A nursing robot control method and robot system

Abstract

The present invention discloses a nursing robot control method and a robot system, which relate to the field of rehabilitation nursing technology. Through personalized rehabilitation support, real-time adjustment of rehabilitation movements, emotion and interaction management, and improved efficiency and accuracy, the rehabilitation treatment process is effectively optimized. It can not only adjust the rehabilitation plan according to the patient's real-time performance to ensure that each patient obtains the most appropriate treatment support, but also improve the patient's rehabilitation motivation and overall treatment effect by analyzing and adjusting the rehabilitation vitality plan. This innovative method that combines artificial intelligence and robotics technology has brought significant progress and application prospects to the future field of intelligent medical care. The nursing robot can judge in real time whether the patient's rehabilitation movements meet the standards and adjust the auxiliary control value when necessary. This timely feedback and adjustment helps to ensure that patients can perform correct rehabilitation movements at each training stage and improve the rehabilitation effect.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation nursing, and in particular to a nursing robot control method and a robot system. Background Art

[0002] With the continuous development of artificial intelligence and robotics, the modern medical field has begun to explore how to use these technologies to improve the effect and quality of rehabilitation treatment, and the application of nursing robots in the medical field is becoming more and more extensive. Using nursing robots to monitor and adjust rehabilitation training can be combined with advanced technical means to improve the effect of rehabilitation training. Therefore, a nursing robot control method and robot system came into being.

[0003] Prior art, such as the interactive management system and method for rehabilitation care based on the Internet of Things disclosed in the invention patent application with announcement number CN117690583A, relates to the field of intelligent management of rehabilitation care, which uses the collected rehabilitation training image data of multiple predetermined time points of the rehabilitation training patient to be monitored as input data, and then extracts and analyzes the patient's facial features, joint features and overall gait features in the rehabilitation training image data respectively through computer vision technology and image processing technology, and combines these features to obtain a classification result for indicating whether the rehabilitation training program of the rehabilitation training patient to be monitored needs to be adjusted. That is, by comprehensively analyzing the facial features, joint features and overall gait features of the patient during the rehabilitation training process, the patient's rehabilitation movement training can be analyzed from all angles. In this way, it helps to provide personalized rehabilitation programs and give rehabilitation suggestions to improve the efficiency and comprehensiveness of rehabilitation care.

[0004] In response to the above scheme, the applicant of the present invention has found that the above technology has at least the following technical problems: 1. The above technology mainly uses computer vision technology and image processing technology to analyze the facial features, joint features and overall gait characteristics of the patient during rehabilitation training, and lacks assessment of the patient's emotions and psychological state, resulting in a lack of comprehensiveness in the rehabilitation plan. It does not take into account the patient's emotional state during rehabilitation training and its impact on the rehabilitation effect. There is a lack of assessment and adjustment mechanism for the patient's emotions, which may cause the patient's emotional fluctuations during rehabilitation training to affect the rehabilitation effect, which may lead to the static state of the rehabilitation training plan and the inability to flexibly respond to the patient's changing needs. It is difficult to provide a personalized rehabilitation plan adjustment plan based on the patient's individual characteristics and conditions. There is a lack of a mechanism to adjust the rehabilitation vitality plan according to the patient's actual situation, which may lead to unsatisfactory rehabilitation results.

[0005] 2. The above-mentioned scheme lacks real-time monitoring and adjustment during the rehabilitation training process, and lacks timely feedback and adjustment mechanisms, which may lead to the inability to correct the limitations and shortcomings of the rehabilitation training plan in a timely manner. It does not provide a quantitative evaluation and feedback mechanism for the rehabilitation effect, lacks quantitative evaluation and monitoring of the rehabilitation training effect, and is difficult to accurately evaluate the improvement of the rehabilitation effect, making it difficult to accurately measure and monitor the improvement of the rehabilitation effect. Summary of the invention

[0006] In view of the above-mentioned technical deficiencies, the object of the present invention is to provide a nursing robot control method and a robot system.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a nursing robot control method, including: Step 1, obtaining the rehabilitation movement evaluation coefficient: When the target patient in the target hospital undergoes leg rehabilitation training, the nursing robot will set several collection time points, so that the nursing robot collects the rehabilitation movement parameters corresponding to the target patient at each collection time point, and then analyzes and obtains the rehabilitation movement evaluation coefficient corresponding to the target patient at each collection time point.

[0008] Step 2. Determination of rehabilitation movements: Based on the rehabilitation movement evaluation coefficient corresponding to the target patient at each collection time point, determine whether the rehabilitation movement corresponding to the target patient at each collection time point meets the standard. If the rehabilitation movement corresponding to the target patient at a certain collection time point does not meet the standard, analyze the auxiliary control value corresponding to the rehabilitation movement of the target patient at the collection time point, and adjust the nursing robot according to the auxiliary control value corresponding to the rehabilitation movement of the target patient at the collection time point.

