CN117457216B - Digital senile comprehensive evaluation system and method - Google Patents
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Abstract
The invention is applicable to the fields of medical rehabilitation and health promotion, and provides a digital senile comprehensive evaluation system and a digital senile comprehensive evaluation method, wherein the system comprises the following components: the system comprises a sensing acquisition module, a questionnaire collection module, a capability assessment module and a user interaction module; the sensing acquisition module can acquire kinematic data of an evaluator in the process of performing an evaluation task and extract motion characteristics of the acquired kinematic data; the questionnaire collecting module is capable of extracting an evaluation questionnaire associated with an evaluator for answering of the evaluator based on the evaluation interface provided by the user interaction module, and submitting the evaluation questionnaire of the answered answer; the ability evaluation module comprises a score prediction module and a standard scale database; the invention has lower requirements on professional experience of an evaluator, can be better popularized and saves human resources; and gives accurate and objective comprehensive evaluation results for the elderly, and improves evaluation efficiency and reliability.
Description
Technical Field
The invention relates to the field of medical rehabilitation and health promotion, in particular to a digital senile comprehensive evaluation system and method.
Background
With the increase of age, the health condition and the body function index of the human body are reduced, and chronic diseases such as apoplexy and Alzheimer disease are common senile diseases. With the progress of the illness, many slow patients have symptoms of slow movement, weak body or weak risk, and the body, function, psychological and social conditions of the old are negatively changed.
The geriatric integrated assessment (CGA) is the basis for assessment, planning and intervention required to meet the health and social care of frail or frail-at-risk elderly people. The CGA uses a multidisciplinary joint method, and is usually closely cooperated with doctors, nurses, physical therapists, and social workers to ensure comprehensive evaluation of the body, functions, psychology and social conditions of the aged, and to make and start a protection plan for protecting the health and functional status of the aged, thereby maximally improving the functional level and quality of life of the aged.
However, in the prior art, the CGA has complicated evaluation content and numerous scales, has high requirements on professional experience of an evaluator, and is difficult to popularize in communities. In addition, the subjective judgment of a single evaluator is used for scoring, the accuracy is low, the evaluation efficiency is low, the two evaluators are used for scoring at the same time, the average value is finally taken as an evaluation result, a large amount of manpower resources are consumed, and when the two evaluators are in large opinion divergence, the final result cannot be judged.
Therefore, there is a need to develop a system and method for digitally assessing the general abilities of elderly people.
Disclosure of Invention
The embodiment of the invention aims to provide a digital comprehensive evaluation system and method for the elderly, which provide technical support for popularization of CGA to communities, reduce subjective judgment during evaluation, save human resources, provide accurate and objective comprehensive evaluation results for the elderly and improve evaluation efficiency and reliability.
The embodiment of the invention is realized in such a way that a digital senile comprehensive evaluation system comprises: the system comprises a sensing acquisition module, a questionnaire collection module, a capability assessment module and a user interaction module;
the sensing acquisition module can acquire kinematic data of an evaluator in the process of performing an evaluation task and extract motion characteristics of the acquired kinematic data;
the questionnaire collecting module is capable of extracting an evaluation questionnaire associated with an evaluator for answering of the evaluator based on the evaluation interface provided by the user interaction module, and submitting the evaluation questionnaire of the answered answer;
The ability evaluation module comprises a score prediction module and a standard scale database; the score prediction module predicts the score of the motion feature based on a preset SVM linear regression model so as to obtain the activity score of daily life and give out a corresponding capability grade;
The standard scale database comprises more than two evaluation scales, and the daily life activity scores and the evaluation volumes are matched to corresponding evaluation scales in the standard scale database to obtain total senile comprehensive capacity evaluation scores and corresponding capacity evaluation results; and outputting the capability assessment result of the evaluator through the user interaction module.
In order to facilitate implementation of the digital senile comprehensive evaluation system, reduce implementation difficulty and improve implementation range, another object of an embodiment of the present invention is to provide a digital senile comprehensive evaluation method, which includes the following steps:
acquiring kinematic data of an evaluator in an evaluation task, and extracting kinematic features of the acquired kinematic data;
Extracting an evaluation questionnaire associated with an evaluator for the evaluator to answer, and submitting the evaluation questionnaire of the answer;
Based on a preset SVM linear regression model, carrying out score prediction on the motion characteristics to obtain daily life activity scores and give corresponding capability grades;
matching the daily life activity score and the assessment questionnaire to corresponding assessment scales in a standard scale database to obtain a total senile comprehensive capacity assessment score and a corresponding capacity assessment result; and outputting the capability assessment result of the evaluator through the user interaction module.
