CN117373613A - Rehabilitation training scheme determining method, system, equipment and readable storage medium - Google Patents
Rehabilitation training scheme determining method, system, equipment and readable storage medium Download PDFInfo
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Abstract
The application provides a rehabilitation training scheme determining method, a system, equipment and a readable storage medium, wherein when a target user performs rehabilitation training, a training image extracting module acquires training videos and extracts a plurality of training images; the action parameter generation module performs skeleton key point identification on the training images by utilizing a human skeleton key point detection algorithm to obtain the position information of each skeleton key point, and determines the action parameters of each skeleton key point of the target user according to the acquisition time of each training image; the myoelectricity parameter acquisition module acquires myoelectricity parameters of each target muscle group; the user characteristic generating module generates target user characteristics of a target user according to each action parameter and each myoelectric parameter; the training scheme determining module compares the characteristics of the target user with the characteristics of the standard user to determine the training scheme of the target user when the target user performs the next rehabilitation training. By adopting the method, the effectiveness and the accuracy of the rehabilitation training scheme obtained by determination are improved.
Description
Technical Field
The invention relates to the field of health management, in particular to a rehabilitation training scheme determining method, a system, equipment and a readable storage medium.
Background
Rehabilitation training is to help patients recover or improve their physiological, cognitive and psychological functions, thereby alleviating symptoms, improving quality of life and even realizing better social participation. Different types of rehabilitation exercises may have different goals, for example physical rehabilitation exercises may help the patient recover motor functions, cognitive rehabilitation exercises may improve memory and thinking ability.
In the prior art, when performing matching of rehabilitation training schemes for patients or users, subjective assessment of medical staff is often relied on, that is, after the medical staff observes the behaviors of the users, training schemes are formulated for the medical staff according to experience. However, in the research, the method may be limited by the insufficient experience of medical staff, the interference of human factors and the influence of subjective judgment, so that the problem of low effectiveness of the determined rehabilitation training scheme is caused; meanwhile, the medical staff only observes the user behaviors by naked eyes and lacks objective and quantitative data, so that the problem that the accuracy of the determined rehabilitation training scheme is low is caused.
Disclosure of Invention
Accordingly, the present invention is directed to a rehabilitation training scheme determining method, system, device and readable storage medium, so as to improve the effectiveness and accuracy of the rehabilitation training scheme obtained by determining.
In a first aspect, an embodiment of the present application provides a rehabilitation training scheme determining method, which is applied to a rehabilitation training scheme determining system, where the system includes a training image extracting module, an action parameter generating module, an myoelectricity parameter collecting module, a user feature generating module and a training scheme determining module, and the method includes:
when a target user performs the rehabilitation training, the training image extraction module acquires a training video containing the target user and extracts a plurality of training images from the training video;
the action parameter generation module carries out bone key point identification on each training image by utilizing a human bone key point detection algorithm to obtain the position information of each bone key point, and then determines the action parameters of each bone key point of the target user according to the position information of each bone key point in each training image and the acquisition time of each training image;
The myoelectricity parameter acquisition module acquires myoelectricity parameters of each target muscle group of the target user;
the user characteristic generation module generates target user characteristics of the target user according to the action parameters of each skeletal key point and the myoelectricity parameters of each target muscle group;
and the training scheme determining module compares the target user characteristics with the standard user characteristics, and determines the training scheme of the target user when performing the next rehabilitation training according to the comparison result.
Optionally, the user feature generating module generates the target user feature of the target user according to the action parameter of each skeletal key point and the myoelectric parameter of each target muscle group, including:
the user characteristic generating module respectively marks the action parameters of each skeletal key point by utilizing an action parameter marking rule to obtain a plurality of action marking data, and respectively marks the myoelectric parameters of each target muscle group by utilizing a myoelectric parameter marking rule to obtain a plurality of myoelectric marking data;
for each action annotation data, the user characteristic generation module generates an annotation data sequence with each myoelectricity annotation data by utilizing the action annotation data;
And the user characteristic generating module gathers each marked data sequence to obtain the target user characteristic.
Optionally, the system further comprises a user feature determination module, and before the training scheme determination module compares the target user feature with a standard user feature, the method further comprises:
and the user characteristics determine that the standard user characteristics are obtained by inputting the action parameters of all skeletal key points and myoelectricity parameters of all target muscle groups of a normal person during the rehabilitation training to a trained user characteristic generation model, wherein the standard user characteristics are standard marking data sequences formed by all standard action marking data of the normal person and standard corresponding myoelectricity marking data of the normal person.
