Method, system, electronic device and storage medium for training cognitive memory

Abstract

The application discloses a cognitive memory training method and system, an electronic device and a storage medium, and relates to the technical field of intelligent medical treatment. According to the scheme, in the training process, a target object wears a wearable smart device and trains according to the prompt of an interactive device, the wearable smart device collects action data in the training process and sends the action data to a visual capturer, the visual capturer corrects the captured movement track of the patient by using the action data, analyzes the corrected movement track, and controls the interactive device according to the analysis result, so as to assist the target object to complete the training. In the scheme, the basic guidance can be provided by the training system, and therefore, the technical problem that the cognitive memory training in the prior art consumes a large amount of human resources can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to the technical field of smart medical treatment, and especially to a cognitive memory training method and system, an electronic device and a storage medium. BACKGROUND

[0002] Cognitive function is one of the high-level functions of the human brain and is an important topic in modern medical research. In a broad sense, cognition refers to the psychological activity of reflecting, analyzing and understanding the characteristics and connections of objective things, and revealing the significance and effect of things on people. Specifically, it includes psychological processes such as perception, attention, representation, learning and memory, thinking and language. Cognition is an activity for people to understand the outside world, that is, the process of obtaining, organizing and applying knowledge, and it is also a cognitive process that reflects intelligence and behavior. Cognition is a necessary condition for people to adapt to the surrounding environment. In addition, some people also point out that cognition is the intelligence of human beings adapting to the surrounding environment. In short, cognition is the individual ability of people to obtain and apply information to adapt to the environment. Stroke (also known as "apoplexy" or "cerebrovascular accident") patients have persistent cognitive impairment, which further leads to difficulty in perceiving and adapting to the external environment, resulting in difficulty in living and working independently.

[0003] The impairment of cognitive function is relatively complex, and the main phenomena of mild cognitive impairment are: 1) memory impairment, such as: recent memory, personal experience memory, and memory impairment of major events in life; 2) disorientation, including time, place, and disorientation; 3) language impairment, including word-finding difficulty, reading, writing and comprehension difficulty; 4) impaired visual-spatial ability; 5) decreased calculation ability; 6) decreased ability to judge and solve problems, etc. Language is a unique cognitive function of human beings, and language symbol information is processed in the cognitive process, from the initial perception and recognition of language symbols to the final speech expression, and the whole psychological process of language communication is inseparable from cognitive function (such as thinking, learning, memory, etc.).

[0004] The rehabilitation of cognitive impairment generally aims to achieve two specific goals: 1) repair of impaired cognitive processes; and 2) compensation for functional defects. The first goal assumes that the impaired cognitive process can be repaired, and the cognitive function can be partially restored. The second goal assumes that in the case where the cognitive impairment is difficult to repair, other compensation methods are used to solve the problems caused by cognitive defects. Compensation methods were once the main method of cognitive rehabilitation, and later the retraining theory of rehabilitation was proposed on the basis of brain plasticity. There are different rehabilitation strategies and methods for each cognitive function, such as attention, memory, executive function and perceptual impairment.

[0005] In the prior art, the recovery of cognitive impairment of patients is mostly managed manually by professional rehabilitation therapists who conduct one-on-one rehabilitation training. In this case, the rehabilitation therapists can still conduct rehabilitation training one by one when the number of patients is not large. If the number of patients is large, the rehabilitation therapists will be overloaded, and even some patients cannot receive corresponding and effective rehabilitation training, thereby affecting the training effect. SUMMARY

[0006] In view of the technical problems of large consumption of human resources and poor training effect in cognitive memory training in the prior art, the present application provides a cognitive memory training method, system, electronic device and storage medium.

[0007] According to a first aspect, the present application provides a cognitive memory training system, comprising: an interactive device, the interactive device being configured to show training content to a target object and interact with the target object during training; a wearable smart device, the wearable smart device being configured to collect action data of the target object during training; and a visual capturer, the visual capturer being in communication connection with the interactive device and the wearable smart device, the visual capturer being configured to capture a movement trajectory of the target object during training, correct the movement trajectory using the action data, analyze the corrected movement trajectory in terms of action standard, and control the interactive content of the interactive device according to the analysis result to assist the target object in completing the training.