[0009] Step 3. Acquisition of rehabilitation emotion assessment coefficient: The nursing robot collects facial state parameters and interaction state parameters corresponding to the target patient at each collection time point, and then analyzes and obtains the facial state influencing factors and rehabilitation emotion assessment coefficients corresponding to the target patient at each collection time point.

[0010] Step 4. Adjustment of the rehabilitation vitality plan: According to the rehabilitation emotion assessment coefficient corresponding to the target patient at each collection time point, the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point is analyzed, and the nursing robot is executed according to the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point.

[0011] Preferably, the determination of whether the rehabilitation movements corresponding to the target patient at each acquisition time point meet the standards is performed by the following specific analysis process: A1. Obtaining the gender, age and disease severity of the target patient, and comparing the gender, age and disease severity of the target patient with the gender, age and disease severity of each standard rehabilitation exercise evaluation coefficient interval collection in the database; if the gender, age and disease severity of the target patient are the same as the gender, age and disease severity of a certain standard rehabilitation exercise evaluation coefficient interval collection in the database, then the standard rehabilitation exercise evaluation coefficient interval collection in the database is recorded as the standard rehabilitation exercise evaluation coefficient interval collection corresponding to the target patient.

[0012] A2. The nursing robot obtains the training items performed on the target patient at each acquisition time point through the camera installed inside the nursing robot, and compares the training items corresponding to each acquisition time point of the target patient with the training items corresponding to each standard rehabilitation movement assessment coefficient interval in the corresponding collection of standard rehabilitation movement assessment coefficient intervals in the database. If the training item corresponding to a certain acquisition time point of the target patient is the same as the training item corresponding to a certain standard rehabilitation movement assessment coefficient interval in the corresponding collection of standard rehabilitation movement assessment coefficient intervals in the database, then the standard rehabilitation movement assessment coefficient interval in the collection of standard rehabilitation movement assessment coefficient intervals in the database is recorded as the standard rehabilitation movement assessment coefficient interval corresponding to the target patient at the acquisition time point.

[0013] A3. Compare the rehabilitation movement evaluation coefficient corresponding to the target patient at each acquisition time point with the corresponding standard rehabilitation movement evaluation coefficient interval in the database. If the rehabilitation movement evaluation coefficient corresponding to the target patient at a certain acquisition time point is within the corresponding standard rehabilitation movement evaluation coefficient interval in the database, then it is determined that the rehabilitation movement corresponding to the target patient at the acquisition time point meets the standard. If the rehabilitation movement evaluation coefficient corresponding to the target patient at a certain acquisition time point is not within the corresponding standard rehabilitation movement evaluation coefficient interval in the database, then it is determined that the rehabilitation movement corresponding to the target patient at the acquisition time point does not meet the standard. In this way, it is determined whether the rehabilitation movement corresponding to the target patient at each acquisition time point meets the standard.

[0014] Preferably, the auxiliary control value corresponding to the rehabilitation action of the target patient at the acquisition time point is analyzed, and the specific analysis process is as follows: if the rehabilitation action corresponding to the target patient at a certain acquisition time point does not meet the standard, the rehabilitation movement evaluation coefficient corresponding to the target patient at the acquisition time point is compared with the rehabilitation movement evaluation coefficients corresponding to each auxiliary control value in the database; if the rehabilitation movement evaluation coefficient corresponding to the target patient at the acquisition time point is the same as the rehabilitation movement evaluation coefficient corresponding to a certain auxiliary control value in the database, the auxiliary control value in the database is recorded as the auxiliary control value corresponding to the rehabilitation action of the target patient at the acquisition time point.

[0015] Preferably, the analysis of the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point is specifically performed as follows: the rehabilitation emotion assessment coefficient corresponding to the target patient at each collection time point is compared with the rehabilitation emotion assessment coefficient interval corresponding to each rehabilitation vitality plan adjustment value in the database; if the rehabilitation emotion assessment coefficient corresponding to the target patient at a certain collection time point is within the rehabilitation emotion assessment coefficient interval corresponding to a certain rehabilitation vitality plan adjustment value in the database, then the rehabilitation vitality plan adjustment value in the database is used as the rehabilitation vitality plan adjustment value corresponding to the target patient at the collection time point; in this way, the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point is analyzed.

[0016] In a second aspect, the present invention provides a nursing robot system, comprising: a rehabilitation movement evaluation coefficient acquisition module: when a target patient in a target hospital performs leg rehabilitation training, the nursing robot will set several collection time points, so that the nursing robot collects rehabilitation movement parameters corresponding to the target patient at each collection time point, and then analyzes and obtains the rehabilitation movement evaluation coefficient corresponding to the target patient at each collection time point.

[0017] Rehabilitation action judgment module: used to judge whether the rehabilitation action corresponding to the target patient at each collection time point meets the standard according to the rehabilitation movement evaluation coefficient corresponding to the target patient at each collection time point. If the rehabilitation action corresponding to the target patient at a certain collection time point does not meet the standard, the auxiliary control value corresponding to the rehabilitation action of the target patient at the collection time point is analyzed, and the nursing robot is adjusted according to the auxiliary control value corresponding to the rehabilitation action of the target patient at the collection time point.

