Intelligent rehabilitation training intervention system for developmental coordination disorder

Abstract

The invention belongs to the technical field of medical care systems, and particularly relates to an intelligent rehabilitation training intervention system for developmental coordination disorder. According to the system, training information and coordination information are obtained and calibrated in real time through the first information acquisition module, the accuracy of data is ensured, conclusion information in coordination data is extracted through the second information acquisition module, a basis is provided for follow-up evaluation, then the coordination adaptability is calculated through the evaluation module, and a corresponding optimization strategy is output. In the training process, the updating module continuously collects latest training information and outputs the latest training information as post-training data so as to guarantee dynamic adjustment of a training scheme, risks such as long-term execution processing delay are avoided, the reduction module and the post-evaluation module are combined, the accuracy and stability of a training result are guaranteed, and the training effect is improved. Finally, an optimal coordination result is output, and the coordination ability of the patient is remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of healthcare systems, and particularly relates to an intelligent rehabilitation training intervention system for developmental coordination disorder. Background Art

[0002] Developmental coordination disorder refers to the impairment of an individual's coordination function due to various reasons during the physiological development process, which affects their normal motor ability and living ability. This disorder is common in children and adolescents, but may also occur in adults. Its main symptoms include clumsy movement, coordination disorder, discontinuous movement, etc. Currently, there are various rehabilitation training methods for developmental coordination disorder, such as physical therapy, exercise therapy, behavior therapy, etc. However, these methods mostly rely on manual execution, lacking systematization and intelligence, and it is difficult to achieve personalized and refined rehabilitation treatment.

[0003] Although there are some rehabilitation training systems in the prior art, most of them cannot achieve intelligent adjustment and cannot dynamically adjust the training plan according to the real-time feedback of patients. This leads to certain differences in the rehabilitation effect. At the same time, the processing of training data by these systems often based on a preset static data set, ignoring individual differences and dynamic changes, and unable to customize the most suitable training plan according to the specific situation of each patient. Obviously, this will lead to an unsatisfactory rehabilitation effect and it is difficult to meet the technical problems of the multi-faceted needs of patients. Summary of the Invention

[0004] The object of the present invention is to provide an intelligent rehabilitation training intervention system for developmental coordination disorder, which can effectively improve the rehabilitation effect of patients' coordination ability. In addition, based on the collection of multiple pieces of information of patients and the adaptive adjustment of the rehabilitation training plan, the rehabilitation process can be established on a scientific and systematic dynamic adjustment mechanism to ensure the effectiveness and adaptability of the training and improve the recovery efficiency of patients.

[0005] The technical solution adopted by the present invention is specifically as follows:

[0006] An intelligent rehabilitation training intervention system for developmental coordination disorder, comprising:

[0007] A first information collection module, used to collect training information and coordination information, respectively perform calibration processing, and output the calibrated training information as pre-training data, and output the calibrated coordination information as multiple groups of pre-coordination data;

[0008] A second information collection module, used to collect the conclusion information in multiple groups of pre-coordination data and synchronously output it as pre-conclusion data;

[0009] An evaluation module, configured to match pre-conclusion data corresponding to pre-training data, output an optimization strategy according to the pre-conclusion data corresponding to the pre-training data, collect optimization information according to the optimization strategy, and output it as pre-optimization data;

[0010] An update module, configured to collect update information of training information and output it as post-training data;

[0011] A restoration module, configured to collect subsequent information of pre-optimization data, output it as post-optimization data, and then output the coordination information of the post-optimization data as multiple groups of post-coordination data;

[0012] A post-evaluation module, configured to match post-coordination data corresponding to the post-training data, output an intervention strategy according to the post-coordination data corresponding to the post-training data, and determine the conclusion information of the optimization strategy as the optimal coordination result according to the intervention strategy.

[0013] In a preferred solution, the first information collection module includes:

[0014] A first information collection unit, configured to collect training information, add a time tag to it according to the chronological order of the training information, and then output the training information with the added time tag as pre-training data;

[0015] A second information collection unit, configured to collect coordination information, perform segmentation processing on the coordination information to obtain multiple execution time periods, output the pre-training data as multiple pre-coordination information according to the multiple execution time periods, arrange the pre-conclusion data in the order of the execution time periods, and summarize them into multiple groups of pre-coordination data.

[0016] In a preferred solution, the second information collection module includes:

[0017] A conclusion information determination unit, configured to collect conclusion information in multiple groups of pre-coordination data;

[0018] A pre-conclusion data output unit, configured to classify the conclusion information into phased first conclusion information and abnormal second conclusion information according to the synchronization calibration result of the pre-coordination information, wherein the phased first conclusion information is synchronized with the execution time period of the pre-coordination information, and the abnormal second conclusion information is synchronized with data less than the execution time period;

[0019] The pre-conclusion data output unit is further configured to add the phased first conclusion information to the first conclusion data and add the abnormal second conclusion information to the second conclusion data.

