CN111248903A - Wearable workload measurement method, system, apparatus and storage medium - Google Patents
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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
- A61B5/369—Electroencephalography [EEG]
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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
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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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
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
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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
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
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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
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/742—Details of notification to user or communication with user or patient ; user input means using visual displays
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2576/00—Medical imaging apparatus involving image processing or analysis
- A61B2576/02—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part
- A61B2576/026—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part for the brain
Abstract
The invention discloses a wearable workload measurement method, a wearable workload measurement system, a wearable workload measurement device and a storage medium, wherein the workload state of a user can be monitored and visually displayed, the normal work of the user cannot be interfered, the identification method is simple and efficient, and real-time feedback can be realized. The signal processing method adopted by the invention only uses a simple method to carry out classification and identification, obtains excellent performance, is simple and effective, and can quickly identify the current workload state of the user.
Description
Technical Field
The invention relates to the technical field of electroencephalogram signal acquisition and processing, in particular to a wearable workload measuring method, a wearable workload measuring system, a wearable workload measuring device and a wearable workload measuring storage medium.
Background
In recent years, with the improvement of informatization and automation degree of a human-computer interaction system, the cognitive demand of people is correspondingly increased, human factors in human-computer interaction are more and more emphasized by designers, and workload becomes one of the most concerned directions in related research fields. In 1977, the commonly accepted view of "theory and measure of workload" (Mental workload and measurement) specialized meetings held by the special Committee organization of the North convention (NATO) recognized workload as a multidimensional concept involving task demand (task demand), time pressure (time pressure), operator's ability and degree of effort (effort), performance (performance), and numerous other factors. Kakizaki et al found that more than 90% of the workload resulted in excessive psychological stress on people, which resulted in a reduction in system reliability and safety, thereby causing serious accidents. Therefore, the research on the workload of people in the human-computer interaction process has important significance on the operation efficiency and the safety of the interaction task.
However, the existing research results are obtained by analyzing data in an off-line environment, the workload state cannot be observed visually in real time, the existing research instruments are too cumbersome for users, the subjective feeling of the users may be interfered, the workload identification in daily work is not facilitated, and the classification results of most of the research are not visual enough. In order to improve the classification precision, the existing method is too complex, the recognition speed is slow, and information cannot be fed back in real time.
Disclosure of Invention
In order to solve the above problems, an object of the present invention is to provide a wearable workload measurement method, system, device and storage medium, which can intuitively monitor the workload state of a user, do not interfere with the normal work of the user, and have a simple and efficient identification method, and can implement real-time feedback.
The technical scheme adopted by the invention for solving the problems is as follows:
in a first aspect, an embodiment of the present invention provides a wearable workload measurement method, including:
collecting real-time electroencephalogram signals;
amplifying the electroencephalogram signals, and eliminating artifacts of the amplified electroencephalogram signals;
processing the EEG signals, extracting a frequency band energy value by Fourier transform and estimating a brain connection strength value by phase measurement;
forming a characteristic vector by the frequency band energy value and the brain connection strength value as a classification sample for judging the workload;
and classifying the classified samples, outputting a classified output value, and displaying the workload state in a color gradient mode.
Further, the acquiring the real-time electroencephalogram signals comprises: the brain electrical signal collected per second is in a matrix form of 64 x 250, and the brain electrical signal per second represents one sample.
Further, the step of amplifying the electroencephalogram signal and eliminating the artifact of the amplified electroencephalogram signal comprises: the electroencephalogram signals are amplified through an amplifier, and artifact elimination is carried out on the amplified electroencephalogram signals through a 1-42HZ FIR band-pass filter and independent component analysis.
Further, the processing of the electroencephalogram signals, extracting band energy values by fourier transform and estimating brain connection strength values by phase measurement includes: the EEG signals are processed, and the frequency band energy values of 64 channels and the brain connection strength values among the channels are extracted by Fourier transform.