[0018] Rehabilitation emotion assessment coefficient acquisition module: used by the nursing robot to collect facial state parameters and interaction state parameters corresponding to the target patient at each collection time point, so as to analyze and obtain the facial state influencing factors and rehabilitation emotion assessment coefficients corresponding to the target patient at each collection time point.

[0019] Rehabilitation vitality plan adjustment module: used to analyze the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point according to the rehabilitation emotion assessment coefficient corresponding to the target patient at each collection time point, and execute the nursing robot according to the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point.

[0020] The beneficial effects of the present invention are as follows: 1. The present invention provides a nursing robot control method and a robot system, which effectively optimizes the rehabilitation treatment process through multiple advantages such as personalized rehabilitation support, real-time adjustment of rehabilitation movements, emotion and interaction management, and improved efficiency and accuracy. It can not only adjust the rehabilitation plan according to the patient's real-time performance to ensure that each patient receives the most appropriate treatment support, but also improve the patient's rehabilitation motivation and overall treatment effect by analyzing and adjusting the rehabilitation vitality plan. This innovative method that combines artificial intelligence and robotics technology has brought significant progress and application prospects to the future field of intelligent medical care.

[0021] 2. In the embodiment of the present invention, by acquiring and analyzing the rehabilitation movement evaluation coefficient and the rehabilitation emotion evaluation coefficient, the nursing robot can provide personalized rehabilitation support for each patient. This personalized capability can adjust the rehabilitation plan according to the performance of each patient at different time points to maximize the rehabilitation process. Based on the judgment and analysis of the rehabilitation movements, the nursing robot can judge in real time whether the patient's rehabilitation movements meet the standards and adjust the auxiliary control value when necessary. This timely feedback and adjustment helps to ensure that patients can perform correct rehabilitation movements at each training stage and improve the rehabilitation effect.

[0022] 3. In the embodiment of the present invention, by collecting facial state and interaction state parameters, the nursing robot can analyze the patient's rehabilitation emotional assessment coefficient, thereby adjusting the rehabilitation vitality plan. This function not only helps to improve the patient's emotional state and rehabilitation motivation, but also enhances the human-computer interaction experience during the nursing process. The automated data collection and analysis process makes the rehabilitation process more efficient and accurate. The nursing robot can quickly respond to changes in the patient and make timely adjustments, reducing human errors and time waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 The present invention is a flowchart of the steps for implementing the method.

[0025] Figure 2 It is a schematic diagram of the connection of the system modules of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Embodiments of the present invention Figure 1 As shown, a nursing robot control method includes: step one, obtaining the rehabilitation movement evaluation coefficient: when the target patient in the target hospital undergoes leg rehabilitation training, the nursing robot will set several collection time points, so that the nursing robot collects the rehabilitation movement parameters corresponding to the target patient at each collection time point, and then analyzes and obtains the rehabilitation movement evaluation coefficient corresponding to the target patient at each collection time point.

[0028] In a specific embodiment, the rehabilitation exercise parameters include flexion angle, extension angle and corresponding contraction strength of each leg muscle.

[0029] It should be noted that the nursing robot is equipped with joint angle sensors and gyroscope sensors to monitor the flexion angle and extension angle of the target patient, and the nursing robot is equipped with electromyography sensors to monitor the corresponding contraction strength of each leg muscle of the target patient.

[0030] In another specific embodiment, the analysis obtains the rehabilitation exercise evaluation coefficient corresponding to the target patient at each acquisition time point, and the specific analysis process is as follows: the flexion angle, extension angle and contraction strength of each leg muscle corresponding to the target patient at each acquisition time point are recorded as Q g , W g and , where g represents the number corresponding to each acquisition time point, g = 1, 2...u, u is any integer greater than 2, k represents the number corresponding to each leg muscle, k = 1, 2...m, m is any integer greater than 2, substitute into the calculation formula

[0031]

[0032] The rehabilitation exercise evaluation coefficient γ corresponding to the target patient at each acquisition time point is obtained g , where Q′, W′, and E′ are the standard flexion angle, standard extension angle, and standard contraction strength of the leg muscles corresponding to the set patients, respectively; τ1, τ2, and τ3 are the weight factors corresponding to the set flexion angle, extension angle, and leg muscle contraction strength of the patients, respectively; ΔQ, ΔW, and ΔE are the allowed flexion angle difference, allowed extension angle difference, and allowed leg muscle contraction strength difference of the patients, respectively.

[0033] It should be noted that τ1, τ2, and τ3 are all greater than 0 and less than 1.