[0020] In a preferred solution, the evaluation module includes:

[0021] An evaluation unit, configured to calculate the coordination cooperation degree according to the pre-conclusion data corresponding to the pre-training data;

[0022] An optimization threshold calculation unit, configured to output an optimization threshold according to an optimization working condition and a standard working condition;

[0023] An optimization unit, configured to output an optimization strategy according to the degree of coordination and the deviation amount of the optimization threshold;

[0024] Wherein, the optimization unit is further configured to collect and increase the execution time period of the coordination information corresponding to the optimization strategy with an output of increase, add the increased execution time period to the coordination information, and then synchronously calibrate the data update of the increased execution time period as the first conclusion information;

[0025] The optimization unit is further configured to collect and compress the execution time period of the coordination information corresponding to the optimization strategy with an output of compression, output the compressed execution time period as an abnormal execution result, and then synchronously calibrate the update of the compressed execution time period as the second conclusion information.

[0026] In a preferred solution, the update module includes:

[0027] An update unit, configured to collect the update information of the training information, perform time tagging processing on it, and then output it as post-training data;

[0028] An evaluation unit, configured to calculate a coordination evaluation score corresponding to the pre-conclusion data according to the post-coordination data and the pre-coordination data, and classify the coordination evaluation score into a non-zero evaluation score and a zero evaluation score;

[0029] The coordination score evaluation unit is further configured to compare the non-zero evaluation score corresponding to the pre-conclusion data with the standard coordination score, and interact the non-zero evaluation score when it is greater than or equal to the standard coordination score; when it is less than the standard coordination score, report and generate a lowering conclusion corresponding to the non-zero evaluation score, and then restore to the non-zero evaluation score, and synchronously clear the subsequent information of the phased second conclusion information in which the post-coordination data corresponding to the lowering conclusion is improved compared with the pre-coordination data.

[0030] In a preferred solution, the post-evaluation module includes:

[0031] A coordination unit, configured to output a coordination score according to the post-coordination data corresponding to the post-training data;

[0032] An evaluation threshold calculation unit, configured to output a standard coordination score according to a coordination measurement standard;

[0033] A basis unit, configured to output an improvement state and a decline state according to the comparison results of the optimization strategy, the coordination score, and the standard coordination score;

[0034] In the lifting state, determine that the post-coordination information corresponding to the post-training data is stable coordination information, measure the proportion of coordinated working conditions for calculating the standard coordination score, and simultaneously transform the increase strategy and compression strategy in the optimization strategy into increase working conditions and compression working conditions;

[0035] In the descending state, measure the non-standard coordination score corresponding to the descending state, and report all compression strategies and non-standard coordination scores less than the standard coordination score in real time, and restore the execution time period of each non-standard coordination score to the coordination evaluation score in the corresponding previous conclusion data.

[0036] In a preferred solution, the system further includes a reporting module, which is used to generate a pre-coordination report and a post-coordination report respectively according to multiple groups of pre-coordination data, multiple groups of post-coordination data, and pre-conclusion data and post-conclusion data output by the update module, and then add the pre-coordination report and the post-coordination report to the report database respectively.

[0037] In a preferred solution, the reporting module further includes a query unit, which is used to output the coordination deviation value according to the pre-coordination report and the post-coordination report respectively, and then output the performance score according to multiple groups of pre-coordination data, multiple groups of post-coordination data, and the post-coordination report, and compare them with the performance score critical threshold respectively, and calibrate the performance score greater than or equal to the performance score critical threshold as non-clearable data, and the score less than the performance score critical as clearable data;

[0038] The query module is also used to calculate whether to perform a clearing process according to the deviation amount between the clearable data and the retention threshold of the pre-coordination report and the post-coordination report, then output the post-training data in the cleared pre-coordination report and post-coordination report as fault-tolerant data, and summarize the fault-tolerant data into a fault-tolerant database, and perform an output reminder process before the clearing process in the fault-tolerant database.

[0039] In a preferred solution, an association module is further included after the restoration module, and the association module is used to output the continuous deviation rate of the pre-conclusion data and the post-conclusion data according to the optimal coordination result;

[0040] The output result of the association module also includes temporary post-coordination information and long-term post-coordination information, and the termination node of the post-coordination report;

[0041] Among them, if the long-term post-coordination information is a non-standard coordination score, output the lower limit of the coordination cooperation degree deviation between the operator and the pre-conclusion data coordination information, and its deviation amount from the evaluation threshold, and give a real-time reminder when it is less than 0, and perform dynamic adjustment allocation processing on the continuous execution time period according to the comparison result with the termination node; otherwise, perform an immediate termination process, and synchronously add the independent evaluation consideration score of the operator.

