Vascular cognitive impairment assessment system based on Internet of Things

Abstract

The invention relates to the technical field of smart medical treatment, and aims to solve the problems that existing vascular cognitive impairment assessment lacks long-term observation and analysis of real cognitive behaviors in a family scene, and structured acquisition and intelligent identification of behavior data of a patient cannot be performed in combination with an Internet of Things technology; the potential cognitive degeneration signal of the patient in the actual life is difficult to reflect; according to the vascular cognitive impairment assessment system based on the Internet of Things, the Internet of Things equipment with the wireless transmission function is introduced, all-weather collection of cognitive behavior data of a patient in a real family environment is achieved, and the cognitive impairment assessment system based on the Internet of Things has the advantages that the cognitive behavior data of the patient in the real family environment can be acquired; according to the method and the system, data are acquired, multi-dimensional analysis is performed on the data, three cognitive dimensions of memory, execution and language are covered, early warning and hierarchical management of the vascular cognitive impairment are realized, and timeliness and accuracy of an evaluation result are improved.

Description

Technical Field

[0001] This invention relates to the field of smart healthcare technology, specifically to an Internet of Things-based assessment system for vascular cognitive impairment. Background Technology

[0002] In medical research, vascular cognitive impairment is a type of chronic neurodegenerative disease associated with cerebrovascular lesions. With the rapid development of smart healthcare technology, traditional cognitive assessment methods, which mainly rely on manual consultation and paper questionnaires, are gradually becoming unable to meet the actual needs of high-frequency, multi-dimensional, and remote continuous monitoring. Against this backdrop, building a systematic platform with automatic information collection, intelligent analysis, and risk warning capabilities, centered on the specific goal of real-time assessment of vascular cognitive impairment through IoT devices, has become an important development direction.

[0003] Currently, the assessment of vascular cognitive impairment still mainly relies on traditional offline testing methods. After patients leave medical institutions, there is a lack of long-term observation and analysis of their real cognitive behavior in home settings, which greatly reduces the accuracy of the assessment cycle. At the same time, most existing systems fail to combine IoT technology to collect and intelligently identify patients' behavioral data (such as household appliance usage habits and voice interaction responses) in a structured manner, making it difficult to reflect potential cognitive decline signals in patients in real life. In addition, because current system assessments cannot be subdivided into multiple cognitive dimensions such as memory, executive function, and language, it is difficult to achieve multidimensional early warning of the early stages of vascular cognitive impairment. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an Internet of Things-based assessment system for vascular cognitive impairment, which solves the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solution: an Internet of Things-based assessment system for vascular cognitive impairment, including a cloud server, which is communicatively connected to a data acquisition unit and a cloud database, and is also communicatively connected to a cognitive sorting unit, a memory cognitive analysis unit, an executive cognitive analysis unit, a language cognitive analysis unit and a display terminal; The data acquisition unit is used to collect historical cognitive test comprehensive score data of subjects with a history of vascular disease within a historical unit of time, and to acquire cognitive assessment scale score data, memory cognitive behavior data, executive cognitive behavior data and language cognitive behavior data of subjects in real time through Internet of Things devices, and send each type of data to the cloud database for storage. The cloud database is also used to store cognitive type level test judgment table. The cognitive ranking unit is used to analyze the periodic setting of cognitive impairment assessment for test subjects based on historical cognitive test comprehensive score data, generate high-level signals, intermediate signals and low-level signals, thereby completing the setting of cognitive impairment assessment period for test subjects, ranking and analyzing the cognitive impairment assessment types of test subjects, thereby generating single assessment signals, multiple assessment signals and normal signals for vascular complex cognitive impairment, and pushing information notifications to relevant personnel through the display terminal for the normal signals for vascular complex cognitive impairment. The memory and cognition analysis unit is used to assess and analyze memory and cognition impairments based on the assessment priority of each type of cognitive impairment determined within the assessment cycle set in the next process, and in combination with the memory and cognition behavior data of the test subjects obtained by real-time monitoring. Based on this, it generates a memory and cognition impairment warning signal and pushes information notifications to relevant personnel through the display terminal. The executive cognitive analysis unit is used to assess and analyze executive cognitive impairment based on the executive cognitive behavior data of the test subjects obtained through real-time monitoring, generate an early warning signal for executive cognitive impairment, and push information notifications to relevant personnel through the display terminal. The language cognitive analysis unit is used to assess and analyze language cognitive impairment based on real-time monitored language cognitive behavior data, generate language cognitive impairment early warning signals, and push information notifications to relevant personnel through a display terminal.

[0006] Preferably, the Internet of Things (IoT) device includes: various types of home appliances with wireless network transmission capabilities and a patchable cognitive state assessment device, wherein the patchable cognitive state assessment device is used to conduct cognitive tests on subjects using a pre-stored cognitive assessment scale.

