WO2019007369A1 - 人体疲劳值的获取方法及装置 - Google Patents

人体疲劳值的获取方法及装置 Download PDF

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Publication number
WO2019007369A1
WO2019007369A1 PCT/CN2018/094508 CN2018094508W WO2019007369A1 WO 2019007369 A1 WO2019007369 A1 WO 2019007369A1 CN 2018094508 W CN2018094508 W CN 2018094508W WO 2019007369 A1 WO2019007369 A1 WO 2019007369A1
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WO
WIPO (PCT)
Prior art keywords
skin resistance
resistance signal
tester
fatigue
fatigue value
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PCT/CN2018/094508
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English (en)
French (fr)
Inventor
朱昕彤
王勇
王曦光
王晨
王真峥
鞠靖
杨育松
Original Assignee
新华网股份有限公司
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Priority to JP2020522776A priority Critical patent/JP6915161B2/ja
Publication of WO2019007369A1 publication Critical patent/WO2019007369A1/zh

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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
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0531—Measuring skin impedance
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/18—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state for vehicle drivers or machine operators
    • 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
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device

Definitions

  • the present application relates to the field of testing technology, and in particular, to a method and device for acquiring human fatigue values.
  • Bioelectricity is the change in potential and polarity that occurs in the organs, tissues, and cells of living things during their life. It is a kind of physical and physico-chemical changes in the process of life activities. It is a manifestation of normal physiological activities and a basic feature of living tissue. When the organism feels external stimuli and emotional excitement, its skin resistance increases due to the potential difference between the two points, causing the resistance between the two points of the skin to become smaller. This phenomenon is called physiological electric reflection.
  • the prior art analysis of the relationship between mood and skin resistance is usually based on changes in the magnitude of the resistance to distinguish whether the tester's mood is in a calm or excited state. However, it is difficult to obtain the fatigue of the tester based on the skin resistance of the tester.
  • the main purpose of the present application is to provide a method and a device for acquiring a human fatigue value, which solve the problem in the related art that it is difficult to obtain the fatigue value of the tester according to the skin resistance of the tester.
  • a method of acquiring a human body fatigue value comprises: collecting a skin resistance signal of the tester; performing low-pass filtering on the collected skin resistance signal to obtain a filtered skin resistance signal; and performing derivative processing on the filtered skin resistance signal to obtain a derivative process a skin resistance signal; a signal conforming to a preset condition is intercepted from the skin resistance signal after the derivation processing to obtain a target skin resistance signal; and the target skin resistance signal is subjected to a moving average processing to obtain a sliding average processing a target skin resistance signal; obtaining a fatigue value of the tester according to the target skin resistance signal after the moving average treatment, wherein the fatigue value of the tester is used to reflect the degree of fatigue of the tester.
  • obtaining the fatigue value of the tester according to the target skin resistance signal after the moving average processing comprises: normalizing the target skin resistance signal after the sliding average processing to obtain a normalized target skin resistance signal; The fatigue value of the tester is obtained based on the normalized target skin resistance signal.
  • the method further includes: analyzing the fatigue value of the tester; based on the fatigue value of the tester The analysis result is generated, and the test result of the tester is generated to generate a fatigue value report of the tester.
  • normalizing the target skin resistance signal after the sliding average processing to obtain the normalized target skin resistance signal further comprises: using the support vector machine classifier to return the target skin resistance signal after the moving average processing Once normalized, a normalized target skin resistance signal is obtained.
  • performing the derivation processing on the filtered skin resistance signal to obtain the skin resistance signal after the derivation processing comprises: performing analog-to-digital conversion on the filtered skin resistance signal to obtain a digital signal of skin resistance; The digital signal of the skin resistance is subjected to a derivation process to obtain a skin resistance signal after the derivation process.
  • acquiring the fatigue value of the tester according to the normalized target skin resistance signal comprises: obtaining, by the tester fatigue analysis model, the normalized target skin resistance signal corresponding to the fatigue value of the tester,
  • the tester fatigue analysis model is a model established after learning and training the fatigue values of a plurality of testers in advance.
  • the method further includes: determining whether the fatigue value of the tester is greater than a preset value; and determining the tester's The fatigue value is greater than a preset value, determining that the tester is in a fatigue period; determining a fatigue level to which the tester's fatigue value belongs, wherein the fatigue level includes: mild fatigue and severe fatigue.
  • an apparatus for acquiring a human body fatigue value comprises: an acquisition unit configured to collect a skin resistance signal of the tester; and a filtering unit configured to perform low-pass filtering on the collected skin resistance signal to obtain a filtered skin resistance signal; the first processing unit is set to be The filtered skin resistance signal is subjected to a derivation process to obtain a skin resistance signal after the derivation process; and the second processing unit is configured to intercept a signal corresponding to the preset condition from the skin resistance signal after the derivation process, Obtaining a target skin resistance signal; a third processing unit configured to perform a moving average processing on the target skin resistance signal to obtain a target skin resistance signal after the moving average processing; and an acquiring unit configured to target the skin resistance after the moving average processing The signal acquires a fatigue value of the tester, wherein the fatigue value of the tester is used to reflect the degree of fatigue of the tester.
  • the obtaining unit includes: a processing module configured to normalize the target skin resistance signal after the sliding average processing to obtain a normalized target skin resistance signal; and the obtaining module is set to be normalized according to The subsequent target skin resistance signal acquires the fatigue value of the tester.
  • a storage medium including a stored program, wherein the program executes the method of acquiring a human body fatigue value according to any one of the above.
  • a processor for executing a program, wherein the program is executed to execute the method of acquiring a human body fatigue value according to any one of the above.
  • the following steps are adopted: collecting the skin resistance signal of the tester; performing low-pass filtering on the collected skin resistance signal to obtain a filtered skin resistance signal; and performing a derivative process on the filtered skin resistance signal to obtain a request
  • the skin resistance signal after the treatment is processed; the signal corresponding to the preset condition is intercepted from the skin resistance signal after the derivation process to obtain the target skin resistance signal; and the target skin resistance signal is subjected to the moving average processing to obtain the target skin after the sliding average treatment
  • the resistance signal is obtained according to the target skin resistance signal after the moving average processing, wherein the fatigue value of the tester is used to reflect the fatigue degree of the tester.