[0034] It should also be noted that through the summary of a large amount of research data and experimental data. According to professional institutions and research institutions, the standard flexion angle, standard extension angle, and standard contraction strength of the leg muscles corresponding to the patient are set. At the same time, based on the professional knowledge and research basis of experts in the field, they are discussed and confirmed with industry organizations or professional institutions. Experts set the weight factors corresponding to the patient's flexion angle, the weight factors corresponding to the extension angle, and the weight factors corresponding to the leg muscle contraction strength based on their own experience and knowledge, and set the patient's allowable flexion angle difference, allowable extension angle difference, and allowable leg muscle contraction strength difference.

[0035] Step 2. Determination of rehabilitation movements: Based on the rehabilitation movement evaluation coefficient corresponding to the target patient at each collection time point, determine whether the rehabilitation movement corresponding to the target patient at each collection time point meets the standard. If the rehabilitation movement corresponding to the target patient at a certain collection time point does not meet the standard, analyze the auxiliary control value corresponding to the rehabilitation movement of the target patient at the collection time point, and adjust the nursing robot according to the auxiliary control value corresponding to the rehabilitation movement of the target patient at the collection time point.

[0036] In a specific embodiment, the determination of whether the rehabilitation movements corresponding to the target patient at each acquisition time point meet the standards is performed by the following specific analysis process: A1. Obtaining the gender, age and severity of the disease corresponding to the target patient, and comparing the gender, age and severity of the disease corresponding to the target patient with the gender, age and severity of the disease corresponding to each set of standard rehabilitation exercise evaluation coefficient intervals in the database; if the gender, age and severity of the disease corresponding to the target patient are the same as the gender, age and severity of the disease corresponding to a certain set of standard rehabilitation exercise evaluation coefficient intervals in the database, then the set of standard rehabilitation exercise evaluation coefficient intervals in the database is recorded as the set of standard rehabilitation exercise evaluation coefficient intervals corresponding to the target patient.

[0037] It should be noted that the database is used to store the gender, age and disease severity corresponding to each set of standard rehabilitation exercise assessment coefficient intervals.

[0038] It should also be noted that the database is also used to store the training items corresponding to each standard rehabilitation exercise assessment coefficient interval in the collection of each standard rehabilitation exercise assessment coefficient interval.

[0039] A2. The nursing robot obtains the training items performed on the target patient at each acquisition time point through the camera installed inside the nursing robot, and compares the training items corresponding to each acquisition time point of the target patient with the training items corresponding to each standard rehabilitation movement assessment coefficient interval in the corresponding collection of standard rehabilitation movement assessment coefficient intervals in the database. If the training item corresponding to a certain acquisition time point of the target patient is the same as the training item corresponding to a certain standard rehabilitation movement assessment coefficient interval in the corresponding collection of standard rehabilitation movement assessment coefficient intervals in the database, then the standard rehabilitation movement assessment coefficient interval in the collection of standard rehabilitation movement assessment coefficient intervals in the database is recorded as the standard rehabilitation movement assessment coefficient interval corresponding to the target patient at the acquisition time point.

[0040] A3. Compare the rehabilitation movement evaluation coefficient corresponding to the target patient at each acquisition time point with the corresponding standard rehabilitation movement evaluation coefficient interval in the database. If the rehabilitation movement evaluation coefficient corresponding to the target patient at a certain acquisition time point is within the corresponding standard rehabilitation movement evaluation coefficient interval in the database, then it is determined that the rehabilitation movement corresponding to the target patient at the acquisition time point meets the standard. If the rehabilitation movement evaluation coefficient corresponding to the target patient at a certain acquisition time point is not within the corresponding standard rehabilitation movement evaluation coefficient interval in the database, then it is determined that the rehabilitation movement corresponding to the target patient at the acquisition time point does not meet the standard. In this way, it is determined whether the rehabilitation movement corresponding to the target patient at each acquisition time point meets the standard.

[0041] In another specific embodiment, the auxiliary control value corresponding to the rehabilitation action of the target patient at the acquisition time point is analyzed, and the specific analysis process is as follows: if the rehabilitation action corresponding to the target patient at a certain acquisition time point does not meet the standard, the rehabilitation movement evaluation coefficient corresponding to the target patient at the acquisition time point is compared with the rehabilitation movement evaluation coefficients corresponding to each auxiliary control value in the database; if the rehabilitation movement evaluation coefficient corresponding to the target patient at the acquisition time point is the same as the rehabilitation movement evaluation coefficient corresponding to a certain auxiliary control value in the database, the auxiliary control value in the database is recorded as the auxiliary control value corresponding to the rehabilitation action of the target patient at the acquisition time point.

[0042] It should be noted that the auxiliary control value: controls the range or amplitude of movement to ensure that the target patient does not exceed or reach the ideal amplitude when performing rehabilitation exercises, for example, adjusting the leg lifting height, squatting height and kicking angle.

[0043] It should also be noted that the nursing robot is equipped with a power assist device, such as an electric power assist device or a pneumatic power assist device, to help patients complete movements that require greater muscle strength. The nursing robot is adjusted according to the auxiliary control value corresponding to the target patient's rehabilitation movement. For example, the nursing robot is equipped with a power assist device to gradually assist the target patient to adjust to the standard squat height, standard leg lift height and standard kicking angle, so as to help the patient gradually achieve standard movements.

