An intelligent remote rehabilitation guidance system

Abstract

The present invention belongs to the technical field of remote rehabilitation guidance, and in particular, discloses an intelligent remote rehabilitation guidance system, which integrates human-computer interaction, information encryption, physiological and psychological assessment, rehabilitation level classification, physician professional assessment, rehabilitation knowledge base construction, rehabilitation matching planning, rehabilitation training monitoring and feedback, and rehabilitation effect prediction. Through a comprehensive fuzzy evaluation algorithm and an emotional depression detection algorithm, the system comprehensively assesses the physiological and mental health conditions of the rehabilitated person, and recommends suitable rehabilitation physicians or health planning guidance opinions. During the rehabilitation process, the system monitors the standardization of training movements and micro-expression changes in real time, and gives timely feedback and adjustment suggestions. At the same time, it uses a machine learning algorithm to predict the rehabilitation trajectory, optimize the subsequent rehabilitation plan, and ensure personalized and accurate rehabilitation. The present invention improves the participation of the rehabilitated person and the scientific nature of the training, and provides an effective solution for remote rehabilitation guidance.

Claims

1. An intelligent remote rehabilitation guidance system, characterized in that: include: Rehabilitation patient portrait module, rehabilitation planning module, and remote interactive monitoring module; The rehabilitation patient portrait module includes a physiological assessment unit, a psychological assessment unit and a rehabilitation level portrait unit; The physiological evaluation unit is used to obtain the first health data of the rehabilitated person and calculate the first health score of the rehabilitated person through the configured comprehensive fuzzy evaluation algorithm; The psychological assessment unit is used to obtain the second rehabilitation data of the rehabilitated person, and detect and calculate the second health score of the rehabilitated person through the configured psychological questionnaire and depression detection algorithm; the rehabilitation level portrait unit is used to calculate the comprehensive rehabilitation score of the rehabilitated person according to the calculated first health score and second health score, and classify the rehabilitation guidance level of the rehabilitated person according to the obtained comprehensive rehabilitation score; The rehabilitation planning module includes a physician portrait unit, a rehabilitation knowledge base unit and a rehabilitation matching planning unit; The physician portrait unit is used to obtain physician professional level and work achievement data, call the comprehensive fuzzy evaluation algorithm in the physiological evaluation unit, and calculate the professional score and coachability level of the rehabilitation physician; the rehabilitation knowledge base unit is used to obtain historical rehabilitation case library data, build a rehabilitation knowledge base through the configured natural language algorithm, and build a reasoning model into the constructed rehabilitation knowledge base to obtain a rehabilitation reasoning knowledge base with reasoning ability; The rehabilitation matching planning unit is used to recommend a matching rehabilitation physician across departments to the rehabilitated person according to the obtained comprehensive rehabilitation score of the rehabilitated person through a matching recommendation algorithm, or to recommend a matching rehabilitation knowledge base to the rehabilitated person through a matching recommendation algorithm and an online health guidance opinion or guidance video generated by an intelligent generation algorithm; The remote interactive monitoring module includes a motion monitoring unit and a micro-expression monitoring unit; the motion monitoring unit is used to monitor the standard of the training motion of the rehabilitator in real time according to the configured training motion evaluation algorithm, and to provide scoring feedback on the standard of the training motion; The micro-expression monitoring unit is used to monitor the changes of micro-expressions of the rehabilitator in real time during the training process according to the configured micro-expression monitoring algorithm, and to provide feedback of the monitoring results when the monitored micro-expressions change abnormally; The specific steps of the action monitoring unit scoring the training action standard include: F1. Obtain rehabilitation physician video rehabilitation guidance data, standardized rehabilitation guidance rule text data and historical rehabilitation training video data, and perform denoising preprocessing on the collected data. At the same time, perform frame extraction on the preprocessed rehabilitation physician video rehabilitation guidance data and historical rehabilitation training video data through the OPenCV library to obtain rehabilitation training time series image data; F2. Construct a training action evaluation model based on the Chinese pre-trained Bert sub-model and the pre-trained HRNet, and input the acquired rehabilitation training time series image data into the HRNet in the training action evaluation model to obtain the training action joint node height and curvature features. At the same time, input the pre-processed standardized rehabilitation guidance rule text data into the Chinese pre-trained Bert sub-model to extract the standard training rule threshold features, and input the acquired training action joint node height and curvature features and the acquired standard training rule threshold features into the cross-attention sub-model for fusion operation to obtain the fusion training standard features. F3, input the acquired fusion training standard features into the output layer of the training action evaluation model, output the corresponding training action score, and use the extracted training action joint node height and curvature features and the standard training rule threshold features to calculate the training standard error, use the training standard error to construct a cross entropy function, train the training action evaluation model, and obtain the completed training action evaluation model; F4. Deploy the training action evaluation model after training to the action monitoring unit. When the rehabilitated person is trained by the rehabilitation physician or rehabilitation video recommended by the guidance system, the deployed sensors collect the training actions of the rehabilitated person in real time, and upload the collected action data to the action monitoring unit for real-time scoring of the training action standard. F5. Set the training action standard score threshold. When the obtained real-time training action standard score is less than the training action standard score threshold, feedback is given and the training action is adjusted in real time.

