A method for evaluating canine emotional state based on skin resistance

Abstract

The present invention provides a canine emotional state assessment method based on skin resistance, the method comprising the following steps: collecting canine skin resistance signals; preprocessing the skin resistance signals to obtain effective skin resistance signals; extracting features from the effective skin resistance signals to obtain physiological characteristics; training a canine emotional state assessment model based on the physiological characteristics, and assessing the canine emotional state based on the trained canine emotional state assessment model. The canine emotional state assessment method based on skin resistance of the present invention assesses the emotional state of a canine based on the canine skin resistance, thereby providing information guidance for canine training.

Description

Technical Field

[0001] The present invention belongs to the technical field of deep learning, and in particular relates to a method for evaluating the emotional state of dogs based on skin resistance. Background Art

[0002] In the prior art, the physiological emotional state of dogs is usually identified through indicators such as body temperature, breathing, barking, movement, and EEG. However, due to individual differences among dogs, the above methods have low accuracy in emotional assessment and cannot meet the needs of actual application scenarios. Summary of the invention

[0003] In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a method for evaluating the emotional state of dogs based on skin resistance, which evaluates the emotional state of dogs based on the skin resistance of dogs, thereby providing information guidance for dog training.

[0004] The present invention provides a method for evaluating the emotional state of a dog based on skin resistance, the method comprising the following steps: collecting skin resistance signals of a dog; preprocessing the skin resistance signals to obtain effective skin resistance signals; extracting features from the effective skin resistance signals to obtain physiological characteristics; training a dog emotional state evaluation model based on the physiological characteristics, so as to evaluate the emotional state of the dog based on the trained dog emotional state evaluation model.

[0005] In one embodiment of the present invention, a skin resistance signal of a dog is collected based on a skin galvanic sensor, wherein the skin galvanic sensor is fixed on the skin of the dog based on a fixing device.

[0006] In one embodiment of the present invention, preprocessing the skin resistance signal to obtain a valid skin resistance signal includes the following steps:

[0007] Performing denoising processing on the skin resistance signal;

[0008] Downsampling the skin resistance signal after denoising;

[0009] The skin resistance signal after downsampling is decomposed to obtain the effective skin resistance signal.

[0010] In one embodiment of the present invention, the skin resistance signal is denoised based on a bandpass filter.

[0011] In one embodiment of the present invention, the physiological characteristics include time domain characteristics and frequency domain characteristics.

[0012] In one embodiment of the present invention, the time domain features include one or more combinations of the mean, median, maximum, minimum, variance, and slope of the effective skin resistance signal; the frequency domain features include one or more combinations of the centroid frequency, average frequency, frequency root mean square, and frequency variance.

[0013] In one embodiment of the present invention, training a canine emotional state assessment model based on the physiological characteristics includes the following steps:

[0014] Dividing the physiological characteristics and corresponding emotions into a training set, a validation set, and a test set;

[0015] Training the canine emotional state assessment model based on the training set;

[0016] Verifying the trained canine emotional state assessment model based on the verification set;

[0017] The trained canine emotional state assessment model is tested based on the test set.

[0018] In one embodiment of the present invention, the canine emotional state assessment model uses a SVM classifier or an artificial neural network.

[0019] In one embodiment of the present invention, the dog emotional state assessment based on the trained dog emotional state assessment model includes the following steps:

[0020] Collect the skin resistance signal to be evaluated of the dog;

[0021] Acquiring the physiological characteristics of the skin resistance signal to be evaluated;

[0022] The physiological characteristics are input into a trained canine emotional state evaluation model to obtain an emotional state evaluation result corresponding to the skin resistance signal to be evaluated.

[0023] In one embodiment of the present invention, the emotional state evaluation results include happiness, anger, disgust, sadness, fear, surprise, excitement and obedience.

[0024] As described above, the dog emotional state assessment method based on skin resistance of the present invention has the following beneficial effects:

[0025] (1) Combined with deep learning algorithms, the emotional state of dogs is evaluated based on their skin resistance, with high accuracy and fast processing speed;

[0026] (2) It can provide information guidance for dog training and is highly practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Shown is a schematic diagram of a scene of an electronic device of the present invention in an embodiment;

[0028] Figure 2 Shown is a flow chart of a canine emotional state assessment method based on skin resistance in one embodiment of the present invention;

[0029] Figure 3 Shown is a schematic diagram of the structure of a dog emotional state assessment system based on skin resistance in one embodiment of the present invention;

[0030] Figure 4 It is a schematic structural diagram of an electronic device of the present invention in one embodiment. DETAILED DESCRIPTION

[0031] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0032] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0033] The following embodiments of the present invention provide a method for evaluating the emotional state of dogs based on skin resistance, which can be applied to Figure 1 The electronic device shown. The electronic device described in the present invention may include a mobile phone 11 with a wireless charging function, a tablet computer 12, a laptop computer 13, a wearable device, a vehicle-mounted device, an augmented reality (AR) / virtual reality (VR) device, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), etc. The embodiment of the present invention does not impose any restrictions on the specific type of the electronic device.