[0044] In the embodiment of the present invention, by acquiring and analyzing the rehabilitation movement evaluation coefficient and the rehabilitation emotion evaluation coefficient, the nursing robot can provide personalized rehabilitation support for each patient. This personalized capability can adjust the rehabilitation plan according to the performance of each patient at different time points to maximize the rehabilitation process. Based on the judgment and analysis of the rehabilitation movements, the nursing robot can judge in real time whether the patient's rehabilitation movements meet the standards and adjust the auxiliary control value when necessary. This timely feedback and adjustment helps to ensure that patients can perform correct rehabilitation movements at each training stage and improve the rehabilitation effect.

[0045] Step 3. Acquisition of rehabilitation emotion assessment coefficient: The nursing robot collects facial state parameters and interaction state parameters corresponding to the target patient at each collection time point, and then analyzes and obtains the facial state influencing factors and rehabilitation emotion assessment coefficients corresponding to the target patient at each collection time point.

[0046] In a specific embodiment, the facial state parameters include the upward curvature of the mouth corners and the muscle activity intensity corresponding to each facial area, and the interactive state parameters include the frequency of occurrence of each type of emotional vocabulary, the duration and speech speed of each dialogue.

[0047] It should be noted that the nursing robot uses facial tracking technology to detect the upward curvature of the mouth corners and the intensity of muscle activity in other facial areas.

[0048] It is also important to note that the nursing robot uses speech recognition technology to transcribe the conversation content, and then combines it with sentiment analysis algorithms to identify different types of emotional vocabulary and emotional expressions. The analysis of the duration and speed of the conversation is achieved through speech recognition software or natural language processing tools.

[0049] In another specific embodiment, the facial state influencing factors corresponding to the target patient at each acquisition time point are obtained by analysis. The specific analysis process is as follows: the upward arc of the mouth corners corresponding to the target patient at each acquisition time point and the muscle activity intensity corresponding to each facial area are respectively recorded as S g and Among them, g represents the number corresponding to each acquisition time point, g = 1, 2...u, u is any integer greater than 2, h represents the number corresponding to each facial area, h = 1, 2...n, n is any integer greater than 2, substitute into the calculation formula The facial state influencing factor φ corresponding to the target patient at each acquisition time point is obtained g , where S′ and Y′ are the standard upward curvature of the patient’s mouth corners and the standard muscle activity intensity of the facial area, respectively; ζ1 and ζ2 are the weight factors corresponding to the upward curvature of the patient’s mouth corners and the weight factors corresponding to the muscle activity intensity of the facial area, respectively.

[0050] It should be noted that ζ1 and ζ2 are both greater than 0 and less than 1.

[0051] It should also be noted that through the summary of a large amount of research data and experimental data, the standard curvature of the patient's mouth corners and the standard muscle activity intensity corresponding to the facial area are set by professional institutions and research institutions. At the same time, based on the professional knowledge and research basis of experts in the field, they are discussed and confirmed with industry organizations or professional institutions. Experts set the weight factors corresponding to the curvature of the patient's mouth corners and the weight factors corresponding to the muscle activity intensity corresponding to the facial area based on their own experience and knowledge.

[0052] In another specific embodiment, the analysis obtains the rehabilitation emotion assessment coefficient corresponding to the target patient at each acquisition time point. The specific analysis process is as follows: the occurrence frequency of each type of emotional vocabulary corresponding to the target patient at each acquisition time point, the duration and speech speed corresponding to each dialogue segment are recorded as and Among them, g represents the number corresponding to each collection time point, g = 1, 2...u, u is any integer greater than 2, f represents the number corresponding to each type of emotional vocabulary, f = 1, 2...b, b is any integer greater than 2, z represents the number corresponding to each dialogue, z = 1, 2...c, c is any integer greater than 2, substitute into the calculation formula The rehabilitation emotion assessment coefficient corresponding to the target patient at each collection time point is obtained Among them, F′, X′, and V′ are the standard occurrence frequency, standard duration, and standard speaking speed of the set patient type emotional vocabulary, respectively; υ1, υ2, and υ3 are the weight factors corresponding to the occurrence frequency, conversation duration, and speaking speed of the set patient type emotional vocabulary, respectively.

[0053] It should be noted that υ1, υ2, and υ3 are all greater than 0 and less than 1.

[0054] It should also be noted that through the summary of a large amount of research data and experimental data, professional institutions and research institutions set the standard frequency of occurrence of patient-type emotional vocabulary, the standard duration of conversation, and the standard speaking speed. At the same time, based on the professional knowledge and research basis of experts in the field, they discuss and confirm with industry organizations or professional institutions. Experts set the weight factors corresponding to the frequency of occurrence of patient-type emotional vocabulary, the weight factors corresponding to the duration of conversation, and the weight factors corresponding to the speaking speed based on their own experience and knowledge.

