CN112370057A - Pressure evaluation method and device, computer equipment and storage medium - Google Patents
Pressure evaluation method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to the technical field of artificial intelligence, and provides a pressure evaluation method, a pressure evaluation device, computer equipment and a storage medium, wherein fingertip videos of a test user are collected based on a mobile terminal; the fingertip video is obtained by shooting when the test user finger covers the camera under the state that the mobile terminal flash lamp is turned on; converting the fingertip video into an RGBA video format, and extracting the summation of all pixel points in an R channel to obtain a PPG signal; extracting HRV characteristics of the test user based on the PPG signal; and inputting the HRV characteristics of the test user into a preset pressure evaluation model to obtain a pressure evaluation result of the test user. When the stress of a user is tested, only the mobile terminal is needed to be used for collecting physiological signals, professional collection equipment is not needed, and the collection cost is reduced.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a pressure assessment method, apparatus, computer device, and storage medium.
Background
With the rapid development of social economy, the pace of work and life of people is faster and faster, the pressure from all aspects is higher and higher, and a plurality of people can present the pressure problems of different degrees and different causes in different periods. When the pressure can not be released and relieved for a long time, the physical and mental health can be seriously influenced, a series of discomforts such as headache, insomnia, anxiety and the like can be caused slightly, and a series of chronic disease risks such as cardiovascular and cerebrovascular diseases, diabetes, cancer and the like can be increased seriously. Therefore, the pressure state of the user is known and mastered, and the pressure reduction measures are taken in time, so that the method is very important for the physical and psychological health of the user.
Stress, also known as stress, is a state in which a physiological system presents special symptoms in response to a stimulus. When a person is under stress, not only the physiological information (body fluid hormone level, heart rate, blood pressure, respiration, pupil diameter, skin electricity, etc.) of the person changes, but also the physical behavior information (expression, sound, limb movement, etc.) changes. The prior art can evaluate the human body pressure level by detecting the changes of the two types of states.
The pressure index evaluation method can be divided into: biochemical method, subjective evaluation method, physiological reaction test method and physiological parameter test method.
1. The biochemical method needs to extract human body fluid (blood, urine, saliva and the like), has high requirements on equipment and operators, can only judge the pressure state at the time of body fluid collection, and cannot be used for long-term pressure monitoring.
2. The subjective evaluation method depends on the self-feeling description of the tested person, and the evaluation is generally carried out by a professional scale, so that the method is simple and easy to implement and is relatively common in application; but the method is more suitable for large sample statistical analysis, and is easily influenced by environment and short-term memory confusion when being used for evaluating the stress of a single individual for a long time.
3. The physiological response test method evaluates the pressure of a tested person through various test methods such as color words, quick calculation test and the like, and quantifies the pressure level according to the test score or the physiological response condition; the method is influenced by individual difference, has higher requirement on test environment, and has higher realization cost because the test content needs to be properly changed every day during long-term pressure monitoring.
4. The physiological parameter measuring method mainly evaluates pressure by measuring and analyzing physiological signals (electroencephalogram, electrooculogram, electrocardio, PPG and the like) of a measured person, and along with the development of a sensing technology, the physiological signals can be collected by special physiological signal collecting equipment, so that the physiological parameter measuring method is convenient to use, and the pressure level is evaluated more objectively by the physiological signals without being influenced by main factor.
In summary, in the current stress index assessment method, the physiological parameter determination method is most suitable for long-term stress assessment, but when the method is used for acquiring physiological signals, professional signal acquisition equipment cost is required, the equipment is usually expensive, the equipment operation is complex, and the wide popularization of the method is influenced.
Disclosure of Invention
The present application mainly aims to provide a pressure assessment method, device, computer equipment and storage medium, and aims to overcome the defect that professional signal acquisition equipment is required when physiological signals are acquired for pressure assessment at present.
To achieve the above object, the present application provides a pressure evaluation method, comprising the steps of:
collecting a fingertip video of a test user based on a mobile terminal; the fingertip video is obtained by shooting when the test user finger covers the camera under the state that the mobile terminal flash lamp is turned on;
converting the fingertip video into an RGBA video format, and extracting the summation of all pixel points in an R channel to obtain a PPG signal;
extracting HRV characteristics of the test user based on the PPG signal;
inputting the HRV characteristics of the test user into a preset pressure evaluation model to obtain a pressure evaluation result of the test user; wherein the stress assessment model is obtained by training a neural network processed by a time series based on an HRV feature training set.