[0007] Preferably, the analysis of the periodic setting for assessing cognitive impairment in the subjects includes the following specific analysis process: The historical cognitive test comprehensive score data of the subjects were selected from several time periods. Based on the historical cognitive test comprehensive score values ​​of each time period in the historical cognitive test comprehensive score data, the mean of the historical cognitive test comprehensive score of the subjects was obtained by statistical mean calculation algorithm. Based on the rating scale criteria, a rating scale interval was pre-set for the average historical cognitive test scores of the participants. The average historical cognitive test scores of the participants were then input into the rating scale interval for comparison and analysis. The specific comparison and analysis process is as follows: If the average score of the test taker's historical cognition test is greater than the maximum value of the rating level interval, it indicates that the test taker's historical cognition test performance is excellent, and a high-level signal is generated. If the average score of the test taker's historical cognition test is within the rating level interval, it indicates that the test taker's historical cognition test performance is good, and a medium-level signal is generated. If the average score of the test taker's historical cognition test is less than the minimum value of the rating level interval, it indicates that the test taker's historical cognition test performance is poor, and a low-level signal is generated. Based on the generated corresponding level signals, the next stage of cognitive impairment assessment cycle for the subjects is matched with the corresponding hierarchical cycle. The specific matching process is as follows: If the subject's historical cognitive test results are determined to be low-level signals, then the cognitive impairment assessment cycle for the next process of the subject is matched with a first-order cycle; if the subject's historical cognitive test results are determined to be intermediate-level signals, then the cognitive impairment assessment cycle for the next process of the subject is matched with a second-order cycle; if the subject's historical cognitive test results are determined to be high-level signals, then the cognitive impairment assessment cycle for the next process of the subject is matched with a third-order cycle. Specifically, the first-level cycle includes: within a unit time T of the next process, with duration H1 as the dividing length, administering [the following] to the subjects. The second-level cognitive impairment assessment procedure; the second-level cycle specifically includes: within the unit time T of the next process, divided into time intervals of duration H2, the subject is subjected to... The cognitive impairment assessment procedure is performed once; the three-level cycle specifically includes: within the unit time T of the next process, the duration is divided into segments of H3, and the subject is subjected to... The cognitive impairment assessment was performed in 1000 times, and H1 < H2 < H3, while T was divisible by H1, H2 and H3.

[0008] Preferably, the ranking and analysis of the cognitive impairment assessment types of the test subjects includes the following specific analysis and processing steps: After setting the cognitive impairment assessment cycle for the subjects, the memory cognitive score, executive cognitive score, and expressive cognitive score of the subjects were obtained in real time using a patch-type cognitive state assessment device. The memory, executive, and expressive cognitive scores from the participants' cognitive assessment scales were input into a cognitive type level test judgment table stored in the cloud database for judgment processing. The specific process is as follows: If the cognitive type level test judgment table outputs two or more failing results, a multi-assessment signal is generated; if the cognitive type level test judgment table outputs only one failing result, a single assessment signal is generated; if the cognitive type level test judgment table outputs no failing results, a normal signal for vascular comprehensive cognitive impairment is generated, and relevant personnel are notified by pushing information through the display terminal. The single assessment signal execution content is as follows: extract behavioral data corresponding to the cognitive type of the failing result from the cloud database, and perform cognitive impairment assessment operations for the corresponding cognitive type within the assessment cycle set in the next process; The multi-assessment signal execution process involves: Based on the numerical values ​​of memory-based cognitive assessment scores, performance-based cognitive assessment scores, and expression-based cognitive assessment scores, sorting them in ascending order to obtain a cognitive type ranking sequence; and determining the priority of each cognitive type's impairment assessment based on the ranking sequence. The specific process is as follows: The cognitive type that is first in the cognitive type numbering sequence will be given priority for cognitive impairment assessment in the next assessment cycle. The cognitive type that is second in the cognitive type numbering sequence will be given the next cognitive impairment assessment in the next assessment cycle. The cognitive type that is third in the cognitive type numbering sequence will be given the last cognitive impairment assessment in the next assessment cycle.

[0009] Preferably, the specific analysis process for assessing and analyzing memory and cognitive impairment includes: The number of times the refrigerator door was not closed, the number of times the memo reminder was repeated, and the number of times the door password was entered incorrectly were monitored in real time by IoT devices. The three data points were correlated and, after dimensionless processing, the degree of memory and cognitive impairment of the subjects was analyzed to determine the subject's memory and cognitive impairment coefficient, specifically: xjy=a1×cgm+a2×cbw+a3×cmm; where xjy represents the subject's memory and cognitive impairment coefficient, cgm, cbw, and cmm represent the number of times the refrigerator door was not closed, the number of times the memo reminder was repeated, and the number of times the door password was entered incorrectly, respectively. Among them, a1, a2, and a3 represent the weight values ​​of the number of times the refrigerator door was not closed, the number of times the memo reminder was repeated, and the number of times the door password was entered incorrectly, respectively. The specific values ​​are set by those skilled in the art. The memory and cognitive impairment coefficient of the test subjects is compared and analyzed with a preset memory and cognitive impairment threshold. If the memory and cognitive impairment coefficient of the test subjects exceeds the preset memory and cognitive impairment threshold, a memory and cognitive impairment warning signal is generated and the relevant personnel are notified through the display terminal. Otherwise, the next cognitive type impairment assessment operation is performed, or if the memory and cognitive impairment assessment and analysis operation is the last cognitive type impairment assessment operation for the test subjects, the process ends directly.

