Old-age service robot based on emotion adjusting system

Abstract

The invention relates to the technical field of service robots, and discloses an elderly care service robot based on an emotion adjustment system, and the robot comprises a multi-mode emotion recognition module which is used for carrying out the comprehensive perception of the emotional state of the elderly through combining various emotion signals of voice, facial expression and limb movement; the personalized emotion adjusting module is used for generating personalized emotion responses meeting the requirements of the old people; the emotion memory module is used for recording emotion changes, important life events and personal preferences of the elderly; the scene self-adaption module is used for sensing the living environment of the old people in real time and adjusting the emotional expression mode of the robot according to the environment information; the health intervention module is used for generating a health management scheme through long-term monitoring of the emotional state and health of the old people; and a safety monitoring module. According to the invention, the emotion state of the elderly is analyzed through the multi-modal fusion network, the emotion misjudgment probability is reduced, and the man-machine interaction experience and the emotion support effect of the elderly are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of service robots, and particularly to a pension service robot based on an emotion regulation system. Background Art

[0002] In the context of the increasingly serious aging of the population, the pension problem has become one of the important social challenges faced globally. With the improvement of the quality of life and the progress of medical technology, the spiritual and emotional needs of the elderly for life are increasing day by day. However, due to the rapid development of modern society, many elderly people, especially empty nesters, are faced with difficulties such as loneliness, lack of emotional companionship, and mental health problems. Therefore, how to effectively provide emotional support and personalized companionship for the elderly has become an important issue in solving the pension problem. The existing pension service robot technology has to a certain extent solved the actual needs in the lives of the elderly, such as providing life assistance, reminding to take medicine and other functions. However, the existing pension service robots still have great deficiencies in terms of emotional companionship.

[0003] Most of the existing pension service robots rely on single-modal emotion recognition means, such as judging the emotional state of the elderly through voice emotion analysis; since emotion is complex multi-dimensional information, relying on single-modal recognition methods often makes it difficult to accurately judge the actual emotional state of the elderly, especially in cases of environmental noise interference, facial features being blocked, etc., the accuracy of emotion recognition is greatly reduced. Summary of the Invention

[0004] To make up for the above deficiencies, the present invention provides a pension service robot based on an emotion regulation system, aiming to improve the problem that most of the existing pension service robots rely on single-modal emotion recognition means and are difficult to accurately judge the actual emotional state of the elderly, especially in cases of environmental noise interference, facial features being blocked, etc., where the accuracy of emotion recognition is greatly reduced.

[0005] In the first aspect, the present invention provides the following technical solution. A pension service robot based on an emotion regulation system includes: A multi-modal emotion recognition module, which is used to comprehensively perceive the emotional state of the elderly by combining various emotion signals such as voice, facial expressions, and body movements; A personalized emotion regulation module, which is used to generate a personalized emotion response that meets the needs of the elderly according to the personality characteristics, hobbies, and living habits of the elderly; An emotion memory module, which is used to record the emotional changes, important life events, and personal preferences of the elderly; A scene adaptation module, which is used to perceive the living environment of the elderly in real time and adjust the emotional expression of the robot according to the environmental information; A health intervention module for generating a health management plan through long-term monitoring of the emotional state and health of the elderly; A safety monitoring module for monitoring the behavior of the elderly and issuing an alarm when abnormal behavior is detected.

[0006] Preferably, the multi-modal emotion recognition module includes: A voice emotion recognition unit for collecting the voice signals of the elderly through a microphone array, extracting voice features using voice signal processing algorithms, and analyzing the emotional information in the voice using a deep learning model; A facial expression recognition unit for capturing the facial images of the elderly through a camera, analyzing the facial expressions using computer vision techniques, extracting expression features, and recognizing the emotional state of the elderly; A body movement recognition unit for collecting the body movements of the elderly through motion sensors, analyzing their behavior patterns, and thus judging the emotional state of the elderly; A multi-modal fusion unit for fusing voice, facial expression, and body movement features, and comprehensively analyzing multi-modal features using deep learning algorithms to improve the accuracy of emotion recognition.