[0008] According to a second aspect, the present application further provides a cognitive memory training method, comprising: instructing an interactive device to show training content to a target object, wherein the interactive device is configured to interact with the target object during training; receiving action data of the target object collected by a wearable smart device during training; capturing a movement trajectory of the target object during training, correcting the movement trajectory using the action data, analyzing the corrected movement trajectory in terms of action standard, and controlling the interactive content of the interactive device according to the analysis result to assist the target object in completing the training.

[0009] According to a third aspect, the present application further provides an electronic device, comprising: one or more processors; and a storage device configured to store one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of the cognitive memory training methods.

[0010] According to a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the method of any one of the cognitive memory training methods.

[0011] According to the scheme of the present application, in the training process, the target object wears the wearable smart device and performs training according to the training content displayed by the interactive device, the wearable smart device collects the action data in the training process and sends it to the visual capturer, the visual capturer corrects the captured motion trajectory by using the action data, analyzes the standard degree of the corrected motion trajectory, and controls the interactive device according to the analysis result to assist the target object to complete the training. In this scheme, the basic training guidance can be provided by the above-mentioned training system, avoiding or reducing the process of manual training guidance, so as to solve the technical problem that the cognitive memory training in the prior art consumes a large amount of human resources. At the same time, the above-mentioned training system can also collect the action data and motion trajectory of the target object in the training process to analyze and evaluate the standard degree of the training action of the target object, and then control the interactive content of the interactive device and the target object according to the analysis result, so as to play a role in supervising and guiding the training action, and thus be conducive to improving the training effect. BRIEF DESCRIPTION OF DRAWINGS

[0012] Other features, objects, and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments made with reference to the drawings:

[0013] Figure 1 A schematic diagram of a cognitive memory training system in an embodiment of the present application;

[0014] Figure 2 A schematic diagram of a cognitive memory training process in an embodiment of the present application;

[0015] Figure 3 A schematic diagram of a cognitive memory training method in an embodiment of the present application;

[0016] Figure 4 A structural schematic diagram of a cognitive memory training device in an embodiment of the present application;

[0017] Figure 5 A block diagram of an electronic device for implementing the cognitive memory training method in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The exemplary embodiments of the present application are described below with reference to the accompanying drawings, which include various details of the embodiments of the present application to assist in understanding, and should be considered as merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Also, in order to be clear and concise, the description below omits the description of well-known functions and structures.

[0019] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0020] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0021] Figure 1 An exemplary system architecture 100 (i.e., a cognitive memory training system) of an embodiment of the cognitive memory training method or the cognitive memory training device to which the present application can be applied is shown.

[0022] As shown in Figure 1 The system architecture 100 can include an interactive device 101, a wearable smart device 102, and a visual capturer 103.

[0023] The interactive device 101 is used to interact with a target object during training to show training content to the target object.

[0024] The wearable smart device 102 is worn on the target object and is used to collect action data of the target object during training.

[0025] The visual capturer 103 can be connected to the interactive device and the wearable smart device in a wired or wireless manner. During training, the visual capturer can capture a motion trajectory of the target object, correct the motion trajectory using the action data, analyze the standard degree of the corrected motion trajectory, and control the interactive content of the interactive device according to the analysis result to assist the target object to complete the training.

[0026] By using the technical solution of the present application, the training system can provide basic training guidance, avoid or reduce the process of manual training guidance, and thus solve the technical problem of the cognitive memory training in the prior art that consumes a large amount of human resources. Meanwhile, the training system can collect action data and a motion trajectory of the target object in real time during training to analyze and evaluate the standard degree of the training action of the target object, and then control the interactive content of the interactive device and the target object according to the analysis result to supervise and guide the training action, thereby improving the training effect.

[0027] In an optional embodiment of the present application, the wearable smart device described above can be a smart bracelet, a mobile phone, a smart watch, various sensors, etc. A smart bracelet is taken as an example for subsequent description. The smart bracelet is built-in with a positioning sensor. In order to accurately and effectively capture the motion trajectory of the target object, a visual capture device is connected to the positioning sensor, so that the visual capture device can capture the motion trajectory of the target object in the training process by using the positioning sensor.

[0028] For example, the visual capture device obtains the three-dimensional coordinates of the key points (such as hands, etc.) on the target object through the positioning data of the positioning sensor of the smart bracelet in real time, and obtains the spatial change value of each group of motion sequences (each spatial change value is the coordinate difference between adjacent two times of collection) by accumulation, that is, the motion trajectory described above.