[0042] The present invention also provides an intelligent rehabilitation training intervention device for developmental coordination disorder, which is characterized by comprising:

[0043] At least one processor;

[0044] A storage device storing at least one program, wherein the at least one program is configured to run on the at least one processor, causing the at least one processor to execute the steps of the intelligent rehabilitation training intervention system for developmental coordination disorder.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: by collecting pre-coordination data and updated post-coordination data, the present invention can more accurately understand the rehabilitation progress of patients, and then automatically output optimization strategies and intervention measures to help patients more effectively improve coordination disorders. By updating the training information in real time, the rehabilitation plan can be dynamically adjusted to ensure that patients are always trained in the best state. In addition, by continuously iterating and optimizing the data and coordination data, the stability and accuracy of the training results are ensured, thereby continuously improving the rehabilitation effect of patients. Through a multi-loop evaluation mechanism, abnormal coordination performance can be detected and adjusted immediately to avoid the emergence of adverse states. A coordination data storage mechanism can also be established to ensure the long-term retention of key information. At the same time, by updating the post-coordination report in real time, detailed training feedback can be provided to help patients and medical staff understand the rehabilitation progress. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the system structure of the present invention;

[0047] Figure 2 It is a schematic diagram of the sub-structure of the post-evaluation module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] Embodiment 1

[0049] As Figure 1 shown, this embodiment provides an intelligent rehabilitation training intervention system for developmental coordination disorder, comprising:

[0050] A first information collection module for collecting training information and coordination information, respectively performing calibration processing, and outputting the calibrated training information as pre-training data, and outputting the calibrated coordination information as multiple groups of pre-coordination data;

[0051] A second information collection module for collecting conclusion information from multiple groups of pre-coordination data and synchronously outputting it as pre-conclusion data;

[0052] An evaluation module, configured to match pre-conclusion data corresponding to pre-training data, output an optimization strategy based on the pre-conclusion data corresponding to the pre-training data, collect optimization information according to the optimization strategy, and output it as pre-optimization data;

[0053] An update module, configured to collect update information of training information and output it as post-training data;

[0054] A restoration module, configured to collect subsequent information of the pre-optimization data, output it as post-optimization data, and then output the coordination information of the post-optimization data as multiple groups of post-coordination data;

[0055] A post-evaluation module, configured to match post-coordination data corresponding to the post-training data, output an intervention strategy based on the post-coordination data corresponding to the post-training data, and determine the conclusion information of the optimization strategy as the optimal coordination result according to the intervention strategy.

[0056] In this embodiment, during the intelligent rehabilitation training process, the system first collects training information and coordination information in real time through an information collection device, and then performs clustering and calibration processing on this information, so that the training information can be integrated into pre-training data, and at the same time the coordination information can be subdivided into pre-coordination data for multiple execution time periods to determine whether the patient has a coordination disorder. Synchronously, it can also be compared with the preliminary diagnosis conclusion provided by the doctor. If the number of execution time periods with data smaller than the unobstructed execution time periods exceeds dozens of times the standard execution interval calibration quantity (such as 60), it can be determined that the patient has a developmental coordination disorder. Otherwise, continue to collect coordination data. After classifying the coordination data into a normal distribution, the monitoring and collection work of this patient can be stopped, and the collection and monitoring work can only be restarted when the active or coordination disorder increases.

[0057] Next, the system will generate pre-conclusion data based on the conclusion information in the pre-coordination data and output it synchronously. These pre-conclusion data will be matched with the pre-training data to evaluate the effect of the rehabilitation training. After having the pre-conclusion data, an appropriate optimization strategy will be output. According to the real-time collected optimization information, pre-optimization data will be generated based on this information to further improve the effect of the rehabilitation training. During the training process, the training information will be continuously updated. The system will collect this update information and integrate it into post-training data to maintain the effectiveness and adaptability of the training plan. After collecting and outputting the post-training data, the subsequent information of these pre-optimization data will be collected, and this information will be integrated into post-optimization data, and then the post-optimization data will be standardized and output for comparison with the current training data.

[0058] Finally, the system will match the corresponding post-coordination data based on the post-training data. By analyzing this data, it will output the corresponding intervention strategies to help patients coordinate rehabilitation training more effectively. The system will determine the conclusion information of the optimization strategy based on the intervention strategies and provide patients with personalized and optimal coordinated rehabilitation programs.

[0059] Furthermore, adjustments such as increasing the execution time period of coordinated information and compressing the execution time period of coordinated information can be made according to the parameters of the optimization strategy. Through these adjustments, even if the developmental coordination disorder is no longer optimized, the corresponding optimal coordinated results can be output, ensuring the scientific and efficient rehabilitation process of patients. A patrol-style monitoring of the stable coordination situation during multiple training processes can ensure the stability of stable coordination information without performing unnecessary monitoring. In the case of patrol-style monitoring and for the score data of sub-standard coordination during the monitoring process, intentional deviation can also be achieved. In this case, the system only records the coordinated evaluation input by the operator compared to the trainee and only makes a chaotic evaluation or an independent evaluation of the operator and relevant receptors, so that the intelligent rehabilitation setting will not be overused under non-execution conditions.

[0060] The first information collection module is used to collect training information and coordinated information, perform calibration processing on them respectively, output the calibrated training information as pre-training data, and output the calibrated coordinated information as multiple groups of pre-coordination data.

