Satisfaction evaluation method and device, electronic equipment, storage medium and program product
By acquiring EEG signals, heart rate variability, and skin conductance levels of disabled elderly individuals, and using a trained model for satisfaction assessment, the problems of low efficiency and inaccurate results in questionnaire surveys were solved, achieving a more accurate satisfaction assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-10
- Publication Date
- 2026-03-13
AI Technical Summary
In existing technologies, the questionnaire survey method for assessing the satisfaction of disabled elderly people in the care process suffers from low efficiency and inaccurate results, mainly due to the subjective intervention and misunderstanding of caregivers.
By acquiring EEG signal data, heart rate variability information, and skin conduction level of disabled elderly people, a trained satisfaction assessment model is used for evaluation. The model is trained based on historical data and includes feature extraction, fusion, and pooling processing to output satisfaction indicators such as focus, pleasure, and discomfort index.
It achieves more accurate satisfaction assessment, avoids subjective bias of nursing staff, improves assessment efficiency, and directly uses objective physiological signals for assessment.
Smart Images
Figure CN121662404A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of artificial intelligence technology, and in particular to a satisfaction assessment method, apparatus, electronic device, storage medium, and program product. Background Technology
[0002] In the field of care for disabled elderly, assessing the satisfaction of disabled elderly during the care process has become a core element in measuring and improving service quality.
[0003] In existing technologies, questionnaire surveys are commonly used to assess the satisfaction of disabled elderly people during the care process. However, when using questionnaires, on the one hand, because disabled elderly people often have difficulty expressing themselves, caregivers usually need to assist them in completing the questionnaires. This process is prone to bias due to the subjective intervention and misunderstandings of caregivers, which can lead to inaccurate satisfaction assessment results and fail to accurately reflect the satisfaction of disabled elderly people. On the other hand, questionnaire surveys require manual collection, summarization, and statistical analysis to obtain satisfaction assessment results, resulting in low assessment efficiency.
[0004] Therefore, how to improve the accuracy and efficiency of satisfaction assessment is a technical problem that technical personnel urgently need to solve. Summary of the Invention
[0005] This application provides a satisfaction assessment method to address the problems of low assessment efficiency and inaccurate satisfaction assessment results when using questionnaire surveys to assess the satisfaction of disabled elderly people during the care process in the prior art.
[0006] This application also provides a satisfaction assessment device, an electronic device, a computer-readable storage medium, and a computer program product.
[0007] The embodiments of this application adopt the following technical solutions: Firstly, this application provides a satisfaction assessment method, including: Acquire EEG signal data, heart rate variability information, and skin conduction levels from service recipients; EEG signal data, heart rate variability information, and skin conduction level are input into the trained satisfaction assessment model to output the assessed satisfaction level. The satisfaction assessment model was trained based on samples of EEG signal data, heart rate variability information, skin conduction level, and satisfaction levels of historical service recipients.
[0008] Optionally, acquire the EEG signal data, heart rate variability information, and skin conduction level of the service recipient, including: Determine whether the service recipient has triggered a preset nursing event; If the client does not trigger a preset nursing event, then acquire EEG signal data, heart rate variability information, and skin conduction level.
[0009] Optionally, determine whether the service recipient has triggered a preset nursing event, including: Obtain the body movement data of the service recipient; Based on the body movement data and the event characteristics of preset nursing events, a preset event detection model is used to determine whether the service recipient has triggered a preset nursing event. The event detection model is trained from body movement data samples and event characteristic samples of preset nursing events.
[0010] Optionally, EEG signal data, heart rate variability information, and skin conduction levels can be input into the trained satisfaction assessment model to output the assessed satisfaction, including: Temporal alignment processing was performed on EEG signal data, heart rate variability information, and skin conduction levels; The time-aligned EEG signal data, heart rate variability information, and skin conduction level are input into the trained satisfaction assessment model to output the assessed satisfaction level.