Further, the step of forming a feature vector by the frequency band energy value and the brain connection intensity value as a classification sample for workload judgment includes: energy extraction was performed on 64 channels, and the energy P of each channel was defined as:
wherein, f (T) is the electroencephalogram signal of each channel of each sample, T represents the point number of each channel sample, T is 250, each sample has 64 channels, each channel extracts the energy of theta and α frequency bands, total 128 energy values are obtained, the brain connection strength among the channels is estimated to obtain 2016 connection strength values, and the energy values and the connection strength values form a feature vector to be used as a classification sample.
Further, the classifying the classification samples, outputting a classification output value, and displaying the workload state in a color gradient manner includes: classifying the classified samples, outputting classified output values, classifying the workload by adopting a support vector machine classifier to perform five times of cross validation, and optimizing the support vector machine classifier as follows for a group of samples N:
wherein C represents an adjustable regularization parameter, x is a mapping function, and I is a relaxation variable;
the support vector machine non-threshold output is:
f(x)＝yiαixTxi+b
the f (x) output of each sample is a value, and when f (x) is greater than 0, the high load state is considered; when f (x) is less than 0, the low load state is considered at this time.
Further, the classifying the classification samples, outputting a classification output value, and displaying the workload state in a color gradient manner includes: the displayed color is a red-to-blue gradient color, a high-load state is red, and a low-load state is blue.
In a second aspect, an embodiment of the present invention further provides a wearable workload measurement system, including:
the signal acquisition module is used for acquiring real-time electroencephalogram signals;
the artifact eliminating module is used for amplifying the electroencephalogram signals and eliminating the artifacts of the amplified electroencephalogram signals;
the extraction module is used for processing the electroencephalogram signals, extracting a frequency band energy value by using Fourier transform and estimating a brain connection strength value by using phase measurement;
the characteristic fusion module is used for forming a characteristic vector by the frequency band energy value and the brain connection strength value and using the characteristic vector as a classification sample for judging the workload;
and the classification output module is used for performing classification processing on the classification samples, outputting classification output values and displaying the workload state in a color gradient mode.
In a third aspect, an embodiment of the present invention further provides a wearable workload measuring apparatus, including:
the electrode brain wave collecting device comprises a hemispherical display screen, a canvas cricket cap and a dry electrode brain wave collecting instrument which are sequentially stacked from top to bottom, wherein the hemispherical display screen is electrically connected with the dry electrode brain wave collecting instrument, and an brain wave signal processor is arranged on the dry electrode brain wave collecting instrument; and the number of the first and second groups,
the memory is in communication connection with the electroencephalogram signal processor; wherein the content of the first and second substances,
the memory stores instructions executable by the brain electrical signal processor to enable the brain electrical signal processor to perform the method of the first aspect of the invention.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, where computer-executable instructions are stored, and the computer-executable instructions are configured to cause a computer to execute the method according to the first aspect of the present invention.
One or more technical schemes provided in the embodiment of the invention have at least the following beneficial effects: the wearable workload measuring method, the wearable workload measuring system, the wearable workload measuring device and the storage medium can monitor and visually display the workload state of a user, cannot interfere with normal work of the user, are simple and efficient in identification method, and can realize real-time feedback. The signal processing method adopted by the invention only uses a simple method to carry out classification and identification, obtains excellent performance, is simple and effective, and can quickly identify the current workload state of the user.
Drawings
The invention is further illustrated with reference to the following figures and examples.
Fig. 1 is a simplified flowchart of a wearable workload measurement method according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a wearable workload measurement system in a second embodiment of the present invention;
fig. 3 is a functional block diagram of a wearable workload measurement device in a third embodiment of the invention;
fig. 4 is a schematic structural diagram of a wearable workload measurement apparatus according to a third embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention 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 invention and are not intended to limit the invention.
It should be noted that, if not conflicted, the various features of the embodiments of the invention may be combined with each other within the scope of protection of the invention. Additionally, while functional block divisions are performed in apparatus schematics, with logical sequences shown in flowcharts, in some cases, steps shown or described may be performed in sequences other than block divisions in apparatus or flowcharts.