Compared with the prior art, the digital senile comprehensive evaluation system provided by the embodiment of the invention has the following beneficial effects: considering human and resource costs, carrying out corresponding capability assessment on some items in the capability assessment items of the old people such as daily life activities, mental states, perception and communication and social participation in a human-computer interaction mode, so that comprehensive evaluation of the old people can be completed rapidly and accurately; the kinematic data of an evaluator in an evaluation task, particularly in a daily life activity task, is acquired through the set sensing acquisition module, so that the daily life activity capability of the evaluator is evaluated, and the evaluation device is suitable for community and household popularization and use; the ability evaluation module takes the acquired kinematic data and test questionnaires as information sources, predicts daily life activity scores based on a machine learning model (namely an SVM linear regression model), evaluates scores of the other three items based on rating scales in a standard scale database (namely a Lawton IADL scale, an MMSE scale, a GDS-15 scale, an industry standard simple screening table and a related test questionnaire (or called an evaluation questionnaire) of a social support rating scale, effectively eliminates subjective judgment factors of an evaluator, enables evaluation results to be objective and accurate, gives accurate and objective senile comprehensive evaluation results, and improves evaluation efficiency and reliability.
Drawings
Fig. 1 is a schematic structural diagram of a digital senile comprehensive evaluation system according to the present embodiment;
FIG. 2 is a block diagram of the internal components of a sensor acquisition module in one embodiment;
FIG. 3 is a map of a standard table database in one embodiment;
fig. 4 is a schematic structural diagram of another digital senile comprehensive evaluation system according to the present embodiment;
FIG. 5 is a block flow diagram of a method for digital geriatric comprehensive assessment provided in this embodiment;
FIG. 6 is a schematic workflow diagram of a review module in one embodiment;
fig. 7 is a flowchart of a digital senile comprehensive evaluation method according to the present embodiment;
Fig. 8 is an internal structural diagram of a computer device in one embodiment.
In the accompanying drawings: the system comprises a sensing acquisition module 1, a questionnaire collection module 2, an information management module 3, a capability assessment module 4 and a user interaction module 5.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In one embodiment, as shown in fig. 1, a structure diagram of a digital senile comprehensive evaluation system specifically includes: the system comprises a sensing acquisition module 1, a questionnaire collection module 2, a capability assessment module 4 and a user interaction module 5;
The sensing acquisition module 1 can acquire kinematic data of an evaluator in an evaluation task and extract motion characteristics of the acquired kinematic data;
The kinematic data includes motion data collected from left/right hand, left/right forearm, left/right upper arm, waist, left/right thigh, left/right calf of the evaluator, and the evaluation task sequentially completes corresponding activity tasks according to prompts (prompts can be performed by the user interaction module 5, for example, corresponding evaluation flow example diagrams and text comments are provided through a display interface), in this process, the limbs of the evaluator can perform corresponding actions, and part or all of the left/right hand, left/right forearm, left/right upper arm, waist, left/right thigh, left/right calf and the like are driven;
The questionnaire collecting module 2 is capable of extracting an evaluation questionnaire associated with an evaluator for answering by the evaluator based on the evaluation interface provided by the user interaction module 5, and submitting the evaluation questionnaire of the answered answer;
The ability evaluation module 4 comprises a score prediction module 401 and a standard scale database 402; the score prediction module 401 predicts the score of the motion feature based on a preset SVM linear regression model to obtain the activity score of daily life and give out a corresponding capability level;
The standard scale database 402 comprises more than two evaluation scales, and the daily life activity scores and the evaluation volumes are matched to corresponding evaluation scales in the standard scale database to obtain total senile comprehensive capacity evaluation scores and corresponding capacity evaluation results; outputting the capability assessment result of the evaluator through the user interaction module;
The standard scale database 402 is provided with a Barthel index rating table, a Lawton IADL scale, an MMSE scale, a GDS-15 scale, an industry standard simple screening table, a social support rating table and the like; as shown in fig. 3, the Barthel index rating table is used for constructing a daily life activity score prediction model; the MMSE scale and the GDS-15 scale are used for formulating a mental state assessment volume; the industry standard simple screening table is used for formulating a sensory perception and communication assessment volume; the Lawton IADL scale and the social support rating scale are used for formulating a social participation evaluation volume;
It will be appreciated that it is prior art for those skilled in the art to correlate daily life activity scores with barchel index rating tables;
The movements of the left/right hand, left/right forearm, left/right upper arm, waist, left/right thigh, left/right calf of the above-mentioned evaluator are generated in daily life activities in the evaluation task. The activities of daily living may be configured with an assessment guidance scheme and displayed and guided by the user interaction module 5.
