula:
{S1，S2，S′3，S′4，...}；
wherein S is1＝{s1，s2，s3，...sk}；S2＝{sk+1，sk+2，sk+3，...s2k}；
S3'＝{s2k+1，s2k+2，s2k+3，...s3k}＝min(max(S1)，max(S2)，max(S3))；
S4'＝{s3k+1，s3k+2，s3k+3，...s4k}＝min(max(S2)，max(S3)，max(S4))；
Setting window data S as S1，...,sk,sk+1，...,s2k,s2k+1,...,s3kDividing the obtained solution into three parts to obtain: (ii) a
S1＝{s1，s2，s3，...sk}；
S2＝{sk+1，sk+2，sk+3，...s2k}；
S3＝{s2k+1，s2k+2，s2k+3，...s3k}；
Sliding the current window to the next window to obtain window data:
S2＝{sk+1，sk+2，sk+3，...s2k}；
S3＝{s2k+1，s2k+2，s2k+3，...s3k}；
S4＝{s3k+1，s3k+2，s3k+3，...s4k}；
where k is the window step, the size of the window data is m, and m is 3 × k.
In a second aspect, the present application provides an action section detecting device of a surface electromyogram signal, the device comprising:
the signal acquisition unit is used for acquiring an initial surface electromyographic signal;
the preprocessing unit is used for preprocessing the initial surface electromyographic signal to obtain a surface electromyographic signal;
the signal conversion unit is used for carrying out conversion operation on the initial surface electromyographic signal to obtain a conversion signal;
a signal correction unit for correcting the converted signal based on a baseline threshold value to obtain a corrected signal;
the signal processing unit is used for carrying out equidistant integration processing on the correction signal through a kernel function to obtain a judgment signal; and
and the determining unit is used for determining that the surface electromyographic signal corresponding to the judging signal is in an action section when the judging signal is larger than a preset threshold value.
With reference to the second aspect, in a first possible implementation manner of the second aspect, the baseline threshold is determined according to the following formula:
thr＝mean{MAV1,MAV2,MAV3,...,MAVm}+A；
where thr is the baseline threshold, MAViThe maximum value of the sliding window in the resting state data of the surface electromyogram signal is represented by i, which is a positive integer between 1 and k, m is the number of the sliding windows, and A is a constant.
With reference to the second aspect, in a second possible implementation manner of the second aspect, the signal processing unit includes:
the initialization subunit is used for initializing the kernel function;
a signal leading-in subunit, configured to lead the correction signals into kernel functions one by one, and update the kernel functions after each leading-in of one correction signal;
and the calculating subunit is used for calculating the kernel function unit equidistant integral based on a trapezoidal method to obtain a judgment signal corresponding to the correction signal.
With reference to the second aspect, in a third possible implementation manner of the second aspect, the determining unit includes:
a start signal determining subunit, configured to determine, if one or more previous determination signals are smaller than or equal to a preset threshold, that a surface electromyographic signal currently corresponding to the determination signal is a start position of the action segment when the determination signal is switched to be greater than the preset threshold;
and the determining termination signal subunit is used for determining that the surface electromyographic signal currently corresponding to the judging signal is the termination position of the action section when the judging signal is switched to be smaller than a preset threshold value if one or more judging signals are larger than or equal to the preset threshold value.
With reference to the second aspect, in a fourth possible implementation manner of the second aspect, the time series of the initial surface electromyography signal is represented by the following formula:
{s1，s2，s3，...，si，...，sn}；
the converted signal is expressed by the following formula:
{S1，S2，S′3，S′4，...}；
wherein S is1＝{s1，s2，s3，...sk}；S2＝{sk+1，sk+2，sk+3，...s2k}；
S3'＝{s2k+1，s2k+2，s2k+3，...s3k}＝min(max(S1)，max(S2)，max(S3))；
S4'＝{s3k+1，s3k+2，s3k+3，...s4k}＝min(max(S2)，max(S3)，max(S4))；
Setting window data S as S1，...,sk,sk+1，...,s2k,s2k+1,...,s3kDividing the obtained solution into three parts to obtain: (ii) a
S1＝{s1，s2，s3，...sk}；
S2＝{sk+1，sk+2，sk+3，...s2k}；
S3＝{s2k+1，s2k+2，s2k+3，...s3k}；
Sliding the current window to the next window to obtain window data:
S2＝{sk+1，sk+2，sk+3，...s2k}；
S3＝{s2k+1，s2k+2，s2k+3，...s3k}；
S4＝{s3k+1，s3k+2，s3k+3，...s4k}；
where k is the window step, the size of the window data is m, and m is 3 × k.