[0055] Step 4. Adjustment of the rehabilitation vitality plan: According to the rehabilitation emotion assessment coefficient corresponding to the target patient at each collection time point, the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point is analyzed, and the nursing robot is executed according to the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point.

[0056] It should be noted that the adjustment values ​​of the rehabilitation vitality plan include but are not limited to: 1. Emotional state adjustment: According to the rehabilitation emotion assessment coefficient, adjust the relevant content of the rehabilitation vitality plan for the patient's emotional state. For example, if the patient is depressed, you can adjust the activity content in the rehabilitation training plan or provide a warmer response to improve the patient's mood. 2. Communication method adjustment: According to the patient's communication needs and emotional state, adjust the interaction and communication methods of the nursing robot. The robot can automatically adjust the response method and content according to the patient's emotions to provide a communication experience that is closer to the patient. 3. Personalized service adjustment: According to the patient's corresponding rehabilitation emotion assessment coefficient, customize the personalized rehabilitation vitality plan adjustment value to better meet the patient's needs and improve the rehabilitation effect.

[0057] In a specific embodiment, the analysis of the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point is performed as follows: the rehabilitation emotion assessment coefficient corresponding to the target patient at each collection time point is compared with the rehabilitation emotion assessment coefficient interval corresponding to each rehabilitation vitality plan adjustment value in the database; if the rehabilitation emotion assessment coefficient corresponding to the target patient at a certain collection time point is within the rehabilitation emotion assessment coefficient interval corresponding to a certain rehabilitation vitality plan adjustment value in the database, then the rehabilitation vitality plan adjustment value in the database is used as the rehabilitation vitality plan adjustment value corresponding to the target patient at the collection time point; in this way, the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point is analyzed.

[0058] In the embodiment of the present invention, by collecting facial state and interaction state parameters, the nursing robot can analyze the patient's rehabilitation emotional assessment coefficient, thereby adjusting the rehabilitation vitality plan. This function not only helps to improve the patient's emotional state and rehabilitation motivation, but also enhances the human-computer interaction experience during the nursing process. The automated data collection and analysis process makes the rehabilitation process more efficient and accurate. The nursing robot can quickly respond to changes in the patient and make timely adjustments, reducing human errors and time waste.

[0059] Embodiments of the present invention Figure 2 As shown, a nursing robot system includes: a rehabilitation movement evaluation coefficient acquisition module, a rehabilitation action judgment module, a rehabilitation emotion evaluation coefficient acquisition module, a rehabilitation vitality plan adjustment module and a database.

[0060] Rehabilitation movement evaluation coefficient acquisition module: When the target patient in the target hospital undergoes leg rehabilitation training, the nursing robot will set several collection time points, so that the nursing robot collects the rehabilitation movement parameters corresponding to the target patient at each collection time point, and then analyzes and obtains the rehabilitation movement evaluation coefficient corresponding to the target patient at each collection time point.

[0061] Rehabilitation action judgment module: used to judge whether the rehabilitation action corresponding to the target patient at each collection time point meets the standard according to the rehabilitation movement evaluation coefficient corresponding to the target patient at each collection time point. If the rehabilitation action corresponding to the target patient at a certain collection time point does not meet the standard, the auxiliary control value corresponding to the rehabilitation action of the target patient at the collection time point is analyzed, and the nursing robot is adjusted according to the auxiliary control value corresponding to the rehabilitation action of the target patient at the collection time point.

[0062] Rehabilitation emotion assessment coefficient acquisition module: used by the nursing robot to collect facial state parameters and interaction state parameters corresponding to the target patient at each collection time point, so as to analyze and obtain the facial state influencing factors and rehabilitation emotion assessment coefficients corresponding to the target patient at each collection time point.

[0063] Rehabilitation vitality plan adjustment module: used to analyze the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point according to the rehabilitation emotion assessment coefficient corresponding to the target patient at each collection time point, and execute the nursing robot according to the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point.

[0064] The present invention provides a nursing robot control method and a robot system, which effectively optimizes the rehabilitation treatment process through multiple advantages such as personalized rehabilitation support, real-time adjustment of rehabilitation movements, emotion and interaction management, and improved efficiency and accuracy. It can not only adjust the rehabilitation plan according to the patient's real-time performance to ensure that each patient receives the most appropriate treatment support, but also improve the patient's rehabilitation motivation and overall treatment effect by analyzing and adjusting the rehabilitation vitality plan. This innovative method that combines artificial intelligence and robotics technology has brought significant progress and application prospects to the future field of intelligent medical care.