Further, the step of extracting the HRV feature of the test user based on the PPG signal includes:
converting the PPG signal into a conversion signal with a specified sampling rate by adopting a spline interpolation method or a least square interpolation method;
extracting RR intervals of sinus heart pulses in the converted signal;
and extracting and obtaining HRV characteristics of the test user based on the effective RR intervals.
Further, the HRV features include HRV time domain parameters, HRV frequency domain parameters;
the HRV time domain parameters comprise at least a standard deviation of RR intervals, SDNN, of the sinus heartbeat;
the HRV frequency domain parameters at least comprise low-frequency power LF and high-frequency power HF, and the ratio of LF to HF.
Further, the step of inputting the HRV characteristics of the test user into a preset stress assessment model is preceded by:
acquiring physiological signals of a sample user based on a preset acquisition period and a preset acquisition duration, and acquiring a pressure measurement result of the sample user when acquiring the physiological signals each time; the pressure measurement result is measured by the sample user based on a preset pressure scale;
obtaining the HRV characteristics corresponding to the sample user based on the physiological signals acquired each time,
taking the HRV characteristics corresponding to the physiological signals acquired by the sample user each time and the corresponding pressure measurement results as a group of HRV characteristic training sets;
and inputting each group of HRV characteristic training set into a neural network for time series processing to train according to the time sequence of collecting physiological signals to obtain the pressure assessment model.
Further, the step of acquiring a physiological signal of a sample user comprises:
collecting fingertip videos of the sample users based on a mobile terminal; the fingertip video is obtained by shooting when a finger of a sample user covers the camera under the starting state of the flash lamp of the mobile terminal;
converting the fingertip video of the sample user into an RGBA video format, and extracting the summation of all pixel points in an R channel to obtain a PPG signal of the sample user.
Further, before the step of acquiring the physiological signal of the sample user based on the preset acquisition cycle and the preset acquisition duration and obtaining the pressure measurement result of the sample user when acquiring the physiological signal each time, the method includes:
acquiring an application scene type of a pressure test and the type of the pressure test; the application scene types of the stress test comprise a short-term stress test, a long-term stress test and a chronic stress test; the types of stress tests comprise perceived stress and psychological stress;
acquiring the preset acquisition cycle and the preset acquisition duration for acquiring the physiological signals of the sample user according to the corresponding relation between the application scene type of the pressure test and the acquisition cycle and the acquisition duration;
and obtaining a preset pressure gauge for measuring the pressure measurement result of the sample user according to the corresponding relation between the type of the pressure test and the pressure gauge.
Further, the time series processed neural network comprises any one of RNN, LSTM, GUR, IndRNN.
The present application also provides a pressure evaluation device, including:
the acquisition unit is used for acquiring a fingertip video of a test user based on the mobile terminal; the fingertip video is obtained by shooting when the test user finger covers the camera under the state that the mobile terminal flash lamp is turned on;
the conversion unit is used for converting the fingertip video into an RGBA video format and extracting the summation of all pixel points in an R channel to obtain a PPG signal;
the extraction unit is used for extracting and obtaining the HRV characteristics of the test user based on the PPG signal;
the evaluation unit is used for inputting the HRV characteristics of the test user into a preset pressure evaluation model to obtain a pressure evaluation result of the test user; wherein the stress assessment model is obtained by training a neural network processed by a time series based on an HRV feature training set.
The present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the method of any of the above.
According to the pressure evaluation method, the pressure evaluation device, the computer equipment and the storage medium, fingertip videos of a test user are collected based on the mobile terminal; the fingertip video is obtained by shooting when the test user finger covers the camera under the state that the mobile terminal flash lamp is turned on; converting the fingertip video into an RGBA video format, and extracting the summation of all pixel points in an R channel to obtain a PPG signal; extracting HRV characteristics of the test user based on the PPG signal; and inputting the HRV characteristics of the test user into a preset pressure evaluation model to obtain a pressure evaluation result of the test user. When the stress of a user is tested, only the mobile terminal is needed to be used for collecting physiological signals, professional collection equipment is not needed, and the collection cost is reduced.