  • the fatigue value of the tester is obtained by collecting the skin resistance signal of the tester, and then filtering, deriving, intercepting, and sliding average processing, thereby achieving the effect of obtaining the fatigue of the tester according to the skin resistance of the tester.
  • FIG. 1 is a flowchart of a method for acquiring a human body fatigue value according to an embodiment of the present application
  • FIG. 2 is a schematic diagram of a support vector machine classifier in a method for acquiring a human body fatigue value according to an embodiment of the present application
  • FIG. 3 is a schematic diagram of an apparatus for acquiring human body fatigue values according to an embodiment of the present application.
  • a method of acquiring a human body fatigue value is provided.
  • FIG. 1 is a flowchart of a method of acquiring a human body fatigue value according to an embodiment of the present application. As shown in Figure 1, the method includes the following steps:
  • step S101 a skin resistance signal of the tester is collected.
  • Step S102 performing low-pass filtering on the collected skin resistance signal to obtain a filtered skin resistance signal.
  • step S103 the filtered skin resistance signal is subjected to a derivation process to obtain a skin resistance signal after the derivation process.
  • Step S104 intercepting a signal conforming to a preset condition from the skin resistance signal after the derivation processing, to obtain a target skin resistance signal.
  • Step S105 performing a moving average processing on the target skin resistance signal to obtain a target skin resistance signal after the moving average processing.
  • Step S106 obtaining a fatigue value of the tester according to the target skin resistance signal after the moving average processing, wherein the fatigue value of the tester is used to reflect the fatigue degree of the tester.
  • the method for obtaining the human body fatigue value collects the skin resistance signal of the tester, performs low-pass filtering on the collected skin resistance signal, and obtains the filtered skin resistance signal; and performs the filtered skin resistance signal.
  • Derivation processing obtaining the skin resistance signal after the derivation processing; intercepting the signal corresponding to the preset condition from the skin resistance signal after the derivation processing to obtain the target skin resistance signal; performing a sliding average processing on the target skin resistance signal to obtain the sliding
  • the average processed skin resistance signal is obtained; the fatigue value of the tester is obtained according to the target skin resistance signal after the moving average treatment, wherein the fatigue value of the tester is used to reflect the fatigue level of the tester.
  • the fatigue value of the tester is obtained by collecting the skin resistance signal of the tester, and then filtering, deriving, intercepting, and sliding average processing, thereby achieving the effect of obtaining the fatigue of the tester according to the skin resistance of the tester.
  • acquiring the fatigue value of the tester according to the target skin resistance signal after the moving average processing comprises: normalizing the target skin resistance signal after the moving average processing The normalized target skin resistance signal is obtained, and the fatigue value of the tester is obtained according to the normalized target skin resistance signal.
  • the target skin resistance signal after the sliding average treatment is normalized to obtain a normalized target skin resistance signal; according to the normalized
  • the target skin resistance signal acquires the fatigue value of the tester.
  • the normalization process in the present application is to limit the target skin resistance signal to a certain range after processing, so as to conveniently obtain the fatigue value data of the tester according to the target skin resistance signal.
  • the method further includes: analyzing the fatigue value of the tester. Based on the analysis result of the tester's fatigue value, the tester's analysis result is generated to generate a tester's fatigue value report.
  • the tester's fatigue value report is generated based on the analysis result, and the fatigue value report can intuitively reflect the tester's fatigue condition, physical condition, and the like.
  • normalizing the target skin resistance signal after the sliding average processing to obtain the normalized target skin resistance signal further includes: adopting support The vector machine classifier normalizes the target skin resistance signal after the moving average processing to obtain a normalized target skin resistance signal.
  • FIG. 2 A schematic diagram of the principle of the support vector machine classifier in the embodiment of the present application is shown in FIG. 2 .
  • ⁇ and ⁇ represent positive and negative samples, respectively, as shown in the optimal hyperplane in Figure 2.
  • the solid sample points in Figure 2 are the points closest to the hyperplane in the positive and negative samples, and the distance between the two dashed lines in Figure 2 represents the distance between the positive and negative samples.
  • the support vector machine is to maximize the distance between positive and negative samples in the case of ensuring that the samples are correctly separated.
  • the mathematical formula can be described as: minimum 1/2
  • 2 subject to y i (w t x i +b) ⁇ 1i 1, 2...m.
  • the target skin resistance signal after the moving average processing is normalized by using the above-mentioned support vector machine classifier to obtain a normalized target skin resistance signal.
  • acquiring the fatigue value of the tester according to the normalized target skin resistance signal includes: obtaining the normalized target by using the tester fatigue analysis model.
  • the skin resistance signal corresponds to the fatigue value of the tester, wherein the tester fatigue analysis model is a model established after learning and training the fatigue values of a plurality of testers in advance.
  • the fatigue analysis model is established by learning training of fatigue values of a plurality of testers in advance, and the fatigue value of the tester corresponding to the digital signal of the skin resistance is obtained based on the fatigue analysis model of the tester, thereby improving the efficiency of obtaining the fatigue value of the tester. Improves the accuracy of obtaining the tester's fatigue value.
  • the filtered skin resistance signal is subjected to a derivation process, and the skin resistance signal after the derivation processing is obtained, including: performing the filtered skin resistance signal
  • the analog-to-digital conversion obtains a digital signal of the skin resistance; the digital signal of the skin resistance is subjected to a derivation process to obtain a skin resistance signal after the derivation process.
  • the digital signal of the skin resistance is obtained by analog-to-digital conversion of the filtered skin resistance signal; and then the digital signal of the skin resistance is subjected to a derivation process to obtain a skin resistance signal after the derivation process.
  • the method further includes: determining whether the fatigue value of the tester is greater than The preset value; if it is judged that the fatigue value of the tester is greater than the preset value, the tester is determined to be in the fatigue period; and the fatigue level to which the fatigue value of the tester belongs is determined, wherein the fatigue level includes: mild fatigue and severe fatigue.
  • the fatigue level is in the range of 60-80, the fatigue level is mild fatigue, and the fatigue value is >80.
  • the fatigue level is severe fatigue. It is determined according to the fatigue value of the tester that the tester is in a fatigue period; the fatigue level to which the tester's fatigue value belongs is determined to more accurately reflect the tester's fatigue condition, physical condition, and the like.
  • the embodiment of the present application further provides a device for acquiring the fatigue value of the human body.
