CN110931103A - Control method and system of rehabilitation equipment - Google Patents
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
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Abstract
The invention discloses a control method and a control system of rehabilitation equipment. The method comprises the steps of obtaining user identity information, selecting a training mode to execute a training process, sending training data collected in the training process to a cloud server for training evaluation, generating a control command according to a training evaluation result to control a rehabilitation device, and generating a current training record and an evaluation suggestion after training is finished. The information acquisition, real-time monitoring, data analysis and remote control of the rehabilitation device can be realized, the detailed training data of the user in the rehabilitation training process is collected and recorded, the evaluation result of the training effect is provided after training, so that the user can efficiently put into the next-stage rehabilitation training, and meanwhile, the current rehabilitation device is controlled by remotely generating a control command through monitoring the training condition in real time to adapt to the rehabilitation plan of the user in time.
Description
Technical Field
The invention relates to the field of exercise rehabilitation control, in particular to a control method and a control system of rehabilitation equipment.
Background
The rehabilitation equipment is quite important in the aspect of helping the user to perform limb rehabilitation, in the traditional rehabilitation process, a physical therapist needs to perform one-to-one rehabilitation training on the user by hands, the training efficiency and the training intensity of the mode are difficult to guarantee, and the training effect is also influenced by the training level of the physical therapist; meanwhile, objective data for evaluating the relation between training process data and the rehabilitation effect is lacked, the training parameters are difficult to optimize so as to obtain the optimal rehabilitation effect, but the cost generated by the physical therapist for long-time accompanying training is quite high, and if the user selects to build the physical therapist, the simple rehabilitation equipment cannot meet the requirement of the user for long-time repetitive rehabilitation training under the condition of lacking scientific guidance of the physical therapist. Therefore, in order to improve the completion level of the rehabilitation training of the user and improve the guidance efficiency of the rehabilitation training, it is necessary to provide a control method of the rehabilitation device, which can realize real-time monitoring of the rehabilitation training process.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. Therefore, the invention aims to provide a rehabilitation device control method capable of realizing real-time monitoring of a rehabilitation training process.
The technical scheme adopted by the invention is as follows:
in a first aspect, the present invention provides a method for controlling a rehabilitation device, including:
acquiring user identity information, and selecting a training mode to execute a training process;
sending the training data acquired in the training process to a cloud server for training evaluation;
generating a control instruction according to the training evaluation result to control the rehabilitation equipment;
and after training is finished, generating a current training record and an evaluation suggestion.
Further, the training data are sent to the cloud server through a real-time MQTT protocol.
Further, the cloud server performs data analysis by training a machine learning neural network to obtain a training evaluation result, the machine learning neural network performs supervised learning by combining with the evaluation scale, and an algorithm of the machine learning neural network includes: SVM algorithms and XGBoost algorithms.
Further, still include: and sending the training data, the training record and the evaluation suggestion to a user terminal.
Further, the training data includes: sensor angle data and sensor force data.
In a second aspect, the present invention also provides a rehabilitation device control system for executing a rehabilitation device control method according to any one of the first aspects, including: a cloud server and a rehabilitation device;
the rehabilitation equipment is used for acquiring user identity information and selecting a training mode to execute a training process;
the cloud server is used for carrying out training evaluation according to the training data collected by the rehabilitation equipment and generating a control instruction according to a training evaluation result to control the rehabilitation equipment.
Further, the system also comprises a user terminal used for receiving the training data, the training records and the evaluation suggestions.
Further, the system also comprises a Web client used for carrying out user information management and inquiry.
Further, the system further comprises a cloud database for storing the training data, the training records and the evaluation suggestions.
In a third aspect, the present invention also provides a rehabilitation device, which is controlled by using the rehabilitation device control method according to any one of the first aspect.