Drawings
FIG. 1 is a schematic diagram of the steps of a pressure estimation method according to an embodiment of the present application;
FIG. 2 is a block diagram of a pressure estimation device according to an embodiment of the present disclosure;
fig. 3 is a block diagram illustrating a structure of a computer device according to an embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Referring to fig. 1, an embodiment of the present application provides a pressure evaluation method, including the following steps:
step S1, collecting fingertip videos of a test user based on the mobile terminal; the fingertip video is obtained by shooting when the test user finger covers the camera under the state that the mobile terminal flash lamp is turned on;
step S2, converting the fingertip video into an RGBA video format, and extracting the summation of all pixel points in an R channel to obtain a PPG signal;
step S3, based on the PPG signal, extracting and obtaining the HRV characteristic of the test user;
step S4, inputting the HRV characteristics of the test user into a preset pressure evaluation model to obtain a pressure evaluation result of the test user; wherein the stress assessment model is obtained by training a neural network processed by a time series based on an HRV feature training set.
In the present embodiment, the above method is applied to assess the current stress of the user, and the assessment of stress has a great significance to the mental health of the user. The method can also be applied to the technical field of intelligent medical treatment of the smart city to promote the construction of the smart city.
As described in the foregoing steps S1-S2, in this embodiment, a professional acquisition device is not needed, and only a mobile terminal (such as a mobile phone, a tablet, or the like) is used to acquire a fingertip video, so as to acquire a PPG signal of a test user. Specifically, a flash lamp of the mobile terminal is turned on, a shooting function is turned on, a test user completely shields a camera from recording a fingertip blood flow video by one finger, and the fingertip blood flow volume fluctuates periodically along with the change of the blood vessel volume, so that the absorption of blood to a flash lamp light source changes, and the pixel value of the recorded video changes periodically. Converting each frame of video into an RGBA format, solving the summation of all pixel points of an R channel, and obtaining a discrete PPG signal, namely a sampling point of the PPG signal corresponding to each frame of video, wherein if the video frame rate is 30fp/s, the PPG sampling rate is 30 Hz.
As described in step S3, the pressure is closely related to the activity of the heart, and exceeds the correlation between some other physiological signals, such as myoelectric and respiratory signals, and the pressure. Taking into account cardiovascular system differences between different individuals, it is necessary to extract universally applicable features to represent cardiac activity. The heart rate variability (HRV characteristic) refers to the tiny difference of successive heartbeat intervals, the index not only can reflect the activity of the autonomic nervous system of the human body, but also can feed back the abnormality of the cardiovascular system, and the index is an effective index for measuring the activity of the heart of the human body. Specifically, the short-term HRV shows a descending trend under the action of the pressure source and is basically in a negative correlation with the pressure. The HRV power spectrum low frequency power, LF, decreases significantly with decreased sympathetic activity, while the high frequency power, HF, increases significantly with increased parasympathetic activity, so that LF and HF can be used to quantitatively assess sympathetic and parasympathetic activity, respectively, and the ratio LF/HF can be used to assess autonomic nervous system balance.
In this embodiment, in the method for evaluating the pressure by using the physiological parameters, compared with the physiological signals such as electroencephalogram, electrooculogram, myoelectricity and the like, the HRV characteristic parameters are low in acquisition difficulty and cost.
As described in step S4, the stress assessment model is obtained by training the neural network processed by time series based on the HRV feature training set, and the HRV feature of the test user is input into a preset stress assessment model to obtain the stress assessment result of the test user.
In the embodiment, a professional physiological signal acquisition device is not needed, and only the mobile terminal is needed, so that the signal acquisition can be realized without wearing a sensor for a long time by a user. In an embodiment, the HRV feature may be obtained by a periodic short-time measurement mode, and the pressure is estimated by combining multiple short-time measurement HRV features, so that a random error caused by single data fluctuation can be avoided, and the robustness of the pressure estimation result is stronger.
It will be appreciated that in other embodiments, HRV features may also be extracted from ECG, BCG signals to evaluate user stress results.
In an embodiment, the step of extracting, based on the PPG signal, an HRV feature of the test user includes:
converting the PPG signal into a conversion signal with a specified sampling rate by adopting a spline interpolation method or a least square interpolation method;
extracting RR intervals of sinus heart pulses in the converted signal;
and extracting and obtaining HRV characteristics of the test user based on the effective RR intervals.