2. The intelligent remote rehabilitation guidance system according to claim 1, characterized in that: The specific steps of the physiological assessment unit calculating the first health score of the recovered person include: A1. Use various physiological monitoring devices to collect the real-time first health data of the rehabilitated person, clean the collected data, remove abnormal values, standardize the preprocessing, and obtain the first health data after preprocessing; A2. Constructing a first-level evaluation factor set s=[s1…s l …s L ], and use the sub-factors corresponding to the first health factor set to construct the secondary evaluation factor set s l =[s l1 …s lk …s lK ],s l represents the lth first-level evaluation factor in the first-level evaluation factor set, slk represents the kth second-level evaluation factor in the lth first-level evaluation factor; L represents the length of the first-level evaluation factor set, and K represents the length of the second-level evaluation factor set corresponding to the lth first-level evaluation factor; A3. Set the rehabilitation index level set to v = [v1…v m …v M ], evaluate and score the constructed first-level evaluation factor set and second-level evaluation factor set by expert scoring, and obtain the fuzzy evaluation matrix R of the first-level evaluation factor set and the fuzzy evaluation matrix R' of the second-level evaluation factor set in turn; where v m represents the mth rehabilitation index level, and M represents the total number of rehabilitation index levels.

3. An intelligent remote rehabilitation guidance system as claimed in claim 2, characterized in that: The specific steps of the physiological assessment unit calculating the first health score of the recovered person also include: A4. According to the obtained R', calculate the weight W of the second-level evaluation factor under the lth first-level evaluation factor through the minimum membership weighted average deviation l =[w l1 …w lk …w lK ], and calculate the comprehensive evaluation vector C of K secondary evaluation factors l =[C l1 …C lm …C lM ], as follows: where w lk represents the weight of the kth secondary evaluation factor under the lth primary evaluation factor, c lm represents the comprehensive evaluation value of the second-level evaluation factor under the lth first-level evaluation factor at the mth rehabilitation index level, g k represents the maximum membership of the kth secondary assessment factor and the rehabilitation indicator level set; r' km It indicates the membership degree of the k-th secondary assessment factor to the m-th rehabilitation indicator level; A5. Based on the secondary assessment factors l The comprehensive evaluation vector C of the evaluation index level l , calculate the first-level evaluation factor index weight W s , by obtaining W s =(w1…w l …w L ), using the fuzzy evaluation matrix R, W of the first-level evaluation factor set s The comprehensive evaluation vector of the first-level evaluation factor is calculated by the comprehensive evaluation result formula of a single first-level evaluation factor A6. Comprehensively evaluate the calculated first-level evaluation factors Add up all the elements to get the first health score of the recovered person.