[0034] For example, the electronic device may be a station (STAION, ST) in a WLAN with a wireless charging function, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with a wireless charging function, a computing device or other processing device, a computer, a laptop computer, a handheld communication device, a handheld computing device, and / or other devices for communicating on a wireless system and a next generation communication system, such as a mobile terminal in a 5G network, a mobile terminal in a future evolved Public Land Mobile Network (PLMN), or a mobile terminal in a future evolved Non-terrestrial Network (NTN).

[0035] For example, the electronic device can communicate with the network and other devices through wireless communication. The above wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobilecommunication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), BT, GNSS, WLAN, NFC, FM, and / or IR technology. The GNSS can include Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou navigation Satellite System (BDS), Quasi-Zenith Satellite System (QZSS) and / or Satellite Based Augmentation Systems (SBAS).

[0036] During the activities of biological organisms, corresponding skin electrical activities are usually generated due to various emotional fluctuations, which are generally referred to as Galvanic Skin Response (GSR). This is because when the body is stimulated by the outside world or the emotional state changes, the activity of its autonomic nervous system will cause changes such as dilation and contraction of blood vessels in the skin and secretion of sweat glands, thereby causing changes in skin resistance. The secretion of sweat glands is regulated by the sympathetic nerves. Once the emotions of an organism are stimulated by the outside world, the sweat glands will secrete vigorously, causing the conductivity of the skin surface area to change. The greater the stimulation, the greater the change in skin resistance, and the organism cannot control the occurrence of such changes by itself. Therefore, the present invention applies the characteristic of skin resistance to the detection of canine emotional states to further improve the accuracy and reliability of canine emotional state detection.

[0037] The technical solutions in the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0038] like Figure 2 As shown, in one embodiment, the canine emotional state assessment method based on skin resistance of the present invention includes steps S1 to S4.

[0039] Step S1, collecting skin resistance signals of dogs.

[0040] Specifically, the skin resistance signal of the dog is collected based on the galvanic skin sensor. In actual operation, the galvanic skin sensor is fixed on the skin of the dog based on a fixing device to obtain an accurate skin resistance signal. Through the fixing device, the galvanic skin sensor can be tightly fixed on the skin of the dog, effectively avoiding the signal collection error caused by the movement of the galvanic skin sensor. Preferably, the fixing device adopts an elastic fixing belt, and the galvanic skin sensor is set on the abdominal skin of the dog.

[0041] Step S2: pre-process the skin resistance signal to obtain a valid skin resistance signal.

[0042] Specifically, in order to improve the effectiveness of the skin resistance signal and eliminate the noise interference contained therein, it is necessary to first pre-process the skin resistance signal to obtain a valid skin resistance signal.

[0043] In one embodiment, preprocessing the skin resistance signal to obtain a valid skin resistance signal includes the following steps:

[0044] 21) De-noising the skin resistance signal.

[0045] The skin resistance signal is denoised based on a bandpass filter to remove low-frequency noise and high-frequency noise.

[0046] 22) Downsampling the denoised skin resistance signal to eliminate computational redundancy and reduce computational complexity.

[0047] Downsampling is to reduce a high sampling rate signal to a low sampling rate signal. In the present invention, downsampling is achieved by decimation.

[0048] 23) Decomposing the down-sampled skin resistance signal to obtain the effective skin resistance signal.

[0049] Among them, by decomposing the skin resistance signal after downsampling processing, the required signals with different characteristics can be obtained to facilitate subsequent signal analysis and processing.

[0050] Step S3: extracting features from the effective skin resistance signal to obtain physiological features.