Optionally, the training scheme determining module compares the target user feature with the standard user feature, and determines a training scheme of the target user when performing the next rehabilitation training according to a comparison result, including:
the training scheme determining module judges whether the target user characteristics are the same as the standard user characteristics;
if the target user characteristics are different from the standard user characteristics, the training scheme determining module increases the training times or training duration of the target user when the target user performs the next rehabilitation training.
Optionally, after the training scheme determination module determines whether the target user feature is the same as the standard user feature, the method further includes:
and if the target user characteristics are the same as the standard user characteristics, the training scheme determining module reduces the training times or training duration of the target user when the target user performs the next rehabilitation training.
Optionally, when the rehabilitation training is facial expression training, the action parameters are large opening angle of the mouth, left and right swing amplitude of the tongue tip, left and right eye closing duration, or eyebrow wrinkle loosening offset; the target muscle group is a facial muscle group;
when the rehabilitation training is head and shoulder training, the action parameters are left and right rotation amplitude of the head, or head forward tilting angle, or upward lifting height of the shoulder, or backward stretching distance of the shoulder; the target muscle group is a head and shoulder muscle group;
when the rehabilitation training is trunk and upper limb training, the action parameters are trunk left and right swing amplitude, head and trunk inclination angle, arm straightening maximum angle, wrist joint left and right rotation maximum angle, or finger-to-finger completion time; the target muscle group is a trunk and upper limb muscle group;
When the rehabilitation training is lower limb training, the action parameters are leg lifting height, quadriceps femoris forward tilting angle, ankle joint rotation angle, toe ground clearance or standing exercise duration; the target muscle group is a lower limb muscle group.
Optionally, the myoelectric parameters include frequency and duration.
In a second aspect, an embodiment of the present application provides a rehabilitation training scheme determining system, where the system includes a training image extracting module, an action parameter generating module, an myoelectric parameter collecting module, a user feature generating module and a training scheme determining module;
the training image extraction module is used for collecting training videos containing the target user when the target user performs the rehabilitation training, and extracting a plurality of training images from the training videos;
the action parameter generation module is used for carrying out skeleton key point identification on each training image by utilizing a human skeleton key point detection algorithm to obtain the position information of each skeleton key point, and then determining the action parameters of each skeleton key point of the target user according to the position information of each skeleton key point in each training image and the acquisition time of each training image;
The myoelectricity parameter acquisition module is used for acquiring myoelectricity parameters of each target muscle group of the target user;
the user characteristic generating module is used for generating target user characteristics of the target user according to the action parameters of each skeletal key point and the myoelectricity parameters of each target muscle group;
the training scheme determining module is used for comparing the target user characteristics with the standard user characteristics and determining the training scheme of the target user when the next rehabilitation training is carried out according to the comparison result.
Optionally, the user feature generation module is specifically configured to, when generating the target user feature of the target user according to the action parameter of each skeletal keypoint and the myoelectric parameter of each target muscle group:
marking the action parameters of each skeletal key point by using an action parameter marking rule to obtain a plurality of action marking data, and marking the myoelectricity parameters of each target muscle group by using a myoelectricity parameter marking rule to obtain a plurality of myoelectricity marking data;
for each action labeling data, generating a labeling data sequence by utilizing the action labeling data and each myoelectricity labeling data;
And collecting each labeling data sequence to obtain the target user characteristics.
Optionally, the system further comprises a user feature determination module;
the user characteristic determining module is used for inputting action parameters of key points of bones and myoelectricity parameters of each target muscle group of a normal person when performing rehabilitation training to a trained user characteristic generating model to obtain the standard user characteristic before the training scheme determining module compares the target user characteristic with the standard user characteristic, wherein the standard user characteristic is a standard marking data sequence formed by each standard action marking data of the normal person and standard corresponding myoelectricity marking data of the normal person.
Optionally, the training scheme determining module is configured to compare the target user characteristic with the standard user characteristic, and determine, according to a comparison result, a training scheme of the target user when performing the next rehabilitation training, where the training scheme determining module is specifically configured to:
judging whether the target user characteristics are the same as the standard user characteristics;
if the target user characteristics are different from the standard user characteristics, the training times or training time of the target user in the next rehabilitation training are increased.
Optionally, the training scheme determining module is configured to reduce training times or training duration of the target user when performing the next rehabilitation training if the target user features are the same as the standard user features after determining whether the target user features are the same as the standard user features.