[0029] In an optional embodiment of the present application, in order to improve the accuracy of the motion trajectory, it is proposed to use the wearable smart device to collect the motion data of the target object in the training process to correct the motion trajectory. The motion data can be the motion related data of the target object in the training process, for example, can be the vibration test data, the moving direction data, etc. of the smart bracelet.

[0030] In an optional embodiment of the present application, in order to accurately, efficiently and uniformly judge the motion standard degree (or motion specification degree) of the motion trajectory of the target object, so as to effectively and accurately supervise and guide the training of the target object, it is proposed to analyze the motion standard degree of the motion trajectory by the following way:

[0031] The visual capture device is embedded with a K-Means visual graph algorithm. The visual capture device is used to analyze the motion standard degree of the corrected motion trajectory by using the K-Means visual graph algorithm. In the case that the analysis result meets the requirements, a new training motion is matched and displayed to the target object through the interactive device. In the case that the analysis result does not meet the requirements, the target object is prompted to retrain according to the currently displayed training motion through the interactive device.

[0032] In specific implementation, the spatial change value of the standard motion sequence for reference can be pre-stored in the visual capture device. Then, the K-Means visual graph algorithm is used to match the spatial change value of the accumulated motion sequence with the pre-stored spatial change value of the standard motion sequence, so as to obtain the similarity between them, judge the size relationship between the similarity and a specified threshold (such as 90%), and determine whether it meets the requirements (i.e. whether the motion is standard) according to the size relationship, so as to obtain the analysis result. The analysis result includes two results of meeting the requirements and not meeting the requirements. The meeting the requirements means that the similarity is greater than the specified threshold, and the motion is standard. The not meeting the requirements means that the similarity is less than the specified threshold, and the motion is not standard.

[0033] In a specific implementation, when the analysis result is that the action of the target object is standard (for example, the matching degree is greater than 90%), it indicates that the action of the target object is standard, and a new training action can be matched and displayed to the target object through the interactive device; when the analysis result is that the action of the target object is not standard (for example, the matching degree is less than or equal to 90%), the target object is prompted to retrain according to the currently displayed training action through the interactive device, for example, the target object can be prompted to correct the action that is not standard or not in place.

[0034] In the related art, the standard degree of the action of the target object in the motion rehabilitation training process is determined by an artificial, which has the problem of low efficiency (generally, a rehabilitation therapist can only determine the action of one patient at the same time), and the determination result is affected by many factors (for example, the standards of different rehabilitation therapists are different, and the standards of the same rehabilitation therapist at different times can also be different). The above-mentioned scheme for analyzing the standard degree of the action of the motion trajectory can accurately analyze the standard degree of the action through the K-Means visual graph algorithm embedded in the visual capture device, has high efficiency, and the determination standard is unified (not affected by artificial factors), so that the determination result is more accurate, and a better experience can be provided for the user.

[0035] In another optional embodiment of the present application, the interactive device can be a display, and all target objects (i.e., patients) or multiple target objects can share one display for training. However, considering that the training progress of different target objects is not necessarily the same, individual conditions, and individual actions are different, in order to realize more accurate training guidance for each target object, it is proposed to configure one display for each target object, that is, the number of displays and visual capture devices is multiple, and the display and the visual capture device correspond one-to-one, and each display is provided with a specified area for one target object to train. In this way, each target object can train according to its own progress and condition, and receive individual guidance on the display, so that the target object can be provided with better service and experience in a targeted and accurate manner.

[0036] In another optional embodiment of the present application, the display can be used to comprehensively and effectively interact with the target object to improve the user experience. For example, the display is used to:

[0037] Before training, an instruction is issued to guide the target object to stand in the specified area, and in the case that multiple target objects exist in the same specified area, an instruction is issued to guide the excess target objects in the specified area to stand in other specified areas;

[0038] During the training process, the target object is shown the training action, and the corresponding interaction content (for example, showing a new training action or prompting or guiding the target object to re-perform the currently shown training action) can be performed according to the analysis result;

[0039] After the training is completed, the training time of the next training is scheduled through the interaction with the target object.

[0040] In another optional embodiment of the present application, the system architecture 100 can further include a 5G base station 104, an intelligent bracelet distribution device 105, an infrared detector 106, and a distance sensor 107.