[0010] Preferably, the specific analysis process for performing the cognitive impairment assessment and analysis includes: The average duration of device operation interruption for the subjects was obtained by real-time monitoring of the subjects' executive cognitive behavior data in each monitoring period using IoT devices and by combining statistical averaging algorithms. The duration of equipment operation interruption in each monitoring period is compared and analyzed with the average duration of equipment operation interruption. If the duration of equipment operation interruption in the corresponding monitoring period exceeds the average duration of equipment operation interruption, the corresponding monitoring period is recorded as an abnormal operation period, and the total duration of the abnormal operation period is calculated. Otherwise, the corresponding monitoring period is recorded as a normal operation period. The ratio of abnormal operation periods to total monitoring time is calculated to determine the percentage of abnormal operation periods. If the percentage of abnormal operation periods exceeds 25%, an early warning signal for cognitive impairment is generated and relevant personnel are notified via the display terminal. Otherwise, the next cognitive impairment assessment operation is performed, or if the cognitive impairment assessment and analysis operation is the subject's last cognitive impairment assessment operation, the process ends directly.

[0011] Preferably, the language cognitive impairment assessment and analysis operation specifically includes the following analysis process: By monitoring the language cognitive behavior data of test subjects in real time using IoT devices, including voice assistant dialogue response time, voice assistant dialogue repetition count, and voice command response error rate, the three data points are correlated and, after dimensionless processing, the degree of language cognitive impairment of the test subjects is analyzed to determine the language cognitive impairment coefficient of the test subjects. Specifically, xyy=(b1×tfy+b2×cdh+b3×vcw) / (b1+b2+b3); where xyy represents the language cognitive impairment coefficient of the test subjects, tfy, cdh, and vcw represent the voice assistant dialogue response time, voice assistant dialogue repetition count, and voice command response error rate, respectively, where / represents a division sign, and b1, b2, and b3 represent the weight values ​​of the voice assistant dialogue response time, voice assistant dialogue repetition count, and voice command response error rate, respectively, and the specific values ​​are set by those skilled in the art. The language cognitive impairment coefficient of the test subjects is compared and analyzed with the preset language cognitive impairment threshold. If the language cognitive impairment coefficient of the test subjects exceeds the preset language cognitive impairment threshold, a language cognitive impairment warning signal is generated and the relevant personnel are notified through the display terminal. Otherwise, the next cognitive type impairment assessment operation is performed. Or, if the language cognitive impairment assessment and analysis operation is the last cognitive type impairment assessment operation for the test subjects, the process ends directly.

[0012] This invention provides an Internet of Things-based assessment system for vascular cognitive impairment, which has the following beneficial effects: (1) The present invention provides an Internet of Things-based assessment system for vascular cognitive impairment, which breaks through the traditional cognitive impairment assessment method based on manual consultation and paper questionnaires. It constructs a system platform covering automatic data collection, intelligent assessment and dynamic early warning, which significantly improves the accuracy and timeliness of monitoring vascular cognitive impairment. The system introduces Internet of Things devices with wireless transmission capabilities, such as home appliances and patch-type cognitive assessment terminals, to realize the 24 / 7 collection of patients' cognitive behavior data in real home environments. It also uses cloud servers to perform structured storage and multi-dimensional analysis of the data, covering the three cognitive dimensions of memory, executive and language, thereby completing the closed-loop design of the system from the data layer to the intelligent analysis layer. Compared with the prior art, the present invention effectively solves the key problems of the cognitive assessment cycle setting relying on subjective judgment, the single dimension of assessment content and the lack of remote continuous monitoring, and realizes early warning and hierarchical management of vascular cognitive impairment.

[0013] (2) By introducing periodic analysis and signal grading mechanisms of historical cognitive test comprehensive score data into the cognitive ranking unit, an intelligent ranking system that can dynamically adapt to changes in the cognitive state of different test subjects is constructed, fundamentally solving the problem that the traditional cognitive assessment cycle setting relies on manual experience and lacks quantitative basis; First, the historical cognitive test comprehensive score data of test subjects in multiple time periods are statistically processed, and the historical score mean is formed by the mean calculation algorithm. The mean is then input into the preset score level division interval for comparison, thereby generating high-level, medium-level, or low-level signals to achieve stratified judgment of the cognitive level of the test subjects; This signal not only reflects the current cognitive state level, but also directly determines the level length matched in the subsequent assessment cycle, for example Low-level signals correspond to high-frequency assessment cycles, while high-level signals correspond to low-frequency cycles, thus forming an adaptive assessment rhythm mechanism that increases the monitoring frequency as cognitive decline worsens. Compared to the static assessment cycles of existing systems, the cognitive ranking unit can automatically optimize the assessment frequency without human intervention, significantly improving the efficiency of assessment resource allocation and the timeliness of cognitive monitoring. Furthermore, through a signal-based hierarchical output mechanism (including single assessment signals, multiple assessment signals, and normal signals for vascular cognitive impairment), the system achieves real-time visualization of cognitive status and early intervention prompts, transforming the entire assessment process from passive tracking to active response, effectively enhancing the sensitivity to early changes in vascular cognitive impairment and the accuracy of cycle matching.