Optionally, when the rehabilitation training is facial expression training, the action parameters are large opening angle of the mouth, left and right swing amplitude of the tongue tip, left and right eye closing duration, or eyebrow wrinkle loosening offset; the target muscle group is a facial muscle group;
when the rehabilitation training is head and shoulder training, the action parameters are left and right rotation amplitude of the head, or head forward tilting angle, or upward lifting height of the shoulder, or backward stretching distance of the shoulder; the target muscle group is a head and shoulder muscle group;
when the rehabilitation training is trunk and upper limb training, the action parameters are trunk left and right swing amplitude, head and trunk inclination angle, arm straightening maximum angle, wrist joint left and right rotation maximum angle, or finger-to-finger completion time; the target muscle group is a trunk and upper limb muscle group;
When the rehabilitation training is lower limb training, the action parameters are leg lifting height, quadriceps femoris forward tilting angle, ankle joint rotation angle, toe ground clearance or standing exercise duration; the target muscle group is a lower limb muscle group.
Optionally, the myoelectric parameters include frequency and duration.
In a third aspect, embodiments of the present application provide a computer device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory in communication via the bus when the computer device is running, the machine-readable instructions when executed by the processor performing the steps of the rehabilitation training protocol determination method of any of the alternative embodiments of the first aspect described above.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the rehabilitation training protocol determination method described in any of the optional embodiments of the first aspect.
The technical scheme provided by the application comprises the following beneficial effects:
when the target user performs the rehabilitation training, the training image extraction module extracts a plurality of training images of the target user, the action parameters of all skeletal key points of the target user and the myoelectric parameters of all target muscle groups are respectively determined by the action parameter generation module and the myoelectric parameter acquisition module, then the user characteristic generation module generates target user characteristics of the target user according to all the action parameters and the myoelectric parameters, objective quantification of target user behavior data is realized, and objective, accurate and effective data support and basis are provided for the follow-up rehabilitation training scheme determination; and then, the training scheme determining module compares the characteristics of the target user with the characteristics of the standard user, and determines the training scheme of the target user when the target user performs the next rehabilitation training according to the comparison result, so that the training scheme of the user is determined by taking the characteristics of the standard user as comparison basis, the need of medical staff to formulate the training scheme for the user according to the experience of the medical staff is avoided, and the interference of human factors and the influence of subjective judgment are avoided.
By adopting the method, the action and myoelectricity of the target user in the rehabilitation training process are objectively and quantitatively processed to obtain the target user characteristics, then the comparison result is obtained by taking the standard user characteristics as the comparison basis of the target user characteristics, finally the rehabilitation training scheme of the user is determined according to the comparison result, the reliability of the user related data according to the determined rehabilitation training scheme is improved, the influence of the interference of human factors and subjective judgment is avoided, and the effectiveness and the accuracy of the determined rehabilitation training scheme are improved.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a rehabilitation training scheme determining method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for generating target user features according to a first embodiment of the present invention;
FIG. 3 is a flowchart of a training scheme adjustment method according to a first embodiment of the present invention;
fig. 4 is a schematic structural diagram of a rehabilitation training scheme determining system according to a second embodiment of the present invention;
fig. 5 shows a schematic structural diagram of a second rehabilitation training scheme determining system according to the second embodiment of the present invention;
Fig. 6 shows a schematic structural diagram of a computer device according to a third embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
Example 1
For the convenience of understanding the present application, the following describes in detail the first embodiment of the present application with reference to the flowchart of the method for determining a rehabilitation training scheme according to the first embodiment of the present invention shown in fig. 1.
Referring to fig. 1, fig. 1 shows a flowchart of a rehabilitation training scheme determining method according to an embodiment of the present invention, where the method is applied to a rehabilitation training scheme determining system, the system includes a training image extracting module, an action parameter generating module, an myoelectricity parameter collecting module, a user feature generating module, and a training scheme determining module, and the method includes steps S101 to S105:
s101: when a target user performs the rehabilitation training, the training image extraction module acquires a training video containing the target user and extracts a plurality of training images from the training video.