[0041] In another optional embodiment of the present application, in order to realize the traceability of the training process, the above-mentioned 5G base station can be in communication connection with the visual capture device, and the data in the training process is aggregated and transmitted to the target device, so as to check the data in the training process on the target device. For example, the target device can be a server, and the above data can be backed up and stored through the server, so as to facilitate subsequent data analysis or checking (the checker can be the target object himself, a rehabilitation therapist, a doctor, the family of the target object, etc.). The target device can also be a device used by a rehabilitation therapist or a doctor, and the rehabilitation therapist or the doctor can remotely view the rehabilitation training process of the patient in real time.

[0042] With the above scheme, in the training process, the 5G base station aggregates the data in the training system and transmits it to the device used by the doctor, so that the doctor can check the completion of the patient on the device, and can remotely guide the patient using 5G. In this scheme, the training system can provide basic training guidance, and for special cases or higher guidance needs, the doctor can also remotely guide the patient through 5G, thereby solving the technical problem that the cognitive memory training in the related art consumes a large amount of human resources.

[0043] The distribution device 105 of the above-mentioned intelligent bracelet is used to provide the target object with an intelligent bracelet. The distribution device can include a charging base, a Bluetooth, and a processor. By providing the distribution device of the intelligent bracelet, the intelligent bracelet can be efficiently managed, for example, the intelligent bracelet is actively charged when it is idle, the data of the intelligent bracelet is collected using Bluetooth, the intelligent bracelet is counted before and after distribution to prevent the bracelet from being lost, etc.

[0044] In a further optional embodiment of the present application, in order to provide individual training guidance and interaction for each target object, a visual capturer is proposed to be combined with a 5G base station, a distance sensor and an infrared detector to determine the number of target objects located in a specified area in front of a display: a 5G base station, which is in communication connection with the visual capturer; an infrared detector, which is located in front of the display together with the 5G base station, and is used to detect the number and position of the target objects in combination with the 5G base station;

[0045] a distance sensor, which is located behind the display, and is used to detect the distance of the target objects;

[0046] The visual capturer is further configured to correct the position according to the distance, and determine the number of target objects located in the specified area in front of the display according to the number and the corrected position of the target objects.

[0047] In a further optional embodiment of the present application, in order to more accurately determine the number and position of the target objects, the number of target objects in a specified area is determined by the following way:

[0048] The wearable smart device is a smart bracelet,

[0049] The signal transmitter of the 5G base station emits a signal; the 5G base station is further configured to determine the distance between the reflecting object at the signal reflection position and the signal transmitter according to the signal and the reflected signal of the signal, and determine the position of the reflecting object according to the distance;

[0050] The infrared detector is configured to collect human thermal imaging data of the target objects;

[0051] The visual capturer is configured to combine the position of the reflecting object with the human thermal imaging data to determine the number of target objects located in the specified area in front of the display, and use the number of smart bracelets in the specified area to verify the number of target objects.

[0052] For example, a signal is emitted by a signal emitter of a 5G base station, which is mainly a signal emitted to a specified area in front of each display, and then the distance between the reflection object (which can be an object, a target object, or other devices that interfere with signal reflection, and is generally a target object) and the signal emitter at the signal reflection position is determined (the distance can be calculated by using the signal emission time t0, the signal propagation speed s, and the signal reception time t1, and the distance = (t1-t0)*s / 2), and the position of the reflection object is determined by using the distance. Then, the number of target objects in the specified area can be determined by combining the position of the reflection object with the human thermal imaging data collected by the infrared detector. When the position of the reflection object is in the specified area and there is human thermal imaging data corresponding to the position, it is determined that there is a target object in the specified area. For example, for each specified area where the signal is reflected, the size of the human thermal imaging image in the specified area is determined. Only when the size of the human thermal imaging image reaches a preset threshold (such as 0.1 square meters), the human thermal imaging image is considered to be the image of the target object (i.e., the patient), i.e., it is determined that there is a target object in the specified area, otherwise it is considered to be an interference image.

[0053] In order to avoid errors in identifying the number of target objects, the number of target objects can also be verified by the number of smart bracelets in the specified area. For example, the number of target objects determined by the 5G base station and the infrared detector is m, and the number of detected smart bracelets is n. If m is greater than n, it means that the number of target objects determined by the 5G base station and the infrared detector is not accurate, and re-detection is needed until m and n are consistent.

[0054] In specific implementation, the infrared detector and the 5G base station are located in front of the display, and the distance sensor is located behind the display. The distance sensor is used to detect the distance of the target object, and the distance is used to correct the position detected by the infrared detector and the 5G base station. For example, the distance between the 5G base station and the distance sensor is L, the distance detected by the distance sensor between the target object is L1, and the distance detected by the 5G base station between the target object is L2. If the sum of L1 and L2 is approximately equal to L, it means that the position distance detected by the 5G base station is accurate, otherwise it is inaccurate and needs to be re-detected.