[0061] Embodiment 2

[0062] Embodiment 2 further limits on the basis of Embodiment 1: If the training is carried out for the first time, the operator's past coordinated data needs to be retrieved. The normal execution interval from the first execution to the classified coordinated data is 3-7 weeks. During this period, there will be conclusion processing of increasing or decreasing that is less than the normal execution interval. On the contrary, if the adapting operator does not change, then only the post-information collection module needs to be started for monitoring and adjustment. The monitoring process is to perform mind map-style correction on the coordinated information verified during the historical adaptation process of the corresponding adaptation period and record the changes in the operator's coordination level, that is, the rehabilitation program of the adaptation period can be executed more coordinately and proficiently. Specifically, the first information collection module includes:

[0063] The first information collection unit is used to collect training information, add time tags to it according to the time sequence of the training information, and then output the training information with time tags as pre-training data;

[0064] The second information collection unit is used to collect coordinated information, perform segmentation processing on the coordinated information to obtain multiple execution time periods, output the pre-training data as multiple pre-coordinated information according to the multiple execution time periods, and then arrange the pre-conclusion data in the order of the execution time periods and summarize them into multiple groups of pre-coordination data.

[0065] Specifically, the first information collection unit collects training information, which includes a smooth conclusion status and a reserved execution time period (i.e., the execution time period of the adopter or the rehabilitation object); after collecting the training information, according to its time sequence, the smooth conclusion status is output as pre-training data, and at the same time, a mark record is made; at the same time, the measured execution time period is output as pre-coordination data, and at the same time, a segmentation process is carried out to obtain multiple execution time periods, and coordination information is added according to the measured execution time period; finally, the pre-conclusion data is arranged in the order of the execution time period and summarized into multiple groups of pre-coordination data. The execution time periods of the multiple groups of pre-coordination data are within the deployment monitoring period of 3-7 weeks, and the normal execution interval corresponds to different adaptation periods, including the initial adaptation period, the end period of semi-cooperation, and the stable period, etc.

[0066] The second information collection module is used to collect the conclusion information in multiple groups of pre-coordination data and synchronously output it as pre-conclusion data;

[0067] Example 3

[0068] Example 3 further limits on the basis of Example 1: The second information collection module includes:

[0069] The conclusion information determination unit is used to collect the conclusion information in multiple groups of pre-coordination data;

[0070] The pre-conclusion data output unit is used to classify the conclusion information into phased first conclusion information and abnormal second conclusion information according to the synchronous calibration result of the pre-coordination information. Among them, the phased first conclusion information is synchronized with the execution time period of the pre-coordination information, and the abnormal second conclusion information is synchronized with the data smaller than the execution time period;

[0071] The pre-conclusion data output unit is also used to add the phased first conclusion information to the first conclusion data and add the abnormal second conclusion information to the second conclusion data.

[0072] Specifically, the second information collection module is used to collect the conclusion information in multiple groups of pre-coordination data. When outputting the pre-conclusion data, by determining that the conclusion information can be synchronized as the pre-conclusion data and classified into phased or abnormal types. At the same time, the phased first conclusion information will be added to the first conclusion data, while the abnormal second conclusion information will be added to the second conclusion data. In this way, the classification output of the pre-conclusion data can be realized.

[0073] The evaluation module is used to match the pre-conclusion data corresponding to the pre-training data, output an optimization strategy according to the pre-conclusion data corresponding to the pre-training data, collect optimization information according to the optimization strategy, and output it as pre-optimization data;

[0074] Example 4

[0075] Example 4 further limits on the basis of Example 1: The evaluation module includes:

[0076] An evaluation unit for calculating the coordination degree according to the pre-conclusion data corresponding to the pre-training data;

[0077] An optimization threshold calculation unit for outputting an optimization threshold according to the optimized working condition and the standard working condition;

[0078] An optimization unit for outputting an optimization strategy according to the deviation amount between the coordination degree and the optimization threshold;

[0079] Wherein, the optimization unit is further configured to collect and increase the execution time period of the coordination information corresponding to the optimization strategy with an output of increase, add the increased execution time period to the coordination information, and then synchronously calibrate the data updated with the increased execution time period as the first conclusion information;

[0080] The optimization unit is further configured to collect and compress the execution time period of the coordination information corresponding to the optimization strategy with an output of compression, output the compressed execution time period as an abnormal execution result, and then synchronously calibrate the update of the compressed execution time period as the second conclusion information.

[0081] Specifically, calculate the coordination degree according to the pre-training data and the pre-conclusion data, then design and calculate the output of the optimization threshold according to the optimized working condition and the standard working condition. After that, it is necessary to compare the coordination degree with the optimization threshold, and then output the corresponding optimization strategy according to the result, including the strategy of increasing the execution time period and the strategy of compressing the execution time period. Then add the increased and / or compressed execution time period to the coordination information and synchronously calibrate it. When the execution time period is increased, it will be updated as the first conclusion information accordingly to ensure the integrity and coordination of the data (thus generating pre-optimized data); when the execution time period is compressed, it will be for the abnormal execution result, and then update the second conclusion information accordingly to ensure its correctness.