In a third aspect, the present application provides a wearable device, comprising: a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the action segment detection method of a surface electromyogram signal according to the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, implements the steps of the method for detecting an action section of a surface electromyogram signal according to the first aspect.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages:
the method provided by the embodiment of the application relates to a method, a device, equipment and a medium for detecting the action section of a surface electromyogram signal, wherein the method comprises the following steps: collecting an initial surface electromyographic signal; preprocessing the initial surface electromyographic signal to obtain a surface electromyographic signal; performing conversion operation on the surface electromyographic signal to obtain a conversion signal; correcting the converted signal based on a baseline threshold to obtain a corrected signal; carrying out equidistant integration processing on the correction signal through a kernel function to obtain a judgment signal; and when the judging signal is larger than a preset threshold value, determining that the surface electromyographic signal corresponding to the judging signal is in an action segment. According to the embodiment of the application, the collected initial surface electromyogram signals are preprocessed, converted and baseline corrected, then the correction signals are processed by utilizing the kernel function to obtain the judgment signals, namely the initial surface electromyogram signals are subjected to simple secondary conversion, the fluctuation of the surface electromyogram signals caused by muscle tension is weakened, the misjudgment of the activity section caused by the muscle tension is reduced, the tiny difference value between the rest potential section and the action potential section is increased, the calculated amount is reduced, the detection time delay is shortened, and the accuracy and precision of the action potential section detection are improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flowchart of a method for detecting an action segment of a surface electromyogram signal according to an embodiment of the present application;
fig. 2 is a schematic flowchart of another method for detecting an action segment of a surface electromyogram signal according to an embodiment of the present application;
fig. 3 is a schematic flowchart of a method for detecting an action segment of a surface electromyogram signal according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a device for detecting an action segment of a surface electromyogram signal according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a wearable device provided in an embodiment of the present application;
fig. 6 is a schematic structural diagram of an intelligent myoelectric arm ring provided in the embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, 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 some embodiments of the present application, but not all 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.
Fig. 1 is a schematic flow chart of a method for detecting an action segment of a surface electromyogram signal according to an embodiment of the present application, where the method specifically includes the following steps:
s101, acquiring an initial surface electromyogram signal, for example, the initial surface electromyogram signal may be acquired by a wearable device.
The surface electromyogram signal is a non-stable and non-linear weak electric signal, has randomness, is very easy to be interfered by the outside, and has low signal-to-noise ratio.
Optionally, in the embodiment of the present application, an 8-channel intelligent myoelectric arm ring 600 (that is, the wearable device in the embodiment of the present application) is worn on the forearm of the upper limb of the object to be tested, as shown in fig. 6, to acquire an initial surface myoelectric signal, the simulation is performed with Matlab 2015a software, the surface myoelectric signal obtained after the initial surface myoelectric signal is preprocessed is substituted into the algorithm code, so that the start position of the active segment can be obtained, and the data in the active segment is subjected to feature extraction and pattern recognition, so that a label of the gesture motion can be obtained.
Optionally, the time series of the initial surface electromyographic signal is represented by the following formula:
{S1，S2，S3，…，Si，…，Sn}；
the converted signal is expressed by the following formula:
{S1，S2，S′3，S′4，...}；
wherein S is1＝{s1，s2，s3，...sk}；S2＝{sk+1，sk+2，sk+3，...s2k}；
S3'＝{s2k+1，s2k+2，s2k+3，...s3k}＝min(max(S1)，max(S2)，max(S3))；
S4'＝{s3k+1，s3k+2，s3k+3，...s4k}＝min(max(S2)，max(S3)，max(S4))；
Setting window data S as S1，...,sk,sk+1，...,s2k,s2k+1,...,s3kDividing the obtained solution into three parts to obtain: (ii) a
S1＝{s1，s2，s3，...sk}；
S2＝{sk+1，sk+2，sk+3，...s2k}；
S3＝{s2k+1，s2k+2，s2k+3，...s3k}；
Sliding the current window to the next window to obtain window data:
S3＝{s2k+1，s2k+2，s2k+3，...s3k}；
S4＝{s3k+1，s3k+2，s3k+3，...s4k}；
where k is the window step, the size of the window data is m, and m is 3 × k.
S102, preprocessing the initial surface electromyographic signals to obtain surface electromyographic signals.
Optionally, the preprocessing includes, but is not limited to, filtering processing and down-sampling processing, and the obtained surface electromyogram signal has a reduced noise content compared with the initial surface electromyogram signal.
S103, performing conversion operation on the surface electromyographic signal to obtain a conversion signal.
By converting the surface electromyogram signal, the difference of different tested objects is effectively corrected, and the influence of impedance and muscle tension on a baseline threshold is reduced.