[0051] Specifically, the physiological characteristics include time domain characteristics and frequency domain characteristics. In one embodiment, the time domain characteristics include one or more combinations of the mean, median, maximum, minimum, variance, and slope of the effective skin resistance signal; the frequency domain characteristics include one or more combinations of the centroid frequency, average frequency, frequency root mean square, and frequency variance. Therefore, for the required physiological characteristics, the effective skin resistance signal is feature extracted. For example, when the physiological characteristic is the mean, the mean of the effective skin resistance signal is calculated. When the physiological characteristic is the frequency variance, the effective skin resistance signal is first converted from the time domain to the frequency domain, and then the corresponding frequency variance is calculated in the frequency domain.

[0052] Step S4: training a canine emotional state assessment model based on the physiological characteristics, and performing canine emotional state assessment based on the trained canine emotional state assessment model.

[0053] Specifically, training a canine emotional state assessment model based on the physiological characteristics includes the following steps:

[0054] 41) Divide the physiological characteristics and corresponding emotions into a training set, a validation set and a test set.

[0055] The training set, the test set and the validation set are divided according to preset ratios, for example, the ratios are 80%, 10% and 10% respectively.

[0056] 42) Training the canine emotional state assessment model based on the training set.

[0057] Wherein, when training the dog emotional state evaluation model, the model parameter information is updated based on the gradient information of the loss function until the dog emotional state evaluation model reaches a preset convergence condition.

[0058] 43) Validating the trained canine emotional state assessment model based on the validation set.

[0059] The validation set is used to further adjust the canine emotional state assessment model.

[0060] 44) Testing the trained canine emotional state assessment model based on the test set.

[0061] The test set is used to test the performance of the canine emotional state assessment model. After the canine emotional state assessment model is trained, the test set is used to evaluate the generalization ability of the canine emotional state assessment model, that is, the model's ability to predict unseen data.

[0062] In one embodiment, the canine emotional state assessment model uses an SVM classifier or an artificial neural network. Among them, the SVM (Support Vector Machine) classifier is a classic supervised learning algorithm used to solve binary and multi-classification problems. Its core idea is to find an optimal hyperplane in the feature space for classification, and the interval is the largest. The SVM classifier can perform linear or nonlinear classification, regression, and even outlier detection tasks. The artificial neural network (ANN) abstracts the human brain neural network from the perspective of information processing, establishes a simple model, and forms different networks according to different connection methods.

[0063] In one embodiment of the present invention, the dog emotional state assessment based on the trained dog emotional state assessment model includes the following steps:

[0064] a) collecting the skin resistance signal to be evaluated of the dog;

[0065] b) obtaining the physiological characteristics of the skin resistance signal to be evaluated;

[0066] c) Inputting the physiological characteristics into the trained canine emotional state evaluation model to obtain the emotional state evaluation results corresponding to the skin resistance signal to be evaluated, wherein the emotional state evaluation results include happiness, anger, disgust, sadness, fear, surprise, excitement and obedience.

[0067] The protection scope of the canine emotional state assessment method based on skin resistance described in the embodiment of the present invention is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present invention are included in the protection scope of the present invention.

[0068] An embodiment of the present invention further provides a canine emotional state assessment system based on skin resistance, and the canine emotional state assessment system based on skin resistance can implement the canine emotional state assessment method based on skin resistance described in the present invention. However, the implementation device of the canine emotional state assessment system based on skin resistance described in the present invention includes but is not limited to the structure of the canine emotional state assessment system based on skin resistance listed in this embodiment. All structural deformations and replacements of the prior art made according to the principles of the present invention are included in the protection scope of the present invention.

[0069] like Figure 3 As shown, in one embodiment, the dog emotional state assessment system based on skin resistance of the present invention includes a collection module 31 , a preprocessing module 32 , an extraction module 33 and an assessment module 34 .

[0070] The acquisition module 31 is used to acquire skin resistance signals of dogs.

[0071] The preprocessing module 32 is connected to the acquisition module 31 and is used to preprocess the skin resistance signal to obtain a valid skin resistance signal.

[0072] The extraction module 33 is connected to the preprocessing module 32 and is used to extract features from the effective skin resistance signal to obtain physiological features.

[0073] The evaluation module 34 is connected to the extraction module 33 and is used to train a dog emotional state evaluation model based on the physiological characteristics, so as to evaluate the dog emotional state based on the trained dog emotional state evaluation model.

[0074] Among them, the structures and principles of the acquisition module 31, the preprocessing module 32, the extraction module 33 and the evaluation module 34 correspond one by one to the steps in the above-mentioned canine emotional state evaluation method based on skin resistance, so they are not repeated here.

[0075] In the several embodiments provided by the present invention, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules / units is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules or units, which can be electrical, mechanical or other forms.