[0014] (3) To address the lack of dimensional segmentation in existing assessment systems, a cognitive ranking module and three types of cognitive analysis units were innovatively introduced to realize dynamic assessment and early warning strategies for vascular cognitive impairment across multiple cognitive dimensions. Based on historical cognitive test scores and real-time collected data, the system automatically matches assessment cycle levels and performs intelligent ranking analysis on the cognitive status of the subjects. When a single or multiple cognitive scores are found to be substandard, the system automatically derives the priority order of the impairments through preset logic and sequentially activates assessment units of different dimensions for in-depth judgment. Attached Figure Description

[0015] Figure 1 This is a block diagram of an Internet of Things-based assessment system for vascular cognitive impairment according to the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example Please see Figure 1 The present invention provides an Internet of Things-based assessment system for vascular cognitive impairment, including a cloud server, which is communicatively connected to a data acquisition unit and a cloud database. The cloud server is also communicatively connected to a cognitive sorting unit, a memory cognitive analysis unit, an executive cognitive analysis unit, a language cognitive analysis unit, and a display terminal. The data acquisition unit is used to collect historical cognitive test comprehensive score data of subjects with a history of vascular disease within a historical unit of time, and to acquire cognitive assessment scale score data, memory cognitive behavior data, executive cognitive behavior data and language cognitive behavior data of subjects in real time through IoT devices, and send each type of data to the cloud database for storage. The cloud database is also used to store cognitive type level test judgment tables. Specifically, IoT devices include: various types of home appliances with wireless network transmission capabilities and patchable cognitive state assessment devices, wherein the patchable cognitive state assessment devices are used to conduct cognitive tests on subjects using pre-stored cognitive assessment scales.

[0018] The cognitive assessment scales include the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment Scale (MoCA), which are standardized cognitive function assessment tools widely used internationally in clinical practice and research. They are used to quickly, quantitatively, and multidimensionally assess an individual's cognitive state. The MMSE mainly assesses the basic cognitive dimensions of memory, language ability, and executive function. The MoCA is designed in a more detailed way, covering the core content of the MMSE and further extending to the cognitive levels of visuospatial structure ability, abstract thinking, delayed recall, and language fluency. It is suitable for screening early cognitive impairment. Specifically, the cognitive ranking unit is used to analyze the periodic setting of cognitive impairment assessments for test subjects based on historical cognitive test comprehensive score data. The specific analysis process includes: The historical cognitive test comprehensive score data of the subjects were selected from several time periods. Based on the historical cognitive test comprehensive score values ​​of each time period in the historical cognitive test comprehensive score data, the mean of the historical cognitive test comprehensive score of the subjects was obtained by statistical mean calculation algorithm. Based on the rating scale criteria, a rating scale interval was pre-set for the average historical cognitive test scores of the participants. The average historical cognitive test scores of the participants were then input into the rating scale interval for comparison and analysis. The specific comparison and analysis process is as follows: If the average score of the test taker's historical cognition test is greater than the maximum value of the rating level interval, it indicates that the test taker's historical cognition test performance is excellent, and a high-level signal is generated. If the average score of the test taker's historical cognition test is within the rating level interval, it indicates that the test taker's historical cognition test performance is good, and a medium-level signal is generated. If the average score of the test taker's historical cognition test is less than the minimum value of the rating level interval, it indicates that the test taker's historical cognition test performance is poor, and a low-level signal is generated. Based on the generated corresponding level signals, the next stage of cognitive impairment assessment cycle for the subjects is matched with the corresponding hierarchical cycle. The specific matching process is as follows: If the subject's historical cognitive test results are determined to be low-level signals, then the cognitive impairment assessment cycle for the next process of the subject is matched with a first-order cycle; if the subject's historical cognitive test results are determined to be intermediate-level signals, then the cognitive impairment assessment cycle for the next process of the subject is matched with a second-order cycle; if the subject's historical cognitive test results are determined to be high-level signals, then the cognitive impairment assessment cycle for the next process of the subject is matched with a third-order cycle. Specifically, the first-level cycle includes: within a unit time T of the next process, with duration H1 as the dividing length, administering [the following] to the subjects. The second-level cognitive impairment assessment procedure; the second-level cycle specifically includes: within the unit time T of the next process, divided into time intervals of duration H2, the subject is subjected to... The cognitive impairment assessment procedure is performed once; the three-level cycle specifically includes: within the unit time T of the next process, the duration is divided into segments of H3, and the subject is subjected to... The cognitive impairment assessment was performed in 1000 times, and H1 < H2 < H3, while T was divisible by H1, H2 and H3.