And S104, correcting the conversion signal based on the baseline threshold value to obtain a correction signal.
Optionally, the baseline threshold is determined according to the following formula:
thr＝mean{MAV1,MAV2,MAV3,...,MAVm}+A
S2＝{sk+1，sk+2，sk+3，...s2k}；
where thr is the baseline threshold, MAViThe maximum value of the sliding window in the resting state data of the surface electromyogram signal is represented by i, which is a positive integer between 1 and k, m is the number of the sliding windows, and A is a constant.
The converted signal obtained by converting the surface electromyogram signal is corrected by using the baseline threshold, and then the corrected signal is processed by using the kernel function to obtain the judgment signal, wherein the judgment signal increases the tiny difference between the action potential section and the rest potential section in the initial surface electromyogram signal, weakens the fluctuation of the surface electromyogram signal caused by muscle tension and reduces the misjudgment of the active section caused by the muscle tension.
And S105, carrying out equidistant integration processing on the correction signal through a kernel function to obtain a judgment signal.
S106, when the judgment signal is larger than a preset threshold value, determining that the surface electromyographic signal corresponding to the judgment signal is in an action segment.
The existing action potential section usually needs to be processed and then compared with a set threshold value to find an initiation point of the action potential, but the waveform amplitude of a resting potential baseline is greatly changed due to the difference of skin impedance and muscle tension between different tested objects, and the initiation point of the action is easily judged by mistake when the action is not started and the termination point when the action is not ended due to the interference of external noise, so that the detection requirement cannot be met with accurate precision.
The embodiment of the application comprises the steps of firstly carrying out preprocessing such as filtering or downsampling on an acquired initial surface electromyographic signal to obtain a surface electromyographic signal, carrying out conversion operation on the surface electromyographic signal to obtain a converted signal, calculating a baseline threshold value after the conversion, correcting the converted surface electromyographic signal by using the baseline threshold value to obtain a correction signal, effectively correcting the difference of different detected objects, reducing the influence of impedance and muscle tension on the baseline threshold value, then processing the correction signal by using a kernel function to obtain a judgment signal, increasing the tiny difference between an action potential section and a rest potential section in the initial surface electromyographic signal by using the judgment signal, equivalently carrying out simple secondary conversion on the initial surface electromyographic signal, and comparing the judgment signal with a preset threshold value to judge whether the surface electromyographic signal corresponding to the judgment signal is in the action section or not, the detection accuracy of the action section is improved. The fluctuation of the surface electromyogram signal caused by muscle tension is weakened, the misjudgment of the active section caused by muscle tension is reduced, the tiny difference value between the rest potential section and the action potential section is increased, the calculated amount is reduced, the detection time delay is shortened, and the accuracy and precision of the detection of the action potential section are improved.
In order to facilitate understanding of the embodiments of the present application, specific examples are described below.
On the basis of the embodiment shown in fig. 1, an embodiment of the present application further provides a method for detecting an action segment of a surface electromyogram signal, as shown in fig. 2, including the following steps:
s201, acquiring an initial surface electromyogram signal, for example, the initial surface electromyogram signal may be acquired by a wearable device.
S202, preprocessing the initial surface electromyographic signals to obtain surface electromyographic signals.
S203, performing conversion operation on the surface electromyographic signal to obtain a conversion signal.
And S204, correcting the conversion signal based on the baseline threshold value to obtain a correction signal.
S205, initializing the kernel function.
The initialized kernel function is represented as: kernel (j)k)＝0，j1,j2,j3,...jn。
S206, leading the correction signals into kernel functions one by one, and updating the kernel functions after leading one correction signal into each kernel function.
Will correct the signal siIn the import kernel function, the kernel function is updated to kernel ═ j2,...jn,si},j2,...jnCalculating the equidistant integral of the kernel function unit based on a trapezoidal method to obtain a judgment signal yi；
Will correct the signal si+1In the import kernel function, the kernel function is updated to kernel ═ j3,...jn,si,si+1},j3,j4...jnCalculating the equidistant integral of the kernel function unit based on a trapezoidal method to obtain a judgment signal yi+1；
By analogy, from the correction signal s1,s2,s3,...si,., calculating to obtain a judgment signal y1,y2,y3,...yi,...}。
And S207, calculating the equidistant integral of the kernel function unit based on a trapezoidal method to obtain a judgment signal corresponding to the correction signal.
S208, when the judging signal is larger than a preset threshold value, determining that the surface electromyographic signal corresponding to the judging signal is in an action segment.
The action segment includes a start position and an end position, and the determination rules of the start position and the end position will be described later, which will not be described herein again.