The embodiments of the present invention will be further explained with reference to the drawings.
As shown in fig. 1, a first embodiment of the present invention provides a wearable workload measurement method, including but not limited to the following steps:
s100: collecting real-time electroencephalogram signals;
s200: amplifying the electroencephalogram signals, and eliminating artifacts of the amplified electroencephalogram signals;
s300: processing the EEG signals, extracting a frequency band energy value by Fourier transform and estimating a brain connection strength value by phase measurement;
s400: forming a characteristic vector by the frequency band energy value and the brain connection strength value as a classification sample for judging the workload;
s500: and classifying the classified samples, outputting a classified output value, and displaying the workload state in a color gradient mode.
In step S100, the electroencephalogram signal collected per second is in a matrix form of 64 × 250, and the electroencephalogram signal per second represents one sample.
In step S200, because the electroencephalogram signal is a tiny signal, the electroencephalogram signal needs to be amplified by an amplifier; meanwhile, artifact elimination is carried out on the amplified electroencephalogram signals by adopting a 1-42HZ FIR band-pass filter and independent component analysis.
In step S300, it is generally considered that the electroencephalogram characteristics of the θ and α frequency bands can significantly reflect the workload state of the brain, so that fourier transform is performed on the electroencephalogram signals of 64 channels to extract the energy value of the frequency band.
In step S400, feature extraction is required after the frequency band is extracted in order to more effectively identify the workload state. At the present stage, the most effective method is to extract energy features. The energy P of each channel is defined as:
wherein, f (T) is the electroencephalogram signal of each channel of each sample, T represents the point number of each channel sample, T is 250, each sample has 64 channels corresponding to 64 energy values, and the 64 energy values are converted into transverse vectors to be used as classification samples.
In step S500, classification processing is required after feature extraction, a support vector machine classifier (SVM) is used for classifying the workload, and for a group of samples N, the SVM classifier is optimized as follows:
where C represents an adjustable regularization parameter, x is a mapping function, and I is a relaxation variable. The linear support vector machine can be solved by a quadratic programming problem and can directly use a corresponding optimization calculation package. However, it generally translates into a dual problem, which can be expressed as follows using Sequential Minimum Optimization (SMO) or more efficient methods to solve SVMs:
the dual problem also needs to satisfy the following KKT (karush-kuhn-tracker) condition:
the support vector machine non-threshold output is:
f(x)＝yiαixTxi+b
the f (x) output of each sample is a value, and when f (x) is greater than 0, the high load state is considered; when f (x) is less than 0, the low load state is considered at this time.
In summary, compared with the prior art, the wearable workload measurement method has the advantages that: the method can monitor and visually display the workload state of the user, does not interfere with the normal work of the user, is simple and efficient in identification method, and can realize real-time feedback. The signal processing method adopted by the invention only uses a simple method to carry out classification and identification, obtains excellent performance, is simple and effective, and can quickly identify the current workload state of the user.
In addition, as shown in fig. 2, a second embodiment of the present invention provides a wearable workload measurement system, including:
the signal acquisition module 110 is used for acquiring real-time electroencephalogram signals;
an artifact removing module 120, configured to amplify the electroencephalogram signal and perform artifact removal on the amplified electroencephalogram signal;
an extraction module 130, configured to process the electroencephalogram signal, extract a frequency band energy value by fourier transform, and estimate a brain connection strength value by phase measurement;
the feature fusion module 140 is configured to combine the frequency band energy value and the brain connection strength value into a feature vector as a classification sample for workload judgment;
and the classification output module 150 is used for performing classification processing on the classification samples, outputting classification output values and displaying the workload state in a color gradient manner.
The wearable workload measurement system in this embodiment is based on the same inventive concept as the wearable workload measurement method in the first embodiment, and therefore, the wearable workload measurement system in this embodiment has the same beneficial effects: the method can monitor and visually display the workload state of the user, does not interfere with the normal work of the user, is simple and efficient in identification method, and can realize real-time feedback. The signal processing method adopted by the system only uses a simple method for classification and identification, obtains excellent performance, is simple and effective, and can quickly identify the current workload state of a user.