[0019] Specifically, the cognitive ranking unit is also used to rank and analyze the cognitive impairment assessment types of the subjects. The specific analysis and processing process includes: After setting the cognitive impairment assessment cycle for the subjects, the memory cognitive score, executive cognitive score, and expressive cognitive score of the subjects were obtained in real time using a patch-type cognitive state assessment device. It should be noted that the memory cognitive score, executive cognitive score, and expressive cognitive score are used to quantitatively assess the functional status of the test subjects in specific cognitive dimensions, respectively representing the test subjects' performance levels in short-term memory, task execution and judgment, and language expression and semantic comprehension. These three types of scores are acquired in real time by the patchable cognitive state assessment device through the execution of preset cognitive assessment scale tasks. The assessment tasks include, but are not limited to, memory recall tests, logical sequencing tasks, and voice question-and-answer interactions. Each task is scored based on the test subjects' reaction time, accuracy, and task completion indicators, and the results are automatically classified into the corresponding cognitive type score. The memory, executive, and expressive cognitive scores from the participants' cognitive assessment scales were input into a cognitive type level test judgment table stored in the cloud database for judgment processing. The specific process is as follows: If the cognitive type level test judgment table outputs two or more failing results, a multi-assessment signal is generated; if the cognitive type level test judgment table outputs only one failing result, a single assessment signal is generated; if the cognitive type level test judgment table outputs no failing results, a normal signal for vascular comprehensive cognitive impairment is generated, and relevant personnel are notified by pushing information through the display terminal. The single assessment signal execution content is as follows: extract behavioral data corresponding to the cognitive type of the failing result from the cloud database, and perform cognitive impairment assessment operations for the corresponding cognitive type within the assessment cycle set in the next process; The multi-assessment signal execution process involves: Based on the numerical values ​​of memory-based cognitive assessment scores, performance-based cognitive assessment scores, and expression-based cognitive assessment scores, sorting them in ascending order to obtain a cognitive type ranking sequence; and determining the priority of each cognitive type's impairment assessment based on the ranking sequence. The specific process is as follows: The cognitive type that is first in the cognitive type numbering sequence will be given priority for cognitive impairment assessment in the next assessment cycle. The cognitive type that is second in the cognitive type numbering sequence will be given the next cognitive impairment assessment in the next assessment cycle. The cognitive type that is third in the cognitive type numbering sequence will be given the last cognitive impairment assessment in the next assessment cycle.

[0020] Specifically, the memory and cognitive analysis unit is used to assess and analyze memory and cognitive impairments based on the assessment priority of each type of cognitive impairment determined within the assessment cycle set in the next process, and in conjunction with the memory and cognitive behavior data of the subjects acquired through real-time monitoring. Based on this, it generates a warning signal for memory and cognitive impairment and pushes information notifications to relevant personnel through a display terminal. The specific process includes: The number of times the refrigerator door was not closed, the number of times the memo reminder was repeated, and the number of times the door password was entered incorrectly were monitored in real time by IoT devices. The three data points were correlated and, after dimensionless processing, the degree of memory and cognitive impairment of the subjects was analyzed to determine the subject's memory and cognitive impairment coefficient, specifically: xjy=a1×cgm+a2×cbw+a3×cmm; where xjy represents the subject's memory and cognitive impairment coefficient, cgm, cbw, and cmm represent the number of times the refrigerator door was not closed, the number of times the memo reminder was repeated, and the number of times the door password was entered incorrectly, respectively. Among them, a1, a2, and a3 represent the weight values ​​of the number of times the refrigerator door was not closed, the number of times the memo reminder was repeated, and the number of times the door password was entered incorrectly, respectively. The specific values ​​are set by those skilled in the art. The memory and cognitive impairment coefficient of the test subjects is compared and analyzed with a preset memory and cognitive impairment threshold. If the memory and cognitive impairment coefficient of the test subjects exceeds the preset memory and cognitive impairment threshold, a memory and cognitive impairment warning signal is generated and the relevant personnel are notified through the display terminal. Otherwise, the next cognitive type impairment assessment operation is performed, or if the memory and cognitive impairment assessment and analysis operation is the last cognitive type impairment assessment operation for the test subjects, the process ends directly.