[0076] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention. For example, the functional modules / units in the various embodiments of the present invention may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0077] Those of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0078] The embodiment of the present invention also provides a computer-readable storage medium. A person of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiment can be completed by instructing the processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state disk (SSD)), etc.

[0079] An embodiment of the present invention further provides an electronic device, which includes a processor and a memory.

[0080] The memory is used to store computer programs.

[0081] The memory includes: ROM, RAM, disk, USB flash drive, memory card or CD and other media that can store program codes.

[0082] The processor is connected to the memory and is used to execute the computer program stored in the memory so that the electronic device executes the above-mentioned canine emotional state assessment method based on skin resistance.

[0083] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0084] like Figure 4As shown, the electronic device of the present invention is in the form of a general computing device. The components of the electronic device may include but are not limited to: one or more processors or processing units 41, a memory 42, and a bus 43 connecting different system components (including the memory 42 and the processing unit 41).

[0085] Bus 43 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0086] Electronic devices typically include a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0087] The memory 42 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 421 and / or cache memory 422. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 423 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 not shown, usually called a "hard drive"). Although Figure 4 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 43 via one or more data medium interfaces. Memory 42 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of various embodiments of the present invention.

[0088] A program / utility 424 having a set (at least one) of program modules 4241 may be stored, for example, in the memory 42, such program modules 4241 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 4241 generally perform the functions and / or methods of the embodiments described herein.

[0089] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, displays, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via input / output (I / O) interface 44. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 45. Figure 4 As shown, the network adapter 45 communicates with other modules of the electronic device via the bus 43. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0090] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for evaluating the emotional state of dogs based on skin resistance, characterized in that: The method comprises the following steps: Collect skin resistance signals of dogs; Preprocessing the skin resistance signal to obtain a valid skin resistance signal; Extracting features from the effective skin resistance signal to obtain physiological features; A canine emotional state assessment model is trained based on the physiological characteristics, and the canine emotional state assessment is performed based on the trained canine emotional state assessment model.

2. The method for evaluating the emotional state of dogs based on skin resistance according to claim 1, characterized in that: The skin resistance signal of the dog is collected based on a skin conductivity sensor, wherein the skin conductivity sensor is fixed on the skin of the dog based on a fixing device.

3. The method for evaluating the emotional state of dogs based on skin resistance according to claim 1, characterized in that: Preprocessing the skin resistance signal to obtain a valid skin resistance signal includes the following steps: Performing denoising on the skin resistance signal; Downsampling the skin resistance signal after denoising; The skin resistance signal after downsampling is decomposed to obtain the effective skin resistance signal.

4. The method for evaluating the emotional state of dogs based on skin resistance according to claim 3, characterized in that: The skin resistance signal is denoised based on a bandpass filter.

5. The method for evaluating the emotional state of dogs based on skin resistance according to claim 1, characterized in that: The physiological characteristics include time domain characteristics and frequency domain characteristics.

6. The method for evaluating the emotional state of dogs based on skin resistance according to claim 5, characterized in that: The time domain features include one or more combinations of the mean, median, maximum, minimum, variance, and slope of the effective skin resistance signal; the frequency domain features include one or more combinations of the centroid frequency, average frequency, frequency root mean square, and frequency variance.

7. The method for evaluating the emotional state of dogs based on skin resistance according to claim 1, characterized in that: Training a canine emotional state assessment model based on the physiological characteristics comprises the following steps: Dividing the physiological characteristics and corresponding emotions into a training set, a validation set, and a test set; Training the canine emotional state assessment model based on the training set; Verifying the trained canine emotional state assessment model based on the verification set; The trained canine emotional state assessment model is tested based on the test set.

8. The method for evaluating the emotional state of dogs based on skin resistance according to claim 1, characterized in that: The canine emotional state evaluation model adopts a SVM classifier or an artificial neural network.

9. The method for evaluating the emotional state of dogs based on skin resistance according to claim 1, characterized in that: The dog emotional state assessment based on the trained dog emotional state assessment model includes the following steps: Collect the skin resistance signal to be evaluated of the dog; Acquiring the physiological characteristics of the skin resistance signal to be evaluated; The physiological characteristics are input into a trained canine emotional state evaluation model to obtain an emotional state evaluation result corresponding to the skin resistance signal to be evaluated.

10. The method for evaluating the emotional state of dogs based on skin resistance according to claim 9, characterized in that: The emotional state evaluation results include happiness, anger, disgust, sadness, fear, surprise, excitement and obedience.

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