  • the device for acquiring the fatigue value of the human body in the embodiment of the present application can be used to perform the acquisition of the fatigue value of the human body provided by the embodiment of the present application. method.
  • an apparatus for acquiring a human body fatigue value provided by an embodiment of the present application will be described.
  • FIG. 3 is a schematic diagram of an apparatus for acquiring a human body fatigue value according to an embodiment of the present application.
  • the apparatus includes: an acquisition unit 10, a filtering unit 20, a first processing unit 30, a second processing unit 40, a third processing unit 50, and an acquisition unit 60.
  • the collecting unit 10 is configured to collect a skin resistance signal of the tester
  • the filtering unit 20 is configured to perform low-pass filtering on the collected skin resistance signal to obtain a filtered skin resistance signal
  • the first processing unit 30 is configured to perform a derivation process on the filtered skin resistance signal to obtain a skin resistance signal after the derivation process;
  • the second processing unit 40 is configured to intercept a signal that meets a preset condition from the skin resistance signal after the derivative processing to obtain a target skin resistance signal;
  • the third processing unit 50 is configured to perform a moving average processing on the target skin resistance signal to obtain a target skin resistance signal after the moving average processing;
  • the obtaining unit 60 is configured to acquire the fatigue value of the tester according to the target skin resistance signal after the moving average processing, wherein the fatigue value of the tester is used to reflect the degree of fatigue of the tester.
  • the device for acquiring human body fatigue value collects the skin resistance signal of the tester through the collecting unit 10; the filtering unit 20 performs low-pass filtering on the collected skin resistance signal to obtain a filtered skin resistance signal;
  • the processing unit 30 performs a derivative process on the filtered skin resistance signal to obtain a skin resistance signal after the derivation process.
  • the second processing unit 40 intercepts the signal corresponding to the preset condition from the skin resistance signal after the derivative process to obtain a target.
  • the third processing unit 50 performs a moving average processing on the target skin resistance signal to obtain a target skin resistance signal after the moving average processing; and the obtaining unit acquires the fatigue value of the tester according to the target skin resistance signal after the moving average processing, wherein
  • the fatigue value of the tester is used to reflect the fatigue degree of the tester, and solves the problem that it is difficult to obtain the fatigue value of the tester according to the skin resistance of the tester in the related art, by collecting the skin resistance signal of the tester, and then filtering and seeking Guided, intercepted, and slid averaged to obtain the tester's Labor value, thereby achieving the fatigue can be acquired in accordance with the test subject's skin resistance test results.
  • the above-mentioned acquisition unit 10, filtering unit 20, first processing unit 30, second processing unit 40, third processing unit 50 and acquisition unit 60 can be operated in a computer terminal as part of the device, and can pass
  • the processor in the computer terminal performs the functions implemented by the above modules, and the computer terminal can also be a smart phone (such as an Android mobile phone, an iOS mobile phone, etc.), a tablet computer, an applause computer, and a mobile Internet device (MID), a PAD, etc. Terminal Equipment.
  • the acquiring unit 60 further includes: a processing module configured to normalize the target skin resistance signal after the sliding average processing to obtain a normalization The target skin resistance signal; the acquisition module is configured to obtain the fatigue value of the tester according to the normalized target skin resistance signal.
  • the acquiring device of the human body fatigue value includes a processor and a memory, and the collecting unit 10, the filtering unit 20, the first processing unit 30, the second processing unit 40, the third processing unit 50, and the like are all stored in the memory as program units.
  • the above described program elements stored in the memory are executed by the processor to implement the corresponding functions.
  • the processor contains a kernel, and the kernel removes the corresponding program unit from the memory.
  • the kernel can be set to one or more, and the kernel parameters can be adjusted to obtain the fatigue of the human body.
  • the memory may include non-persistent memory, random access memory (RAM), and/or non-volatile memory in a computer readable medium, such as read only memory (ROM) or flash memory (flash RAM), the memory including at least one Memory chip.
  • RAM random access memory
  • ROM read only memory
  • flash RAM flash memory
  • Embodiments of the present invention provide a storage medium on which a program is stored, and when the program is executed by a processor, the method for acquiring the human body fatigue value is implemented.
  • An embodiment of the present invention provides a processor, where the processor is configured to run a program, wherein the method for acquiring the human body fatigue value is executed when the program is running.
  • An embodiment of the present invention provides a device, including a processor, a memory, and a program stored on the memory and operable on the processor.
  • the processor executes the program, the following steps are performed: collecting a skin resistance signal of the tester; The skin resistance signal is low-pass filtered to obtain a filtered skin resistance signal; the filtered skin resistance signal is subjected to a derivative process to obtain a skin resistance signal after the derivation process; from the skin resistance signal after the derivation processing The signal corresponding to the preset condition is intercepted to obtain the target skin resistance signal; the target skin resistance signal is subjected to moving average processing to obtain the target skin resistance signal after the moving average treatment; and the tester's fatigue is obtained according to the target skin resistance signal after the moving average processing The value, wherein the tester's fatigue value is used to reflect the tester's fatigue level.
  • Obtaining the fatigue value of the tester according to the target skin resistance signal after the moving average treatment includes: normalizing the target skin resistance signal after the sliding average processing to obtain a normalized target skin resistance signal; according to the normalized The target skin resistance signal acquires the fatigue value of the tester.
  • the method further comprises: analyzing the fatigue value of the tester; generating an analysis result of the tester based on the analysis result of the fatigue value of the tester The tester's fatigue value report.
  • Normalizing the target skin resistance signal after the sliding average processing to obtain the normalized target skin resistance signal further includes: normalizing the target skin resistance signal after the moving average processing by using the support vector machine classifier , the normalized target skin resistance signal is obtained.
  • Deriving the filtered skin resistance signal to obtain a skin resistance signal after the derivation processing comprises: performing analog-to-digital conversion on the filtered skin resistance signal to obtain a digital signal of skin resistance; and performing a digital signal on the skin resistance The treatment process is performed to obtain a skin resistance signal after the derivation treatment.
  • Obtaining the fatigue value of the tester according to the normalized target skin resistance signal includes: obtaining a normalized target skin resistance signal corresponding to the tester's fatigue value by the tester fatigue analysis model, wherein the tester fatigue analysis model is A model established after learning and training the fatigue values of a plurality of testers.