[0082] An update module for collecting the update information of the training information and outputting it as post-training data;

[0083] Example 5

[0084] Example 5 further limits on the basis of Example 1: The update module includes:

[0085] An update unit for collecting the update information of the training information, performing time-tagging processing on it, and then outputting it as post-training data;

[0086] An evaluation unit is used to calculate a coordination evaluation score corresponding to the pre-conclusion data based on the post-coordination data and the pre-coordination data, and classify the coordination evaluation score into a non-zero evaluation score and a zero evaluation score;

[0087] The coordination score evaluation unit is also used to compare the non-zero evaluation score corresponding to the pre-conclusion data with the standard coordination score, and interact the non-zero evaluation score when it is greater than or equal to the standard coordination score; when it is less than the standard coordination score, it reports the generation of a lowered conclusion corresponding to the non-zero evaluation score, and then resumes to the non-zero evaluation score, and synchronously clears the subsequent information of the phased second conclusion information that the post-coordination data corresponding to the lowered conclusion improves compared with the pre-coordination data.

[0088] Specifically, data is read for relevant modules, corresponding data is updated and marked as post-training data, and then the statistically obtained post-synchronization coordination data is output. This process will not repeatedly update the historical state data, and the relevant historical state data is updated only after being called.

[0089] During the update, it is necessary to calculate and output the corresponding coordination evaluation score according to the post-coordination data corresponding to the pre-conclusion data and the pre-coordination data. The coordination evaluation score will be classified into a non-zero evaluation score and a zero evaluation score. After that, it is necessary to continue to compare them. During the comparison process, the non-zero evaluation score of the pre-conclusion data will be compared with the standard coordination score, and then it is judged whether it is greater than or equal to the standard coordination score.

[0090] After confirming that it is greater than or equal to the standard coordination score, the non-zero evaluation score will be adjusted and updated accordingly (that is, it is clear that continuous monitoring is required). For example, risk prediction control is performed based on the difference between the front and back evaluation scores. During the prediction execution process, the right is to implement the execution time period of the coordination information in the adjusted prediction direction. The purpose is to avoid the output of the phased second conclusion information (executable in the non-lowered conclusion state. If a lowered conclusion is output, it is necessary to execute and immediately optimize to the phased second conclusion information, and then redefine the lowered conclusion of the previous data according to the second conclusion information, but the conclusion timely state of the previous data will not be lowered).

[0091] Conversely, if it is less than the standard coordination score and a lowering conclusion is generated synchronously, corrective adjustments will be made according to real-time actions, restoring to the state of generating the lowering conclusion, and an evaluation report will be generated accordingly. The report will include non-zero evaluation scores and clearly defined lowering conclusions, etc. After lowering, the generation of the second lowering conclusion in stages will be repeated. The specific stage-defined time period, for example, is 30 historical coordination information, accounting for 1 / 2 of the executed historical coordination data. Then, the score data of the standard coordination will be further determined (the corresponding proficiency of precise measurement error), etc. Then, based on the post-coordination data and pre-coordination data corresponding to the lowering conclusion, the stage two conclusion information will be improved (i.e., optimizing the proficiency of the operator during the adaptation period, etc.), and the calibration of the second lowering conclusion will be cleared synchronously;

[0092] A restoration module, configured to collect subsequent information of the pre-optimized data, output it as post-optimized data, and then output the coordination information of the post-optimized data as multiple groups of post-coordination data;

[0093] Embodiment 6

[0094] Embodiment 6 further limits on the basis of Embodiment 1: a correlation module is further included after the restoration module, and the correlation module is configured to output the continuous deviation rate of the pre-conclusion data and the post-conclusion data according to the optimal coordination result;

[0095] The output result of the correlation module further includes temporary post-coordination information and long-term post-coordination information, as well as the termination node of the post-coordination report;

[0096] Among them, if the long-term post-coordination information is a non-standard coordination score, the lower limit of the coordination cooperation degree between the operator and the pre-conclusion data coordination information and its deviation amount from the evaluation threshold will be output, and real-time reminder will be given when it is less than 0, and dynamic adjustment allocation processing will be performed on the continuous execution time period according to the comparison result with the termination node; conversely, immediate termination processing will be performed, and the independent evaluation consideration score of the operator will be added synchronously.

[0097] Specifically, during the corrective optimization process, it is executed step by step. The preset first stage includes collecting subsequent information of the pre-optimized data (no real-time reminder will be given according to the deviation amount between the coordination information of the post-optimized data and the coordination information of the pre-coordination data).