On the basis of the embodiment shown in fig. 1, an embodiment of the present application further provides a method for detecting an action segment of a surface electromyogram signal, as shown in fig. 3, including the following steps:
s301, acquiring an initial surface electromyography signal, for example, the initial surface electromyography signal may be acquired by a wearable device.
S302, preprocessing the initial surface electromyographic signals to obtain surface electromyographic signals.
S303, performing conversion operation on the surface electromyographic signal to obtain a conversion signal.
And S304, correcting the conversion signal based on the baseline threshold value to obtain a correction signal.
S305, carrying out equidistant integration processing on the correction signal through a kernel function to obtain a judgment signal.
S306, if one or more of the previous judging signals is smaller than or equal to a preset threshold value, when the judging signals are switched to be larger than the preset threshold value, determining that the surface electromyographic signals corresponding to the judging signals currently are the initial positions of the action sections.
Optionally, when the determination signal is greater than a preset threshold, it is determined that the surface electromyogram signal corresponding to the determination signal is in an action segment, where the action segment includes a start position and an end position.
S307, if one or more of the previous judging signals is larger than or equal to a preset threshold value, when the judging signals are switched to be smaller than the preset threshold value, determining that the surface electromyographic signals corresponding to the judging signals currently are the end positions of the action sections.
The recorded surface electromyogram signal usually comprises a rest section and an action section, wherein the rest section is useless for subsequent pattern recognition, the rest section means that muscles are in a relaxed state, no action is performed in the section of signal, and the pattern recognition belongs to noise-like useless signals, so that the rest section and the action section need to be effectively separated.
As shown in fig. 4, an embodiment of the present application further provides an action segment detection device for a surface electromyogram signal, where the device includes:
a signal acquisition unit 41, configured to acquire an initial surface electromyogram signal;
a preprocessing unit 42, configured to preprocess the initial surface electromyogram signal to obtain a surface electromyogram signal
A signal conversion unit 43, configured to perform a conversion operation on the surface electromyogram signal to obtain a conversion signal;
a signal correction unit 44 for correcting the converted signal based on a baseline threshold value to obtain a corrected signal;
a signal processing unit 45, configured to perform equidistant integration processing on the correction signal through a kernel function to obtain a determination signal; and
and the determining unit 46 is used for determining that the surface electromyographic signal corresponding to the determination signal is in an action section when the determination signal is greater than a preset threshold value.
Optionally, the baseline threshold is determined according to the following formula:
thr＝mean{MAV1,MAV2,MAV3,...,MAVm}+A；
where thr is the baseline threshold, MAViThe maximum value of the sliding window in the resting state data of the surface electromyogram signal is represented by i, which is a positive integer between 1 and k, m is the number of the sliding windows, and A is a constant.
Optionally, the signal processing unit 45 includes:
an initialization subunit 451, configured to initialize the kernel function;
a signal introducing subunit 452 configured to introduce the correction signals into kernel functions one by one, and update the kernel functions after each introduction of one of the correction signals;
and a calculation subunit 453, configured to calculate the kernel unit equidistant integral based on a trapezoidal method, so as to obtain a determination signal corresponding to the correction signal.
Optionally, the determining unit 46 includes:
a start signal determining subunit 461, configured to determine, if one or more previous determination signals are smaller than or equal to a preset threshold, that a surface electromyographic signal currently corresponding to the determination signal is a start position of the action segment when the determination signal is switched to be greater than the preset threshold;
a determining termination signal subunit 462, configured to determine, if one or more previous determination signals are greater than or equal to a preset threshold, that a surface electromyogram signal currently corresponding to the determination signal is a termination position of the action segment when the determination signal is switched to be smaller than the preset threshold.
Optionally, the time series of the initial surface electromyographic signal is represented by the following formula:
{s1，s2，s3，...，si，...，sn}；
the converted signal is expressed by the following formula:
{S1，S2，S′3，S′4，...}；
wherein S is1＝{s1，s2，s3，...sk}；S2＝{sk+1，sk+2，sk+3，...s2k}；
S3'＝{s2k+1，s2k+2，s2k+3，...s3k}＝min(max(S1)，max(S2)，max(S3))；
S4'＝{s3k+1，s3k+2，s3k+3，...s4k}＝min(max(S2)，max(S3)，max(S4))；
Setting window data S as S1，...,sk,sk+1，...,s2k,s2k+1,...,s3kDividing the obtained solution into three parts to obtain: (ii) a
S1＝{s1，s2，s3，...sk}；
S2＝{sk+1，sk+2，sk+3，...s2k}；
S3＝{s2k+1，s2k+2，s2k+3，...s3k}；
Sliding the current window to the next window to obtain window data:
S2＝{sk+1，sk+2，sk+3，...s2k}；
S3＝{s2k+1，s2k+2，s2k+3，...s3k}；
S4＝{s3k+1，s3k+2，s3k+3，...s4k}；
where k is the window step, the size of the window data is m, and m is 3 × k.