  • the method further comprises: determining whether the fatigue value of the tester is greater than a preset value; and determining the tester's fatigue value is greater than a preset value, determining the tester In the fatigue period; determine the fatigue level to which the tester's fatigue value belongs, wherein the fatigue level includes: mild fatigue and severe fatigue.
  • the devices in this document can be servers, PCs, PADs, mobile phones, and the like.
  • the present application also provides a computer program product, when executed on a data processing device, adapted to perform a process of initializing a method of acquiring a skin resistance signal of a tester; low pass filtering the collected skin resistance signal Obtaining the filtered skin resistance signal; deriving the filtered skin resistance signal to obtain a skin resistance signal after the derivation processing; and intercepting the signal corresponding to the preset condition from the skin resistance signal after the derivation processing Target skin resistance signal; performing a moving average processing on the target skin resistance signal to obtain a target skin resistance signal after the moving average treatment; obtaining a tester's fatigue value according to the target skin resistance signal after the moving average treatment, wherein the tester's fatigue value Used to reflect the degree of fatigue of the tester.
  • Obtaining the fatigue value of the tester according to the target skin resistance signal after the moving average treatment includes: normalizing the target skin resistance signal after the sliding average processing to obtain a normalized target skin resistance signal; according to the normalized The target skin resistance signal acquires the fatigue value of the tester.
  • the method further comprises: analyzing the fatigue value of the tester; generating an analysis result of the tester based on the analysis result of the fatigue value of the tester The tester's fatigue value report.
  • Normalizing the target skin resistance signal after the sliding average processing to obtain the normalized target skin resistance signal further includes: normalizing the target skin resistance signal after the moving average processing by using the support vector machine classifier , the normalized target skin resistance signal is obtained.
  • Deriving the filtered skin resistance signal to obtain a skin resistance signal after the derivation processing comprises: performing analog-to-digital conversion on the filtered skin resistance signal to obtain a digital signal of skin resistance; and performing a digital signal on the skin resistance The treatment process is performed to obtain a skin resistance signal after the derivation treatment.
  • Obtaining the fatigue value of the tester according to the normalized target skin resistance signal includes: obtaining a normalized target skin resistance signal corresponding to the tester's fatigue value by the tester fatigue analysis model, wherein the tester fatigue analysis model is A model established after learning and training the fatigue values of a plurality of testers.
  • the method further comprises: determining whether the fatigue value of the tester is greater than a preset value; and determining the tester's fatigue value is greater than a preset value, determining the tester In the fatigue period; determine the fatigue level to which the tester's fatigue value belongs, wherein the fatigue level includes: mild fatigue and severe fatigue.
  • embodiments of the present application can be provided as a method, system, or computer program product.
  • the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment in combination of software and hardware.