Specifically, when the target user performs the rehabilitation training, the acquisition of the training image includes two methods. The method comprises the following steps: and acquiring training videos containing the target user by a camera in the training image extraction module, and then extracting a plurality of training images from the training videos at preset acquisition intervals. The second method is as follows: emitting infrared light beams by a laser emitter in the training image extraction module to form an infrared light grid or pattern; when the infrared light beam intersects with the user, the infrared camera in the training image extraction module captures the change of the light, distance information between the user and the training image extraction module is calculated by measuring the time flight or the structured light projection of the light, a three-dimensional depth map of the user in a scene is generated, and a three-dimensional depth map is generated at preset acquisition intervals, so that a plurality of training images are obtained.
S102: and the action parameter generation module carries out bone key point identification on each training image by utilizing a human bone key point detection algorithm to obtain the position information of each bone key point, and then determines the action parameters of each bone key point of the target user according to the position information of each bone key point in each training image and the acquisition time of each training image.
Specifically, the motion parameter generation module recognizes and tracks key joints and bones of a human body through computer vision and machine learning technologies, and captures and tracks body motions of a user in real time, so that tracking of body gestures and motions is realized.
The key point detection algorithm of the skeleton is characterized in that an input image or video is processed through a deep learning model, and finally the position coordinates of each key point are output. This process can be divided into two key steps, feature extraction and keypoint regression. In the feature extraction stage, the algorithm pre-processes the input image or video to extract features suitable for skeleton key point detection. Common feature extraction methods include Convolutional Neural Networks (CNN), residual networks (res net), and the like. The method can effectively extract the space and semantic information in the image and provide powerful support for subsequent key point regression. In the key point regression stage, the algorithm maps the feature map to the position coordinates of the key points through a trained deep learning model. Common Regression methods include Convolutional Neural Networks (CNN), regression trees (Regression Tree), gaussian mixture models (Gaussian Mixture Model), and the like. The method can accurately predict the positions of every other key point according to the information of the feature map and output corresponding coordinates, namely position information.
Since each training image is acquired at preset time intervals, the acquisition time of each training image is known, and according to the relationship among time, speed and distance, the action parameters (the movement speed, the angle change speed and the like of each bone key point of the target user) of each bone key point of the target user can be determined according to the position information of each bone key point in each training image and the acquisition time of each training image.
S103: the myoelectricity parameter acquisition module acquires myoelectricity parameters of each target muscle group of the target user.
In particular, myoelectricity tests typically place small electrodes on the skin of a user, which electrodes can record the electrical activity of the muscle in a resting state and in an active state, and measure the electrical activity response of different muscles as the user is doing an action. For example, the parameters of electrical activity, including continuity, frequency and duration, the speed at which the nerve reaches the muscle, etc., of the electrical activity of the muscle, including peaks and troughs, etc., in a resting state or when performing a particular action or contraction. The shape and characteristics of these waveforms enable real-time recording of information of muscle status.
When the target user performs the rehabilitation training, one or more target muscle groups of the rehabilitation are determined in advance according to the type of the rehabilitation training, and then myoelectricity parameters of each target muscle group of the target user are obtained through the myoelectricity test method.
S104: and the user characteristic generation module generates target user characteristics of the target user according to the action parameters of each skeletal key point and the myoelectricity parameters of each target muscle group.
Specifically, the user characteristic generation module binds and associates the action parameters of each skeletal key point and the myoelectricity parameters of each target muscle group of the target user with the target user, and generates target user characteristics of the target user, which are used for describing the training state and training result of the target user when the rehabilitation training is performed.
S105: and the training scheme determining module compares the target user characteristics with the standard user characteristics, and determines the training scheme of the target user when performing the next rehabilitation training according to the comparison result.
Specifically, the standard user features are user features of a normal person during rehabilitation training, and a comparison result obtained by comparing the target user features with the standard user features can indicate whether the target user is different from the normal person during rehabilitation training. If the target user is not different from the normal person in performing rehabilitation training, the fact that the rehabilitation training of the target user has effect is indicated, the next training can be omitted, or the training times or time in the next rehabilitation training scheme is reduced. If the target user is different from the normal person in performing rehabilitation training, the target user is required to perform multiple rehabilitation training, so that the target user can achieve the purpose of rehabilitation through rehabilitation training.
In a possible implementation manner, referring to fig. 2, fig. 2 shows a flowchart of a target user feature generating method according to an embodiment of the present invention, where the user feature generating module generates a target user feature of the target user according to an action parameter of each skeletal key and an myoelectric parameter of each target muscle group, and the method includes steps S201 to S203:
s201: and the user characteristic generating module marks the action parameters of each skeletal key point by using action parameter marking rules to obtain a plurality of action marking data, and marks the myoelectric parameters of each target muscle group by using myoelectric parameter marking rules to obtain a plurality of myoelectric marking data.