Embodiments of the present application further provide a computer-readable storage medium, on which a resource allocation program is stored, where the resource allocation program, when executed by a processor, implements the steps as described in the method embodiments, for example, including:
collecting an initial surface electromyographic signal;
preprocessing the initial surface electromyographic signals to obtain surface electromyographic signals;
performing conversion operation on the surface myoelectric signal to obtain a conversion signal;
correcting the converted signal based on a baseline threshold to obtain a corrected signal;
carrying out equidistant integration processing on the correction signal through a kernel function to obtain a judgment signal;
and when the judging signal is larger than a preset threshold value, determining that the surface electromyographic signal corresponding to the judging signal is in an action segment.
Fig. 5 is a schematic structural diagram of a wearable device according to another embodiment of the present invention. The wearable device provided in the embodiment of the present invention may adopt an 8-channel intelligent myoelectric arm ring 600, and the wearable device may include: a Radio Frequency (RF) unit, a WiFi module, an audio output unit, an a/V (audio/video) input unit, a sensor, a display unit, a user input unit, an interface unit, a memory, a processor, and a power supply.
In the following description, a wearable device will be taken as an example, please refer to fig. 5, which is a schematic diagram of a hardware structure of a wearable device for implementing various embodiments of the present invention, where the wearable device 500 may include: RF (radio frequency) unit 501, WiFi module 502, audio output unit 503, a/V (audio/video) input unit 504, sensor 505, display unit 506, user input unit 507, interface unit 508, memory 509, processor 510, and power supply 511. Those skilled in the art will appreciate that the wearable device structure shown in fig. 5 does not constitute a limitation of the wearable device, and that the wearable device may include more or fewer components than shown, or combine certain components, or a different arrangement of components.
The following describes the various components of the wearable device in detail with reference to fig. 5:
the Radio Frequency unit 501 may be configured to receive and transmit messages or during a call, specifically, the Radio Frequency unit 501 may transmit uplink information to the base station, and may also receive downlink information transmitted by the base station, and then transmit the received downlink information to the processor 510 of the wearable device for processing, the downlink information transmitted by the base station to the Radio Frequency unit 501 may be generated according to the uplink information transmitted by the Radio Frequency unit 501, or may be actively pushed to the Radio Frequency unit 501 after detecting an information update of the wearable device, for example, after detecting that a geographic location of the wearable device changes, the base station may transmit a message notification of the change of the geographic location to the Radio Frequency unit 501 of the wearable device, the Radio Frequency unit 501 may transmit the message notification to the processor 510 of the wearable device after receiving the message notification, the processor 510 of the wearable device may control the message notification to be displayed on the display panel 61 of the wearable device, generally, the Radio Frequency unit includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, a Radio Frequency transceiver, a long-Frequency transceiver, a wireless communication System application, a wireless communication server, a wireless communication System application, a wireless communication System, a wireless communication server, a wireless communication System, a wireless communication server, a wireless communication System, a wireless network Access server, a wireless network Access server, a wireless network Access System, a wireless network Access server, a wireless network communication System, a wireless network Access server, a wireless network Access System, a.
In one embodiment, the wearable device 500 may access an existing communication network by inserting a SIM card.
In another embodiment, the wearable device 500 may be configured with an esim card (Embedded-SIM) to access an existing communication network, and the esim card may be used to save an internal space of the wearable device and reduce a thickness of the wearable device.
It is understood that although fig. 1 shows the radio frequency unit 501, it is understood that the radio frequency unit 101 does not belong to the essential constituents of the wearable device, and can be omitted entirely as required within the scope not changing the essence of the invention. The wearable device 500 may implement a communication connection with other devices or a communication network through the wifi module 102 alone, which is not limited by the embodiments of the present invention.
WiFi belongs to short-distance wireless transmission technology, and wearable equipment can help a user to send and receive e-mails, browse webpages, access streaming media and the like through a WiFi module 502, and provides wireless broadband internet access for the user. Although fig. 5 shows the WiFi module 502, it is understood that it does not belong to the essential constitution of the wearable device, and may be omitted entirely as needed within the scope not changing the essence of the invention.