The invention has the beneficial effects that:
according to the invention, the user identity information is acquired, the training process is executed by selecting the training mode, the training data acquired in the training process is sent to the cloud server for training evaluation, the control instruction is generated according to the training evaluation result to control the rehabilitation equipment, and after the training is finished, the current training record and the evaluation suggestion are generated. The information acquisition, real-time monitoring, data analysis and remote control of the rehabilitation device can be realized, the detailed training data of the user in the rehabilitation training process is collected and recorded, the evaluation result of the training effect is provided after training, so that the user can efficiently put into the next-stage rehabilitation training, and meanwhile, the current rehabilitation device is controlled by remotely generating a control command through monitoring the training condition in real time to adapt to the rehabilitation plan of the user in time.
The invention can be widely applied to a rehabilitation equipment control system.
Drawings
FIG. 1 is a flowchart illustrating an implementation of an embodiment of a control method for a rehabilitation device according to the present invention;
fig. 2 is a block diagram of a rehabilitation device control system according to an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The first embodiment is as follows:
an embodiment of the present invention provides a method for controlling a rehabilitation device, and fig. 1 is a flowchart illustrating an implementation of the method for controlling the rehabilitation device according to the embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
s1: and acquiring user identity information, and selecting a training mode to execute a training process.
S2: and sending the training data acquired in the training process to a cloud server for training and evaluation.
S3: and generating a control instruction according to the training evaluation result to control the rehabilitation equipment.
S4: and after training is finished, generating a current training record and an evaluation suggestion.
S5: and sending the training data, the training records and the evaluation suggestions to a user terminal for the user to train and refer.
Specifically, in step S1, it is optionally implemented that the user logs in the current rehabilitation device in a card swiping manner, for example, the user swipes a card on a card swiping device of the device using a device card containing personal information and sends the card swiping information to the cloud server at the same time, so that the information of the person using the current device can be obtained without manual input before each training.
In addition, the training mode in this embodiment includes: the system comprises an active mode and a passive mode, wherein corresponding power-assisted grades, training time and the like can be selected at the same time, wherein the active mode is suitable for users who have certain muscle tension but cannot completely control limbs; the passive mode is suitable for users who cannot independently control limbs to perform basic activities, and during the mode training, the rehabilitation equipment drives joints corresponding to the limb parts to move, so that the users are helped to complete the rehabilitation training of the corresponding limbs.
In step S2, the training data includes: the sensor angle data and the sensor force data are different for different rehabilitation devices, and the change data of the rehabilitation part can be detected for analyzing the rehabilitation training effect. For example, a hand rehabilitation device is used for rehabilitation of a hand hemiplegic user, and the hand rehabilitation device collects sensor angle data and sensor force data of hand joints through an angle sensor and a force sensor, wherein the sensor angle data and the sensor force data comprise angle information of finger joints of the hand and force feedback information contacted with the rehabilitation device; the lower limb rehabilitation device is used for rehabilitation of users with injured legs, and collects sensor angle data and sensor force data of joints of the legs.
The existing upper computer of the computer is generally connected through a USB mode, and analysis data is recorded on the computer, and the scheme has the defects that: the data only exists in the local computer, and the cross-device analysis of the historical training data of the user cannot be carried out. In addition, the existing Bluetooth is connected with a computer upper computer and sends data to a server through the computer, in the scheme, the Bluetooth has requirements on distance, the Bluetooth is not stable enough in the process of large data volume transmission, and an additional computer or handheld equipment is needed to be used as transfer equipment.
Specifically, in this embodiment, the training data is sent to the cloud server through a real-time MQTT (message queue telemetry transport) protocol, the training data is directly sent to the cloud server through a 4G/5G internet of things communication module without being transferred through equipment, the MQTT is an instant communication protocol with low overhead and low bandwidth occupation, a real-time reliable message service can be provided for connecting remote equipment by using few codes and bandwidths, and the MQTT is suitable for the environment with low hardware performance and poor network conditions, so that the MQTT has a wide application in the aspects of the internet of things and the like, and is a universal internet of things communication standard.
The embodiment uses the 4G/5G communication module, can not be restricted to using the place to carry out the rehabilitation training, for example there is not wifi's clinic, the place such as park even carries the training at home to need not to join in marriage net input password operation in the use, promote user experience, more importantly, the 5G communication module has the characteristics of low time delay high bandwidth, can support a large amount of data high frequency to upload and quick response, and the method of this embodiment has more universality.