Specifically, in order to facilitate the processing of the data by the computer, labeling each action parameter and each myoelectric parameter by adopting a data unification rule to obtain labeling data with unified format and form.
When the training types of rehabilitation training are different, the action parameters and the marking method required for marking are different. When facial expression training is carried out, the action parameters of the skeletal key points comprise a large opening angle of a mouth, a left-right swing amplitude of a tongue tip, a left-right eye closing time length and an eyebrow wrinkle loosening offset; the large angle of the open mouth is marked as Zb 1 The amplitude of the left-right swing of the tongue tip is marked as Sj 1 Marking the closing time length of the left eye and the right eye as Yb 1 Marking the offset of the eyebrow wrinkle as Ms 1 . When the head and shoulder training is carried out, the action parameters comprise left and right rotation amplitude of the head, forward inclination angle of the head, upward lifting height of the shoulder and backward extension distance of the shoulder; marking the left-right rotation amplitude of the head as Tb 1 The head rake angle is noted as Td 1 Lifting the shoulder upwardDegree is marked as Js 1 The backward extension distance of the shoulder is designated Jh 1 . When the trunk and upper limbs are trained, the action parameters comprise the trunk left and right swinging amplitude, the head and trunk inclination angle, the maximum arm straightening angle, the maximum wrist joint left and right rotation angle and the finger-to-finger completion time; the swing amplitude of the trunk is marked as Qg 1 The head and torso tilt angles are noted as Tq 1 The maximum arm straightening angle is denoted as Ss 1 The maximum left-right rotation angle of the wrist joint is marked as Wz 1 Marking the finger-to-finger completion time as Ts 1 . When the lower limb is trained, the action parameters comprise the lifting height of the leg, the forward inclination angle of quadriceps, the rotation angle of ankle joints, the toe off clearance and the standing exercise time; the leg lifting height is denoted as Tt 1 The pretilt angle of quadriceps femoris is denoted Gq 1 The ankle joint rotation angle is noted as Hz 1 Marking the toe-off gap as Zl 1 The standing exercise time length is marked as Zs 1 。
Correspondingly, when the training types of rehabilitation training are different, myoelectric parameters and marking methods of the target muscle groups required to be marked are different. In performing facial expression training, the target muscle group includes a facial muscle group; in performing head and shoulder exercises, the target muscle groups include head and shoulder muscle groups; when the trunk and upper limbs are trained, the target muscle group comprises trunk and upper limb muscle groups; in performing lower limb training, the target muscle group includes a lower limb muscle group. Myoelectric parameters include continuity, frequency, and duration. Marking continuity of facial muscle groups as Mblx 1 The frequency of facial muscle groups is marked as Mbpl 1 The duration of the facial muscle groups is labeled Mbcs 1 The method comprises the steps of carrying out a first treatment on the surface of the Marking continuity of head and shoulder muscle groups as Tjlx 1 The frequency of the head and shoulder muscle groups is marked as Tjpl 1 The duration of the head and shoulder muscle groups is labeled Tjcs 1 The method comprises the steps of carrying out a first treatment on the surface of the The continuity of the trunk and upper limb muscle groups is marked as Qslx 1 The frequency of the trunk and upper limb muscle groups was labeled Qspl 1 The duration of the trunk and upper limb muscle groups is labeled asQscs 1 The method comprises the steps of carrying out a first treatment on the surface of the The continuity of the lower limb muscle group was marked as Xzlx 1 The frequency of the lower limb muscle group is marked as Xzpl 1 The duration of the lower limb muscle group is labeled Xzcs 1 。
S202: and for each action annotation data, the user characteristic generation module generates an annotation data sequence by utilizing the action annotation data and each myoelectricity annotation data.
Specifically, each action labeling data is taken as the first element of the sequence, and a plurality of myoelectricity labeling data exist behind each action labeling data. For example, if n pieces of action annotation data and m pieces of myoelectricity annotation data exist, the first action annotation data is taken as the first element of one annotation data sequence, the first myoelectricity annotation data and the second myoelectricity annotation data up to the mth myoelectricity annotation data are taken as the subsequent elements to form an annotation data sequence (the first action annotation data, the first myoelectricity annotation data, the second myoelectricity annotation data, … and the mth myoelectricity annotation data), and the annotation data sequence is taken as the first annotation data sequence. Because n action annotation data exist, each action annotation data can generate one annotation data sequence, and finally n annotation data sequences can be generated.