[0055] By using the above scheme for determining the number of target objects in the specified area, the number of people in the specified area can be accurately determined. When the number of people in the specified area is greater than 1, the display interaction is used to guide the excess target objects in the specified area to move to other specified areas, which can efficiently and accurately achieve the purpose of individual training of the target objects. In addition, the target objects can be guided to the best position in the specified area (watching the display, not interfering with each other, etc.), which is convenient for the visual capture device to capture the motion trajectory.

[0056] In the technical solution of the present application, the detection function can be realized by decomposing the 5G large broadband band, the dynamic capture of the cognitive memory training action can be realized by changing the threshold value using the visual capture technology, and the orientation of the visual capture is calibrated using the distance data detected by the distance sensor. Through the above technology, cognitive memory training is realized without the need for artificial guidance and supervision of rehabilitation trainers, and multiple patients can be supported for cognitive memory training at the same time. At the same time, using 5G technology can support long-distance information transmission and solve the problem of uneven distribution of medical resources.

[0057] In specific implementation, the training system described above can be deployed in a closed space (for example, indoors) to avoid interference.

[0058] It should be understood that Figure 1 The number of components such as interactive devices, wearable smart devices, and visual capturers in the training system is only illustrative, and can be set according to actual needs and requirements.

[0059] To further understand the technical solution of the present application, the training system of the present application is further described below in combination with the specific implementation mode:

[0060] 1) Taking the above training system deployed indoors as an example, a cognitive training room is formed, 1 5G base station and 1 detection system (taking the system containing 10 visual capturers, 1 infrared detector, 1 distance sensor, 10 displays, smart bracelet distribution equipment and containing 20 smart bracelets as an example) are deployed in the cognitive training room. The number of the above 5G base station, detection system and internal components is only used for illustrative description, and the training related data is collected by the cognitive training system and transmitted by 5G.

[0061] 2) Device space arrangement: the infrared detector and the 5G base station are on one side of the patient (i.e., the target object) (i.e., in front of the display), and the position and the number of the patient in front of the display are positioned; the distance sensor is on the other side of the patient (i.e., behind the display), and the distance data detected by the distance sensor is used to calibrate the position parameters detected by the infrared detector and the 5G base station (since the detection parameters of the distance sensor are generally 2-dimensional data, it is suitable for use in a limited space, and when the patient enters a generally specified area, the distance parameter can be detected to calibrate and correct the position data detected by the infrared detector and the 5G base station), and the device arrangement increases the accuracy of the data collected by the device, while reducing other interference.

[0062] 3) Human-computer interaction: to ensure the training effect of the patient and improve the human-computer interaction mode. In the 5G intelligent cognitive training room, the patient can interact through the smart bracelet, the visual capturer and the display to complete the training.

[0063] The smart bracelet is provided with a positioning sensor, and the movement trajectory of the smart bracelet in space can be displayed in real time within the detection range of the visual capture sensor, that is, the movement trajectory of the patient wearing the smart bracelet can be obtained; at the same time, in order to ensure the effectiveness of the movement trajectory data, the smart bracelet supports Bluetooth data transmission with the visual capture device, and the vibration test data and movement direction of the smart bracelet are transmitted to the visual capture device, and the movement trajectory data is calibrated according to the action data.

[0064] The visual capture device locates the movement trajectory of the patient through the bracelet worn by the patient, and calibrates the movement trajectory of the patient through Bluetooth data transmission with the smart bracelet. At the same time, the embedded visual graphics algorithm K-Means is supported to analyze the standard degree of the movement trajectory of the patient, so that when the movement trajectory meets the requirements, a new action instruction is matched and displayed to the patient.

[0065] Through the cooperation of the smart bracelet and the visual capture device (including the embedded algorithm), the patient can be automatically matched with appropriate training content by using the training scheme, and the capture and calibration of the movement trajectory are implemented to ensure that the training movement of the patient is correct and standardized. For specific process, please refer to Figure 2 :

[0066] Step 201, the patient takes out and wears the bracelet from the smart bracelet issuing device.

[0067] Step 202, the patient in front of the display, according to the pre-rehearsed action, imitates (that is, performs the training action).