[0021] Specifically, the executive cognitive analysis unit is used to assess and analyze executive cognitive impairment based on real-time monitoring data of the subjects' executive cognitive behaviors, generate early warning signals for executive cognitive impairment, and push notifications to relevant personnel via a display terminal. The specific process includes: Using IoT devices, the device operation interruption duration in each monitoring period of the subjects' executive cognitive behavior data is monitored in real time. Combined with a statistical averaging algorithm, the average device operation interruption duration of the subjects is obtained. It should be noted that the device operation interruption duration refers to the cumulative time during which the device is interrupted or paused after being started due to incomplete operation while the subject is using the smart home device. This reflects the subject's ability to perform continuous tasks. For example, if a subject is distracted after starting the microwave to heat food and fails to complete the subsequent actions of retrieving the food for an extended period, causing the device to remain in standby mode for a long time, this period will be recorded as an operation interruption duration. For example, if a subject frequently cancels the washing program or has long gaps between operation commands while using a smart washing machine, the system will also judge this as an interruption. The duration of equipment operation interruption in each monitoring period is compared and analyzed with the average duration of equipment operation interruption. If the duration of equipment operation interruption in the corresponding monitoring period exceeds the average duration of equipment operation interruption, the corresponding monitoring period is recorded as an abnormal operation period, and the total duration of the abnormal operation period is calculated. Otherwise, the corresponding monitoring period is recorded as a normal operation period. The ratio of abnormal operation periods to total monitoring time is calculated to determine the percentage of abnormal operation periods. If the percentage of abnormal operation periods exceeds 25%, an early warning signal for cognitive impairment is generated and relevant personnel are notified via the display terminal. Otherwise, the next cognitive impairment assessment operation is performed, or if the cognitive impairment assessment and analysis operation is the subject's last cognitive impairment assessment operation, the process ends directly.

[0022] Specifically, the language cognitive analysis unit is used to assess and analyze language cognitive impairment based on real-time monitored language cognitive behavior data, generate early warning signals for language cognitive impairment, and push notifications to relevant personnel through a display terminal. The specific process includes: By monitoring the language cognitive behavior data of test subjects in real time using IoT devices, including voice assistant dialogue response time, voice assistant dialogue repetition count, and voice command response error rate, the three data points are correlated and, after dimensionless processing, the degree of language cognitive impairment of the test subjects is analyzed to determine the language cognitive impairment coefficient of the test subjects. Specifically, xyy=(b1×tfy+b2×cdh+b3×vcw) / (b1+b2+b3); where xyy represents the language cognitive impairment coefficient of the test subjects, tfy, cdh, and vcw represent the voice assistant dialogue response time, voice assistant dialogue repetition count, and voice command response error rate, respectively, where / represents a division sign, and b1, b2, and b3 represent the weight values ​​of the voice assistant dialogue response time, voice assistant dialogue repetition count, and voice command response error rate, respectively, and the specific values ​​are set by those skilled in the art. It should be noted that the voice assistant dialogue response time represents the average time consumed by the subject during verbal interaction with the voice assistant, from the moment the voice assistant issues a question or instruction to the moment the subject provides an effective voice response; the voice assistant dialogue repetition number represents the total number of times the voice assistant needs to repeat the same question or instruction because the subject's initial expression was unclear or irrelevant; and the voice instruction response error rate represents the proportion of incorrect voice instructions issued by the voice assistant to the subject. The language cognitive impairment coefficient of the test subjects is compared and analyzed with the preset language cognitive impairment threshold. If the language cognitive impairment coefficient of the test subjects exceeds the preset language cognitive impairment threshold, a language cognitive impairment warning signal is generated and the relevant personnel are notified through the display terminal. Otherwise, the next cognitive type impairment assessment operation is performed. Or, if the language cognitive impairment assessment and analysis operation is the last cognitive type impairment assessment operation for the test subjects, the process ends directly.

[0023] In the process of using this invention, individuals with a history of vascular disease are used as subjects. Historical cognitive test comprehensive score data of the subjects are collected within a historical unit of time. Cognitive assessment scale score data, memory cognitive behavior data, executive cognitive behavior data, and language cognitive behavior data of the subjects are acquired in real time through IoT devices. All types of data are sent to a cloud database for storage. The cloud database is also used to store cognitive type level test judgment tables. This realizes the automation, multi-source, and high-frequency collection of basic data for cognitive impairment assessment, which significantly improves the data integrity and scenario authenticity of the assessment system. Based on historical cognitive test comprehensive score data, the cycle setting for cognitive impairment assessment of subjects is analyzed, generating high-level, intermediate-level, and low-level signals. Based on this, the cognitive impairment assessment cycle setting for subjects is completed. The cognitive impairment assessment types of subjects are sorted and analyzed, generating single assessment signals, multiple assessment signals, and normal signals for vascular complex cognitive impairment. The normal signals for vascular complex cognitive impairment are pushed to relevant personnel through the display terminal, realizing dynamic linkage and control of assessment cycle and assessment type, and improving the system's accuracy and flexibility in responding to cognitive decline trends. Based on the assessment priority of each cognitive type of impairment determined within the assessment cycle set in the next process, and combined with the memory and cognitive behavior data of the subjects obtained by real-time monitoring, the memory and cognitive impairment assessment and analysis operation is performed, thereby generating a memory and cognitive impairment early warning signal, and pushing information notification to relevant personnel through the display terminal, realizing early identification and personalized early warning of memory impairment, and enhancing the system's sensitivity to detecting the decline of the subjects' short-term memory ability. Based on the executive cognitive behavior data of the subjects obtained through real-time monitoring, the executive cognitive impairment assessment and analysis operation is performed, and an early warning signal for executive cognitive impairment is generated accordingly. The information is pushed to relevant personnel through the display terminal, realizing the quantitative assessment of executive function impairment and focusing on abnormal behavior, and improving the efficiency of identifying variations in cognitive executive ability. Based on real-time monitoring data of language cognitive behavior, language cognitive impairment is assessed and analyzed, and an early warning signal for language cognitive impairment is generated. This signal is then pushed to relevant personnel via a display terminal, enabling real-time assessment and early warning of the degree of language function decline and expanding the assessment dimensions of multimodal cognitive monitoring.