[0007] Preferably, the personalized emotion regulation module includes: A personalized learning unit for collecting information on the personality traits, hobbies, and living habits of the elderly through long-term interaction with them, and constructing a personalized emotion model using machine learning algorithms; An emotion state analysis unit for analyzing the current emotional state of the elderly and generating an emotional response that meets the needs of the elderly in combination with their historical emotion records; An emotion response generation unit for generating an appropriate emotion response according to the results of the emotion state analysis, including verbal comfort and body movement simulation.

[0008] Preferably, the emotion memory module includes: An emotion recording unit for recording the emotional changes of the elderly in daily interactions, as well as life events and personal preferences; An emotion recall unit for invoking emotion memories in subsequent interactions to increase the continuity of emotional companionship.

[0009] Preferably, the scene adaptation module includes: An environment perception unit for real-time sensing of noise, light, and temperature factors in the environment through sensors to obtain environmental information; A scene analysis unit for analyzing environmental information to judge the characteristics of the current scene; An emotion expression adjustment unit for adjusting the emotion expression of the robot according to the results of the scene analysis.

[0010] Preferably, the health intervention module includes: An emotion monitoring unit for long-term monitoring of the emotional state of the elderly and recording the trend of emotional changes; A health data collection unit for collecting the health data of the elderly through wearable devices; A health advice generation unit for generating a health management plan by combining the emotion monitoring results and health data; A behavior recording unit for recording the health behaviors of the elderly to further optimize the personalized emotion model.

[0011] Preferably, the safety monitoring module includes: A behavior monitoring unit for monitoring the behaviors of the elderly and identifying abnormal behaviors; An alarm unit for sending an alarm to family members or caregivers when an abnormal behavior is detected.

[0012] In a second aspect, the present invention provides the following technical solution, a method for operating a pension service robot based on an emotion regulation system, including the following steps: S1. Collect the voice, facial expressions and body movement signals of the elderly through the multi-modal emotion recognition module, and use the multi-modal fusion unit for emotion recognition; S2. Use the personalized learning unit in the personalized emotion regulation module to construct a personalized emotion model based on long-term interaction with the elderly; analyze the current emotional state of the elderly through the emotional state analysis unit and generate an emotional response that meets the needs of the elderly; S3. The emotion memory module records the emotional changes and important life events of the elderly, and calls emotion recall in subsequent interactions to enhance emotional interaction; S4. Perceive the current environmental characteristics through the environmental perception unit in the scene adaptation module and adjust the emotional expression to adapt to environmental changes; S5. The health intervention module generates a health management plan through emotion monitoring and health data collection, and records health behaviors to optimize the emotion model; S6. The safety monitoring module monitors the behaviors of the elderly and issues an alarm after identifying abnormal behaviors.

[0013] In a third aspect, the invention provides the following technical solution, a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, it implements the above-mentioned method for operating a pension service robot based on an emotion regulation system.

[0014] Fourthly, the present invention provides the following technical solution: a readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned operation method of the elderly care service robot based on the emotion regulation system is realized.

[0015] The present invention has the following beneficial effects: 1. In the present invention, a multi-modal emotion recognition module is adopted to combine information such as speech, facial expressions, and body movements, and analyze the emotional state of the elderly through a multi-modal fusion network; this method not only improves the accuracy and robustness of emotion recognition, but also compensates for the lack of perceptual information in different scenarios; for example, when the environmental light is insufficient to recognize facial expressions, the features of body movements and speech can assist in judging emotions; therefore, multi-modal emotion recognition greatly improves the accuracy of emotion understanding, reduces the probability of emotion misjudgment, and significantly enhances the experience of human-computer interaction and the emotional support effect for the elderly.

[0016] 2. In the present invention, the personalized emotion regulation module establishes an emotion model for the individual elderly through long-term interaction with the elderly and using a personalized learning mechanism; this makes the emotional response of the robot more in line with the personality, hobbies, and emotional characteristics of the elderly; for example, when interacting with an elderly person who likes quietness, the robot will lower the speech rate and volume and maintain a gentle tone, and this meticulous personalized regulation significantly improves the comfort and trust of the elderly and meets the needs of the elderly for personalized companionship.