  • the application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.
  • a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
  • processors CPUs
  • input/output interfaces network interfaces
  • memory volatile and non-volatile memory
  • the memory may include non-persistent memory, random access memory (RAM), and/or non-volatile memory in a computer readable medium, such as read only memory (ROM) or flash memory.
  • RAM random access memory
  • ROM read only memory
  • Memory is an example of a computer readable medium.
  • Computer readable media includes both permanent and non-persistent, removable and non-removable media.
  • Information storage can be implemented by any method or technology.
  • the information can be computer readable instructions, data structures, modules of programs, or other data.
  • Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory. (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, Magnetic tape cartridges, magnetic tape storage or other magnetic storage devices or any other non-transportable media can be used to store information that can be accessed by a computing device.
  • computer readable media does not include temporary storage of computer readable media, such as modulated data signals and carrier waves.
  • embodiments of the present application can be provided as a method, system, or computer program product.
  • the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment in combination of software and hardware.
  • the application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.

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Abstract

本申请公开了一种人体疲劳值的获取方法及装置。该方法包括：采集测试者的皮肤电阻信号；对采集到的皮肤电阻信号进行低通滤波，得到滤波后的皮肤电阻信号；对滤波后的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号；从求导处理后的皮肤电阻信号中截取符合预设条件的信号，得到目标皮肤电阻信号；对目标皮肤电阻信号进行滑动平均处理，得到滑动平均处理后的目标皮肤电阻信号；根据滑动平均处理后的目标皮肤电阻信号获取测试者的疲劳值，其中，测试者的疲劳值用于反映测试者的疲劳程度。通过本申请，解决了相关技术中难以根据测试者的皮肤电阻获取测试者的疲劳值的问题。

Description

人体疲劳值的获取方法及装置 技术领域
本申请涉及测试技术领域，具体而言，涉及一种人体疲劳值的获取方法及装置。
背景技术
生物电，是生物的器官、组织和细胞在生命活动过程中发生的电位和极性变化。它是生命活动过程中的一类物理、物理-化学变化，是正常生理活动的表现，也是生物活组织的一个基本特征。生物在感受到外界刺激以及情绪激动时，其皮肤电阻由于两点之间的电位差增大，导致其皮肤两点之间的电阻变小，这种现象称之为生理电反射。现有技术中对情绪和皮肤电阻之间关系的分析，通常是以电阻大小的变化来区分测试者的情绪是处在平静还是兴奋状态。然而难以根据测试者的皮肤电阻获取测试者的疲劳度。
针对相关技术中难以根据测试者的皮肤电阻获取测试者的疲劳值的问题，目前尚未提出有效的解决方案。
发明内容
本申请的主要目的在于提供一种人体疲劳值的获取方法及装置，以解决相关技术中难以根据测试者的皮肤电阻获取测试者的疲劳值的问题。
为了实现上述目的，根据本申请的一个方面，提供了一种人体疲劳值的获取方法。该方法包括：采集测试者的皮肤电阻信号；对采集到的皮肤电阻信号进行低通滤波，得到滤波后的皮肤电阻信号；对所述滤波后的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号；从所述求导处理后的皮肤电阻信号中截取符合预设条件的信号，得到目标皮肤电阻信号；对所述目标皮肤电阻信号进行滑动平均处理，得到滑动平均处理后的目标皮肤电阻信号；根据滑动平均处理后的目标皮肤电阻信号获取所述测试者的疲劳值，其中，所述测试者的疲劳值用于反映所述测试者的疲劳程度。
进一步地，根据滑动平均处理后的目标皮肤电阻信号获取所述测试者的疲劳值包括：对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号；根据归一化后的目标皮肤电阻信号获取所述测试者的疲劳值。
进一步地，根据归一化后的目标皮肤电阻信号获取所述测试者的疲劳值之后，所述方法还包括：对所述测试者的疲劳值进行分析；基于对所述测试者的疲劳值的分析 结果，生成所述测试者的分析结果生成所述测试者的疲劳值报告。
进一步地，对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号还包括：采用支持向量机分类器对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号。
进一步地，对所述滤波后的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号包括：对所述滤波后的皮肤电阻信号进行模数转换，得到皮肤电阻的数字信号；对所述皮肤电阻的数字信号进行求导处理，得到求导处理后的皮肤电阻信号。
进一步地，根据归一化后的目标皮肤电阻信号获取所述测试者的疲劳值包括：通过所述测试者疲劳分析模型获取所述归一化后的目标皮肤电阻信号对应测试者的疲劳值，其中，所述测试者疲劳分析模型为预先对多个测试者的疲劳值进行学习训练后建立的模型。
进一步地，根据归一化后的目标皮肤电阻信号获取所述测试者的疲劳值之后，所述方法还包括：判断所述测试者的疲劳值是否大于预设数值；若判断所述测试者的疲劳值大于预设数值，确定所述测试者处于疲劳期；确定所述测试者的疲劳值所属的疲劳等级，其中，所述疲劳等级包括：轻度疲劳和重度疲劳。
为了实现上述目的，根据本申请的另一方面，提供了一种人体疲劳值的获取装置。该装置包括：采集单元，设置为采集测试者的皮肤电阻信号；滤波单元，设置为对采集到的皮肤电阻信号进行低通滤波，得到滤波后的皮肤电阻信号；第一处理单元，设置为对所述滤波后的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号；第二处理单元，设置为从所述求导处理后的皮肤电阻信号中截取符合预设条件的信号，得到目标皮肤电阻信号；第三处理单元，设置为对所述目标皮肤电阻信号进行滑动平均处理，得到滑动平均处理后的目标皮肤电阻信号；获取单元，设置为根据滑动平均处理后的目标皮肤电阻信号获取所述测试者的疲劳值，其中，所述测试者的疲劳值用于反映所述测试者的疲劳程度。