[0068] Step 203, the visual capture device captures the movement trajectory of the patient, and at the same time transmits the action data through Bluetooth to judge whether the movement trajectory capture is successful, if successful, execute step 205, otherwise execute step 204.

[0069] Step 204, the movement trajectory capture fails, and returns to step 202.

[0070] Step 205, the visual capture device algorithm calculates the accuracy of the movement trajectory of the patient, and if the accuracy of the movement trajectory meets the requirements, a new training action is matched.

[0071] Step 206, dynamically adjust the training scheme, the patient enters the training, and in the training process, the movement trajectory is captured in real time, and steps 203 to 206 are repeated until the training is completed.

[0072] 4) The patient enters the 5G intelligent cognitive training room, receives the smart bracelet and wears it. The 5G base station transmits signals, the bracelet collects the signals and returns them to the 5G base station, and the position of the bracelet is determined. In this way, even if multiple people enter the 5G intelligent cognitive functional rehabilitation training room at different times, they can also be positioned respectively.

[0073] 5) The patient goes to the designated position according to the guidance issued by the display. Through 5G positioning, smart bracelet and thermal imaging analysis results, the display determines the number of patients in the designated area in front of the display.

[0074] Using the 3.1-10.6 GHz bandwidth of 5G, the distance between the transmitter and the object is calculated in real time to determine the position of the object. After calculating the position of the object, the data (including three-dimensional coordinate data: X, Y, Z) and the thermal imaging data detected by the infrared detector are combined for analysis to determine the number of patients in the designated area. For example, when the coordinate data detects that the infrared detection data thermal map area is > 0.5 mm, it is determined that there are multiple patients in the area.

[0075] If it is determined that there are multiple patients in the same area, the display will guide the patient to another designated area for training. At the same time, the 5G is used to collect the area where each patient's smart bracelet is located, and this is used to calibrate the number of patients in the designated area, ensuring that only one patient is training in each designated area.

[0076] 6) When 5G and thermal imaging determine that the number of patients in each designated area is correct and the position is correct, the display plays the training action, and the visual capture device captures the patient's action to determine and calibrate the training posture. The distance sensor monitors whether the patient is in the designated area in real time and returns the distance parameter to the system and the 5G positioning function data for calibration to recalculate the positioning result.

[0077] 7) The patient's training data is transmitted to the doctor's terminal in real time (the doctor is the designated doctor selected by the patient in the system). The doctor supervises the patient according to the training system's prompts (patient training score results), on the one hand to prevent accidents, and on the other hand to give suggestions based on the patient's real-time training effect.

[0078] 8) After the patient finishes the current training, the next training time in the 5G intelligent cognitive training room is scheduled.

[0079] Using the technical solution of the present application, brain imaging data can be used in combination with traditional video and audio data to analyze memory problems caused by cognitive impairment in patients.

[0080] To address the technical problems mentioned in this application, one embodiment utilizes 5G high-bandwidth detection to detect patient location; transmits multimodal patient motion information via high-bandwidth data transmission; acquires patient motion information using visual capture technology; performs posture interpretation and correction using embedded algorithms; assesses patient energy expenditure via infrared detection; performs position calibration for multiple patients via distance detection; and provides accurate training plans for patients through intelligent cognitive training and multimodal data analysis. By integrating these technologies to create a 5G intelligent cognitive function training room, the uneven distribution of medical resources can be alleviated.

[0081] Continue to refer to Figure 3 The diagram illustrates a flow 300 of an embodiment of a cognitive memory training method according to this application. The cognitive memory training method provided in this embodiment can be executed by a visual capture device, which can be configured within the visual capture device. The method includes the following steps:

[0082] Step 301: Instruct the interactive device to display the training content to the target object. The interactive device is used to interact with the target object during the training process.

[0083] Step 302: Receive motion data of the target object collected by the wearable smart device during the training process.

[0084] Step 303: During the training process, capture the motion trajectory of the target object, correct the motion trajectory using the motion data, analyze the motion standard of the corrected motion trajectory, and control the interactive content of the interactive device based on the analysis results to assist the target object in completing the training.

[0085] The detailed implementation process of steps 301 to 303 above can be found in the preceding description of the visual capture device. Using the technical solution of this application, during training, the target subject wears a wearable smart device and trains according to the prompts of the interactive device. The wearable smart device collects motion data during training and sends it to the visual capture device. The visual capture device uses the motion data to correct the captured motion trajectory, analyzes the corrected motion trajectory, and controls the interactive device based on the analysis results to assist the target subject in completing the training. In this solution, the training system can provide basic guidance, thereby solving the technical problem of high human resource consumption in cognitive memory training in existing technologies.