[0017] 3. In the present invention, the emotion memory module can call these memories in appropriate situations by recording the emotional changes and important life events of the elderly; for example, sending blessings actively on the elderly person's birthday or recalling past happy moments when the elderly person is in a low mood; this memory function significantly enhances the continuity and depth of human-computer interaction, making the elderly feel that the robot is not only an intelligent device, but more like a companion with the ability to understand emotions, thus significantly improving the psychological satisfaction and sense of security of the elderly.

[0018] 4. In the present invention, the scene adaptation module adjusts the emotional expression mode of the robot by sensing information such as light, noise, and temperature in the environment in real time through sensors; for example, when the environment is quiet at night, the robot will lower the volume and use a gentle tone to talk to the elderly, thus reducing the disturbance to the elderly; by adapting to the environmental characteristics, the scene adaptation module enables the robot to better meet the needs of the elderly in different life scenarios and greatly improves the comfort and user experience of the elderly using the robot. Description of the Drawings

[0019] Figure 1 It is the system architecture diagram of the elderly care service robot based on the emotion regulation system proposed by the present invention; Figure 2 System architecture diagram of the multi-modal emotion recognition module of the elderly care service robot based on the emotion regulation system proposed by the present invention; Figure 3 System architecture diagram of the personalized emotion regulation module of the elderly care service robot based on the emotion regulation system proposed by the present invention; Figure 4 System architecture diagram of the emotion memory module of the elderly care service robot based on the emotion regulation system proposed by the present invention; Figure 5 System architecture diagram of the scene adaptation module of the elderly care service robot based on the emotion regulation system proposed by the present invention; Figure 6 System architecture diagram of the health intervention module of the elderly care service robot based on the emotion regulation system proposed by the present invention; Figure 7 System architecture diagram of the safety monitoring module of the elderly care service robot based on the emotion regulation system proposed by the present invention; Figure 8 Method flow chart of the operation method of the elderly care service robot based on the emotion regulation system proposed by the present invention. Specific embodiments

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] Embodiment 1 Refer to Figures 1 - 7 , in the first embodiment of the present invention, the present invention provides an elderly care service robot based on an emotion regulation system, including: A multi-modal emotion recognition module for comprehensively perceiving the emotional state of the elderly by combining various emotional signals such as voice, facial expressions, and body movements; A personalized emotion regulation module for generating personalized emotional responses that meet the needs of the elderly according to their personality characteristics, hobbies, and living habits; An emotion memory module for recording the emotional changes, important life events, and personal preferences of the elderly; A scene adaptation module for real-time perceiving the living environment of the elderly and adjusting the emotional expression of the robot according to the environmental information; A health intervention module for generating a health management plan through long-term monitoring of the emotional state and health of the elderly; The safety monitoring module is used to monitor the behavior of the elderly and issue an alarm when abnormal behavior is detected.

[0022] Specifically, the present invention is a geriatric service robot based on multi-modal emotion recognition and personalized regulation, mainly composed of multiple modules, including a multi-modal emotion recognition module, a personalized emotion regulation module, an emotion memory module, a scene adaptation module, a health intervention module, and a safety monitoring module. The robot perceives the emotional state of the elderly through the multi-modal emotion recognition module, combines multi-dimensional information such as voice, facial expression, and body movement to accurately judge the psychological state of the elderly. The personalized emotion regulation module generates emotional responses using the personalized data of the elderly collected, ensuring that each interaction meets the needs and preferences of the elderly, thereby providing more considerate emotional companionship. The emotion memory module is used to record important emotional events and life details of the elderly and call them appropriately in subsequent interactions to enhance the continuity and authenticity of companionship. The scene adaptation module judges the characteristics of the living scene through environmental perception and adjusts the emotional expression of the robot according to different environments (such as day or night, quiet or noisy), so that the robot can provide a comfortable and appropriate interaction in various situations. The health intervention module provides personalized health management suggestions for the elderly through comprehensive analysis of emotion and health data to help maintain a good physical and mental state. The safety monitoring module is used to monitor the behavior of the elderly in real time, identify potential safety hazards in a timely manner, and issue an alarm to ensure the safety of the elderly.