[0098] Then, through corresponding restoration optimization calculations, this information will be further summarized into post-optimized data. Next, the coordination information in the post-optimized data will be extracted, and then multiple groups of post-coordination data will be output according to the deviation amount calculation. Then, the post-optimized data will be given to the correlation module. The correlation relationship between the subsequent optimization data is divided into the output key parameters of the correlation module based on the data input by the operator, helping to determine the continuous deviation rate between the pre-conclusion data and the post-conclusion data.

[0099] The results of its output association module include the continuation deviation rate of pre-conclusion data and post-conclusion data (measuring the instantaneous sum conclusion and the forgetting index), and importantly also include the termination nodes for generating post-coordination reports based on the sampling execution frequency of non-long-term post-coordination information. Then, it will output the optimized execution time period according to the execution situation, allocate resources in the direction of measurement compensation, timely correct the planned execution path, etc., and make several reasonable arrangements to reduce the interruption of the operator's training frequency. Then, it decides whether to continue monitoring. If it continues to monitor, it will allocate and process the continuous execution time period in a dynamically adjusted manner. If it exceeds the execution time period, it will directly terminate the execution time period immediately (in this case, there is an automatic trigger). At the same time, it will synchronously add the measurement results of the operator's fitness to the independent evaluation consideration score.

[0100] A post-evaluation module is used to match post-coordination data corresponding to post-training data, output an intervention strategy based on the post-coordination data corresponding to the post-training data, and determine that the conclusion information of the optimization strategy is the optimal coordination result according to the intervention strategy.

[0101] Embodiment 7

[0102] As Figure 2 shown, Embodiment 7 further limits on the basis of Embodiment 1: The post-evaluation module includes:

[0103] A coordination unit is used to output a coordination score according to the post-coordination data corresponding to the post-training data;

[0104] An evaluation threshold calculation unit is used to output a standard coordination score according to the coordination measurement standard;

[0105] A basis unit is used to output an improvement state and a decline state according to the comparison results of the optimization strategy, the coordination score, and the standard coordination score;

[0106] In the improvement state, determine the post-coordination information corresponding to the post-training data as stable coordination information, calculate the proportion of the coordination working conditions of the standard coordination score, and synchronously convert the increase strategy and compression strategy in the optimization strategy into increase working conditions and compression working conditions;

[0107] In the decline state, calculate the non-standard coordination score corresponding to the decline state, report all compression strategies and non-standard coordination scores less than the standard coordination score in real time, and restore the execution time period of each non-standard coordination score to the coordination evaluation score in the corresponding pre-conclusion data.

[0108] Specifically, subsequent survey data will first be collected under the standard coordinated configuration (which determines the system pressure and the energy supply allocation strategy), and the coordinated value of the post-coordinated data will be calculated. Then, the coordinated measurement standard will be evaluated and measured to output the corresponding standard coordinated score. Subsequently, the measured coordinated score, the standard coordinated score, and the optimization strategy will be compared, and then it will be determined whether it is in an improving state or a declining state. In the improving state, the post-coordinated information is stable coordinated information, and then the proportion of the coordinated working conditions of its standard coordinated score will be measured, and the optimization strategy will be adjusted accordingly.

[0109] Specifically, the meeting will synchronously transform the increase strategy and the compression strategy in the optimization strategy into the corresponding increase working condition and compression working condition. When the system is in a declining state, the non-standard coordinated score corresponding to the declining state will be measured, and a detailed report will be made on all compression strategies and non-standard coordinated scores less than the standard coordinated score. The execution time period of each non-standard coordinated score will be restored to the coordinated measurement score in the corresponding pre-conclusion data, and then it will be decided whether continuous monitoring can be carried out. During the execution process, a reasonable arrangement will be made for the execution frequency of the coordinated information with a small deviation amount to optimize the entire execution system, and then the corresponding replacement stimulation recovery plan will be executed. After the end, the plan execution path will be corrected in a timely manner to ensure that the training task will not be overly interfered.

[0110] Embodiment 7

[0111] Embodiment 7 further limits on the basis of Embodiment 1: The system further includes a reporting module, and the reporting module is used to generate a pre-coordinated report and a post-coordinated report respectively according to multiple groups of pre-coordinated data, multiple groups of post-coordinated data, pre-conclusion data, and post-conclusion data output by the update module, and then add the pre-coordinated report and the post-coordinated report to the report database respectively.

[0112] Specifically, in this reporting module, pre-coordinated data and types will be output according to the updated module. Among them, the protection period of the data in the pre-coordinated report is longer than that of the data in the post-coordinated report (for example, the former matches the execution time, and the latter is adaptability data, including 1 to 3 weeks). Then, the corresponding post-coordinated data will be synchronously output. After that, the pre-conclusion data and the post-conclusion data will be determined for the pre-coordinated data and the post-coordinated data. Then, the relevant pre-conclusion data and post-conclusion data will be subjected to corresponding information extraction, and the pre-coordinated report and the post-coordinated report will be synchronously output. Subsequently, the pre-coordinated report and the post-coordinated report after sorting, processing, and summarizing will be added to the corresponding databases respectively.