In this embodiment, under the influence of the mobile terminal, the sampling rates of the devices are different, and the sampling rates of the obtained PPG signals are different, so that in the scheme, the sampling rate of the PPG signals needs to be converted into a specified sampling rate (for example, 250Hz) by an up-sampling or down-sampling method, and in the up-sampling or down-sampling process, a spline interpolation or least square interpolation method is usually used in order to ensure that the waveform shape is not deformed.
Since the signal may be interfered by random action or hardware noise, and the RR interval of the noise signal segment is invalid, the RR interval of the noise segment is processed (removed or interpolated) in this embodiment, so as to avoid affecting the subsequent HRV feature extraction. Finally, only the HRV feature of the sinus cardiac rhythm is valid, and in order to avoid the abnormal cardiac cycle from affecting the calculation of the HRV feature, the adjacent RR intervals with larger difference (for example: the difference exceeds 20% or 30%) are also subjected to the same elimination or interpolation processing. After the RR interval of the sinus heartbeat is obtained, HRV characteristics of the tested user can be extracted and obtained.
Specifically, the HRV features include HRV time domain parameters, HRV frequency domain parameters;
the HRV time domain parameters comprise at least a standard deviation of RR intervals, SDNN, of the sinus heartbeat;
the HRV frequency domain parameters at least comprise low-frequency power LF and high-frequency power HF, and the ratio of LF to HF. The low-frequency power LF is low-frequency energy of an RR interphase power spectrum, and the high-frequency power HF is high-frequency energy of the RR interphase power spectrum.
In other embodiments, the HRV time domain parameters further include rmsd, PNN50, and the HRV frequency domain parameters further include LF _ nu (low frequency), HF _ nu (high frequency), and the like.
In one embodiment, the step S4 of inputting the HRV characteristic of the test user into a preset stress assessment model includes:
step S10, acquiring physiological signals of a sample user based on a preset acquisition cycle and a preset acquisition duration, and acquiring a pressure measurement result of the sample user each time the physiological signals are acquired; the pressure measurement result is measured by the sample user based on a preset pressure scale;
step S20, obtaining the HRV characteristics corresponding to the sample user based on the physiological signals acquired each time;
step S30, taking the HRV characteristics corresponding to the physiological signals acquired by the sample user each time and the corresponding pressure measurement results as a group of HRV characteristic training sets;
and step S40, inputting each group of HRV characteristic training set into a neural network for time series processing to train according to the time sequence of collecting physiological signals, and obtaining the pressure evaluation model.
In this embodiment, a method for training the pressure estimation model is provided.
The preset acquisition period and the preset acquisition duration are determined according to the current application scenario, the pressure is a variable representing the body state, and is different from a complex and changeable physiological signal, the human body pressure state is stable for a short time, and real-time monitoring is not required, so that only the periodic detection pressure of the user is considered in the embodiment, and the monitoring period length is related to the application scenario, for example: when the work/study arrangement of one day is reasonably planned, a short-term pressure test needs to be carried out, and the pressure of a user is monitored for multiple times in one day; during long-term pressure test, monitoring is only needed once in 1-2 days; the monitoring period may be longer with chronic stress testing.
The longer the acquisition time interval is, the higher the requirement on the stability of the acquired data quality is, so that the acquisition time is 30-60 s when the measurement is carried out for multiple times in one day; when the measurement is carried out once a day, the collection time is 1-5 min; once in 1 week, the collection time was >10 min. In this embodiment, the pressure gauge is mainly used in an application scenario in which measurement is performed once a day, the single data acquisition time is set to 2min, when data is acquired, a mobile terminal (capable of acquiring PPG and BCG signals) without additional hardware cost is adopted, and the pressure gauge selects "PERCEIVED STRESS quationnaie" capable of representing the short-term pressure of the user.
The physiological signal collection of the sample user is generally collected when the sample user is not interfered by external things in the early morning, and the initial state of the daily pressure of the sample user is collected. In order to embody the real stress state of the sample user in daily life, no stress induction operation is performed. The sample user is required to collect PPG signals by a mobile phone in a calm state in a fixed time period (such as 7-8 am) every morning, and then a pressure measurement result is obtained by filling a pressure gauge, namely the training data set label.