The evaluation guiding scheme can be formulated and stored in advance, and can also be formulated in real time through a specified template; for example: the settings may be programmed by the therapist or may be automatically generated by a test analysis software program in conjunction with the evaluator's clinical data.
In this embodiment, the evaluation guidance scheme includes, but is not limited to, a test gesture, a guidance pattern, the number of action groups, the number of actions, the order of actions, the time of actions, and the like; the method can be realized by the devices such as an image display, a voice broadcasting device and the like adopted by the user interaction module 5.
In the embodiment, human and resource costs are considered in the evaluation, and the corresponding capability evaluation of daily life activities, mental states, perception and communication and social participation in the capability evaluation project of the old is performed in a human-computer interaction mode, so that the comprehensive evaluation of the old can be completed rapidly and accurately; the kinematic data of an evaluator in an evaluation task, particularly in a daily life activity task, is acquired through the set sensing acquisition module, so that the daily life activity capability of the evaluator is evaluated, and the evaluation device is suitable for community and household popularization and use; the ability evaluation module takes the acquired kinematic data and the test questionnaires as information sources, predicts daily life activity scores based on an SVM linear regression model, evaluates scores of the other three items based on Lawton IADL scale, MMSE scale, GDS-15 scale, industry standard simple screening table, and related test questionnaires (or called evaluation questionnaires) of a social support evaluation scale in a standard scale database, effectively eliminates subjective judgment factors of an evaluator, and enables evaluation results to be objective and accurate, so that accurate and objective senile comprehensive evaluation results are given, and evaluation efficiency and reliability are improved.
As shown in fig. 2 and 3, in one example of the present embodiment, the SVM linear regression model is completed by constructing a linear relationship between the kinematic data and the scoring criteria of the Barthel index rating table. This linear relationship is mainly set in accordance with the framework of a support vector machine model (SVM).
In the embodiment, the kinematic data is mainly generated by daily life activities and is output in the process of being carried out by daily life activity evaluation items, wherein each evaluation item comprises sub-evaluation items, and each daily life activity evaluation item comprises feeding, bathing, decoration, dressing, stool control, urine control, toilet use, bed-chair transfer, walking on level ground and going up and down stairs;
in addition, mental state assessment items include cognitive ability, aggression, depression symptoms; sensory perception and communication include conscious level, vision, hearing, communication; social participation includes life ability, work ability, temporal/spatial orientation, character orientation, social interaction ability; these items are all digitized by the above-mentioned conventional evaluation values, and quantization can be achieved.
As shown in fig. 2, in one embodiment, the sensing acquisition module 1 includes an inertial measurement unit 101 and a data processing unit 102;
the inertial measurement unit 101 is configured to obtain kinematic data when an evaluator performs an evaluation task;
The data processing unit 102 is configured to perform data preprocessing and kinematic feature extraction on the kinematic data.
The inertial measurement unit 101 comprises a plurality of inertial sensors, and during evaluation, the inertial sensors are tied to a left/right hand, a left/right forearm, a left/right upper arm, a waist, a left/right thigh and a left/right calf according to interface prompt information, and corresponding activity tasks are completed in sequence according to the prompt; the data processing unit 102 may be a microprocessor or a programmable logic controller.
In the process of completing the corresponding activity tasks, the inertial sensor acquires sensing data in real time and feeds back the sensing data to the data processing unit 102, and the data processing unit 102 performs preprocessing of the data and extraction of kinematic features;
As shown in fig. 2, preprocessing includes, but is not limited to, alignment of data, denoising, conversion, etc.; alignment can be performed by sliding window segmentation, and window data are subjected to average value, standard deviation, maximum value, minimum value, kurtosis, skewness, amplitude and energy treatment; the specific process of feature extraction can be flexibly selected according to the items of the Barthel index evaluation table, and can be easily realized by a person skilled in the art.