[0113] Embodiment 8

[0114] Embodiment 8 further limits on the basis of Embodiment 1: The reporting module further includes a query unit, which is used to output coordination deviation values according to the pre-coordination report and the post-coordination report respectively, and then output performance scores according to multiple groups of pre-coordination data, multiple groups of post-coordination data, and the post-coordination report, and compare them with the performance score critical thresholds respectively, and calibrate the performance scores greater than or equal to the performance score critical threshold as non-clearable data, and the scores less than the performance score critical threshold as clearable data;

[0115] The query module is also used to calculate whether to perform clearing processing according to the deviation amount between the clearable data and the retention thresholds of the pre-coordination report and the post-coordination report, and then output the post-training data in the cleared pre-coordination report and post-coordination report as fault-tolerant data, and summarize the fault-tolerant data into a fault-tolerant database, and perform output reminder processing before clearing processing in the fault-tolerant database.

[0116] Specifically, a corresponding report query will be first executed for the user, and the pre-coordination report and the post-coordination report will be input to output the coordination deviation value.

[0117] Then, the determined pre-coordination data and post-coordination data will be compared accordingly, and then the corresponding performance score results will be output (measuring the load performance of the measurement system in response to coordination obstacles and optimizing the results of the automatic scheduling plan for this purpose).

[0118] Finally, by combining the non-response status data for the coordination score in the database, the corresponding lower main information will be screened out, and then the relevant non-clearable data will be obtained; secondly, the deviation threshold and the retention threshold deviation amount between the non-clearable data and the pre-coordination report will be measured, and then whether the threshold will be quickly cleared will be calculated according to the deviation amount of the post-coordination report corresponding to the report retention threshold (delayed clearing and immediate clearing).

[0119] Then, according to the relevant results, the fault-tolerant data of the key report labels in the cleared data will be retained and re-output, and then the data will be classified and submitted accordingly, and these fault-tolerant data will be used for emergency allocation. It should be noted that in this case, only the corresponding data information will be output before the clearing processing in the fault-tolerant database until the data information participated later completes reasonable replacement (it cannot be timely regulated according to the needs of the situation, so as to more effectively ensure the internal response effect).

[0120] In this embodiment, a comprehensive and dynamic intelligent monitoring and intervention for the rehabilitation training of developmental coordination disorder is realized, with multiple training data and coordination information. According to real-time collection, the training information and coordination information are synchronously collected and output as pre-training data and multiple groups of pre-coordination data, which can help clarify the degree of coordination disorder of the patient. Then, for the patient's characteristic pre-coordination data, it can be judged whether the current situation can be used as the initial combination construction or combined with historical execution assistance information for the initial execution information, so as to help enhance the rehabilitation effect of the patient. Then, by combining these data, it can further help to establish the evaluation and adjustment of the pre-training data and the updated post-training data continuously, so as to ensure the rationality and adaptability of the training plan.

[0121] Through the multi-loop comparison monitoring of the standard coordination score, the optimal coordination support is provided for the execution of each response task. It can also re-evaluate and output the fault-tolerant data according to the urgency of the data and the response effect, so as to respond to the first execution in other better situations in the monitoring plan, etc. Through this solution, a dynamic adjustment mechanism can be established more effectively, ensuring the effectiveness of the training, implementing the coping strategies, making the coordination score of the training be preferentially executed compared with the key score of the rehabilitation, and the adjustment of the execution plan can be further delayed based on the data logic of the execution support of the previous execution task, etc., improving the rehabilitation efficiency of the patient. And through continuous and accurate diagnosis and timely adjustment, not only can targeted intervention measures be provided through accurate data analysis, but also the coordination ability of the patient can be accurately judged and the coordination ability of the patient can be improved.

[0122] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent rehabilitation training intervention system for developmental coordination disorder, characterized in that, Including: A first information acquisition module, configured to acquire training information and coordination information, perform calibration processing on each of them respectively, output the calibrated training information as pre-training data, and output the calibrated coordination information as multiple groups of pre-coordination data; A second information acquisition module, configured to acquire conclusion information from multiple groups of pre-coordination data and synchronously output it as pre-conclusion data; An evaluation module, configured to match pre-conclusion data corresponding to the pre-training data, output an optimization strategy according to the pre-conclusion data corresponding to the pre-training data, acquire optimization information according to the optimization strategy, and output it as pre-optimization data; An update module, configured to acquire update information of the training information and output it as post-training data; A restoration module, configured to acquire subsequent information of the pre-optimization data, output it as post-optimization data, and then output the coordination information of the post-optimization data as multiple groups of post-coordination data; A post-evaluation module, configured to match post-coordination data corresponding to the post-training data, output an intervention strategy according to the post-coordination data corresponding to the post-training data, and determine the conclusion information of the optimization strategy as the optimal coordination result according to the intervention strategy.