4. The intelligent remote rehabilitation guidance system according to claim 3, characterized in that: The specific workflow steps of the rehabilitation matching planning unit include: E1. Set a rehabilitation score selection threshold. When the comprehensive rehabilitation score obtained is lower than the rehabilitation score selection threshold, two selection buttons, namely rehabilitation physician and rehabilitation reasoning knowledge base, are provided through the guidance system interactive interface for the rehabilitation patient to choose. E2. When the patient selects a rehabilitation video generated by the rehabilitation reasoning knowledge base, the corresponding rehabilitation guidance video and rehabilitation guidance suggestions are recommended to the patient through a matching recommendation algorithm based on the patient's comprehensive rehabilitation score, the first health score, and the second health score; E3. When the rehabilitation reasoning knowledge base does not contain rehabilitation videos and rehabilitation suggestions corresponding to the comprehensive rehabilitation score, first health score and second health score of the rehabilitated person, the reasoning model is called to generate corresponding rehabilitation guidance videos and rehabilitation guidance suggestions for the rehabilitated person through the existing rehabilitation knowledge base using the video generation algorithm and the text generation algorithm.

5. The intelligent remote rehabilitation guidance system according to claim 4, characterized in that: The specific workflow steps of the rehabilitation matching planning unit also include: E4. When the patient selects a rehabilitation physician, a matching threshold is set, and the matching coefficient between the patient and the rehabilitation physician is calculated through a matching recommendation algorithm based on the patient's comprehensive rehabilitation score, the first health score and the second health score, and the rehabilitation physician's professional score and coachability level. When the matching coefficient is greater than the matching threshold, the corresponding rehabilitation physician is recommended. If it is less than the matching coefficient, the matching continues until the matching is completed. If the rehabilitation physician whose matching coefficient is greater than the matching threshold cannot be matched after the matching is completed, the rehabilitation physician with the largest matching coefficient with the patient is recommended to the corresponding patient; E5. Set a mental health threshold. When the second health score is greater than the mental health threshold, in addition to the rehabilitation physician recommended by E4, the corresponding mental health physician is recommended to the rehabilitated person across departments through a matching recommendation algorithm based on the second health score. If it is less than the threshold, no mental health physician is recommended.

6. The intelligent remote rehabilitation guidance system according to claim 5, characterized in that: The specific workflow steps of the rehabilitation matching planning unit also include: E6. When the comprehensive rehabilitation score obtained is higher than the rehabilitation score selection threshold, the matching recommendation algorithm is used to directly recommend the corresponding rehabilitation physician to the patient, and at the same time, the matching recommendation algorithm is used to recommend a mental health physician to the patient across departments based on the second health score; E7. When the comprehensive rehabilitation score of the patient is lower than the rehabilitation score selection threshold after training guided by the rehabilitation physician and mental health physician recommended by E6, the patient will undergo the E1-E5 matching recommendation process.

7. The intelligent remote rehabilitation guidance system according to claim 6, characterized in that: The specific workflow of the micro-expression monitoring unit includes: G1. Obtain an annotated public micro-expression database and historical training video data collected during rehabilitation training, use a pre-trained face detection model to extract and annotate the face area from the collected historical training video data, and integrate it with the obtained public micro-expression database; G2, perform key point labeling and image alignment on the unified and integrated micro-expression data, and perform filtering and enhancement processing on the aligned images to obtain a micro-expression training data set; G3, construct a micro-expression detection model, input the images in the acquired micro-expression training data set into the initial feature extraction layer of the micro-expression detection model in order of time points, and obtain the key features of facial expressions; G4, input the key features of the acquired facial expression into the optical flow sub-model layer in the micro-expression detection model to obtain the dynamic change features of the local micro-expressions in the key features of the facial expression; G5, input the key features of facial expressions and the dynamic change features of local micro-expressions into the collaborative attention layer, perform feature fusion, and obtain fused micro-expression features; G6. Input the acquired fused micro-expression features into the classification layer in the model, output the probability of detecting the corresponding type of micro-expression, set the cross entropy function as the model loss function, and set the training cycle to 200 to train the micro-expression detection model to obtain the trained micro-expression detection model.

8. The intelligent remote rehabilitation guidance system according to claim 7, characterized in that: The specific workflow of the micro-expression monitoring unit also includes: G7. Deploy the acquired micro-expression detection model to the micro-expression monitoring unit. When the rehabilitated person is training, the sensor collects the facial image or video data of the rehabilitated person in real time, transmits the collected data to the micro-expression monitoring unit for detection, and outputs the micro-expression detection results. G8. Set an abnormal probability threshold. When the probability that the detected output micro-expression is a negative micro-expression is greater than the abnormal probability threshold, an alarm feedback is performed, otherwise no alarm feedback is performed.

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