S203: and the user characteristic generating module gathers each marked data sequence to obtain the target user characteristic.
Specifically, the user characteristic generating module gathers n labeling data sequences to obtain a target user characteristic, and the target user characteristic may be in the form of a sequence matrix or a sequence table. Through the steps S201 to S203, each action parameter and myoelectricity parameter are marked to obtain a data sequence, so that a subsequent system can compare user characteristics presented in a data sequence form conveniently, and the data processing efficiency is improved.
In a possible embodiment, the system further comprises a user feature determination module, and before the training scheme determination module compares the target user feature with a standard user feature, the method further comprises:
and the user characteristics determine that the standard user characteristics are obtained by inputting the action parameters of all skeletal key points and myoelectricity parameters of all target muscle groups of a normal person during the rehabilitation training to a trained user characteristic generation model, wherein the standard user characteristics are standard marking data sequences formed by all standard action marking data of the normal person and standard corresponding myoelectricity marking data of the normal person.
Specifically, the user characteristic generation model is a logistic regression model established based on a large number of previous normal person test data, and the model can determine the user characteristic according to the action parameters of each skeletal key point and the myoelectricity parameters of each target muscle group.
In a possible implementation manner, referring to fig. 3, fig. 3 shows a flowchart of a training scheme adjustment method provided by an embodiment of the present invention, where the training scheme determining module compares the target user feature with the standard user feature, and determines, according to a comparison result, a training scheme of the target user when performing a next rehabilitation training, including steps S301 to S302:
s301: the training scheme determination module determines whether the target user characteristic is the same as the standard user characteristic.
S302: if the target user characteristics are different from the standard user characteristics, the training scheme determining module increases the training times or training duration of the target user when the target user performs the next rehabilitation training.
Specifically, if the target user features are different from the standard user features, the training scheme of the target user during the next training needs to be adjusted, and the adjustment direction is used for improving the training effect, so that the target user can achieve the effect of recovering the behavior model of the normal person through the next training.
The adjusting method comprises the steps of increasing training times of the target user when the target user performs the next rehabilitation training and increasing training time of the target user when the target user performs the next rehabilitation training.
In one possible embodiment, after the training scheme determination module determines whether the target user characteristic is the same as the standard user characteristic, the method further comprises:
and if the target user characteristics are the same as the standard user characteristics, the training scheme determining module reduces the training times or training duration of the target user when the target user performs the next rehabilitation training.
In a possible implementation manner, when the rehabilitation training is facial expression training, the action parameters are large opening angle of the mouth, left and right swing amplitude of the tip of the tongue, closing time of the left and right eyes, or eyebrow crumpling and loosening offset; the target muscle group is a facial muscle group.
When the rehabilitation training is head and shoulder training, the action parameters are left and right rotation amplitude of the head, or head forward tilting angle, or upward lifting height of the shoulder, or backward stretching distance of the shoulder; the target muscle group is a head and shoulder muscle group.
When the rehabilitation training is trunk and upper limb training, the action parameters are trunk left and right swing amplitude, head and trunk inclination angle, arm straightening maximum angle, wrist joint left and right rotation maximum angle, or finger-to-finger completion time; the target muscle group is a trunk and upper limb muscle group.
When the rehabilitation training is lower limb training, the action parameters are leg lifting height, quadriceps femoris forward tilting angle, ankle joint rotation angle, toe ground clearance or standing exercise duration; the target muscle group is a lower limb muscle group.
In one possible embodiment, the myoelectric parameters include frequency and duration.
Example two
Referring to fig. 4, fig. 4 shows a schematic structural diagram of a rehabilitation training scheme determining system according to a second embodiment of the present invention, where the system includes a training image extracting module 401, an action parameter generating module 402, an myoelectric parameter collecting module 403, a user feature generating module 404, and a training scheme determining module 405;
the training image extraction module is used for collecting training videos containing the target user when the target user performs the rehabilitation training, and extracting a plurality of training images from the training videos;
The action parameter generation module is used for carrying out skeleton key point identification on each training image by utilizing a human skeleton key point detection algorithm to obtain the position information of each skeleton key point, and then determining the action parameters of each skeleton key point of the target user according to the position information of each skeleton key point in each training image and the acquisition time of each training image;
the myoelectricity parameter acquisition module is used for acquiring myoelectricity parameters of each target muscle group of the target user;
the user characteristic generating module is used for generating target user characteristics of the target user according to the action parameters of each skeletal key point and the myoelectricity parameters of each target muscle group;
the training scheme determining module is used for comparing the target user characteristics with the standard user characteristics and determining the training scheme of the target user when the next rehabilitation training is carried out according to the comparison result.