The audio output unit 503 may convert audio data received by the radio frequency unit 501 or the WiFi module 502 or stored in the memory 509 into an audio signal and output as sound when the wearable device 500 is in a call signal reception mode, a talk mode, a recording mode, a voice recognition mode, a broadcast reception mode, or the like. Also, the audio output unit 503 may also provide audio output related to a specific function performed by the wearable device 500 (e.g., a call signal reception sound, a message reception sound, etc.). The audio output unit 503 may include a speaker, a buzzer, and the like.
The a/V input unit 504 is used to receive audio or video signals. The a/V input Unit 504 may include a Graphics Processing Unit (GPU) 1041 and a microphone 5042, and the Graphics processor 5041 processes image data of still pictures or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The processed image frames may be displayed on the display unit 506. The image frames processed by the graphics processor 5041 may be stored in the memory 509 (or other storage medium) or transmitted via the radio frequency unit 501 or the WiFi module 502. The microphone 5042 can receive sounds (audio data) via the microphone 5042 in a phone call mode, a recording mode, a voice recognition mode, or the like, and can process such sounds into audio data. The processed audio (voice) data may be converted into a format output transmittable to a mobile communication base station via the radio frequency unit 501 in case of a phone call mode. The microphone 5042 may implement various types of noise cancellation (or suppression) algorithms to cancel (or suppress) noise or interference generated in the course of receiving and transmitting audio signals.
In one embodiment, the wearable device 500 includes one or more cameras, and by turning on the cameras, capturing of images can be realized, functions such as photographing and recording can be realized, and the positions of the cameras can be set as required.
The wearable device 500 also includes at least one sensor 505, such as light sensors, motion sensors, and other sensors. Specifically, the light sensor includes an ambient light sensor that can adjust the brightness of the display panel 5061 according to the brightness of ambient light, and a proximity sensor that can turn off the display panel 5061 and/or backlight when the wearable device 500 is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), detect the magnitude and direction of gravity when stationary, and can be used for applications of recognizing the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), and the like.
In one embodiment, the wearable device 500 further comprises a proximity sensor, and by adopting the proximity sensor, the wearable device can realize non-contact operation, thereby providing more operation modes.
In one embodiment, the wearable device 500 further comprises a heart rate sensor, which, when worn, enables detection of heart rate by proximity to the user.
In one embodiment, the wearable device 500 may further include a fingerprint sensor, and by reading the fingerprint, functions such as security verification can be implemented.
The display unit 506 may include a display panel 5061, and the display panel 5061 may be configured in the form of a liquid crystal display (L acquired crystal display, <tttranslation = L "&tttl &ttt/t >tcd), an Organic light Emitting Diode (Organic L light-Emitting Diode, O L ED), or the like.
Optionally, the flexible display screen may adopt an O L ED screen body and a graphene screen body, and in other embodiments, the flexible display screen may also be made of other display materials, which is not limited in this embodiment.
In one embodiment, the display panel 5061 of the wearable device may take a rectangular shape, which is convenient to wrap around when worn. In other embodiments, other approaches may be taken.
The user input unit 507 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the wearable device. Specifically, the user input unit 507 may include a touch panel 5071 and other input devices 5072. Touch panel 5071, also referred to as a touch screen, may collect touch operations by a user (e.g., operations by a user on or near touch panel 5071 using a finger, a stylus, or any other suitable object or accessory) and drive the corresponding connection device according to a predetermined program. The touch panel 5071 may include two parts of a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 510, and can receive and execute commands sent by the processor 510. In addition, the touch panel 5071 may be implemented in various types such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. In addition to the touch panel 5071, the user input unit 507 may include other input devices 5072. In particular, other input devices 5072 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like, and are not limited to these specific examples.
In one embodiment, the side of the wearable device 500 may be provided with one or more buttons. The button can realize various modes such as short-time pressing, long-time pressing, rotation and the like, thereby realizing various operation effects. The number of the buttons can be multiple, and different buttons can be combined for use to realize multiple operation functions.
Further, the touch panel 5071 may overlay the display panel 5061, and when the touch panel 5071 detects a touch operation thereon or nearby, the touch panel is transmitted to the processor 510 to determine the type of the touch event, and then the processor 510 provides a corresponding visual output on the display panel 5061 according to the type of the touch event. Although in fig. 5, the touch panel 5071 and the display panel 5061 are two independent components to implement the input and output functions of the wearable device, in some embodiments, the touch panel 5071 and the display panel 5061 may be integrated to implement the input and output functions of the wearable device, and is not limited herein. For example, when receiving a message notification of an application program through the radio frequency unit 501, the processor 510 may control the message notification to be displayed in a predetermined area of the display panel 5061, where the predetermined area corresponds to a certain area of the touch panel 5071, and by performing a touch operation on a certain area of the touch panel 5071, the message notification displayed in the corresponding area on the display panel 5061 may be controlled.