[0024] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An Internet of Things-based assessment system for vascular cognitive impairment, comprising a cloud server, wherein the cloud server is communicatively connected to a data acquisition unit and a cloud database, characterized in that: The cloud server also has communication connections to a cognitive sorting unit, a memory cognitive analysis unit, an executive cognitive analysis unit, a language cognitive analysis unit, and a display terminal; The data acquisition unit is used to collect historical cognitive test comprehensive score data of subjects with a history of vascular disease within a historical unit of time, and to acquire cognitive assessment scale score data, memory cognitive behavior data, executive cognitive behavior data and language cognitive behavior data of subjects in real time through Internet of Things devices, and send each type of data to the cloud database for storage. The cloud database is also used to store cognitive type level test judgment table. The cognitive ranking unit is used to analyze the periodic setting of cognitive impairment assessment for test subjects based on historical cognitive test comprehensive score data, generate high-level signals, intermediate signals and low-level signals, thereby completing the setting of cognitive impairment assessment period for test subjects, ranking and analyzing the cognitive impairment assessment types of test subjects, thereby generating single assessment signals, multiple assessment signals and normal signals for vascular complex cognitive impairment, and pushing information notifications to relevant personnel through the display terminal for the normal signals for vascular complex cognitive impairment. The memory and cognition analysis unit is used to assess and analyze memory and cognition impairments based on the assessment priority of each type of cognitive impairment determined within the assessment cycle set in the next process, and in combination with the memory and cognition behavior data of the test subjects obtained by real-time monitoring. Based on this, it generates a memory and cognition impairment warning signal and pushes information notifications to relevant personnel through the display terminal. The executive cognitive analysis unit is used to assess and analyze executive cognitive impairment based on the executive cognitive behavior data of the test subjects obtained through real-time monitoring, generate an early warning signal for executive cognitive impairment, and push information notifications to relevant personnel through the display terminal. The language cognitive analysis unit is used to assess and analyze language cognitive impairment based on real-time monitored language cognitive behavior data, generate language cognitive impairment early warning signals, and push information notifications to relevant personnel through a display terminal.

2. The IoT-based vascular cognitive impairment assessment system according to claim 1, characterized in that: The Internet of Things (IoT) devices include: various types of home appliances with wireless network transmission capabilities and patchable cognitive state assessment devices, wherein the patchable cognitive state assessment devices are used to conduct cognitive tests on subjects using pre-stored cognitive assessment scales.

3. The IoT-based assessment system for vascular cognitive impairment according to claim 1, characterized in that: The analysis of the timeline for assessing cognitive impairment in the subjects included the following specific steps: The historical cognitive test comprehensive score data of the subjects were selected from several time periods. Based on the historical cognitive test comprehensive score values ​​of each time period in the historical cognitive test comprehensive score data, the mean of the historical cognitive test comprehensive score of the subjects was obtained by statistical mean calculation algorithm. Based on the rating scale criteria, a rating scale interval was pre-set for the average historical cognitive test scores of the participants. The average historical cognitive test scores of the participants were then input into the rating scale interval for comparison and analysis. The specific comparison and analysis process is as follows: If the average score of the test taker's historical cognition test is greater than the maximum value of the rating level interval, it indicates that the test taker's historical cognition test performance is excellent, and a high-level signal is generated. If the average score of the test taker's historical cognition test is within the rating level interval, it indicates that the test taker's historical cognition test performance is good, and a medium-level signal is generated. If the average score of the test taker's historical cognition test is less than the minimum value of the rating level interval, it indicates that the test taker's historical cognition test performance is poor, and a low-level signal is generated. Based on the generated corresponding level signals, the next stage of cognitive impairment assessment cycle for the subjects is matched with the corresponding hierarchical cycle. The specific matching process is as follows: If the subject's historical cognitive test results are determined to be low-level signals, then the cognitive impairment assessment cycle for the next process is matched with a first-order cycle; if the subject's historical cognitive test results are determined to be intermediate-level signals, then the cognitive impairment assessment cycle for the next process is matched with a second-order cycle; if the subject's historical cognitive test results are determined to be high-level signals, then the cognitive impairment assessment cycle for the next process is matched with a third-order cycle.