[0011] Optionally, EEG signal data, heart rate variability information, and skin conduction levels can be input into the trained satisfaction assessment model to output the assessed satisfaction, including: EEG signal data, heart rate variability information, and skin conduction level are input into the feature extraction layer of the satisfaction assessment model to extract waveform feature information and long-term temporal dependencies of EEG signal data, heart rate variability information, and skin conduction level, respectively. Waveform feature information and long-term temporal dependencies are input into the fusion layer of the satisfaction assessment model to perform feature fusion processing on the waveform feature information and long-term temporal dependencies; The feature fusion result after feature fusion processing is input into the pooling layer of the satisfaction evaluation model to obtain the focus, pleasure and discomfort index corresponding to EEG signal data, heart rate variability information and skin conduction level; Focus, pleasure, and discomfort index are input into the output layer of the satisfaction assessment model to obtain the satisfaction level of the service recipients.
[0012] Optionally, the EEG signal data is acquired using a dry EEG signal acquisition device with flexible microneedle array electrodes, wherein the sampling frequency of the dry EEG signal acquisition device is greater than or equal to 500Hz.
[0013] Optionally, the feature extraction layer of the satisfaction assessment model includes a one-dimensional residual network and a bidirectional gated recurrent unit; The one-dimensional residual network consists of three residual blocks, with convolution kernel sizes of 15, 9, and 5, respectively. A bidirectional gated recurrent unit is used to capture the long-term temporal dependencies of the input information of the satisfaction assessment model.
[0014] Secondly, this application provides a satisfaction assessment device, including an acquisition module and a processing module, wherein: The acquisition module is used to acquire the EEG signal data, heart rate variability information, and skin conduction level of the service recipient; The processing module is used to input EEG signal data, heart rate variability information and skin conduction level into the trained satisfaction assessment model to output the satisfaction assessment. The satisfaction assessment model was trained based on samples of EEG signal data, heart rate variability information, skin conduction level, and satisfaction levels of historical service recipients.
[0015] Optional, the acquisition module includes: The judgment unit is used to determine whether the service object has triggered a preset nursing event; The acquisition unit is used to acquire EEG signal data, heart rate variability information, and skin conduction level if the service recipient does not trigger a preset nursing event.
[0016] Optional, a decision unit, used for: Obtain the body movement data of the service recipient; Based on the body movement data and the event characteristics of preset nursing events, a preset event detection model is used to determine whether the service recipient has triggered a preset nursing event. The event detection model is trained from body movement data samples and event characteristic samples of preset nursing events.
[0017] Optional, processing module, used for: Temporal alignment processing was performed on EEG signal data, heart rate variability information, and skin conduction levels; The time-aligned EEG signal data, heart rate variability information, and skin conduction level are input into the trained satisfaction assessment model to output the assessed satisfaction level.
[0018] Optional, processing module, used for: EEG signal data, heart rate variability information, and skin conduction level are input into the feature extraction layer of the satisfaction assessment model to extract waveform feature information and long-term temporal dependencies of EEG signal data, heart rate variability information, and skin conduction level, respectively. Waveform feature information and long-term temporal dependencies are input into the fusion layer of the satisfaction assessment model to perform feature fusion processing on the waveform feature information and long-term temporal dependencies; The feature fusion result after feature fusion processing is input into the pooling layer of the satisfaction evaluation model to obtain the focus, pleasure and discomfort index corresponding to EEG signal data, heart rate variability information and skin conduction level; Focus, pleasure, and discomfort index are input into the output layer of the satisfaction assessment model to obtain the satisfaction level of the service recipients.
[0019] Optionally, the EEG signal data is acquired using a dry EEG signal acquisition device with flexible microneedle array electrodes, wherein the sampling frequency of the dry EEG signal acquisition device is greater than or equal to 500Hz.
[0020] Optionally, the feature extraction layer of the satisfaction assessment model includes a one-dimensional residual network and a bidirectional gated recurrent unit; The one-dimensional residual network consists of three residual blocks, with convolution kernel sizes of 15, 9, and 5, respectively. A bidirectional gated recurrent unit is used to capture the long-term temporal dependencies of the input information of the satisfaction assessment model.
[0021] Thirdly, this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the satisfaction evaluation method as described above.
[0022] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the satisfaction evaluation method described above.
[0023] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the satisfaction evaluation method described above.
[0024] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: Using the method provided in this application, the satisfaction level of the service recipient can be predicted through a satisfaction assessment model based on the recipient's electroencephalogram (EEG) signal data, heart rate variability information, and skin conductance level. Since the satisfaction assessment is based on directly collected objective physiological signals such as EEG signal data, heart rate variability information, and skin conductance level, it effectively avoids the problem in existing questionnaire surveys where subjective intervention and misunderstandings by caregivers can lead to biased satisfaction assessment results that fail to accurately reflect the satisfaction of disabled elderly individuals. This ensures the accuracy of the predicted satisfaction level.