In one example of this embodiment, the inertial sensor collects acceleration, angular velocity, and joint angle of each limb segment of the evaluator in real time, and the data processing unit 102 performs data preprocessing on the obtained acceleration, angular velocity, and joint angle, that is, first, sets a sliding window with a window size of 3s and an overlapping rate of 50% to perform signal segmentation, and then performs feature extraction on data of each window to obtain an average value, a standard deviation, a maximum value, a minimum value, kurtosis, a skewness, an amplitude, and energy.
In one embodiment, the system further comprises: an information management module 3; the information management module 3 is used for storing test data submitted by an evaluator and providing a data query interface; the test data includes personal information, clinical data, kinematic data, an assessment questionnaire, and a capability assessment result.
In one example of this embodiment, the data query interface may be connected to the user interaction module 5, and input, output, and display are performed through the user interaction module 5.
In one example of the present embodiment, the personal information includes at least: name, age, gender, contact details, identification card number information, etc.
In one example of the present embodiment, the information management module 3 may employ a host computer, a cloud server, or a computer device.
Of course, as shown in fig. 4, in an example of an embodiment, the system may not include the information management module 3; thus, when the system is used for comprehensive evaluation of the elderly, personal information and evaluation results are not saved, and privacy protection of patients is better.
In one embodiment, the system further comprises: review module and expert database;
The review module determines classical values of all evaluations in the evaluation scale based on big data to determine difference thresholds of all the evaluations; comparing the daily life activity scores with the evaluation scores obtained after the evaluation questionnaires are matched with the corresponding evaluation scales in the standard scale database item by item; if the comparison result of any similar item exceeds the difference threshold, marking the corresponding item exceeding the difference threshold;
And based on the expert database, periodically checking the rechecking result of the rechecking module, and maintaining the standard scale database and optimizing the capability assessment module according to the checking condition.
In one example of this embodiment, take the Barthel index rating table as an example: classical values of feeding are 0, 5 and 10 minutes, classical values of bathing are 0 and 5 minutes, classical values of modification are 0 and 5 minutes, and classical values of dressing are 0, 5 and 10 minutes; typical values for stool control are 0, 5, 10 minutes, typical values for urine control are 0, 5, 10 minutes, etc. The bath and the decoration are similar items, the stool control and the urine control are similar items, and the bed-chair transfer and the flat walking are similar items;
When the bath is 5 points and the modification is 0 points, the comparison result of the similar items is 5, and the difference threshold is set to 4, the corresponding items exceeding the difference threshold can be considered, and the bath and the modification need to be marked so as to be further checked, and an accurate evaluation result is obtained; or the setting of the capability assessment module 4 is problematic and requires optimization;
Another example is: the score of the bed-chair transfer is 15, the score of the flat walking is 5, the corresponding difference threshold is 5, the comparison result of the similar item is 10, and the score exceeds the difference threshold 5, so that the bed-chair transfer and the flat walking can be marked for checking.
Table 1 shows the Barthel index evaluation table
In an example of this embodiment, the expert database is preset with information of a senior expert, so that the marking condition of the review module can be pushed to the senior expert timely, or the pre-assigned path is fed back periodically, the senior expert checks the review condition of the review module periodically, and after the review is complete, the corresponding marking is cancelled, and the checking result is output.
As shown in fig. 1, in an example of the present embodiment, the user interaction module 5 is configured to execute image guidance and broadcast guidance in the assessment and guidance scheme in the assessment task through an image display and a voice broadcast device connected to the host computer or the cloud server.
Specifically, the evaluation guiding scheme is arranged in a direction which is convenient for an evaluator to observe and listen through an image display and voice broadcasting equipment (sound equipment and loudspeaker), and the evaluator is guided to recognize the target and move feet according to the scene displayed on the image display and the voice broadcasting target; prompting test safety notice, timely performing voice questions and answers, and the like.
In one example of the present embodiment, the review module includes a similar item determination unit, a similar item comparison unit, and a sub item marking unit;
the similar item determining unit is used for determining similar items of all the neutron items based on the big data;
the similarity item comparison unit is used for carrying out item-by-item comparison according to the similarity items determined in each item;
and the sub item marking unit marks the corresponding item exceeding the difference threshold value.