In a possible embodiment, the user feature generation module is specifically configured to, when generating the target user feature of the target user according to the action parameter of each of the skeletal keypoints and the myoelectric parameter of each of the target muscle groups:
Marking the action parameters of each skeletal key point by using an action parameter marking rule to obtain a plurality of action marking data, and marking the myoelectricity parameters of each target muscle group by using a myoelectricity parameter marking rule to obtain a plurality of myoelectricity marking data;
for each action labeling data, generating a labeling data sequence by utilizing the action labeling data and each myoelectricity labeling data;
and collecting each labeling data sequence to obtain the target user characteristics.
In one possible implementation, referring to fig. 5, fig. 5 shows a schematic structural diagram of a second rehabilitation training scheme determining system according to a second embodiment of the present invention, where the system further includes a user feature determining module 501;
the user characteristic determining module is used for inputting action parameters of key points of bones and myoelectricity parameters of each target muscle group of a normal person when performing rehabilitation training to a trained user characteristic generating model to obtain the standard user characteristic before the training scheme determining module compares the target user characteristic with the standard user characteristic, wherein the standard user characteristic is a standard marking data sequence formed by each standard action marking data of the normal person and standard corresponding myoelectricity marking data of the normal person.
In a possible implementation manner, the training scheme determining module is configured to compare the target user characteristic with the standard user characteristic, and determine, according to a comparison result, a training scheme of the target user when performing the next rehabilitation training, where the training scheme is specifically configured to:
judging whether the target user characteristics are the same as the standard user characteristics;
if the target user characteristics are different from the standard user characteristics, the training times or training time of the target user in the next rehabilitation training are increased.
In a possible implementation manner, the training scheme determining module is configured to reduce the training frequency or training duration of the target user when performing the next rehabilitation training after determining whether the target user feature is the same as the standard user feature.
In a possible implementation manner, when the rehabilitation training is facial expression training, the action parameters are large opening angle of the mouth, left and right swing amplitude of the tip of the tongue, closing time of the left and right eyes, or eyebrow crumpling and loosening offset; the target muscle group is a facial muscle group;
When the rehabilitation training is head and shoulder training, the action parameters are left and right rotation amplitude of the head, or head forward tilting angle, or upward lifting height of the shoulder, or backward stretching distance of the shoulder; the target muscle group is a head and shoulder muscle group;
when the rehabilitation training is trunk and upper limb training, the action parameters are trunk left and right swing amplitude, head and trunk inclination angle, arm straightening maximum angle, wrist joint left and right rotation maximum angle, or finger-to-finger completion time; the target muscle group is a trunk and upper limb muscle group;
when the rehabilitation training is lower limb training, the action parameters are leg lifting height, quadriceps femoris forward tilting angle, ankle joint rotation angle, toe ground clearance or standing exercise duration; the target muscle group is a lower limb muscle group.
In one possible embodiment, the myoelectric parameters include frequency and duration.
Example III
Based on the same application concept, referring to fig. 6, fig. 6 shows a schematic structural diagram of a computer device provided in a third embodiment of the present invention, where, as shown in fig. 6, a computer device 600 provided in the third embodiment of the present invention includes:
The rehabilitation training device comprises a processor 601, a memory 602 and a bus 603, wherein the memory 602 stores machine-readable instructions executable by the processor 601, when the computer device 600 is running, the processor 601 and the memory 602 communicate through the bus 603, and the machine-readable instructions are executed by the processor 601 to perform the steps of the rehabilitation training scheme determining method shown in the first embodiment.
Example IV
Based on the same application concept, the embodiment of the present invention further provides a computer readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the rehabilitation training scheme determining method according to any one of the above embodiments are executed.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system and apparatus may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.
The computer program product for performing rehabilitation training scheme determination provided by the embodiment of the invention comprises a computer readable storage medium storing program codes, wherein the instructions included in the program codes can be used for executing the method described in the method embodiment, and specific implementation can be referred to the method embodiment and will not be repeated here.
The rehabilitation training scheme determining system provided by the embodiment of the invention can be specific hardware on equipment or software or firmware installed on the equipment. The system provided by the embodiment of the present invention has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding content in the foregoing method embodiment where the system embodiment is not mentioned. It will be clear to those skilled in the art that, for convenience and brevity of description, the specific operation of the system, system and unit described above may refer to the corresponding process in the above method embodiment, which is not repeated here.