进一步地，所述获取单元包括：处理模块，设置为对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号；获取模块，设置为根据归一化后的目标皮肤电阻信号获取所述测试者的疲劳值。
为了实现上述目的，根据本申请的另一方面，提供了一种存储介质，所述存储介质包括存储的程序，其中，所述程序执行上述任意一项所述的人体疲劳值的获取方法。
为了实现上述目的，根据本申请的另一方面，提供了一种处理器，所述处理器用于运行程序，其中，所述程序运行时执行上述任意一项所述的人体疲劳值的获取方法。
通过本申请，采用以下步骤：采集测试者的皮肤电阻信号；对采集到的皮肤电阻信号进行低通滤波，得到滤波后的皮肤电阻信号；对滤波后的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号；从求导处理后的皮肤电阻信号中截取符合预设条件的信号，得到目标皮肤电阻信号；对目标皮肤电阻信号进行滑动平均处理，得到滑动平均处理后的目标皮肤电阻信号；根据滑动平均处理后的目标皮肤电阻信号获取测试者的疲劳值，其中，测试者的疲劳值用于反映测试者的疲劳程度。通过本申请，解决了相关技术中难以根据测试者的皮肤电阻获取测试者的疲劳值的问题。通过采集测试者的皮肤电阻信号、然后经过滤波、求导、截取、滑动平均处理后获取到测试者的疲劳值，进而达到了能够根据测试者的皮肤电阻获取到测试者的疲劳度的效果。
附图说明
构成本申请的一部分的附图用来提供对本申请的进一步理解，本申请的示意性实施例及其说明用于解释本申请，并不构成对本申请的不当限定。在附图中：
图1是根据本申请实施例提供的人体疲劳值的获取方法的流程图；
图2是根据本申请实施例提供的人体疲劳值的获取方法中支持向量机分类器的示意图；以及
图3是根据本申请实施例提供的人体疲劳值的获取装置的示意图。
具体实施方式
需要说明的是，在不冲突的情况下，本申请中的实施例及实施例中的特征可以相互组合。下面将参考附图并结合实施例来详细说明本申请。
为了使本技术领域的人员更好地理解本申请方案，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分的实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本申请保护的范围。
需要说明的是，本申请的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本申请的实施例。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的 其它步骤或单元。
根据本申请的实施例，提供了一种人体疲劳值的获取方法。
图1是根据本申请实施例的人体疲劳值的获取方法的流程图。如图1所示，该方法包括以下步骤：
步骤S101，采集测试者的皮肤电阻信号。
步骤S102，对采集到的皮肤电阻信号进行低通滤波，得到滤波后的皮肤电阻信号。
步骤S103，对滤波后的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号。
步骤S104，从求导处理后的皮肤电阻信号中截取符合预设条件的信号，得到目标皮肤电阻信号。
步骤S105，对目标皮肤电阻信号进行滑动平均处理，得到滑动平均处理后的目标皮肤电阻信号。
步骤S106，根据滑动平均处理后的目标皮肤电阻信号获取测试者的疲劳值，其中，测试者的疲劳值用于反映测试者的疲劳程度。
本申请实施例提供的人体疲劳值的获取方法，通过采集测试者的皮肤电阻信号；对采集到的皮肤电阻信号进行低通滤波，得到滤波后的皮肤电阻信号；对滤波后的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号；从求导处理后的皮肤电阻信号中截取符合预设条件的信号，得到目标皮肤电阻信号；对目标皮肤电阻信号进行滑动平均处理，得到滑动平均处理后的目标皮肤电阻信号；根据滑动平均处理后的目标皮肤电阻信号获取测试者的疲劳值，其中，测试者的疲劳值用于反映测试者的疲劳程度。通过本申请，解决了相关技术中难以根据测试者的皮肤电阻获取测试者的疲劳值的问题。通过采集测试者的皮肤电阻信号、然后经过滤波、求导、截取、滑动平均处理后获取到测试者的疲劳值，进而达到了能够根据测试者的皮肤电阻获取到测试者的疲劳度的效果。
可选地，在本申请实施例提供的人体疲劳值的获取方法中，根据滑动平均处理后的目标皮肤电阻信号获取测试者的疲劳值包括：对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号；根据归一化后的目标皮肤电阻信号获取测试者的疲劳值。
为了提升获取测试者的疲劳值的准确性，在本方案中，对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号；根据归一化后的 目标皮肤电阻信号获取测试者的疲劳值。需要说明的是，本申请中的归一化处理是把目标皮肤电阻信号经过处理后限制在一定范围内，以便后续根据目标皮肤电阻信号获取测试者的疲劳值数据的方便。
可选地，在本申请实施例提供的人体疲劳值的获取方法中，根据归一化后的目标皮肤电阻信号获取测试者的疲劳值之后，该方法还包括：对测试者的疲劳值进行分析；基于对测试者的疲劳值的分析结果，生成测试者的分析结果生成测试者的疲劳值报告。
通过对测试者的疲劳值进行分析，基于分析结果生成测试者的疲劳值报告，通过疲劳值报告可以直观的反映测试者的疲劳情况、身体情况等等。
可选地，在本申请实施例提供的人体疲劳值的获取方法中，对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号还包括：采用支持向量机分类器对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号。
本申请实施例中的支持向量机分类器其原理的示意图如图2所示。其中○和□分别代表正负样本，如图2中的optimal hyperplane(最优超平面)所示。图2中实心的样本点，分别是正负样本中距离超平面最近的点，图2中两条虚线之间的距离就表征了正负样本之间的距离(gap)。支持向量机就是在保证将样本正确分开的情况下，使得正负样本之间的距离(gap)最大，用数学公式可以描述为：minimize 1/2||w|| 2subject to y i(w tx i+b)≥1i＝1，2……m。采用上述的支持向量机分类器对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号。
可选地，在本申请实施例提供的人体疲劳值的获取方法中，根据归一化后的目标皮肤电阻信号获取测试者的疲劳值包括：通过测试者疲劳分析模型获取归一化后的目标皮肤电阻信号对应测试者的疲劳值，其中，测试者疲劳分析模型为预先对多个测试者的疲劳值进行学习训练后建立的模型。
通过预先对多个测试者的疲劳值进行学习训练建立疲劳分析模型，基于测试者疲劳分析模型获取皮肤电阻的数字信号对应的测试者的疲劳值，提升了获取测试者的疲劳值的效率，也提升了获取测试者的疲劳值的准确性。
可选地，在本申请实施例提供的人体疲劳值的获取方法中，对滤波后的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号包括：对滤波后的皮肤电阻信号进行模数转换，得到皮肤电阻的数字信号；对皮肤电阻的数字信号进行求导处理，得到求导处理后的皮肤电阻信号。
通过上述方案，通过对滤波后的皮肤电阻信号进行模数转换，得到皮肤电阻的数字信号；然后对皮肤电阻的数字信号进行求导处理，从而得到求导处理后的皮肤电阻 信号。
可选地，在本申请实施例提供的人体疲劳值的获取方法中，根据归一化后的目标皮肤电阻信号获取测试者的疲劳值之后，该方法还包括：判断测试者的疲劳值是否大于预设数值；若判断测试者的疲劳值大于预设数值，确定测试者处于疲劳期；确定测试者的疲劳值所属的疲劳等级，其中，疲劳等级包括：轻度疲劳和重度疲劳。
例如，疲劳值在60-80范围内疲劳等级为轻度疲劳，疲劳值＞80疲劳等级为重度疲劳。在根据测试者的疲劳值判断出确定测试者处于疲劳期；确定测试者的疲劳值所属的疲劳等级，以便更加准确的反映测试者的疲劳情况、身体情况等等。
需要说明的是，在附图的流程图示出的步骤可以在诸如一组计算机可执行指令的计算机系统中执行，并且，虽然在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。
本申请实施例还提供了一种人体疲劳值的获取装置，需要说明的是，本申请实施例的人体疲劳值的获取装置可以用于执行本申请实施例所提供的用于人体疲劳值的获取方法。以下对本申请实施例提供的人体疲劳值的获取装置进行介绍。