[0086] Further reference Figure 4 As an implementation of the methods shown in the above figures, this application provides an embodiment of a cognitive memory training device, which is similar to... Figure 3 Corresponding to the method embodiment shown, in addition to the features described below, the device embodiment may also include [features related to...].Figure 3 The method embodiments shown have the same or corresponding features or effects. The device can be particularly applied in various electronic devices.

[0087] As Figure 4 shown, the cognitive memory training device 400 of the embodiment includes an indication unit 401, a receiving unit 402, and a training unit 403. The indication unit 401 is configured to instruct an interactive device to show training content to a target object, wherein the interactive device is used to interact with the target object during the training process. The receiving unit 402 is configured to receive action data of the target object collected by a wearable smart device during the training process. The training unit 403 is configured to capture a motion trajectory of the target object during the training process, correct the motion trajectory by using the action data, analyze the action standard degree of the corrected motion trajectory, and control the interaction content of the interactive device according to the analysis result to assist the target object to complete the training.

[0088] In the embodiment, the specific processing of the indication unit 401, the receiving unit 402, and the training unit 403 of the cognitive memory training device 400 and the technical effects brought by the specific processing can be respectively referred to the related description in the foregoing, which will not be described here.

[0089] According to the embodiments of the present application, the present application also provides an electronic device and a readable storage medium.

[0090] As Figure 5 shown, is a block diagram of an electronic device according to the cognitive memory training method of the embodiments of the present application. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown in the figure, their connections, and their functions, are meant only as examples, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0091] As Figure 5As shown, the electronic device includes one or more processors 501, a memory 502, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected by different buses, and can be mounted on a common main board or otherwise installed as desired. The processor can process instructions executed within the electronic device, including instructions stored in the memory or graphical information stored in the memory to display a GUI on an external input / output device, such as a display device coupled to the interface. In other embodiments, multiple processors and / or buses can be used with multiple memories and multiple memory, if desired. Likewise, multiple electronic devices can be connected, each device providing part of the necessary operations (e.g., as a server array, a set of blade servers, or a multi-processor system). Figure 5 The processor 501 is taken as an example.

[0092] The memory 502 is a non-transitory computer readable storage medium provided by the present application. The memory stores instructions executable by at least one processor, so that the at least one processor executes the cognitive memory training method provided by the present application. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to execute the cognitive memory training method provided by the present application.

[0093] The memory 502 is a non-transitory computer readable storage medium, which can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules of the cognitive memory training method in the embodiments of the present application (for example, the indication unit 401, the receiving unit 402 and the training unit 403 shown in the figure). The processor 501 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions and modules stored in the memory 502, that is, implements the cognitive memory training method in the above method embodiments. Figure 4 The processor 501 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions and modules stored in the memory 502, that is, implements the cognitive memory training method in the above method embodiments.

[0094] The memory 502 can include a program storage area and a data storage area, wherein the program storage area can store an operating system and application programs required by at least one function; the data storage area can store data created by use of the electronic device, etc. In addition, the memory 502 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory 502 can optionally include a memory disposed remotely with respect to the processor 501, which can be connected to the electronic device through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0095] The electronic device of the cognitive memory training method can further include an input device 503 and an output device 504. The processor 501, the memory 502, the input device 503, and the output device 504 can be connected through a bus or other means, Figure 5 The connection through the bus is taken as an example.

[0096] The input device 503 can receive inputted digital or character information, and generate key signal input related to user settings and function control of the cognitive memory training electronic device, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 504 can include a display device, an auxiliary lighting device (e.g., an LED), a tactile feedback device (e.g., a vibration motor), etc. The display device can include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device can be a touch screen.

[0097] Various embodiments of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0098] These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and / or object-oriented programming language, and / or in assembly / machine language. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus and / or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0099] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0100] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0101] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0102] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the block can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and

[0103] The units described in the embodiments of the present application can be implemented in the form of software, or can be implemented in the form of hardware. The described units can also be arranged in a processor, for example, a processor can be described as including an indication unit, a receiving unit and a training unit. In some cases, the names of these units do not constitute a limitation on the units themselves.