[0023] The multi-modal emotion recognition module includes: The voice emotion recognition unit is used to collect the voice signals of the elderly through a microphone array, extract voice features using voice signal processing algorithms, and analyze the emotional information in the voice using a deep learning model; The facial expression recognition unit is used to capture the facial images of the elderly through a camera, analyze the facial expressions using computer vision technology, extract expression features and identify the emotional state of the elderly; The body movement recognition unit is used to collect the body movements of the elderly through motion sensors, analyze their behavior patterns, and thus judge the emotional state of the elderly; The multi-modal fusion unit is used to fuse voice, facial expression, and body movement features, and perform comprehensive analysis of multi-modal features using deep learning algorithms to improve the accuracy of emotion recognition.

[0024] Specifically, the multi-modal emotion recognition module is the core perception part of the geriatric service robot, used to comprehensively perceive the emotional state of the elderly through multiple perception channels. It consists of the following main units: Speech Emotion Recognition Unit: This unit collects the speech information of the elderly through a microphone array, uses speech signal processing techniques (such as MFCC feature extraction) to convert the speech signal into feature vectors, and combines deep learning models (such as LSTM or CNN) to classify and analyze the emotions in the speech. This unit can not only identify the categories of emotions (such as happy, sad, angry), but also judge the strength and changes of the tone.

[0025] Facial Expression Recognition Unit: Captures the facial expressions of the elderly through a high-definition camera, uses computer vision algorithms (such as OpenCV or Dlib library) to detect facial feature points, and extracts key Facial Action Units. Analyzes these feature points through a deep convolutional neural network (CNN) to identify changes in facial expressions, thereby judging the emotional state of the elderly.

[0026] Body Movement Recognition Unit: Captures and analyzes the body movements of the elderly through sensors (such as accelerometers, gyroscopes) and cameras installed on the robot. Using action recognition models (such as RNN or pose estimation algorithms), it can judge the physical state of the elderly, such as whether they are in a tense, relaxed or other emotional states.

[0027] Multi-modal Fusion Unit: This unit fuses data from different sources such as speech, facial expressions, and body movements, and realizes comprehensive analysis of features through a weighting strategy or using a deep learning model (such as a multi-modal fusion network), thereby improving the accuracy and robustness of emotion recognition.

[0028] The personalized emotion regulation module includes: Personalized Learning Unit: Used to collect information about the elderly's personality traits, hobbies, and living habits through long-term interaction with the elderly, and use machine learning algorithms to build a personalized emotion model; Emotion State Analysis Unit: Used to analyze the current emotional state of the elderly, and combine their historical emotion records to generate an emotion response that meets the needs of the elderly; Emotion Response Generation Unit: Used to generate appropriate emotion responses according to the results of emotion state analysis, including verbal comfort and body movement simulation.

[0029] Specifically, the personalized emotion regulation module generates emotion responses based on the elderly's personality traits and historical interaction data, improving the effectiveness and pertinence of companionship. It includes the following units: Personalized Learning Unit: Through long-term interaction with the elderly, collects their living habits, preferences, and personality traits, and builds a user profile. Machine learning algorithms (such as collaborative filtering or K-means clustering) are used to analyze this data and update the personalized emotion model, making the emotion response more in line with personal preferences.

[0030] Emotional state analysis unit: This unit analyzes the current emotional state in real time and combines historical data to judge the emotional change trend of the elderly. For example, when it recognizes that the elderly are in a low mood, it analyzes whether it is related to the recent health status or life events and prepares appropriate emotional responses.

[0031] Emotional response generation unit: According to the results of emotional state analysis, it generates diverse emotional responses. It can be voice comfort (such as encouragement in a soft tone), physical actions (such as simulating a light touch on the arm), or playing the music the elderly like, helping them regulate their emotions in various ways.