图3是根据本申请实施例的人体疲劳值的获取装置的示意图。如图3所示，该装置包括：采集单元10，滤波单元20，第一处理单元30，第二处理单元40、第三处理单元50和获取单元60。
具体地，采集单元10，设置为采集测试者的皮肤电阻信号；
滤波单元20，设置为对采集到的皮肤电阻信号进行低通滤波，得到滤波后的皮肤电阻信号；
第一处理单元30，设置为对滤波后的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号；
第二处理单元40，设置为从求导处理后的皮肤电阻信号中截取符合预设条件的信号，得到目标皮肤电阻信号；
第三处理单元50，设置为对目标皮肤电阻信号进行滑动平均处理，得到滑动平均处理后的目标皮肤电阻信号；
获取单元60，设置为根据滑动平均处理后的目标皮肤电阻信号获取测试者的疲劳值，其中，测试者的疲劳值用于反映测试者的疲劳程度。
本申请实施例提供的人体疲劳值的获取装置，通过采集单元10采集测试者的皮肤电阻信号；滤波单元20对采集到的皮肤电阻信号进行低通滤波，得到滤波后的皮肤电 阻信号；第一处理单元30对滤波后的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号；第二处理单元40从求导处理后的皮肤电阻信号中截取符合预设条件的信号，得到目标皮肤电阻信号；第三处理单元50对目标皮肤电阻信号进行滑动平均处理，得到滑动平均处理后的目标皮肤电阻信号；获取单元根据滑动平均处理后的目标皮肤电阻信号获取测试者的疲劳值，其中，测试者的疲劳值用于反映测试者的疲劳程度，解决了相关技术中难以根据测试者的皮肤电阻获取测试者的疲劳值的问题，通过采集测试者的皮肤电阻信号、然后经过滤波、求导、截取、滑动平均处理后获取到测试者的疲劳值，进而达到了能够根据测试者的皮肤电阻获取到测试者的疲劳度的效果。
此处需要说明的是，上述采集单元10，滤波单元20，第一处理单元30，第二处理单元40、第三处理单元50和获取单元60可以作为装置的一部分运行在计算机终端中，可以通过计算机终端中的处理器来执行上述模块实现的功能，计算机终端也可以是智能手机(如Android手机、iOS手机等)、平板电脑、掌声电脑以及移动互联网设备(Mobile Internet Devices，MID)、PAD等终端设备。
可选地，在本申请实施例提供的人体疲劳值的获取装置中，获取单元60还包括：处理模块，设置为对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号；获取模块，设置为根据归一化后的目标皮肤电阻信号获取测试者的疲劳值。
所述人体疲劳值的获取装置包括处理器和存储器，上述采集单元10，滤波单元20，第一处理单元30，第二处理单元40和第三处理单元50等均作为程序单元存储在存储器中，由处理器执行存储在存储器中的上述程序单元来实现相应的功能。
处理器中包含内核，由内核去存储器中调取相应的程序单元。内核可以设置一个或以上，通过调整内核参数来获取人体的疲劳度。
存储器可能包括计算机可读介质中的非永久性存储器，随机存取存储器(RAM)和/或非易失性内存等形式，如只读存储器(ROM)或闪存(flash RAM)，存储器包括至少一个存储芯片。
本发明实施例提供了一种存储介质，其上存储有程序，该程序被处理器执行时实现所述人体疲劳值的获取方法。
本发明实施例提供了一种处理器，所述处理器用于运行程序，其中，所述程序运行时执行所述人体疲劳值的获取方法。
本发明实施例提供了一种设备，设备包括处理器、存储器及存储在存储器上并可在处理器上运行的程序，处理器执行程序时实现以下步骤：采集测试者的皮肤电阻信号；对采集到的皮肤电阻信号进行低通滤波，得到滤波后的皮肤电阻信号；对滤波后 的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号；从求导处理后的皮肤电阻信号中截取符合预设条件的信号，得到目标皮肤电阻信号；对目标皮肤电阻信号进行滑动平均处理，得到滑动平均处理后的目标皮肤电阻信号；根据滑动平均处理后的目标皮肤电阻信号获取测试者的疲劳值，其中，测试者的疲劳值用于反映测试者的疲劳程度。
根据滑动平均处理后的目标皮肤电阻信号获取测试者的疲劳值包括：对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号；根据归一化后的目标皮肤电阻信号获取测试者的疲劳值。
根据归一化后的目标皮肤电阻信号获取测试者的疲劳值之后，该方法还包括：对测试者的疲劳值进行分析；基于对测试者的疲劳值的分析结果，生成测试者的分析结果生成测试者的疲劳值报告。
对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号还包括：采用支持向量机分类器对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号。
对滤波后的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号包括：对滤波后的皮肤电阻信号进行模数转换，得到皮肤电阻的数字信号；对皮肤电阻的数字信号进行求导处理，得到求导处理后的皮肤电阻信号。
根据归一化后的目标皮肤电阻信号获取测试者的疲劳值包括：通过测试者疲劳分析模型获取归一化后的目标皮肤电阻信号对应测试者的疲劳值，其中，测试者疲劳分析模型为预先对多个测试者的疲劳值进行学习训练后建立的模型。
根据归一化后的目标皮肤电阻信号获取测试者的疲劳值之后，该方法还包括：判断测试者的疲劳值是否大于预设数值；若判断测试者的疲劳值大于预设数值，确定测试者处于疲劳期；确定测试者的疲劳值所属的疲劳等级，其中，疲劳等级包括：轻度疲劳和重度疲劳。本文中的设备可以是服务器、PC、PAD、手机等。
本申请还提供了一种计算机程序产品，当在数据处理设备上执行时，适于执行初始化有如下方法步骤的程序：采集测试者的皮肤电阻信号；对采集到的皮肤电阻信号进行低通滤波，得到滤波后的皮肤电阻信号；对滤波后的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号；从求导处理后的皮肤电阻信号中截取符合预设条件的信号，得到目标皮肤电阻信号；对目标皮肤电阻信号进行滑动平均处理，得到滑动平均处理后的目标皮肤电阻信号；根据滑动平均处理后的目标皮肤电阻信号获取测试者的疲劳值，其中，测试者的疲劳值用于反映测试者的疲劳程度。
根据滑动平均处理后的目标皮肤电阻信号获取测试者的疲劳值包括：对滑动平均 处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号；根据归一化后的目标皮肤电阻信号获取测试者的疲劳值。
根据归一化后的目标皮肤电阻信号获取测试者的疲劳值之后，该方法还包括：对测试者的疲劳值进行分析；基于对测试者的疲劳值的分析结果，生成测试者的分析结果生成测试者的疲劳值报告。
对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号还包括：采用支持向量机分类器对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号。
对滤波后的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号包括：对滤波后的皮肤电阻信号进行模数转换，得到皮肤电阻的数字信号；对皮肤电阻的数字信号进行求导处理，得到求导处理后的皮肤电阻信号。
根据归一化后的目标皮肤电阻信号获取测试者的疲劳值包括：通过测试者疲劳分析模型获取归一化后的目标皮肤电阻信号对应测试者的疲劳值，其中，测试者疲劳分析模型为预先对多个测试者的疲劳值进行学习训练后建立的模型。
根据归一化后的目标皮肤电阻信号获取测试者的疲劳值之后，该方法还包括：判断测试者的疲劳值是否大于预设数值；若判断测试者的疲劳值大于预设数值，确定测试者处于疲劳期；确定测试者的疲劳值所属的疲劳等级，其中，疲劳等级包括：轻度疲劳和重度疲劳。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括 指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
在一个典型的配置中，计算设备包括一个或多个处理器(CPU)、输入/输出接口、网络接口和内存。
存储器可能包括计算机可读介质中的非永久性存储器，随机存取存储器(RAM)和/或非易失性内存等形式，如只读存储器(ROM)或闪存(flash RAM)。存储器是计算机可读介质的示例。