[0104] As another aspect, the present application also provides a computer readable medium, which can be included in the apparatus described in the above embodiments, or can exist independently without being assembled into the apparatus. The above computer readable medium carries one or more programs, when the one or more programs are executed by the apparatus, the apparatus is caused to: instruct an interactive device to show training content to a target object, wherein the interactive device is used to interact with the target object in a training process; receive action data of the target object collected by a wearable smart device in the training process; correct a motion trajectory of the target object captured in the training process by using the action data, analyze the corrected motion trajectory, and control interaction content of the interactive device according to an analysis result to assist the target object to complete the training.

[0105] The above description is merely preferred embodiments of the present application and a description of the principles of the applied technology. It should be understood by those skilled in the art that the scope of the application disclosed in the present application is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combinations of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features can be replaced with the technical features disclosed in the present application (but not limited to) having similar functions to form technical solutions.

Claims

1. A cognitive memory training system, comprising: an interactive device configured to present training content to a target object and interact with the target object during a training process; a wearable smart device configured to collect motion data of the target object during the training process; a visual capturer communicatively connected to the interactive device and the wearable smart device, the visual capturer configured to capture a motion trajectory of the target object during the training process, correct the motion trajectory using the motion data, analyze a motion standard degree of the corrected motion trajectory, and control an interactive content of the interactive device according to an analysis result to assist the target object to complete the training; the interactive device is a display, and the display and the visual capturer are in one-to-one correspondence, and each of the display corresponds to a designated area for training one target object; the training system further comprises: a 5G base station communicatively connected to the visual capturer; an infrared detector located in front of the display, the infrared detector configured to detect a number and a position of the target objects in combination with the 5G base station; a distance sensor located behind the display, the distance sensor configured to detect a distance of the target objects; the visual capturer is further configured to correct the position according to the distance and determine the number of the target objects in the designated area in front of the display according to the number and the corrected position of the target objects.

2. The training system of claim 1, wherein, the visual capturer is embedded with a K-Means visual graph algorithm, and the visual capturer is configured to analyze the motion standard degree of the corrected motion trajectory using the K-Means visual graph algorithm, match new training motions and present the new training motions to the target object through the interactive device when the analysis result meets the requirements, and prompt the target object to retrain according to the currently presented training motions through the interactive device when the analysis result does not meet the requirements. the display is configured to:

3. The training system of claim 1, wherein, issue a guide before the training to guide the target object to stand in the designated area, and issue a guide to guide the excess target objects in the designated area to stand in other designated areas when there are multiple target objects in the same designated area; present training motions to the target object during the training process; reserve a training time for the next training through the interaction with the target object after the training is completed. the wearable smart device is a smart bracelet, 4. The training system of claim 1, wherein, a signal transmitter of the 5G base station emits a signal, and the 5G base station is further configured to determine a distance between a reflection object of a signal reflection position and the signal transmitter according to the signal and a reflected signal of the signal, and determine the position of the reflection object according to the distance; the infrared detector is configured to collect human thermal imaging data of the target object. ​ The visual capturer is configured to combine the position of the reflective object with the human thermal imaging data, determine the number of the target objects located in the designated area in front of the display, and verify the number of the target objects by the number of the smart bands in the designated area.

5. The training system of claim 4, wherein, The smart band is provided with a positioning sensor, and the visual capturer is connected to the positioning sensor and configured to capture the movement track of the target object in the training process by the positioning sensor.

6. A cognitive memory training method, comprising: indicating an interactive device to show training content to a target object, wherein the interactive device is configured to interact with the target object in a training process, the interactive device is a display, the number of the display and visual capturer is multiple, and each of the display and the visual capturer corresponds to a designated area for training one target object; receiving action data of the target object collected by a wearable smart device in the training process; capturing the movement track of the target object in the training process by the visual capturer, correcting the movement track by the action data, analyzing the standard degree of action of the corrected movement track, and controlling the interaction content of the interactive device according to the analysis result to assist the target object to complete the training; further comprising: communicating with the visual capturer through a 5G base station; the infrared detector and the 5G base station are located in front of the display, and the number and position of the target object are detected by the infrared detector and the 5G base station; a distance sensor is located behind the display, and the distance of the target object is detected by the distance sensor; the position is corrected according to the distance by the visual capturer, and the number of the target objects located in the designated area in front of the display is determined according to the number of the target objects and the corrected position.

7. An electronic device, comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of claim 6.

8. A computer readable storage medium having stored thereon a computer program, wherein, The program is executed by the processor to implement the method of claim 6. The program is executed by the processor to implement the method of claim 6.

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