And obtaining the HRV feature corresponding to the sample user based on the physiological signal acquired each time, where the extraction process of the HRV feature is the same as that in the above embodiment, and is not repeated here.
And taking the HRV characteristics corresponding to the physiological signals acquired by the sample user every time and the corresponding pressure measurement results as a group of HRV characteristic training sets, wherein the HRV characteristic training sets are used for training the neural network processed by the time series.
In this embodiment, the time-series processed neural network includes any one of RNN, LSTM, GUR, IndRNN.
The traditional BP neural network can only process input features in sequence independently, and the front input feature and the rear input feature have no relation at all, so that the BP neural network cannot reflect the relation between historical HRV features and current pressure. The Recurrent Neural Network (RNN) can be divided into an input layer, a hidden layer and an output layer.
In this embodiment, RNN may be used, and the values St and Ot of the hidden layer and the output layer of RNN are not only related to the current input xt, but also depend on the value St-1 of the previous hidden layer. The pressure evaluation model shows the interaction between the front and the back of the sequence data, and is more suitable for processing the sequence with time sequence.
The RNN unit can be replaced by a plurality of time sequence processing units with better performance, such as LSTM/GUR/IndRNN. The traditional RNN is easy to have the problem of gradient disappearance/explosion due to parameter sharing in time, and the behavior of neurons is difficult to reasonably explain due to interconnection of neurons in layers. LSTM/GRU can solve the problem of gradient disappearance/explosion in the traditional RNN layer, while IndRNN improves the interpretability of neurons and can be used for processing longer sequence information.
Finally, the model structure can be used not only for regression calculation of pressure index, but also for pressure grading judgment (e.g. large pressure, small pressure, no pressure classification, etc.), and only the output layer and the loss function need to be modified (e.g. the output layer activation function is set to softmax, and the loss function is set to multi-class cross entry).
In an embodiment, the step S10 of acquiring the physiological signal of the sample user includes:
collecting fingertip videos of the sample users based on a mobile terminal; the fingertip video is obtained by shooting when a finger of a sample user covers the camera under the starting state of the flash lamp of the mobile terminal;
converting the fingertip video of the sample user into an RGBA video format, and extracting the summation of all pixel points in an R channel to obtain a PPG signal of the sample user.
In this embodiment, the specific implementation process of acquiring the physiological signal is the same as that in the above embodiment, and is not described herein again.
In an embodiment, before the step S10 of acquiring a physiological signal of a sample user based on a preset acquisition cycle and a preset acquisition duration, and acquiring a pressure measurement result of the sample user each time the physiological signal is acquired, the method includes:
acquiring an application scene type of a pressure test and the type of the pressure test; the application scene types of the stress test comprise a short-term stress test, a long-term stress test and a chronic stress test; the types of stress tests comprise perceived stress and psychological stress;
acquiring the preset acquisition cycle and the preset acquisition duration for acquiring the physiological signals of the sample user according to the corresponding relation between the application scene type of the pressure test and the acquisition cycle and the acquisition duration;
and obtaining a preset pressure gauge for measuring the pressure measurement result of the sample user according to the corresponding relation between the type of the pressure test and the pressure gauge.
In this embodiment, the preset acquisition period and the preset acquisition duration are both determined according to the current application scenario, pressure is a variable representing a body state, and is different from a complex and changeable physiological signal, a human body pressure state is stable for a short time, and does not need to be monitored in real time, so that only the periodic detection pressure of the user is considered in this embodiment, and the length of the monitoring period is related to the application scenario, for example: when the work/study arrangement of one day is reasonably planned, a short-term pressure test needs to be carried out, and the pressure of a user is monitored for multiple times in one day; during long-term pressure test, monitoring is only needed once in 1-2 days; the monitoring period may be longer with chronic stress testing.
The stress to which the human body is subjected can be classified into various types according to different classification methods, for example: sensing stress and psychological stress; the above-mentioned sensing stress and psychological stress include working stress, living stress, learning stress, training stress, chronic stress, sudden stress, physical induced stress, emotional induced stress, and the like. Different applications require different types of pressure to be evaluated, and researchers studying pressure have designed corresponding evaluation scales or test methods for different types of pressure. For example: the scale for perceived stress "PERCEIVED STRESS QUESTIONNAIRE," the perceptual mental stress Scale (CPSS) "for mental stress, and the like.