Taking the Barthel index rating table as an example: based on big data, the classical values of feeding are 0, 5 and 10 points, the classical values of bathing are 0 and 5 points, the modified classical values are 0 and 5 points, and the classical values of dressing are 0, 5 and 10 points; typical values for stool control are 0, 5, 10 minutes, typical values for urine control are 0, 5, 10 minutes, etc. Determining that bathing and modification are similar items, controlling stool and urine, and transferring a bed chair and walking on level ground are similar items; etc.
Wherein the criteria are determined according to the execution degree of the same part of the limb, the actions of the left/right hand, the left/right forearm, the left/right upper arm, the waist, the left/right thigh and the left/right calf in the limb correspond to the higher similarity in terms, which indicate that the similarity term is defined as similarity terms when the scores of the corresponding sub-terms are evaluated, and the scores of the similarity terms are also similar.
When the bath is 5 points and the modification is 0 points, the comparison result of the similar items is 5, and the difference threshold is set to 4, the corresponding items exceeding the difference threshold can be considered, and the bath and the modification need to be marked so as to be further checked, and an accurate evaluation result is obtained; or the setting of the capability assessment module 4 is problematic and requires optimization; the determination of the variance threshold may be performed by a senior citizen in advance.
Another example is: the score of the bed-chair transfer is 15, the score of the flat walking is 5, the corresponding difference threshold is 5, the comparison result of the similar item is 10, and the score exceeds the difference threshold 5, so that the bed-chair transfer and the flat walking can be marked for checking.
In another embodiment, as shown in fig. 5-7, a digitized combined senile assessment method is used for the digitized combined senile assessment system as described above, the method comprising the following steps S101-S109:
S101, acquiring kinematic data of an evaluator in an evaluation task, and extracting kinematic features of the acquired kinematic data;
In the step, kinematic data, namely data acquired by inertial sensors tied to left/right hands, left/right forearm, left/right upper arm, waist, left/right thigh and left/right calf of an evaluator, are obtained, so that the acceleration, angular velocity and joint angle of each limb segment are obtained, and the obtained acceleration, angular velocity and joint angle are subjected to data preprocessing; pretreatment: firstly, a sliding window with the window size of 3s and the overlapping rate of 50% is set for signal segmentation, and then the data of each window is subjected to feature extraction to obtain an average value, a standard deviation, a maximum value, a minimum value, kurtosis, a skewness, an amplitude and energy; namely, motion feature extraction is performed.
S103, extracting an evaluation questionnaire associated with the evaluator for the evaluator to answer, and submitting the answer questionnaire;
For example: barthel index rating table, lawton IADL scale, MMSE scale, GDS-15 scale, industry standard easy sieve look-up table, social support rating table; for some evaluators, not all scale evaluations are necessarily required; a portion may be extracted for evaluation as appropriate. The Barthel index evaluation table is used for constructing a daily life activity score prediction model; the MMSE scale and the GDS-15 scale are used for formulating a mental state assessment volume; the industry standard simple screening table is used for formulating a sensory perception and communication assessment volume; the Lawton IADL scale, the social support rating scale, is used to formulate a social engagement assessment questionnaire, as shown in fig. 3.
S105, based on a preset SVM linear regression model, carrying out score prediction on the motion characteristics to obtain daily life activity scores and give corresponding capability grades;
S107, matching the daily life activity score and the measurement and evaluation volume to corresponding evaluation scales in a standard scale database to obtain a total senile comprehensive capacity evaluation score and a corresponding capacity evaluation result;
S109, outputting the capability assessment result of the evaluator through the user interaction module.
In one embodiment, the method further comprises the steps of:
And establishing an SVM linear regression model by constructing a linear relation between the kinematic data and the scoring standard of the Barthel index evaluation table.
As shown in fig. 6, in one embodiment, the method further comprises the following steps S202-S206:
S202, determining classical values of all evaluations in an evaluation scale based on big data so as to determine difference thresholds of all the evaluations;
s204, comparing each evaluation value obtained after the daily life activity value and the evaluation questionnaire are matched to the corresponding evaluation scale in the standard scale database item by item;
s206, if the comparison result of any similar item exceeds the difference threshold, marking the corresponding item exceeding the difference threshold.