4. The IoT-based assessment system for vascular cognitive impairment according to claim 1, characterized in that: The ranking and analysis of the cognitive impairment assessment types of the test subjects includes the following specific analysis and processing procedures: After setting the cognitive impairment assessment cycle for the subjects, the memory cognitive score, executive cognitive score, and expressive cognitive score of the subjects were obtained in real time using a patch-type cognitive state assessment device. The memory, executive, and expressive cognitive scores from the participants' cognitive assessment scales were input into a cognitive type level test judgment table stored in the cloud database for judgment processing. The specific process is as follows: If the cognitive type level test judgment table outputs two or more failing results, a multi-assessment signal is generated; if the cognitive type level test judgment table outputs only one failing result, a single assessment signal is generated; if the cognitive type level test judgment table outputs no failing results, a normal signal for vascular comprehensive cognitive impairment is generated, and relevant personnel are notified by pushing information through the display terminal. The single assessment signal execution content is as follows: extract behavioral data corresponding to the cognitive type of the failing result from the cloud database, and perform cognitive impairment assessment operations for the corresponding cognitive type within the assessment cycle set in the next process; The multi-assessment signal execution process involves: Based on the numerical values ​​of memory-based cognitive assessment scores, performance-based cognitive assessment scores, and expression-based cognitive assessment scores, sorting them in ascending order to obtain a cognitive type ranking sequence; and determining the priority of each cognitive type's impairment assessment based on the ranking sequence. The specific process is as follows: The cognitive type that is first in the cognitive type numbering sequence will be given priority for cognitive impairment assessment in the next assessment cycle. The cognitive type that is second in the cognitive type numbering sequence will be given the next cognitive impairment assessment in the next assessment cycle. The cognitive type that is third in the cognitive type numbering sequence will be given the last cognitive impairment assessment in the next assessment cycle.

5. The IoT-based assessment system for vascular cognitive impairment according to claim 1, characterized in that: The specific analysis process for assessing and analyzing memory and cognitive impairment includes: The number of times the refrigerator door was not closed, the number of times the memo was repeatedly reminded, and the number of times the door password was entered incorrectly were monitored in real time by the participants using IoT devices. The three data items were correlated and processed without dimensions to analyze the degree of memory and cognitive impairment of the participants and determine the participants' memory and cognitive impairment coefficient. The memory and cognitive impairment coefficient of the test subjects is compared and analyzed with a preset memory and cognitive impairment threshold. If the memory and cognitive impairment coefficient of the test subjects exceeds the preset memory and cognitive impairment threshold, a memory and cognitive impairment warning signal is generated and the relevant personnel are notified through the display terminal. Otherwise, the next cognitive type impairment assessment operation is performed, or if the memory and cognitive impairment assessment and analysis operation is the last cognitive type impairment assessment operation for the test subjects, the process ends directly.

6. The IoT-based assessment system for vascular cognitive impairment according to claim 1, characterized in that: The specific analysis process for performing cognitive impairment assessment and analysis includes: The average duration of device operation interruption for the subjects was obtained by real-time monitoring of the subjects' executive cognitive behavior data in each monitoring period using IoT devices and by combining statistical averaging algorithms. The duration of equipment operation interruption in each monitoring period is compared and analyzed with the average duration of equipment operation interruption. If the duration of equipment operation interruption in the corresponding monitoring period exceeds the average duration of equipment operation interruption, the corresponding monitoring period is recorded as an abnormal operation period, and the total duration of the abnormal operation period is calculated. Otherwise, the corresponding monitoring period is recorded as a normal operation period. The ratio of abnormal operation periods to total monitoring time is calculated to determine the percentage of abnormal operation periods. If the percentage of abnormal operation periods exceeds 25%, an early warning signal for cognitive impairment is generated and relevant personnel are notified via the display terminal. Otherwise, the next cognitive impairment assessment operation is performed, or if the cognitive impairment assessment and analysis operation is the subject's last cognitive impairment assessment operation, the process ends directly.

7. The IoT-based assessment system for vascular cognitive impairment according to claim 1, characterized in that: The specific analysis process for assessing and analyzing language cognitive impairment includes: By using IoT devices to monitor the language cognitive behavior data of test subjects in real time, including voice assistant dialogue response time, voice assistant dialogue repetition number, and voice command response error rate, the three data items are correlated and processed without dimensions to analyze the degree of language cognitive impairment of the test subjects and determine the language cognitive impairment coefficient of the test subjects. The language cognitive impairment coefficient of the test subjects is compared and analyzed with the preset language cognitive impairment threshold. If the language cognitive impairment coefficient of the test subjects exceeds the preset language cognitive impairment threshold, a language cognitive impairment warning signal is generated and the relevant personnel are notified through the display terminal. Otherwise, the next cognitive type impairment assessment operation is performed. Or, if the language cognitive impairment assessment and analysis operation is the last cognitive type impairment assessment operation for the test subjects, the process ends directly.

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