[0032] The emotional memory module includes: Emotional recording unit, which is used to record the emotional changes of the elderly in daily interactions, as well as life events and personal preferences; Emotional recall unit, which is used to call emotional memories in subsequent interactions to increase the continuity of emotional companionship.

[0033] Specifically, the emotional memory module strengthens the emotional connection by recording the interaction information with the elderly and enhances the continuity of companionship. It includes the following units: Emotional recording unit: Records the emotional reactions of the elderly in daily interactions and related events (such as family gatherings, birthdays). This helps the system accumulate important information related to the elderly for subsequent use.

[0034] Emotional recall unit: Calls emotional records in appropriate situations. For example, it actively sends blessings on the elderly's birthday, or when the elderly mentions a certain memory, the robot can resonate with it, showing understanding and memory, creating a more real sense of companionship.

[0035] The scene adaptation module includes: Environmental perception unit, which is used to perceive environmental factors such as noise, light, and temperature in the environment in real time through sensors to obtain environmental information; Scene analysis unit, which is used to analyze environmental information and judge the characteristics of the current scene; Emotional expression adjustment unit, which is used to adjust the emotional expression of the robot according to the results of scene analysis.

[0036] Specifically, the scene adaptation module is used to perceive the surrounding environment and adjust the robot's behavior to adapt to different life scenes, improving the naturalness and comfort of interaction. It includes the following units: Environmental perception unit: Real-time obtains environmental parameters such as surrounding noise, light, and temperature through sensors. This module uses multiple sensors (such as light sensors, temperature and humidity sensors) to judge the state of the current environment.

[0037] Scene analysis unit: Analyze the perceived environmental data. For example, determine whether it is day or night currently, whether the surroundings are quiet, etc., providing a basis for the robot to adapt to the environment.

[0038] Emotion expression adjustment unit: Adjust the robot's emotion expression according to environmental characteristics. For example, at a quiet night, the robot will adjust the volume to a lower level, the tone becomes gentle, reducing the disturbance to the elderly.

[0039] The health intervention module includes: Emotion monitoring unit, used to monitor the elderly's emotional state in the long term and record the trend of emotional changes; Health data collection unit, used to collect the elderly's health data through wearable devices; Health advice generation unit, used to generate a health management plan by combining the emotion monitoring results and health data; Behavior recording unit, used to record the elderly's health behaviors to further optimize the personalized emotion model.

[0040] Specifically, the health intervention module provides targeted health advice and intervention means through comprehensive analysis of the elderly's emotions and health data. It includes the following units: Emotion monitoring unit: Monitor the elderly's emotional state in the long term and use the trend of emotional state changes to identify potential psychological problems or health hazards.

[0041] Health data collection unit: Obtain health data such as heart rate, blood pressure, and blood oxygen through health monitoring devices (such as smart bracelets) and integrate and analyze them with emotional state data.

[0042] Health advice generation unit: Combine emotions and health data to generate health advice for the elderly. For example, recommend some low-intensity exercises or meditation practices to improve the emotional state.

[0043] Behavior recording unit: Record the elderly's health behaviors, such as exercise conditions and eating habits. These data will be further used to optimize the personalized emotion model and provide services more in line with individual needs.

[0044] The safety monitoring module includes: Behavior monitoring unit, used to monitor the elderly's behaviors and identify abnormal behaviors; Alarm unit, used to send an alarm to family members or caregivers when abnormal behaviors are detected.

[0045] Specifically, the safety monitoring module is used to monitor the elderly's behaviors in real time to make a quick response in case of emergencies and ensure the safety of the elderly. It consists of the following units: Behavior Monitoring Unit: Through the cameras and motion sensors installed on the robot, it monitors the behaviors of the elderly in real time. Through motion analysis algorithms, it identifies possible abnormal behaviors, such as falls and long periods of immobility.