As shown in fig. 3 to 4, the third embodiment of the present invention also provides a wearable workload measuring device 200, including:
the electrode electroencephalogram acquisition instrument comprises a hemispherical display screen 210, a canvas cricket cap 220 and a dry electrode electroencephalogram acquisition instrument 230 which are sequentially stacked from top to bottom, wherein the hemispherical display screen 210 is electrically connected with the dry electrode electroencephalogram acquisition instrument 230, and an electroencephalogram signal processor is arranged on the dry electrode electroencephalogram acquisition instrument 230; and the number of the first and second groups,
the memory is in communication connection with the electroencephalogram signal processor;
wherein the memory stores instructions executable by the brain electrical signal processor to enable the brain electrical signal processor to perform any of the wearable workload measurement methods described above in the first embodiment.
The canvas cricket cap 220 covers the dry electrode electroencephalogram acquisition instrument 230, the main appearance image of the device is established, the appearance is simple, easy and attractive, and the device is suitable for daily wearing of a user.
The hemispherical display screen 210 is uniformly covered around the canvas peak cap 220, and the hemispherical structure is adopted to better conform to the shape of the brain of a user, so that the appearance is more attractive. The hemispherical display screen 210 is electrically connected with the dry electrode electroencephalogram acquisition instrument 230, and the result of the analysis of the electroencephalogram signal processor is visually displayed on the hemispherical display screen 210. The color displayed by the hemispherical display screen 210 is a gradual change from red to blue, and is red in the high load state and blue in the low load state. In a high-load state, the larger the classification output value of the dry electrode electroencephalogram acquisition instrument 230 is, the darker the color is, the smaller the output value is, and the lighter the color is; in a low-load state, the classification output value of the dry electrode electroencephalogram collector 230 is smaller, the color is darker, and the output value is larger and the color is lighter.
The work load state of the user can be monitored visually in real time, the work load state is displayed on the top of the head in a hemispherical display screen 210 mode, the precision is high, the whole device is simple, easy and attractive, the device is suitable for daily wearing of the user, and interference influence cannot be caused.
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, such as program instructions/modules corresponding to the virtual image control method in the embodiments of the present invention. The processor executes various functional applications and data processing of the stereoscopic imaging processing apparatus by executing the non-transitory software program, instructions and modules stored in the memory, namely, the wearable workload measurement method of any of the above method embodiments is realized.
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 created according to the use of the stereoscopic imaging processing device, 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 remotely located from the processor, and the remote memory may be connected to the stereoscopic projection device via 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 one or more modules are stored in the memory and, when executed by the one or more processors, perform the wearable workload measurement method of any of the method embodiments described above, e.g. the method steps S100 to S500 of the first embodiment.
The fourth embodiment of the present invention also provides a computer-readable storage medium storing computer-executable instructions that, when executed by one or more control processors, cause the one or more processors to perform a wearable workload measurement method of the above-described method embodiments, e.g., method steps S100 to S500 of the first embodiment.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, may be located in one place, or may be distributed over 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.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a general hardware platform, and certainly can also be implemented by hardware. It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a computer readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
While the preferred embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that the foregoing and various other changes, omissions and deviations in the form and detail thereof may be made without departing from the scope of this invention.
Claims (10)
1. A wearable workload measurement method, comprising:
collecting real-time electroencephalogram signals;
amplifying the electroencephalogram signals, and eliminating artifacts of the amplified electroencephalogram signals;
processing the EEG signals, extracting a frequency band energy value by Fourier transform and estimating a brain connection strength value by phase measurement;
forming a characteristic vector by the frequency band energy value and the brain connection strength value as a classification sample for judging the workload;
and classifying the classified samples, outputting a classified output value, and displaying the workload state in a color gradient mode.