[0065] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. A nursing robot control method, characterized in that: include: Step 1: Acquisition of rehabilitation exercise evaluation coefficient: When the target patient in the target hospital undergoes leg rehabilitation training, the nursing robot will set several collection time points, so that the nursing robot collects the rehabilitation exercise parameters corresponding to the target patient at each collection time point, and then analyzes and obtains the rehabilitation exercise evaluation coefficient corresponding to the target patient at each collection time point; Step 2: Determination of rehabilitation actions: According to the rehabilitation movement evaluation coefficient corresponding to the target patient at each acquisition time point, it is determined whether the rehabilitation action corresponding to the target patient at each acquisition time point meets the standard. If the rehabilitation action corresponding to the target patient at a certain acquisition time point does not meet the standard, the auxiliary control value corresponding to the rehabilitation action of the target patient at the acquisition time point is analyzed, and the nursing robot is adjusted according to the auxiliary control value corresponding to the rehabilitation action of the target patient at the acquisition time point; Step 3: Acquisition of rehabilitation emotion assessment coefficient: The nursing robot collects facial state parameters and interaction state parameters corresponding to the target patient at each collection time point, thereby analyzing and obtaining facial state influencing factors and rehabilitation emotion assessment coefficients corresponding to the target patient at each collection time point; Step 4. Adjustment of the rehabilitation vitality plan: According to the rehabilitation emotion assessment coefficient corresponding to the target patient at each collection time point, the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point is analyzed, and the nursing robot is executed according to the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point.

2. A nursing robot control method as claimed in claim 1, characterized in that: The rehabilitation exercise parameters include flexion angle, extension angle and corresponding contraction strength of each leg muscle.

3. A nursing robot control method as claimed in claim 2, characterized in that: The analysis obtains the rehabilitation exercise assessment coefficient corresponding to the target patient at each acquisition time point. The specific analysis process is as follows: The flexion angle, extension angle and contraction strength of each leg muscle corresponding to the target patient at each acquisition time point are recorded as Q g , W g and Where g represents the number corresponding to each acquisition time point, g = 1, 2...u, u is any integer greater than 2, k represents the number corresponding to each leg muscle, k = 1, 2...m, m is any integer greater than 2, substitute into the calculation formula The rehabilitation exercise evaluation coefficient γ corresponding to the target patient at each acquisition time point is obtained g , where Q′, W′, and E′ are the standard flexion angle, standard extension angle, and standard contraction strength of the leg muscles corresponding to the set patients, respectively; τ1, τ2, and τ3 are the weight factors corresponding to the set flexion angle, extension angle, and leg muscle contraction strength of the patients, respectively; ΔQ, ΔW, and ΔE are the allowed flexion angle difference, allowed extension angle difference, and allowed leg muscle contraction strength difference of the patients, respectively.

4. A nursing robot control method as claimed in claim 3, characterized in that: The specific analysis process of judging whether the rehabilitation action corresponding to the target patient at each acquisition time point meets the standard is as follows: A1. Obtain the gender, age and disease severity of the target patient, and compare the gender, age and disease severity of the target patient with the gender, age and disease severity of each standard rehabilitation exercise assessment coefficient interval collection in the database. If the gender, age and disease severity of the target patient are the same as the gender, age and disease severity of a certain standard rehabilitation exercise assessment coefficient interval collection in the database, then record the standard rehabilitation exercise assessment coefficient interval collection in the database as the standard rehabilitation exercise assessment coefficient interval collection corresponding to the target patient; A2. The nursing robot obtains the training items performed by the target patient at each acquisition time point through the camera installed inside the nursing robot, and compares the training items corresponding to each acquisition time point of the target patient with the training items corresponding to each standard rehabilitation movement assessment coefficient interval in the corresponding database standard rehabilitation movement assessment coefficient interval collection. If the training item corresponding to a certain acquisition time point of the target patient is the same as the training item corresponding to a certain standard rehabilitation movement assessment coefficient interval in the corresponding database standard rehabilitation movement assessment coefficient interval collection, then the standard rehabilitation movement assessment coefficient interval in the database standard rehabilitation movement assessment coefficient interval collection is recorded as the standard rehabilitation movement assessment coefficient interval corresponding to the target patient at the acquisition time point; A3. Compare the rehabilitation movement evaluation coefficient corresponding to the target patient at each acquisition time point with the corresponding standard rehabilitation movement evaluation coefficient interval in the database. If the rehabilitation movement evaluation coefficient corresponding to the target patient at a certain acquisition time point is within the corresponding standard rehabilitation movement evaluation coefficient interval in the database, then it is determined that the rehabilitation movement corresponding to the target patient at the acquisition time point meets the standard. If the rehabilitation movement evaluation coefficient corresponding to the target patient at a certain acquisition time point is not within the corresponding standard rehabilitation movement evaluation coefficient interval in the database, then it is determined that the rehabilitation movement corresponding to the target patient at the acquisition time point does not meet the standard. In this way, it is determined whether the rehabilitation movement corresponding to the target patient at each acquisition time point meets the standard.

5. A nursing robot control method as claimed in claim 4, characterized in that: The auxiliary control value corresponding to the rehabilitation action of the target patient at the acquisition time point is analyzed, and the specific analysis process is as follows: If the rehabilitation movement corresponding to the target patient at a certain acquisition time point does not meet the standard, the rehabilitation movement evaluation coefficient corresponding to the target patient at the acquisition time point is compared with the rehabilitation movement evaluation coefficients corresponding to each auxiliary control value in the database. If the rehabilitation movement evaluation coefficient corresponding to the target patient at the acquisition time point is the same as the rehabilitation movement evaluation coefficient corresponding to a certain auxiliary control value in the database, the auxiliary control value in the database is recorded as the auxiliary control value corresponding to the rehabilitation movement of the target patient at the acquisition time point.