In an embodiment, the method further comprises:
and storing the preset pressure evaluation model, the pressure evaluation result, the fingertip video and the HRV characteristic into a block chain. The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism and an encryption algorithm. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product services layer, and an application services layer.
Referring to fig. 2, an embodiment of the present application further provides a pressure evaluation apparatus, including:
the acquisition unit 10 is used for acquiring a fingertip video of a test user based on the mobile terminal; the fingertip video is obtained by shooting when the test user finger covers the camera under the state that the mobile terminal flash lamp is turned on;
the conversion unit 20 is configured to convert the fingertip video into an RGBA video format, and extract a sum of all pixel points in the R channel to obtain a PPG signal;
an extracting unit 30, configured to extract, based on the PPG signal, an HRV feature of the test user;
the evaluation unit 40 is configured to input the HRV characteristics of the test user into a preset pressure evaluation model to obtain a pressure evaluation result of the test user; wherein the stress assessment model is obtained by training a neural network processed by a time series based on an HRV feature training set.
In an embodiment, the extracting unit 30 is specifically configured to:
converting the PPG signal into a conversion signal with a specified sampling rate by adopting a spline interpolation method or a least square interpolation method;
extracting RR intervals of sinus heart pulses in the converted signal;
and extracting and obtaining HRV characteristics of the test user based on the effective RR intervals.
In an embodiment, the HRV features include HRV temporal parameters, HRV frequency domain parameters;
the HRV time domain parameters comprise at least a standard deviation of RR intervals, SDNN, of the sinus heartbeat;
the HRV frequency domain parameters at least comprise low-frequency power LF and high-frequency power HF, and the ratio of LF to HF.
In an embodiment, the pressure evaluation apparatus further includes:
the system comprises a sample acquisition unit, a pressure measurement unit and a control unit, wherein the sample acquisition unit is used for acquiring physiological signals of a sample user based on a preset acquisition cycle and a preset acquisition duration and acquiring a pressure measurement result of the sample user when acquiring the physiological signals each time; the pressure measurement result is measured by the sample user based on a preset pressure scale;
a sample feature extraction unit for obtaining the HRV feature corresponding to the sample user based on the physiological signal acquired each time,
the training set construction unit is used for taking the HRV characteristics corresponding to the physiological signals acquired by the sample user each time and the corresponding pressure measurement results as a group of HRV characteristic training sets;
and the training unit is used for inputting each group of HRV characteristic training set into the neural network for time sequence processing to train according to the time sequence of acquiring physiological signals to obtain the pressure evaluation model.
In one embodiment, the sample acquisition unit acquires physiological signals of a sample user, comprising:
collecting fingertip videos of the sample users based on a mobile terminal; the fingertip video is obtained by shooting when a finger of a sample user covers the camera under the starting state of the flash lamp of the mobile terminal;
converting the fingertip video of the sample user into an RGBA video format, and extracting the summation of all pixel points in an R channel to obtain a PPG signal of the sample user.
In an embodiment, the pressure evaluation apparatus further includes:
the type acquisition unit is used for acquiring the application scene type of the pressure test and the type of the pressure test; the application scene types of the stress test comprise a short-term stress test, a long-term stress test and a chronic stress test; the types of stress tests comprise perceived stress and psychological stress;
the first acquisition unit is used for acquiring the preset acquisition cycle and the preset acquisition duration for acquiring the physiological signals of the sample user according to the corresponding relation between the application scene type of the pressure test and the acquisition cycle and the acquisition duration;
and the second obtaining unit is used for obtaining a preset pressure gauge for measuring the pressure measurement result of the sample user according to the corresponding relation between the type of the pressure test and the pressure gauge.
In an embodiment, the time series processed neural network comprises any one of RNN, LSTM, GUR, IndRNN.
In this embodiment, please refer to the method described in the above embodiment for specific implementation of each unit in the above apparatus embodiment, which is not described herein again.
Referring to fig. 3, a computer device, which may be a server and whose internal structure may be as shown in fig. 3, is also provided in the embodiment of the present application. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the computer designed processor is used to provide computational and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing pressure evaluation results and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a stress assessment method.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is only a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects may be applied.
An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method of stress assessment. It is to be understood that the computer-readable storage medium in the present embodiment may be a volatile-readable storage medium or a non-volatile-readable storage medium.