2. The wearable workload measurement method according to claim 1, wherein the acquiring real-time brain electrical signals comprises: the brain electrical signal collected per second is in a matrix form of 64 x 250, and the brain electrical signal per second represents one sample.
3. The wearable workload measurement method according to claim 2, wherein the amplifying the electroencephalogram signal and the artifact removing the amplified electroencephalogram signal comprises: the electroencephalogram signals are amplified through an amplifier, and artifact elimination is carried out on the amplified electroencephalogram signals through a 1-42HZ FIR band-pass filter and independent component analysis.
4. The wearable workload measurement method according to claim 3, wherein the processing of the brain electrical signals, extracting band energy values using a Fourier transform and estimating brain connection strength values using phase measurements comprises: the EEG signals are processed, and the frequency band energy values of 64 channels and the brain connection strength values among the channels are extracted by Fourier transform.
5. The method according to claim 4, wherein the using the feature vectors of the band energy values and the brain connection strength values as the classification samples for workload determination comprises: energy extraction was performed on 64 channels, and the energy P of each channel was defined as:
wherein, f (T) is the electroencephalogram signal of each channel of each sample, T represents the point number of each channel sample, T is 250, each sample has 64 channels, each channel extracts the energy of theta and α frequency bands, total 128 energy values are obtained, the brain connection strength among the channels is estimated to obtain 2016 connection strength values, and the energy values and the connection strength values form a feature vector to be used as a classification sample.
6. The wearable workload measurement method according to claim 5, wherein the classifying the classification samples, outputting classification output values, and displaying the workload status in a color gradient manner comprises: classifying the classified samples, outputting classified output values, classifying the workloads by adopting a support vector machine classifier, and optimizing the support vector machine classifier as follows for a group of samples N:
ξi≥0,i＝1,2,…,N
wherein C represents an adjustable regularization parameter, x is a mapping function, and I is a relaxation variable;
the support vector machine non-threshold output is:
f(x)＝yiαixTxi+b
the f (x) output of each sample is a value, and when f (x) is greater than 0, the high load state is considered; when f (x) is less than 0, the low load state is considered at this time.
7. The wearable workload measurement method according to claim 5, wherein the classifying the classification samples, outputting classification output values, and displaying the workload status in a color gradient manner comprises: the displayed color is a red-to-blue gradient color, a high-load state is red, and a low-load state is blue.
8. A wearable workload measurement system, comprising:
the signal acquisition module is used for acquiring real-time electroencephalogram signals;
the artifact eliminating module is used for amplifying the electroencephalogram signals and eliminating the artifacts of the amplified electroencephalogram signals;
the extraction module is used for processing the electroencephalogram signals, extracting a frequency band energy value by using Fourier transform and estimating a brain connection strength value by using phase measurement;
the characteristic fusion module is used for forming a characteristic vector by the frequency band energy value and the brain connection strength value and using the characteristic vector as a classification sample for judging the workload;
and the classification output module is used for performing classification processing on the classification samples, outputting classification output values and displaying the workload state in a color gradient mode.
9. A wearable workload measurement device, comprising:
the electrode brain wave collecting device comprises a hemispherical display screen, a canvas cricket cap and a dry electrode brain wave collecting instrument which are sequentially stacked from top to bottom, wherein the hemispherical display screen is electrically connected with the dry electrode brain wave collecting instrument, and an brain wave signal processor is arranged on the dry electrode brain wave collecting instrument; and the number of the first and second groups,
the memory is in communication connection with the electroencephalogram signal processor; wherein the content of the first and second substances,
the memory stores instructions executable by the brain electrical signal processor to enable the brain electrical signal processor to perform the method of any one of claims 1-7.
10. A computer-readable storage medium having computer-executable instructions stored thereon for causing a computer to perform the method of any one of claims 1-7.
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| CN105751961B (en) * | 2016-03-21 | 2018-11-16 | 建湖金洋科技有限公司 | It is capable of the automobile-used overhead console of health monitoring |
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