6. A nursing robot control method as claimed in claim 1, characterized in that: The facial state parameters include the upward curvature of the mouth corners and the muscle activity intensity corresponding to each facial area, and the interactive state parameters include the appearance frequency of each type of emotional vocabulary, the duration and speech speed of each dialogue.

7. A nursing robot control method as claimed in claim 6, characterized in that: The analysis obtains the facial state influencing factors corresponding to the target patient at each acquisition time point. The specific analysis process is as follows: The curvature of the mouth corners of the target patient at each acquisition time point and the muscle activity intensity of each facial area are recorded as S g and Among them, g represents the number corresponding to each acquisition time point, g = 1, 2...u, u is any integer greater than 2, h represents the number corresponding to each facial area, h = 1, 2...n, n is any integer greater than 2, substitute into the calculation formula The facial state influencing factor φ corresponding to the target patient at each acquisition time point is obtained g , where S′ and Y′ are the standard upward curvature of the patient’s mouth corners and the standard muscle activity intensity of the facial area, respectively; ζ1 and ζ2 are the weight factors corresponding to the upward curvature of the patient’s mouth corners and the weight factors corresponding to the muscle activity intensity of the facial area, respectively.

8. A nursing robot control method as claimed in claim 7, characterized in that: The analysis obtains the rehabilitation emotion assessment coefficient corresponding to the target patient at each collection time point. The specific analysis process is as follows: The frequency of occurrence of each type of emotional vocabulary of the target patient at each acquisition time point, the duration and speech speed of each conversation segment are recorded as and Among them, g represents the number corresponding to each collection time point, g = 1, 2...u, u is any integer greater than 2, f represents the number corresponding to each type of emotional vocabulary, f = 1, 2...b, b is any integer greater than 2, z represents the number corresponding to each dialogue, z = 1, 2...c, c is any integer greater than 2, substitute into the calculation formula The rehabilitation emotion assessment coefficient corresponding to the target patient at each collection time point is obtained Among them, F′, X′, and V′ are the standard occurrence frequency, standard duration, and standard speaking speed of the set patient type emotional vocabulary, respectively; υ1, υ2, and υ3 are the weight factors corresponding to the occurrence frequency, conversation duration, and speaking speed of the set patient type emotional vocabulary, respectively.

9. A nursing robot control method as claimed in claim 8, characterized in that: The specific analysis process of analyzing the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point is as follows: The rehabilitation emotion assessment coefficient corresponding to the target patient at each collection time point is compared with the rehabilitation emotion assessment coefficient interval corresponding to each rehabilitation vitality plan adjustment value in the database. If the rehabilitation emotion assessment coefficient corresponding to the target patient at a certain collection time point is within the rehabilitation emotion assessment coefficient interval corresponding to a certain rehabilitation vitality plan adjustment value in the database, the rehabilitation vitality plan adjustment value in the database is used as the rehabilitation vitality plan adjustment value corresponding to the target patient at the collection time point. In this way, the rehabilitation vitality plan adjustment values ​​corresponding to the target patients at each collection time point are analyzed.

10. A nursing robot system for executing the nursing robot control method according to any one of claims 1 to 9, characterized in that: include: Rehabilitation movement evaluation coefficient acquisition module: when the target patient in the target hospital undergoes leg rehabilitation training, the nursing robot will set several collection time points, so that the nursing robot collects the rehabilitation movement parameters corresponding to the target patient at each collection time point, and then analyzes and obtains the rehabilitation movement evaluation coefficient corresponding to the target patient at each collection time point; Rehabilitation action judgment module: used to judge whether the rehabilitation action corresponding to the target patient at each acquisition time point meets the standard according to the rehabilitation movement evaluation coefficient corresponding to the target patient at each acquisition time point. If the rehabilitation action corresponding to the target patient at a certain acquisition time point does not meet the standard, the auxiliary control value corresponding to the rehabilitation action of the target patient at the acquisition time point is analyzed, and the nursing robot is adjusted according to the auxiliary control value corresponding to the rehabilitation action of the target patient at the acquisition time point; Rehabilitation emotion assessment coefficient acquisition module: used by the nursing robot to collect facial state parameters and interaction state parameters corresponding to the target patient at each collection time point, so as to analyze and obtain the facial state influencing factors and rehabilitation emotion assessment coefficients corresponding to the target patient at each collection time point; Rehabilitation vitality plan adjustment module: used to analyze the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point according to the rehabilitation emotion assessment coefficient corresponding to the target patient at each collection time point, and execute the nursing robot according to the rehabilitation vitality plan adjustment value corresponding to the target patient at each collection time point.

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