In step S3, the cloud server performs data analysis by training a machine learning neural network to obtain a training evaluation result, where the machine learning neural network performs supervised learning by combining with the evaluation scale, and an algorithm of the machine learning neural network includes: SVM algorithms and XGBoost algorithms.
Supervised learning is a machine learning task that infers a function from labeled training data, where each instance consists of an input object (i.e., training data in this embodiment) and a desired output value (i.e., label in this embodiment), and the supervised learning algorithm analyzes the training data and generates an inferred function that can be used to map out a new instance. In this embodiment, a machine learning neural network is obtained through training of a large amount of sample data, and a specific training and learning process is as follows.
Firstly, collecting enough user training information, carrying out data label judgment by combining a universal evaluation scale, namely carrying out rating classification according to training data generated by a user in a man-machine interaction training process to form a judgment label, and constructing a sample training set, wherein the evaluation scale optionally comprises: the brunnstrom scale or the ashworth scale, both of which belong to the conventional rehabilitation rating scales, and the contents of which are not described herein.
Then, a supervised learning and classification algorithm in machine learning is used for realizing the evaluation of a training result, namely model training is carried out according to sensor angle data and sensor force data in a sample, and the grades of the angle and the force are obtained and are expressed as follows:
function (angle, force) scale rating
When new training data is input, a corresponding rating, that is, a training evaluation result, can be obtained, the SVM algorithm and the XGBoost algorithm used in this embodiment both belong to a conventional classification learning algorithm, and specific contents thereof are not described herein in detail.
In this embodiment, it is further determined whether the training mode and the training intensity of the user need to be adaptively modified according to the obtained training evaluation result, and a control instruction corresponding to the training evaluation result is generated and sent to the rehabilitation device to control the rehabilitation device.
For example, in a specific application scenario, when lower limb strength training is performed, after a period of training, the lower limb strength of the user is evaluated and judged to be improved, and meanwhile, the current physical state of the user is judged to enter the next stage according to the evaluation scale, the training strength needs to be modified, for example, the training strength is enhanced, and according to the evaluation result, the rehabilitation device is adjusted to a new training strength for training.
Specifically, for example, the rehabilitation device has a plurality of training modes with training intensity and corresponding assistance levels, and the implementation process is as follows:
1) firstly, acquiring a medical suggestion of a user according to identity information of the user, and selecting the user to perform rehabilitation training in a certain training mode and assistance level;
2) recording the training parameters of the user in the training process of the user under the training parameters;
3) and judging a training evaluation result of the user by combining the training scale, for example, obtaining a high score of the user in coordination and action in the training process by contrasting the training scale according to the training parameters, then obtaining a suggestion that the user adjusts the assistance level to enter the next stage of training, informing the user through a client or other reminding modes, carrying out corresponding setting on equipment by the user according to the suggestion, or carrying out remote setting through the client, and carrying out rehabilitation training according to a new training mode or assistance level after the setting is finished. In step S4, when the user finishes training, the training process may be terminated, the current training cycle is completed, the cloud server performs sorting and analysis on the current training process to generate a current training record, and generates an evaluation suggestion by combining the historical training condition and the current training evaluation result, for example, to prompt the user of a training mode, training intensity, and the like of the next rehabilitation training.
In step S5, the embodiment may send the training record and the evaluation suggestion to the relevant APP on the user terminal, and the user obtains the real-time training situation through the APP, and performs the historical training situation query statistics and the rehabilitation course tracking, thereby providing a perfect rehabilitation training process for the user.
The embodiment can realize information acquisition, real-time monitoring, data analysis and remote control of the rehabilitation equipment, acquire and record detailed training data of a user in the rehabilitation training process, provide an evaluation result of a training effect after training, so that the user can efficiently put into next-stage rehabilitation training, and simultaneously remotely generate a control instruction to remotely guide and control the current rehabilitation equipment and adapt to the rehabilitation plan of the user in time by monitoring the training condition in real time.