[0025] Secondly, compared with existing questionnaire survey methods, the method provided in this application embodiment can improve the efficiency of satisfaction assessment because it does not require manual collection, summarization and statistics to obtain satisfaction assessment results. Attached Figure Description
[0026] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A schematic diagram illustrating the implementation process of a satisfaction assessment method provided in this application embodiment; Figure 2 This application provides a schematic diagram of the specific structure of a satisfaction assessment device. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0027] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0028] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.
[0029] Example 1 To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0030] As will be known to those skilled in the art, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.
[0031] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application.
[0032] To address the problems of low assessment efficiency and inaccurate satisfaction assessment results when using questionnaires to evaluate the satisfaction of disabled elderly people during the care process in existing technologies, this application provides a satisfaction assessment method.
[0033] The execution subject of this method can be various types of computing devices, or it can be an application or app installed on the computing device. The computing device can be a user terminal such as a mobile phone, tablet computer, or smart wearable device, or it can be a server.
[0034] For ease of description, this application uses a server as the execution subject of the method in its embodiments to illustrate the method. Those skilled in the art will understand that this embodiment uses a server as an example to describe the method, which is merely an illustrative example and does not limit the scope of protection of the corresponding claims.
[0035] Specifically, the implementation flow of the method provided in this application embodiment is as follows: Figure 1 As shown, it includes the following steps: Step 11: Obtain the EEG signal data, heart rate variability information, and skin conduction level of the service recipient.
[0036] In this embodiment, considering that head-mounted electroencephalography (EEG) devices are typically used in practical applications, such as multi-channel headbands with dry electrodes, the EEG signal data of the service recipient is collected in real time via Bluetooth or USB connection. Therefore, the server in this embodiment can establish a communication connection with the head-mounted EEG device and then obtain the EEG signal data of the service recipient from the head-mounted EEG device based on this communication connection.
[0037] In one alternative implementation, the EEG signal data can be acquired specifically through a dry EEG signal acquisition device with a flexible microneedle array electrode, wherein the sampling frequency of the dry EEG signal acquisition device is greater than or equal to 500Hz.
[0038] Regarding heart rate variability information, in this embodiment of the application, wearable devices such as ECG (electrocardiogram) chest straps or PPG (optical pulse sensor) watches can be used to collect the electrocardiogram (ECG) or pulse wave (PPG) signals of the service recipients, and combine them with time-domain and frequency-domain analysis methods of heart rate variability (HRV) to monitor the heart rate variability information of the service recipients in real time.
[0039] In this embodiment, an electrical conductance sensor (EDA), such as a finger clip or wristband conductivity sensor, can be used to measure the electrical conductance level of the service recipient.
[0040] It should be noted that the above-described methods for obtaining the EEG signal data, heart rate variability information, and skin conduction level of the service recipient are merely illustrative examples of the embodiments of this application and do not impose any limitations on the embodiments of this application.
[0041] Optionally, to achieve real-time feedback and processing of information such as EEG signal data, heart rate variability information, and skin conductance levels, in this embodiment, edge computing nodes can be deployed using NVIDIA Jetson Orin modules on each wearable device terminal, and continuously powered by a dedicated USB-C parallel power management circuit. These edge nodes are responsible for performing localized real-time preprocessing (such as feature extraction) on the collected EEG signal data, heart rate variability information, and skin conductance levels, thereby significantly improving the timeliness of data processing and laying the foundation for subsequent real-time analysis and feedback.
[0042] In one alternative implementation, considering that some pre-set care events, such as turning over or feeding, may affect the client's EEG signal data, heart rate variability information, and skin conductance levels, leading to inaccurate satisfaction results based on this information, to avoid interference from these pre-set care events and ensure the accuracy of the satisfaction assessment results, when acquiring this data, it can be first determined whether the client has triggered a pre-set care event; if the client has not triggered a pre-set care event, then the EEG signal data, heart rate variability information, and skin conductance levels are acquired.
[0043] Pre-set nursing events refer to operations or activities during the nursing process that may interfere with the collection of physiological data such as electroencephalogram (EEG) signals, heart rate variability, and skin conductance. Examples of pre-set nursing events include feeding, hydration, turning over, bowel movements, intravenous infusion, and wound care.