In summary, according to the pressure evaluation method, the pressure evaluation device, the computer device, and the storage medium provided in the embodiments of the present application, a fingertip video of a test user is collected based on a mobile terminal; the fingertip video is obtained by shooting when the test user finger covers the camera under the state that the mobile terminal flash lamp is turned on; converting the fingertip video into an RGBA video format, and extracting the summation of all pixel points in an R channel to obtain a PPG signal; extracting HRV characteristics of the test user based on the PPG signal; and inputting the HRV characteristics of the test user into a preset pressure evaluation model to obtain a pressure evaluation result of the test user. When the stress of a user is tested, only the mobile terminal is needed to be used for collecting physiological signals, professional collection equipment is not needed, and the collection cost is reduced.
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 instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium provided herein and used in the examples may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double-rate SDRAM (SSRSDRAM), Enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method 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, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.
The above description is only for the preferred embodiment of the present application and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are intended to be included within the scope of the present application.
Claims (10)
1. A method of pressure assessment, comprising the steps of:
collecting a fingertip video of a test user based on a mobile terminal; the fingertip video is obtained by shooting when the test user finger covers the camera under the state that the mobile terminal flash lamp is turned on;
converting the fingertip video into an RGBA video format, and extracting the summation of all pixel points in an R channel to obtain a PPG signal;
extracting HRV characteristics of the test user based on the PPG signal;
inputting the HRV characteristics of the test user into a preset pressure evaluation model to obtain a pressure evaluation result of the test user; wherein the stress assessment model is obtained by training a neural network processed by a time series based on an HRV feature training set.
2. The pressure assessment method according to claim 1, wherein the step of extracting the HRV feature of the test user based on the PPG signal comprises:
converting the PPG signal into a conversion signal with a specified sampling rate by adopting a spline interpolation method or a least square interpolation method;
extracting RR intervals of sinus heart pulses in the converted signal;
and extracting and obtaining HRV characteristics of the test user based on the effective RR intervals.
3. The pressure assessment method of claim 2, wherein the HRV features comprise HRV temporal parameters, HRV frequency domain parameters;
the HRV time domain parameters comprise at least a standard deviation of RR intervals, SDNN, of the sinus heartbeat;
the HRV frequency domain parameters at least comprise low-frequency power LF and high-frequency power HF, and the ratio of LF to HF.
4. The pressure assessment method of claim 1, wherein said step of inputting the HRV characteristics of the test user into a preset pressure assessment model is preceded by:
acquiring physiological signals of a sample user based on a preset acquisition period and a preset acquisition duration, and acquiring a pressure measurement result of the sample user when acquiring the physiological signals each time; the pressure measurement result is measured by the sample user based on a preset pressure scale;
obtaining the HRV characteristics corresponding to the sample user based on the physiological signals acquired each time,
taking the HRV characteristics corresponding to the physiological signals acquired by the sample user each time and the corresponding pressure measurement results as a group of HRV characteristic training sets;
and inputting each group of HRV characteristic training set into a neural network for time series processing to train according to the time sequence of collecting physiological signals to obtain the pressure assessment model.
5. The stress-assessing method of claim 4, wherein the step of acquiring physiological signals of the sample user comprises:
collecting fingertip videos of the sample users based on a mobile terminal; the fingertip video is obtained by shooting when a finger of a sample user covers the camera under the starting state of the flash lamp of the mobile terminal;
converting the fingertip video of the sample user into an RGBA video format, and extracting the summation of all pixel points in an R channel to obtain a PPG signal of the sample user.
6. The stress assessment method according to claim 4, wherein the step of acquiring the physiological signal of the sample user based on the preset acquisition period and the preset acquisition duration and obtaining the stress measurement result of the sample user each time the physiological signal is acquired comprises:
acquiring an application scene type of a pressure test and the type of the pressure test; the application scene types of the stress test comprise a short-term stress test, a long-term stress test and a chronic stress test; the types of stress tests comprise perceived stress and psychological stress;
acquiring the preset acquisition cycle and the preset acquisition duration for acquiring the physiological signals of the sample user according to the corresponding relation between the application scene type of the pressure test and the acquisition cycle and the acquisition duration;
and obtaining a preset pressure gauge for measuring the pressure measurement result of the sample user according to the corresponding relation between the type of the pressure test and the pressure gauge.