Example two:
as shown in fig. 2, which is a block diagram of a rehabilitation device control system in this embodiment, the method for controlling a rehabilitation device according to any one of the embodiments includes: the system comprises a cloud server 10, a rehabilitation device 20, a user terminal 30 and a Web client 40, wherein data transmission is carried out through a 4G/5G communication module.
The rehabilitation device 20 is used for acquiring the user identity information and selecting a training mode to execute a training process;
the cloud server 10 is configured to perform training evaluation according to the training data collected by the rehabilitation device 20, and generate a control instruction according to a training evaluation result to control the rehabilitation device 20.
The user terminal 30 is used for receiving training data, training records and evaluation suggestions, meanwhile, a doctor can also check training effects in real time through an APP on the user terminal 30 during training so as to generate an adaptive training evaluation report after training is finished, and the doctor can interact with the cloud server 10 through the user terminal 30 when necessary and send a control instruction to remotely control the rehabilitation device.
The Web client 40 is used for user information management and query, for example, a doctor views historical training data of all users and corresponding devices through the Web client 40 so as to perform tracking treatment, and meanwhile, the operation condition of the current device is evaluated according to the historical training data.
The cloud server 10 further includes: the system comprises a cloud database 11 and a data analysis module 12, wherein the cloud database 11 is used for storing training data, training records and evaluation suggestions, the training data generated in the training process is recorded in real time through the standardized storage for tracing, and the recovery trend and the training progress of a user are judged by combining different training performances of time trends; the data analysis module 12 is configured to perform data analysis through the training machine learning neural network to obtain a training evaluation result, which is convenient for the user to perform subsequent rehabilitation adjustment.
Example three:
in addition, the invention also provides a rehabilitation device, which is controlled by using the control method of the rehabilitation device according to any one of the embodiments, such as a hand exoskeleton for hand rehabilitation, a lower limb exoskeleton for lower limb rehabilitation and the like, and all the rehabilitation devices which can be controlled by using the control method of the first embodiment are within the protection scope of the embodiment.
The above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same, although the present invention is described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. A control method of a rehabilitation apparatus, characterized by comprising:
acquiring user identity information, and selecting a training mode to execute a training process;
sending the training data acquired in the training process to a cloud server for training evaluation;
generating a control instruction according to the training evaluation result to control the rehabilitation equipment;
and after training is finished, generating a current training record and an evaluation suggestion.
2. The control method of a rehabilitation device according to claim 1, wherein the training data is sent to the cloud server via a real-time MQTT protocol.
3. The control method of the rehabilitation device according to claim 1, wherein the training evaluation result is obtained by performing data analysis by the cloud server through a training machine learning neural network, the machine learning neural network performs supervised learning by combining with an evaluation scale, and an algorithm of the machine learning neural network comprises: SVM algorithms and XGBoost algorithms.
4. The control method of a rehabilitation apparatus according to claim 1, further comprising: and sending the training data, the training record and the evaluation suggestion to a user terminal.
5. The control method of a rehabilitation apparatus according to any one of claims 1 to 4, wherein the training data includes: sensor angle data and sensor force data.
6. A rehabilitation device control system characterized by executing a rehabilitation device control method according to any one of claims 1 to 5, comprising: a cloud server and a rehabilitation device;
the rehabilitation equipment is used for acquiring user identity information and selecting a training mode to execute a training process;
the cloud server is used for carrying out training evaluation according to the training data collected by the rehabilitation equipment and generating a control instruction according to a training evaluation result to control the rehabilitation equipment.
7. The rehabilitation device control system of claim 6, further comprising a user terminal for receiving the training data, the training records, and evaluation recommendations.
8. The rehabilitation device control system according to claim 6, further comprising a Web client for user information management and query.
9. The rehabilitation device control system according to any one of claims 6 to 8, further comprising a cloud database for storing the training data, the training records and the evaluation recommendations.
10. A rehabilitation device characterized by being controlled by a rehabilitation device control method according to any one of claims 1 to 5.
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