2. The intelligent rehabilitation training intervention system for developmental coordination disorder according to claim 1, wherein: The first information acquisition module includes: A first information acquisition unit, configured to acquire training information, add a time tag to it according to the time sequence of the training information, and then output the training information with the time tag added as pre-training data; A second information acquisition unit, configured to acquire coordination information, perform segmentation processing on the coordination information to obtain multiple execution time periods, output the pre-training data as multiple pieces of pre-coordination information according to the multiple execution time periods, arrange the pre-conclusion data in the order of the execution time periods, and summarize them into multiple groups of pre-coordination data.

3. The intelligent rehabilitation training intervention system for developmental coordination disorder according to claim 1, characterized in that: The second information acquisition module includes: A conclusion information determination unit, configured to acquire conclusion information from multiple groups of pre-coordination data; A pre-conclusion data output unit, configured to classify the conclusion information into stage-based first conclusion information and abnormal second conclusion information according to the synchronous calibration result of the pre-coordination information, wherein the stage-based first conclusion information is synchronized with the execution time period of the pre-coordination information, and the abnormal second conclusion information is synchronized with data smaller than the execution time period; The pre-conclusion data output unit is further configured to add the stage-based first conclusion information to the first conclusion data and add the abnormal second conclusion information to the second conclusion data.

4. The intelligent rehabilitation training intervention system for developmental coordination disorder according to claim 1, wherein: The evaluation module includes: An evaluation unit, configured to calculate the coordination cooperation degree according to the pre-conclusion data corresponding to the pre-training data; an optimization threshold measurement unit, configured to output an optimization threshold according to the optimization working condition and the standard working condition; An optimization unit, configured to output an optimization strategy according to the deviation amount between the coordination cooperation degree and the optimization threshold; wherein, the optimization unit is further configured to correspondingly acquire and increase the execution time period of the coordination information according to the output optimization strategy for increase, add the increased execution time period to the coordination information, and synchronously calibrate the updated data of the increased execution time period as the first conclusion information; The optimization unit is further configured to correspondingly acquire and compress the execution time period of the coordination information according to the output optimization strategy for compression, output the compressed execution time period as an abnormal execution result, and synchronously calibrate the update of the compressed execution time period as the second conclusion information.

5. The intelligent rehabilitation training intervention system for developmental coordination disorder according to claim 1, wherein: The update module includes: An update unit, configured to collect update information of training information, process it by adding a time tag, and then output it as post-training data; An evaluation unit, configured to calculate a coordination evaluation score corresponding to the pre-conclusion data according to the post-coordination data and the pre-coordination data, and classify the coordination evaluation score into a non-zero evaluation score and a zero evaluation score; The coordination score evaluation unit is further configured to compare the non-zero evaluation score corresponding to the pre-conclusion data with the standard coordination score, and interact the non-zero evaluation score when it is greater than or equal to the standard coordination score; when it is less than the standard coordination score, report and generate a lowered conclusion corresponding to the non-zero evaluation score, and then restore to the non-zero evaluation score, and synchronously clear the subsequent information of the phased second conclusion information that the post-coordination data corresponding to the lowered conclusion improves compared with the pre-coordination data.

6. The intelligent rehabilitation training intervention system for developmental coordination disorder according to claim 1, wherein: The post-evaluation module includes: A coordination unit, configured to output a coordination score according to the post-coordination data corresponding to the post-training data; An evaluation threshold calculation unit, configured to output a standard coordination score according to the coordination measurement standard; A basis unit, configured to output an improvement state and a decline state according to the comparison results of the optimization strategy, the coordination score, and the standard coordination score.

7. The intelligent rehabilitation training intervention system for developmental coordination disorder according to any one of claims 1 to 6, characterized in that: The system further includes a reporting module, which is configured to respectively generate a pre-coordination report and a post-coordination report according to multiple groups of pre-coordination data, multiple groups of post-coordination data, pre-conclusion data, and post-conclusion data output by the update module, and then add the pre-coordination report and the post-coordination report to the report database respectively.

8. The intelligent rehabilitation training intervention system for developmental coordination disorder according to any one of claims 1 to 6, characterized in that: After the restoration module, there is further an association module, which is configured to output a continuous deviation rate of the pre-conclusion data and the post-conclusion data according to the optimal coordination result; The output result of the association module further includes temporary post-coordination information, long-term post-coordination information, and termination nodes of the post-coordination report; wherein, if the long-term post-coordination information is a non-standard coordination score, the lower limit of the coordination cooperation degree between the operator and the pre-conclusion data coordination information, and its deviation amount from the evaluation threshold are output, and real-time reminder is performed when it is less than 0, and dynamic adjustment allocation processing is performed on the continuous execution time period according to the comparison result with the termination node; otherwise, immediate termination processing is performed, and the independent evaluation consideration score of the operator is synchronously added.

9. An intelligent rehabilitation training intervention device for developmental coordination disorder, characterized in that: Including: At least one processor; A storage device, on which at least one program is stored, wherein the at least one program is configured to be run on the at least one processor, so that the at least one processor executes the steps of the intelligent rehabilitation training intervention system for developmental coordination disorder.

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