[0044] It should be noted that the preset nursing events exemplified above are merely an exemplary illustration of the embodiments of this application and do not impose any limitations on the embodiments of this application.
[0045] In one optional implementation, when determining whether a service recipient has triggered a preset nursing event, the service recipient's body movement data can be obtained first. Based on the body movement data and the event characteristics of the preset nursing event, a preset event detection model can be used to determine whether the service recipient has triggered the preset nursing event. The event detection model is trained from body movement data samples and event characteristic samples of the preset nursing event.
[0046] Step 12: Input the EEG signal data, heart rate variability information and skin conduction level into the trained satisfaction assessment model to output the satisfaction assessment. The satisfaction assessment model was trained based on samples of EEG signal data, heart rate variability information, skin conduction level, and satisfaction levels of historical service recipients.
[0047] Optionally, the feature extraction layer of the satisfaction assessment model may include a one-dimensional residual network and a bidirectional gated recurrent unit; wherein the one-dimensional residual network includes three residual blocks, and the convolutional kernel sizes of the three residual blocks are 15, 9 and 5, respectively. The bidirectional gated recurrent unit is used to capture the long-term temporal dependencies of the input information of the satisfaction assessment model.
[0048] In one alternative implementation, considering that EEG signal data, heart rate variability information, and skin conductance levels come from different devices, their acquisition time points may not be completely synchronized. Therefore, this may lead to a break in the correlation between physiological signals (i.e., EEG signal data, heart rate variability information, and skin conductance levels), making it impossible to accurately establish the correspondence between EEG signal data, heart rate variability information, and skin conductance levels at the same moment. To avoid this problem, in this embodiment, the EEG signal data, heart rate variability information, and skin conductance levels can be time-aligned; then, the time-aligned EEG signal data, heart rate variability information, and skin conductance levels are input into the trained satisfaction assessment model to output the assessed satisfaction level. This ensures that the EEG signal data, heart rate variability information, and skin conductance levels acquired at a specific moment can be correctly correlated and fused, thereby improving the consistency and reliability of the features on which the satisfaction assessment model is based, and ultimately enhancing the accuracy of the satisfaction assessment results.
[0049] In this embodiment, EEG signal data, heart rate variability information, and skin conduction level can be input into the feature extraction layer of the satisfaction assessment model to extract waveform feature information and long-term temporal dependencies of the EEG signal data, heart rate variability information, and skin conduction level, respectively. Then, the waveform feature information and long-term temporal dependencies are input into the fusion layer of the satisfaction assessment model to perform feature fusion processing on the waveform feature information and long-term temporal dependencies. Afterward, the feature fusion result after feature fusion processing can be input into the pooling layer of the satisfaction assessment model to obtain the focus, pleasure, and discomfort index corresponding to the EEG signal data, heart rate variability information, and skin conduction level. Finally, the focus, pleasure, and discomfort index are input into the output layer of the satisfaction assessment model to obtain the satisfaction of the service recipient.
[0050] Using the method provided in this application, the satisfaction level of the service recipient can be predicted through a satisfaction assessment model based on the recipient's electroencephalogram (EEG) signal data, heart rate variability information, and skin conductance level. Since the satisfaction assessment is based on directly collected objective physiological signals such as EEG signal data, heart rate variability information, and skin conductance level, it effectively avoids the problem in existing questionnaire surveys where subjective intervention and misunderstandings by caregivers can lead to biased satisfaction assessment results that fail to accurately reflect the satisfaction of disabled elderly individuals. This ensures the accuracy of the predicted satisfaction level.
[0051] Secondly, compared with existing questionnaire survey methods, the method provided in this application embodiment can improve the efficiency of satisfaction assessment because it does not require manual collection, summarization and statistics to obtain satisfaction assessment results.
[0052] Example 2 To address the problems of low assessment efficiency and inaccurate satisfaction assessment results when using questionnaires to evaluate the satisfaction of disabled elderly people during the care process in existing technologies, this application provides a satisfaction assessment device, the specific structure of which is shown in the schematic diagram below. Figure 2 As shown, it includes an acquisition module 21 and a processing module 22, wherein: The acquisition module 21 is used to acquire the EEG signal data, heart rate variability information and skin conduction level of the service recipient; Processing module 22 is used to input EEG signal data, heart rate variability information and skin conduction level into the trained satisfaction assessment model to output the assessed satisfaction level. The satisfaction assessment model was trained based on samples of EEG signal data, heart rate variability information, skin conduction level, and satisfaction levels of historical service recipients.