The interface unit 508 serves as an interface through which at least one external device is connected to the wearable apparatus 500. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. The interface unit 508 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements within the wearable apparatus 500 or may be used to transmit data between the wearable apparatus 500 and the external device.
In one embodiment, the interface unit 508 of the wearable device 500 is configured as a contact, and is connected to another corresponding device through the contact to implement functions such as charging and connection. The contact can also be waterproof.
The memory 509 may be used to store software programs as well as various data. The memory 509 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 509 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
The processor 510 is a control center of the wearable device, and is connected to various parts of the entire wearable device through various interfaces and lines, and performs various functions of the wearable device and processes data by operating or executing software programs and/or modules stored in the memory 509 and calling up the data stored in the memory 509, thereby performing overall monitoring of the wearable device. Processor 510 may include one or more processing units; preferably, the processor 510 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into processor 510.
The wearable device 500 may further include a power source 511 (such as a battery) for supplying power to various components, and preferably, the power source 511 may be logically connected to the processor 510 via a power management system, so as to implement functions of managing charging, discharging, and power consumption via the power management system.
Although not shown in fig. 5, the wearable device 500 may further include a bluetooth module or the like, which is not described herein. The wearable device 500 can be connected with other terminal devices through Bluetooth to realize communication and information interaction.
For a hardware implementation, the processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable logic devices (P L D), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented by means of units performing the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the units may be implemented in the same software and/or hardware or in a plurality of software and/or hardware when implementing the invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. 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 invention. Thus, the present invention 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 (12)
1. A method for detecting an action segment of a surface electromyogram signal, the method comprising the steps of:
collecting an initial surface electromyographic signal;
preprocessing the initial surface electromyographic signal to obtain a surface electromyographic signal;
performing conversion operation on the surface electromyographic signal to obtain a conversion signal;
correcting the converted signal based on a baseline threshold to obtain a corrected signal;
carrying out equidistant integration processing on the correction signal through a kernel function to obtain a judgment signal;
and when the judging signal is larger than a preset threshold value, determining that the surface electromyographic signal corresponding to the judging signal is in an action segment.
2. The method of claim 1, wherein the baseline threshold is determined according to the following equation:
thr＝mean{MAV1,MAV2,MAV3,...,MAVm}+A；
where thr is the baseline threshold, MAViThe maximum value of the sliding window in the resting state data of the surface electromyogram signal is represented by i, which is a positive integer between 1 and k, m is the number of the sliding windows, and A is a constant.
3. The method of claim 1, wherein the equidistant integration processing of the correction signal by the kernel function to obtain the decision signal comprises:
initializing the kernel function;
introducing the correction signals into kernel functions one by one, and updating the kernel functions after introducing one correction signal;
and calculating the unit equidistant integral of the kernel function based on a trapezoidal method to obtain a judgment signal corresponding to the correction signal.
4. The method according to claim 1, wherein the determining that the surface electromyography signal corresponding to the determination signal is in an action segment when the determination signal is greater than a preset threshold value comprises:
if one or more previous judging signals are smaller than or equal to a preset threshold value, when the judging signals are switched to be larger than the preset threshold value, determining that the surface electromyographic signals currently corresponding to the judging signals are the initial positions of the action sections;
and if the previous one or more judgment signals are larger than or equal to a preset threshold value, when the judgment signals are switched to be smaller than the preset threshold value, determining that the surface electromyographic signals currently corresponding to the judgment signals are the termination positions of the action sections.
5. The method according to claim 1, characterized in that the time series of the initial surface electromyographic signals is represented by the formula:
{S1，S2，S3，...，Si，...，Sn}；
the converted signal is expressed by the following formula:
{S1，S2，S’3，S’4，...}；
wherein S is1＝{s1，s2，s3，...sk}；S2＝{sk+1，sk+2，sk+3，...s2k}；
S3'＝{s2k+1，s2k+2，s2k+3，...s3k}＝min(max(S1)，max(S2)，max(S3))；
S4'＝{s3k+1，s3k+2，s3k+3，...s4k}＝min(max(S2)，max(S3)，max(S4))；
Setting window data S as S1，...,sk,sk+1，...,s2k,s2k+1,...,s3kDividing the obtained solution into three parts to obtain: (ii) a
S1＝{s1，s2，s3，...sk}；
S2＝{sk+1，sk+2，sk+3，...s2k}；
S3＝{s2k+1，s2k+2，s2k+3，...s3k}；
Sliding the current window to the next window to obtain window data:
S2＝{sk+1，sk+2，sk+3，...s2k}；
S3＝{s2k+1，s2k+2，s2k+3，...s3k}；
S4＝{s3k+1，s3k+2，s3k+3，...s4k}；
where k is the window step, the size of the window data is m, and m is 3 × k.