[0046] Alarm Unit: When abnormal behaviors are detected, the alarm unit will remind the elderly through voice or send an alarm to the family members and caregivers to ensure timely response. At the same time, the robot can take some countermeasures, such as trying to talk to the elderly to confirm their status.

[0047] Embodiment 2: Refer to Figure 8 , in the second embodiment of the present invention, the present invention provides an operation method for a pension service robot based on an emotion regulation system, including the following steps: S1. Collect the voice, facial expressions, and body movement signals of the elderly through a multi-modal emotion recognition module, and use the multi-modal fusion unit for emotion recognition; S2. Use the personalized learning unit in the personalized emotion regulation module to build a personalized emotion model based on long-term interaction with the elderly; analyze the current emotional state of the elderly through the emotion state analysis unit and generate an emotion response that meets the needs of the elderly; S3. The emotion memory module records the emotional changes and important life events of the elderly, and calls emotion recall in subsequent interactions to enhance emotional interaction; S4. Perceive the current environmental characteristics through the environmental perception unit in the scene adaptation module, and adjust the emotional expression to adapt to environmental changes; S5. The health intervention module generates a health management plan through emotion monitoring and health data collection, and records health behaviors to optimize the emotion model; S6. The safety monitoring module monitors the behaviors of the elderly and issues an alarm after identifying abnormal behaviors.

[0048] Specifically, S1. Multi-modal Emotion Collection and Recognition Voice Data Collection: The robot captures the voice of the elderly through a microphone array, and converts the sound signal into a digital signal in real time. Then, through voice signal processing technologies (such as noise reduction and feature extraction), voice features such as pitch, rhythm, and speech rate are extracted.

[0049] Facial Expression Data Collection: The robot uses a camera to capture the facial images of the elderly, and uses facial recognition algorithms to detect the positions of key points on the face, including eyebrows, eyes, and corners of the mouth. These key features are extracted to judge the facial expressions of the elderly.

[0050] Limb movement data collection: By using motion sensors (such as accelerometers and gyroscopes) and cameras installed on the robot, the limb movements of the elderly are monitored in real time. This step mainly focuses on the behavior patterns of the elderly, such as walking, sitting down, gestures, etc., to infer their emotional states.

[0051] Multimodal fusion analysis: By inputting the data of speech, facial expressions, and limb movements into a deep learning model, multimodal fusion analysis is carried out to comprehensively judge the emotional state of the elderly. A fusion model (such as a deep neural network or a Bayesian network) is used to improve the accuracy of recognition.

[0052] S2. Personalized learning and model construction Long-term interaction data collection: The robot interacts with the elderly for a long time, and collects information about personality traits, preferences, and living habits from daily conversations and behaviors. These data are continuously accumulated through the data collection module to build a complete user profile.

[0053] Personalized model training: Through machine learning algorithms (such as K-means clustering and collaborative filtering), the collected data is processed and analyzed to establish a personalized emotion model for the elderly. This model can be dynamically updated to more accurately reflect the emotional needs of the elderly over time.

[0054] Adjust emotional response: Using the personalized emotion model, the robot can adjust the type and manner of response according to the current emotional state and past emotional records of the elderly. For example, when detecting that the elderly are in a low mood, the robot will choose a more caring and comforting way of conversation.

[0055] Emotion analysis: Combining the recognition results of speech, facial expressions, and limb movements, the current emotional state of the elderly is analyzed. The emotional state includes emotion types (such as happy, sad, angry) and emotion intensities (such as slight, unstable, strong).

[0056] Generate emotional response: According to the analysis results, the robot generates appropriate emotional responses. For example, when detecting that the elderly are in a high mood, the robot may maintain this mood through interaction, such as playing favorite music; when the elderly are in a low mood, the robot may comfort them in a gentle tone or encourage the elderly to do some favorite activities.

[0057] S3. Establishment and invocation of emotional memory Emotional data recording: The emotional memory module records the emotional changes and related life events of the elderly in daily interactions. For example, it records that the elderly are in a particularly happy mood on a certain day and the possible reasons (such as a family member's visit).