As shown in fig. 7, in an example of the present embodiment, a digital senile comprehensive evaluation method is provided, which includes the steps of:
Initializing a system;
recording personal information and clinical data of an evaluator;
Starting or extracting an evaluation task (i.e. an evaluation item), and acquiring the evaluation item currently participated by an evaluator, wherein the evaluation item comprises four evaluation items of daily life activities, mental states, perception and communication and social participation;
Each evaluation item comprises sub-evaluation items, and the daily life activity evaluation items comprise eating, bathing, modifying, dressing, controlling stool, controlling urine, going to the toilet, transferring a bed and a chair, walking on a flat ground and going up and down stairs; mental state assessment items include cognitive ability, aggression, depression symptoms; sensory perception and communication include conscious level, vision, hearing, communication; social participation includes lifestyle, work ability, temporal/spatial orientation, character orientation, and social interaction ability.
When an evaluator performs a daily life activity evaluation project, the inertial sensor is bound on a left/right hand, a left/right forearm, a left/right upper arm, a waist, a left/right thigh and a left/right calf according to interface prompt information, and corresponding action tasks are completed in sequence according to the prompt;
When the evaluator performs mental state, perception and communication and social participation evaluation items, the display interface provides a corresponding test questionnaire (or an evaluation questionnaire), and the evaluator fills out the questionnaire faithfully according to personal conditions;
Predicting daily life activity scores through an SVM linear regression model; respectively loading test questionnaire results of three evaluation items of mental state, perception and communication and social participation into corresponding evaluation scales, and calculating the score and grade of each evaluation item according to the evaluation scale scoring standard;
Summarizing the evaluation results of the four evaluation items, accumulating the predicted values and the evaluation scale scores to obtain total values of the comprehensive evaluation of the elderly, and dividing the corresponding ability grades of the elderly based on the total values of the comprehensive evaluation of the elderly.
In one example of this embodiment, the method further comprises:
and sending the evaluation result to a display interface, and providing the evaluation result for an evaluator and a doctor to refer to the evaluation result so as to make a reasonable protection plan.
The digital senile comprehensive evaluation system performs corresponding capacity evaluation on some of the senile capacity evaluation items such as daily life activities, mental states, perception and communication and social participation in a man-machine interaction mode, and can rapidly and accurately complete senile comprehensive evaluation; the kinematic data of an evaluator in an evaluation task, particularly in a daily life activity task, is acquired through the set sensing acquisition module, so that the daily life activity capability of the evaluator is evaluated, and the evaluation device is suitable for community and household popularization and use; the ability evaluation module takes the acquired kinematic data and the test questionnaire as information sources, predicts the activities of daily living scores based on a machine learning model (namely an SVM linear regression model), evaluates the scores of the other three items based on the rating scale in the standard scale database, effectively eliminates subjective judgment factors of an evaluator, and enables the evaluation result to be objective and accurate, thereby giving an accurate and objective comprehensive evaluation result for the elderly and improving the evaluation efficiency and reliability.
As shown in fig. 8, in one embodiment, a computer device is provided that includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform steps S101-S109 of the digitized geriatric integrated assessment method as described above:
S101, acquiring kinematic data of an evaluator in an evaluation task, and extracting kinematic features of the acquired kinematic data;
s103, extracting an evaluation questionnaire associated with the evaluator for the evaluator to answer, and submitting the answer questionnaire;
s105, based on a preset SVM linear regression model, carrying out score prediction on the motion characteristics to obtain daily life activity scores and give corresponding capability grades;
S107, matching the daily life activity score and the measurement and evaluation volume to corresponding evaluation scales in a standard scale database to obtain a total senile comprehensive capacity evaluation score and a corresponding capacity evaluation result;
S109, outputting the capability assessment result of the evaluator through the user interaction module.