6. An action section detection device of a surface electromyogram signal, the device comprising:
the signal acquisition unit is used for acquiring an initial surface electromyographic signal;
the preprocessing unit is used for preprocessing the initial surface electromyographic signal to obtain a surface electromyographic signal;
the signal conversion unit is used for carrying out conversion operation on the surface electromyographic signals to obtain conversion signals;
a signal correction unit for correcting the converted signal based on a baseline threshold value to obtain a corrected signal;
the signal processing unit is used for carrying out equidistant integration processing on the correction signal through a kernel function to obtain a judgment signal; and
and the determining unit is used for determining that the surface electromyographic signal corresponding to the judging signal is in an action section when the judging signal is larger than a preset threshold value.
7. The apparatus of claim 6, wherein the baseline threshold is determined according to the following equation:
thr＝mean{MAV1,MAV2,MAV3,...,MAVm}+A；
where thr is the baseline threshold, MAViThe maximum value of the sliding window in the resting state data of the surface electromyogram signal is represented by i, which is a positive integer between 1 and k, m is the number of the sliding windows, and A is a constant.
8. The apparatus of claim 6, wherein the signal processing unit comprises:
the initialization subunit is used for initializing the kernel function;
a signal leading-in subunit, configured to lead the correction signals into kernel functions one by one, and update the kernel functions after each leading-in of one correction signal;
and the calculating subunit is used for calculating the kernel function unit equidistant integral based on a trapezoidal method to obtain a judgment signal corresponding to the correction signal.
9. The apparatus of claim 6, wherein the determining unit comprises:
a start signal determining subunit, configured to determine, if one or more previous determination signals are smaller than or equal to a preset threshold, that a surface electromyographic signal currently corresponding to the determination signal is a start position of the action segment when the determination signal is switched to be greater than the preset threshold;
and the determining termination signal subunit is used for determining that the surface electromyographic signal currently corresponding to the judging signal is the termination position of the action section when the judging signal is switched to be smaller than a preset threshold value if one or more judging signals are larger than or equal to the preset threshold value.
10. The apparatus according to claim 6, characterized in that the time series of the initial surface electromyographic signals is represented by the following formula:
{S1，S2，S3，...，Si，...，Sn}；
the converted signal is expressed by the following formula:
(S1，S2，S’3，S’4，...}；
wherein S is1＝{s1，s2，s3，...sk}；S2＝{sk+1，sk+2，sk+3，...s2k}；
S3'＝{s2k+1，s2k+2，s2k+3，...s3k}＝min(max(S1)，max(S2)，max(S3))；
S4'＝{s3k+1，s3k+2，s3k+3，...s4k}＝min(max(S2)，max(S3)，max(S4))；
Setting window data S as S1，...,sk,sk+1，...,s2k,s2k+1,...,s3kDividing the obtained solution into three parts to obtain: (ii) a
S1＝{s1，s2，s3，...sk}；
S2＝{sk+1，sk+2，sk+3，...s2k}；
S3＝{s2k+1，s2k+2，s2k+3，...s3k}；
Sliding the current window to the next window to obtain window data:
S2＝{sk+1，sk+2，sk+3，...s2k}；
S3＝{s2k+1，s2k+2，s2k+3，...s3k}；
S4＝{s3k+1，s3k+2，s3k+3，...s4k}；
where k is the window step, the size of the window data is m, and m is 3 × k.
11. A wearable device, characterized in that the wearable device comprises: a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the action segment detection method of a surface electromyogram signal according to any one of claims 1 to 5.
12. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, implements the steps of the method for detecting an action section of a surface electromyogram signal according to any one of claims 1 to 5.
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| CN110420025A (en) * | 2019-09-03 | 2019-11-08 | 北京海益同展信息科技有限公司 | Surface electromyogram signal processing method, device and wearable device |
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| CN111844032A (en) * | 2020-07-15 | 2020-10-30 | 北京海益同展信息科技有限公司 | Electromyographic signal processing and exoskeleton robot control method and device |
| CN111844032B (en) * | 2020-07-15 | 2022-04-12 | 京东科技信息技术有限公司 | Electromyographic signal processing and exoskeleton robot control method and device |
| CN112123332A (en) * | 2020-08-10 | 2020-12-25 | 北京海益同展信息科技有限公司 | Construction method of gesture classifier, exoskeleton robot control method and device |
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| CN113303816A (en) * | 2021-04-30 | 2021-08-27 | 浙江大学 | Taste perception identification method and system based on facial surface electromyographic signals |
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