[0053] Optionally, module 21 includes: The judgment unit is used to determine whether the service object has triggered a preset nursing event; The acquisition unit is used to acquire EEG signal data, heart rate variability information, and skin conduction level if the service recipient does not trigger a preset nursing event.
[0054] Optional, a decision unit, used for: Obtain the body movement data of the service recipient; Based on the body movement data and the event characteristics of preset nursing events, a preset event detection model is used to determine whether the service recipient has triggered a preset nursing event. The event detection model is trained from body movement data samples and event characteristic samples of preset nursing events.
[0055] Optionally, processing module 22 is used for: Temporal alignment processing was performed on EEG signal data, heart rate variability information, and skin conduction levels; The time-aligned EEG signal data, heart rate variability information, and skin conduction level are input into the trained satisfaction assessment model to output the assessed satisfaction level.
[0056] Optionally, processing module 22 is used for: EEG signal data, heart rate variability information, and skin conduction level are input into the feature extraction layer of the satisfaction assessment model to extract waveform feature information and long-term temporal dependencies of EEG signal data, heart rate variability information, and skin conduction level, respectively. Waveform feature information and long-term temporal dependencies are input into the fusion layer of the satisfaction assessment model to perform feature fusion processing on the waveform feature information and long-term temporal dependencies; The feature fusion result after feature fusion processing is input into the pooling layer of the satisfaction evaluation model to obtain the focus, pleasure and discomfort index corresponding to EEG signal data, heart rate variability information and skin conduction level; Focus, pleasure, and discomfort index are input into the output layer of the satisfaction assessment model to obtain the satisfaction level of the service recipients.
[0057] Optionally, the EEG signal data is acquired using a dry EEG signal acquisition device with flexible microneedle array electrodes, wherein the sampling frequency of the dry EEG signal acquisition device is greater than or equal to 500Hz.
[0058] Optionally, the feature extraction layer of the satisfaction assessment model includes a one-dimensional residual network and a bidirectional gated recurrent unit; The one-dimensional residual network consists of three residual blocks, with convolution kernel sizes of 15, 9, and 5, respectively. A bidirectional gated recurrent unit is used to capture the long-term temporal dependencies of the input information of the satisfaction assessment model.
[0059] Using the device provided in this application embodiment, the satisfaction level of the service recipient can be predicted through a satisfaction assessment model based on the recipient's electroencephalogram (EEG) signal data, heart rate variability information, and skin conductance level. Since the satisfaction assessment is based on directly collected objective physiological signals such as EEG signal data, heart rate variability information, and skin conductance level, it effectively avoids the problem in existing questionnaire surveys where subjective intervention and misunderstandings by caregivers can lead to biased satisfaction assessment results that fail to accurately reflect the satisfaction of disabled elderly individuals. This ensures the accuracy of the predicted satisfaction level.
[0060] Secondly, compared with existing questionnaire survey methods, the device provided in this application embodiment can improve the efficiency of satisfaction assessment because it can obtain satisfaction assessment results without manual collection, summarization and statistics.
[0061] Example 3 Figure 3 To illustrate the hardware structure of an electronic device according to various embodiments of this application, the electronic device may include a processor 301 and a memory 302 storing computer program instructions. Specifically, the processor 301 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of this application.
[0062] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 302 may include removable or non-removable (or fixed) media. Where appropriate, memory 302 may be internal or external to an electronic device. In a particular embodiment, memory 302 may be a non-volatile solid-state memory.
[0063] In one embodiment, memory 302 may be read-only memory (ROM). In one embodiment, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.
[0064] The processor 301 reads and executes computer program instructions stored in the memory 302 to implement any of the satisfaction evaluation methods in the above embodiments.
[0065] In one example, the electronic device may also include a communication interface 303 and a bus 310. For example, Figure 3 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 310 and complete communication with each other.
[0066] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.
[0067] Bus 310 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 310 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.