7. The stress assessment method according to claim 1, wherein said time series processed neural network comprises any one of RNN, LSTM, GUR, IndRNN.
8. A pressure evaluation device, comprising:
the acquisition unit is used for acquiring a fingertip video of a test user based on the mobile terminal; the fingertip video is obtained by shooting when the test user finger covers the camera under the state that the mobile terminal flash lamp is turned on;
the conversion unit is used for converting the fingertip video into an RGBA video format and extracting the summation of all pixel points in an R channel to obtain a PPG signal;
the extraction unit is used for extracting and obtaining the HRV characteristics of the test user based on the PPG signal;
the evaluation unit is used for inputting the HRV characteristics of the test user into a preset pressure evaluation model to obtain a pressure evaluation result of the test user; wherein the stress assessment model is obtained by training a neural network processed by a time series based on an HRV feature training set.
9. A computer device comprising a memory and a processor, the memory having stored therein a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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|---|---|---|---|---|
| CN113257418A (en) * | 2021-03-29 | 2021-08-13 | 广州科克里特生命科技有限公司 | Risk detection system and method for low back pain |
| WO2022095331A1 (en) * | 2020-11-09 | 2022-05-12 | 平安科技（深圳）有限公司 | Stress assessment method and apparatus, computer device and storage medium |
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| CN115281676B (en) * | 2022-10-08 | 2023-01-31 | 齐鲁工业大学 | Fatigue detection method based on GRU neural network and ECG signal |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019180083A1 (en) * | 2018-03-21 | 2019-09-26 | Axelife | System for monitoring stress in an individual |
| CN110477932A (en) * | 2019-07-31 | 2019-11-22 | 商丘师范学院 | A kind of students psychology Stress appraisal method and system based on internet and cloud computing |
| CN111513730A (en) * | 2020-03-20 | 2020-08-11 | 合肥工业大学 | Psychological stress prediction method and system based on multi-channel physiological data |
| CN111714144A (en) * | 2020-07-24 | 2020-09-29 | 长春理工大学 | Mental stress analysis method based on video non-contact measurement |
| CN111797817A (en) * | 2020-07-30 | 2020-10-20 | 平安科技（深圳）有限公司 | Emotion recognition method and device, computer equipment and computer-readable storage medium |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105147248B (en) * | 2015-07-30 | 2019-02-05 | 华南理工大学 | Depression assessment system and its appraisal procedure based on physiologic information |
| CN109106350A (en) * | 2018-09-20 | 2019-01-01 | 中山大学 | The preliminary screening method of the Sleep Apnea-hypopnea Syndrome of low physiological stress |
| CN110477895B (en) * | 2019-07-24 | 2022-11-11 | 苏州国科医工科技发展(集团)有限公司 | Multi-light-source detector continuous heart rate measuring method based on blood volume waves |
| CN112370057A (en) * | 2020-11-09 | 2021-02-19 | 平安科技（深圳）有限公司 | Pressure evaluation method and device, computer equipment and storage medium |
-
2020
- 2020-11-09 CN CN202011240444.4A patent/CN112370057A/en active Pending
-
2021
- 2021-03-31 WO PCT/CN2021/084544 patent/WO2022095331A1/en active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019180083A1 (en) * | 2018-03-21 | 2019-09-26 | Axelife | System for monitoring stress in an individual |
| CN110477932A (en) * | 2019-07-31 | 2019-11-22 | 商丘师范学院 | A kind of students psychology Stress appraisal method and system based on internet and cloud computing |
| CN111513730A (en) * | 2020-03-20 | 2020-08-11 | 合肥工业大学 | Psychological stress prediction method and system based on multi-channel physiological data |
| CN111714144A (en) * | 2020-07-24 | 2020-09-29 | 长春理工大学 | Mental stress analysis method based on video non-contact measurement |
| CN111797817A (en) * | 2020-07-30 | 2020-10-20 | 平安科技（深圳）有限公司 | Emotion recognition method and device, computer equipment and computer-readable storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022095331A1 (en) * | 2020-11-09 | 2022-05-12 | 平安科技（深圳）有限公司 | Stress assessment method and apparatus, computer device and storage medium |
| CN113257418A (en) * | 2021-03-29 | 2021-08-13 | 广州科克里特生命科技有限公司 | Risk detection system and method for low back pain |
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