FIG. 8 illustrates an internal block diagram of a computer device in one embodiment. The computer device includes a processor, a memory, a network interface, an input device, and a display screen connected by a system bus. The memory includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system, and may also store a computer program that, when executed by a processor, causes the processor to implement a digitized geriatric comprehensive assessment method. The internal memory may also store a computer program that, when executed by the processor, causes the processor to perform a digitized geriatric integrated assessment method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (8)
1. A digital geriatric integrated assessment system, the system comprising: the system comprises a sensing acquisition module, a questionnaire collection module, a capability assessment module and a user interaction module;
the sensing acquisition module can acquire kinematic data of an evaluator in the process of performing an evaluation task and extract motion characteristics of the acquired kinematic data;
the questionnaire collecting module is capable of extracting an evaluation questionnaire associated with an evaluator for answering of the evaluator based on the evaluation interface provided by the user interaction module, and submitting the evaluation questionnaire of the answered answer;
the ability evaluation module comprises a score prediction module and a standard scale database; the score prediction module predicts the score of the motion feature based on a preset SVM linear regression model so as to obtain the activity score of daily life and give out a corresponding capability grade; the SVM linear regression model is completed by constructing a linear relation between the kinematic data and the scoring standard of the evaluation scale;
the standard scale database comprises more than two evaluation scales, and the daily life activity scores and the evaluation volumes are matched to corresponding evaluation scales in the standard scale database to obtain total senile comprehensive capacity evaluation scores and corresponding capacity evaluation results; outputting the capability assessment result of the evaluator through the user interaction module;
the system further comprises: review module and expert database;
The review module determines the evaluation classical value of each item in the evaluation scale based on the big data to determine the difference threshold of each item; comparing the daily life activity scores with the evaluation scores obtained after the evaluation questionnaires are matched with the corresponding evaluation scales in the standard scale database item by item; the review module comprises a similarity item determining unit, a similarity item comparing unit and a sub item marking unit, wherein the similarity item determining unit is used for determining the similarity item of each sub item based on big data, the determined standard is determined according to the execution degree of the same part of the limb, and the action corresponds to the higher similarity in each item, indicates that the corresponding sub item scores are similar and similar when being evaluated, and can be defined as a similarity item; the similarity item comparison unit is used for carrying out item-by-item comparison according to the similarity items determined in each item; if the comparison result of any similar item exceeds the difference threshold, the sub item marking unit marks the corresponding item exceeding the difference threshold so as to be convenient for further review to obtain an accurate evaluation result, or the setting of the capability evaluation module is problematic and needs to be optimized;
And based on the expert database, periodically checking the rechecking result of the rechecking module, and maintaining the standard scale database and optimizing the capability assessment module according to the checking condition.
2. The digitized geriatric integrated assessment system of claim 1, further comprising: an information management module; the information management module is used for saving test data submitted by an evaluator and providing a data query interface; the test data includes personal information, clinical data, kinematic data, an assessment questionnaire, and a capability assessment result.
3. The digital geriatric integrated assessment system of claim 1, wherein the sensing acquisition module comprises an inertial measurement unit, a data processing unit;
The inertia measurement unit is used for acquiring kinematic data of an evaluator during an evaluation task;
the data processing unit is used for carrying out data preprocessing and kinematic feature extraction on the kinematic data.
4. The digitized geriatric integrated assessment system of claim 1, wherein the two or more assessment scales are each: barthel index rating scale, lawton IADL scale, MMSE scale, GDS-15 scale, industry standard easy sieve look-up table, social support rating scale.
5. The digitized geriatric integrated assessment system of claim 4, wherein the SVM linear regression model is accomplished by constructing a linear relationship between the kinematic data and scoring criteria of the barchel index rating form.
6. A method for digital geriatric integrated assessment according to any one of claims 1 to 5, the method comprising the steps of:
acquiring kinematic data of an evaluator in an evaluation task, and extracting kinematic features of the acquired kinematic data;
Extracting an evaluation questionnaire associated with an evaluator for the evaluator to answer, and submitting the evaluation questionnaire of the answer;
Based on a preset SVM linear regression model, carrying out score prediction on the motion characteristics to obtain daily life activity scores and give corresponding capability grades;
matching the daily life activity score and the assessment questionnaire to corresponding assessment scales in a standard scale database to obtain a total senile comprehensive capacity assessment score and a corresponding capacity assessment result; and outputting the capability assessment result of the evaluator through the user interaction module.
7. The method for digitized geriatric integrated assessment of claim 6, further comprising the steps of:
And establishing an SVM linear regression model by constructing a linear relation between the kinematic data and the scoring standard of the Barthel index evaluation table.
8. The method for digitized geriatric integrated assessment of claim 7, further comprising the steps of:
determining an evaluation classical value of each item in the evaluation scale based on the big data to determine a difference threshold of each item;
Comparing the daily life activity scores with the evaluation scores obtained after the evaluation questionnaires are matched with the corresponding evaluation scales in the standard scale database item by item;
and if the comparison result of any similar item exceeds the difference threshold, marking the corresponding item exceeding the difference threshold.
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