[0058] Emotional Memory Invocation: In subsequent interactions, the robot appropriately invokes emotional memory according to the situation. For example, on the birthday of the elderly, the robot actively sends blessings and mentions past pleasant birthday experiences; when the elderly feel lonely, the robot can help improve their mood and increase the continuity of emotional companionship by referring to past warm moments.

[0059] S4. Scene Adaptive Adjustment Environmental Perception: The robot collects environmental data through sensors, including light, noise, temperature, etc. For example, a light sensor is used to determine the current environmental brightness state.

[0060] Scene Feature Analysis: The scene is judged by analyzing the current environmental features, such as whether it is night or day, whether the environment is quiet, whether the temperature is appropriate, etc. The robot judges the possible needs of the elderly based on these features.

[0061] Emotional Expression Adjustment: The performance of the robot is adjusted according to the analyzed environmental features. For example, at a quiet night, the robot will talk to the elderly in a lower volume and its movement amplitude will also become gentler to avoid disturbing the elderly; while in a more active environment during the day, the robot can be more active.

[0062] S5. Health Monitoring and Intervention Emotion and Health Data Collection: The emotion monitoring unit continuously monitors the emotional state of the elderly, and combines with the physiological data (heart rate, blood pressure, blood oxygen, etc.) collected by health monitoring devices (such as smart bracelets) to obtain the overall health status of the elderly.

[0063] Health Data Analysis: Analyze the relationship between the emotional state and health data to judge whether there are potential health hazards for the elderly. For example, if the mood is low for a long time and accompanied by a high heart rate, it may indicate mental health problems or excessive stress.

[0064] Health Advice Generation: Provide health management advice for the elderly according to the analysis results. For example, if it is detected that the elderly sit still for a long time and are in a low mood, the robot may suggest taking a walk or playing some relaxing music to relieve the mood.

[0065] Behavior Record and Optimization: Record the feedback and behavior of the elderly on health advice, such as whether the mood has improved after accepting the exercise advice, and use this data to further optimize the personalized emotion model.

[0066] S6. Safety Monitoring and Alarm Behavior Monitoring: The robot monitors the behavior of the elderly through cameras and motion sensors, focusing on whether there are abnormal situations. For example, whether the elderly fall, whether they stay still for a long time, etc. Through behavior pattern recognition algorithms, early warnings are given for potential dangerous behaviors.

[0067] Abnormal situation handling: When detecting abnormal behavior of the elderly, the alarm unit will respond immediately. For example, if it recognizes that the elderly person has fallen, the robot will try to communicate with the elderly person to confirm their status and, if necessary, send an alarm to the family members or caregivers. At the same time, the robot can record the detailed information of this abnormal behavior for subsequent analysis and care.

[0068] Embodiment III In the third embodiment of the present invention, based on the same inventive concept, a computer-readable storage medium is proposed. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the operation method of the elderly care service robot based on the emotion regulation system in the above embodiment.

[0069] Embodiment IV In the fourth embodiment of the present invention, based on the same inventive concept, a computer device is proposed. The terminal includes: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory and execute the operation method of the elderly care service robot based on the emotion regulation system in the above embodiment.

[0070] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0071] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Elderly care service robot based on emotion regulation system, characterized by: include: Multimodal emotion recognition module, which is used to comprehensively perceive the emotional state of the elderly by combining multiple emotional signals such as voice, facial expressions and body movements; Personalized emotion regulation module, which is used to generate personalized emotional responses that meet the needs of the elderly based on their personality traits, hobbies and living habits; The emotional memory module is used to record the elderly’s emotional changes, important life events and personal preferences; The scene adaptation module is used to perceive the living environment of the elderly in real time and adjust the robot's emotional expression according to the environmental information; The health intervention module is used to generate health management plans through long-term monitoring of the emotional state and health of the elderly; The safety monitoring module is used to monitor the behavior of the elderly and issue an alarm when abnormal behavior is detected.