计算机可读介质包括永久性和非永久性、可移动和非可移动媒体可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括，但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带，磁带磁磁盘存储或其他磁性存储设备或任何其他非传输介质，可用于存储可以被计算设备访问的信息。按照本文中的界定，计算机可读介质不包括暂存电脑可读媒体(transitory media)，如调制的数据信号和载波。
还需要说明的是，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、商品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、商品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括要素的过程、方法、商品或者设备中还存在另外的相同要素。
本领域技术人员应明白，本申请的实施例可提供为方法、系统或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
以上仅为本申请的实施例而已，并不用于限制本申请。对于本领域技术人员来说，本申请可以有各种更改和变化。凡在本申请的精神和原理之内所作的任何修改、等同 替换、改进等，均应包含在本申请的权利要求范围之内。

Claims (10)

  1. 一种人体疲劳值的获取方法，包括：
    采集测试者的皮肤电阻信号；
    对采集到的皮肤电阻信号进行低通滤波，得到滤波后的皮肤电阻信号；
    对所述滤波后的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号；
    从所述求导处理后的皮肤电阻信号中截取符合预设条件的信号，得到目标皮肤电阻信号；
    对所述目标皮肤电阻信号进行滑动平均处理，得到滑动平均处理后的目标皮肤电阻信号；
    根据滑动平均处理后的目标皮肤电阻信号获取所述测试者的疲劳值，其中，所述测试者的疲劳值用于反映所述测试者的疲劳程度。
  2. 根据权利要求1所述的方法，其中，根据滑动平均处理后的目标皮肤电阻信号获取所述测试者的疲劳值包括：
    对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号；
    根据归一化后的目标皮肤电阻信号获取所述测试者的疲劳值。
  3. 根据权利要求2所述的方法，其中，根据归一化后的目标皮肤电阻信号获取所述测试者的疲劳值之后，所述方法还包括：
    对所述测试者的疲劳值进行分析；
    基于对所述测试者的疲劳值的分析结果，生成所述测试者的分析结果生成所述测试者的疲劳值报告。
  4. 根据权利要求2述的方法，其中，对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号还包括：
    采用支持向量机分类器对滑动平均处理后的目标皮肤电阻信号进行归一化处理，得到归一化后的目标皮肤电阻信号。
  5. 根据权利要求2所述的方法，其中，对所述滤波后的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号包括：
    对所述滤波后的皮肤电阻信号进行模数转换，得到皮肤电阻的数字信号；
    对所述皮肤电阻的数字信号进行求导处理，得到求导处理后的皮肤电阻信号。
  6. 根据权利要求2所述的方法，其中，根据归一化后的目标皮肤电阻信号获取所述测试者的疲劳值包括：
    通过所述测试者疲劳分析模型获取所述归一化后的目标皮肤电阻信号对应测试者的疲劳值，其中，所述测试者疲劳分析模型为预先对多个测试者的疲劳值进行学习训练后建立的模型。
  7. 根据权利要求4述的方法，其中，根据归一化后的目标皮肤电阻信号获取所述测试者的疲劳值之后，所述方法还包括：
    判断所述测试者的疲劳值是否大于预设数值；
    若判断所述测试者的疲劳值大于预设数值，确定所述测试者处于疲劳期；
    确定所述测试者的疲劳值所属的疲劳等级，其中，所述疲劳等级包括：轻度疲劳和重度疲劳。
  8. 一种人体疲劳值的获取装置，包括：
    采集单元，设置为采集测试者的皮肤电阻信号；
    滤波单元，设置为对采集到的皮肤电阻信号进行低通滤波，得到滤波后的皮肤电阻信号；
    第一处理单元，设置为对所述滤波后的皮肤电阻信号进行求导处理，得到求导处理后的皮肤电阻信号；
    第二处理单元，设置为从所述求导处理后的皮肤电阻信号中截取符合预设条件的信号，得到目标皮肤电阻信号；
    第三处理单元，设置为对所述目标皮肤电阻信号进行滑动平均处理，得到滑动平均处理后的目标皮肤电阻信号；
    获取单元，设置为根据滑动平均处理后的目标皮肤电阻信号获取所述测试者的疲劳值，其中，所述测试者的疲劳值用于反映所述测试者的疲劳程度。
  9. 一种存储介质，其中，所述存储介质包括存储的程序，其中，所述程序执行权利要求1至7中任意一项所述的人体疲劳值的获取方法。
  10. 一种处理器，其中，所述处理器用于运行程序，其中，所述程序运行时执行权利要求1至7中任意一项所述的人体疲劳值的获取方法。
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CN110085335B (zh) * 2019-05-14 2022-03-08 广西防城港核电有限公司 安全壳泄漏率在线监测信号处理方法
CN113855505A (zh) * 2021-10-13 2021-12-31 青岛海尔空调器有限总公司 用于缓解用户疲劳的方法及装置、按摩设备

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104044460A (zh) * 2014-03-20 2014-09-17 徐健 一种机动车防疲劳驾驶的报警方法及装置
CN106073806A (zh) * 2016-06-01 2016-11-09 深圳市宏电技术股份有限公司 一种用于穿戴式设备的疲劳检测方法、装置及穿戴式设备
CN106725537A (zh) * 2016-12-06 2017-05-31 北京欧德蒙科技有限公司 基于人体皮肤电的疲劳分析方法及系统

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20040032451A (ko) * 2002-10-09 2004-04-17 삼성전자주식회사 생체신호 기반의 건강 관리 기능을 갖는 모바일 기기 및이를 이용한 건강 관리 방법
EP1551282B1 (en) * 2002-10-09 2015-11-18 BodyMedia, Inc. Apparatus for detecting, receiving, deriving and displaying human physiological and contextual information
CN100476894C (zh) * 2007-09-26 2009-04-08 曹明礼 汽车疲劳驾驶报警方法
JP5665241B2 (ja) * 2012-03-21 2015-02-04 パナソニックＩｐマネジメント株式会社 眼球疲労判定装置およびその作動方法
JP6182282B2 (ja) * 2014-06-12 2017-08-16 コーニンクレッカ フィリップス エヌ ヴェＫｏｎｉｎｋｌｉｊｋｅ Ｐｈｉｌｉｐｓ Ｎ．Ｖ． 日周期検出システム
CN204207728U (zh) * 2014-08-14 2015-03-18 绍兴县中国轻纺城纽妃诗服装服饰有限公司 一种可监测人体健康状况的服装
CN104720787A (zh) * 2015-04-01 2015-06-24 深圳柔微传感科技有限公司 一种实现疲劳实时监测的方法和智能服装
CN105212949A (zh) * 2015-08-25 2016-01-06 西南大学 一种使用皮肤电信号进行文化体验情感识别的方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104044460A (zh) * 2014-03-20 2014-09-17 徐健 一种机动车防疲劳驾驶的报警方法及装置
CN106073806A (zh) * 2016-06-01 2016-11-09 深圳市宏电技术股份有限公司 一种用于穿戴式设备的疲劳检测方法、装置及穿戴式设备
CN106725537A (zh) * 2016-12-06 2017-05-31 北京欧德蒙科技有限公司 基于人体皮肤电的疲劳分析方法及系统

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