[0068] Furthermore, in conjunction with the satisfaction evaluation methods in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when executed by a processor, these computer program instructions implement any of the satisfaction evaluation methods in the above embodiments.
[0069] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.
[0070] The above description is merely a specific implementation example of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0071] Secondly, those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this 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.) containing computer-usable program code.
[0072] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0075] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0076] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0077] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0078] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0079] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A satisfaction assessment method, characterized in that, include: Acquire EEG signal data, heart rate variability information, and skin conduction levels from service recipients; The electroencephalogram (EEG) signal data, the heart rate variability information, and the skin conduction level are input into the trained satisfaction assessment model to output the assessed satisfaction level. The satisfaction assessment model is trained based on samples of EEG signal data, heart rate variability information, and skin conduction level from historical service recipients, as well as the satisfaction levels of those historical service recipients.
2. The method as described in claim 1, characterized in that, The acquisition of the EEG signal data, heart rate variability information, and skin conduction level of the service recipient includes: Determine whether the service recipient has triggered a preset nursing event; If the service recipient does not trigger a preset nursing event, then the electroencephalogram (EEG) signal data, the heart rate variability information, and the skin conduction level are acquired.
3. The method as described in claim 2, characterized in that, The step of determining whether the service recipient has triggered a preset nursing event includes: Obtain the body movement data of the service object; Based on the body movement data and the event characteristics of the preset nursing event, a preset event detection model is used to determine whether the service recipient has triggered the preset nursing event. The event detection model is trained from the body movement data sample and the event characteristic sample of the preset nursing event.
4. The method as described in claim 1, characterized in that, The step of inputting the electroencephalogram (EEG) signal data, the heart rate variability information, and the skin conduction level into the trained satisfaction assessment model to output the assessed satisfaction includes: The electroencephalogram (EEG) signal data, the heart rate variability information, and the skin conduction level are subjected to time-series alignment processing. The time-aligned EEG signal data, heart rate variability information, and skin conduction level are input into the trained satisfaction assessment model to output the assessed satisfaction level.
5. The method as described in claim 1, characterized in that, The step of inputting the electroencephalogram (EEG) signal data, the heart rate variability information, and the skin conduction level into the trained satisfaction assessment model to output the assessed satisfaction includes: The EEG signal data, the heart rate variability information, and the skin conduction level are input into the feature extraction layer of the satisfaction assessment model to extract waveform feature information and long-term temporal dependencies of the EEG signal data, the heart rate variability information, and the skin conduction level, respectively. The waveform feature information and the long-term temporal dependency are input into the fusion layer of the satisfaction evaluation model to perform feature fusion processing on the waveform feature information and the long-term temporal dependency. The feature fusion result after feature fusion processing is input into the pooling layer of the satisfaction evaluation model to obtain the focus, pleasure and discomfort index corresponding to the EEG signal data, the heart rate variability information and the skin conduction level; The focus level, pleasure level, and discomfort index are input into the output layer of the satisfaction assessment model to obtain the satisfaction level of the service recipient.
6. The method as described in claim 1, characterized in that, The EEG signal data is acquired through a dry EEG signal acquisition device with flexible microneedle array electrodes, and the sampling frequency of the dry EEG signal acquisition device is greater than or equal to 500Hz.
7. The method as described in claim 1, characterized in that, The feature extraction layer of the satisfaction assessment model includes a one-dimensional residual network and a bidirectional gated recurrent unit. The one-dimensional residual network includes three residual blocks, and the kernel sizes of the three residual blocks are 15, 9 and 5, respectively; The bidirectional gated loop unit is used to capture the long-term temporal dependencies of the input information of the satisfaction assessment model.
8. A satisfaction assessment device, characterized in that, It includes an acquisition module and a processing module, wherein: The acquisition module is used to acquire the EEG signal data, heart rate variability information, and skin conduction level of the service recipient; The processing module is used to input the electroencephalogram (EEG) signal data, the heart rate variability information, and the skin conduction level into the trained satisfaction assessment model to output the assessed satisfaction level. The satisfaction assessment model is trained based on samples of EEG signal data, heart rate variability information, and skin conduction level from historical service recipients, as well as the satisfaction levels of those historical service recipients.
9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the satisfaction assessment method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the satisfaction assessment method as described in any one of claims 1 to 7.
11. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the satisfaction assessment method according to any one of claims 1 to 7.