2. The elderly care service robot based on the emotion regulation system according to claim 1, characterized in that: The multimodal emotion recognition module comprises: The speech emotion recognition unit is used to collect the speech signals of the elderly through a microphone array, extract the speech features using a speech signal processing algorithm, and analyze the emotional information in the speech using a deep learning model; A facial expression recognition unit is used to capture facial images of the elderly through a camera, analyze facial expressions using computer vision technology, extract expression features and recognize the emotional state of the elderly; The body movement recognition unit is used to collect the body movements of the elderly through motion sensors, analyze their behavior patterns, and thus determine the emotional state of the elderly; The multimodal fusion unit is used to fuse the features of speech, facial expression and body movement, and use deep learning algorithms to perform comprehensive analysis of multimodal features to improve the accuracy of emotion recognition.

3. The elderly care service robot based on the emotion regulation system according to claim 1, characterized in that: The personalized emotion regulation module includes: Personalized learning unit, which is used to collect information about the personality traits, hobbies and living habits of the elderly through long-term interaction with them, and to build a personalized emotion model using machine learning algorithms; The emotional state analysis unit is used to analyze the current emotional state of the elderly and generate an emotional response that meets the needs of the elderly in combination with their historical emotional records; The emotional response generation unit is used to generate appropriate emotional responses according to the results of the emotional state analysis, including language comfort and body movement simulation.

4. The elderly care service robot based on the emotion regulation system according to claim 1, characterized in that: The emotional memory module includes: An emotion recording unit to record the older adult’s mood changes during daily interactions, as well as life events and personal preferences; The emotional recall unit is used to call upon emotional memories in subsequent interactions and increase the continuity of emotional companionship.

5. The elderly care service robot based on the emotion regulation system according to claim 1, characterized in that: The scene adaptation module comprises: The environmental sensing unit is used to sense the noise, light, and temperature factors in the environment in real time through sensors to obtain environmental information; A scene analysis unit, used to analyze environmental information and determine the characteristics of the current scene; The emotion expression adjustment unit is used to adjust the robot's emotion expression according to the results of scene analysis.

6. The elderly care service robot based on the emotion regulation system according to claim 1, characterized in that: The health intervention module includes: Emotion monitoring unit, used to monitor the emotional state of the elderly over a long period of time and record the trend of emotional changes; A health data collection unit, used to collect health data of the elderly through wearable devices; A health advice generation unit, which is used to combine emotion monitoring results and health data to generate a health management plan; Behavior recording unit, used to record the health behaviors of the elderly to further optimize the personalized emotion model.

7. The elderly care service robot based on the emotion regulation system according to claim 1, characterized in that: The safety monitoring module comprises: Behavior monitoring unit, used to monitor the behavior of the elderly and identify abnormal behavior; An alarm unit is used to alert family members or caregivers when abnormal behavior is detected.

8. The operation method of the elderly care service robot based on the emotion regulation system is characterized in that: The elderly care service robot based on the emotion regulation system according to any one of claims 1 to 7 comprises the following steps: S1. Collect the elderly’s voice, facial expression and body movement signals through the multimodal emotion recognition module, and use the multimodal fusion unit to perform emotion recognition; S2. Use the personalized learning unit in the personalized emotion regulation module to build a personalized emotion model based on long-term interaction with the elderly; analyze the current emotional state of the elderly through the emotion state analysis unit to generate an emotional response that meets the needs of the elderly; S3, the emotional memory module records the emotional changes and important life events of the elderly, and calls on emotional memories in subsequent interactions to enhance emotional interaction; S4, perceive the current environment characteristics through the environment perception unit in the scene adaptation module, and adjust the emotional expression method to adapt to environmental changes; S5, the health intervention module generates health management plans through emotion monitoring and health data collection, and records health behaviors to optimize the emotion model; S6. The safety monitoring module monitors the behavior of the elderly and issues an alarm after identifying abnormal behavior.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for operating an elderly care service robot based on an emotion regulation system as described in any one of claims 1 to 7 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for operating an elderly care service robot based on an emotion regulation system as described in any one of claims 1 to 7 is implemented.

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