In the embodiments provided in the present invention, it should be understood that the disclosed system and method may be implemented in other manners. The system embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions in actual implementation, and e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments provided in the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that: like reference numerals and letters in the following figures denote like items, and thus once an item is defined in one figure, no further definition or explanation of it is required in the following figures, and furthermore, the terms "first," "second," "third," etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the corresponding technical solutions. Are intended to be encompassed within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. The utility model provides a rehabilitation training scheme determining method which is characterized in that the method is applied to a rehabilitation training scheme determining system, the system comprises a training image extracting module, an action parameter generating module, an myoelectricity parameter collecting module, a user characteristic generating module and a training scheme determining module, and the method comprises the following steps:
when a target user performs the rehabilitation training, the training image extraction module acquires a training video containing the target user and extracts a plurality of training images from the training video;
the action parameter generation module carries out bone key point identification on each training image by utilizing a human bone key point detection algorithm to obtain the position information of each bone key point, and then determines the action parameters of each bone key point of the target user according to the position information of each bone key point in each training image and the acquisition time of each training image;
the myoelectricity parameter acquisition module acquires myoelectricity parameters of each target muscle group of the target user;
the user characteristic generation module generates target user characteristics of the target user according to the action parameters of each skeletal key point and the myoelectricity parameters of each target muscle group;
And the training scheme determining module compares the target user characteristics with the standard user characteristics, and determines the training scheme of the target user when performing the next rehabilitation training according to the comparison result.
2. The method of claim 1, wherein the user feature generation module generates the target user feature of the target user based on the action parameters of each of the skeletal keypoints and the myoelectrical parameters of each of the target muscle groups, comprising:
the user characteristic generating module respectively marks the action parameters of each skeletal key point by utilizing an action parameter marking rule to obtain a plurality of action marking data, and respectively marks the myoelectric parameters of each target muscle group by utilizing a myoelectric parameter marking rule to obtain a plurality of myoelectric marking data;
for each action annotation data, the user characteristic generation module generates an annotation data sequence with each myoelectricity annotation data by utilizing the action annotation data;
and the user characteristic generating module gathers each marked data sequence to obtain the target user characteristic.
3. The method of claim 1, wherein the system further comprises a user feature determination module, the method further comprising, prior to the training regimen determination module comparing the target user feature to a standard user feature:
And the user characteristics determine that the standard user characteristics are obtained by inputting the action parameters of all skeletal key points and myoelectricity parameters of all target muscle groups of a normal person during the rehabilitation training to a trained user characteristic generation model, wherein the standard user characteristics are standard marking data sequences formed by all standard action marking data of the normal person and standard corresponding myoelectricity marking data of the normal person.
4. The method according to claim 1, wherein the training scheme determining module compares the target user characteristic with a standard user characteristic, and determines a training scheme of the target user when performing the next rehabilitation training according to a comparison result, including:
the training scheme determining module judges whether the target user characteristics are the same as the standard user characteristics;
if the target user characteristics are different from the standard user characteristics, the training scheme determining module increases the training times or training duration of the target user when the target user performs the next rehabilitation training.
5. The method of claim 4, wherein after the training scheme determination module determines whether the target user characteristic is the same as the standard user characteristic, the method further comprises:
And if the target user characteristics are the same as the standard user characteristics, the training scheme determining module reduces the training times or training duration of the target user when the target user performs the next rehabilitation training.
6. The method according to claim 1, wherein when the rehabilitation training is facial expression training, the action parameter is a large opening angle of a mouth, or a left-right swing amplitude of a tongue tip, or a left-right eye closing time length, or an eyebrow wrinkle loosening offset; the target muscle group is a facial muscle group;
when the rehabilitation training is head and shoulder training, the action parameters are left and right rotation amplitude of the head, or head forward tilting angle, or upward lifting height of the shoulder, or backward stretching distance of the shoulder; the target muscle group is a head and shoulder muscle group;
when the rehabilitation training is trunk and upper limb training, the action parameters are trunk left and right swing amplitude, head and trunk inclination angle, arm straightening maximum angle, wrist joint left and right rotation maximum angle, or finger-to-finger completion time; the target muscle group is a trunk and upper limb muscle group;
when the rehabilitation training is lower limb training, the action parameters are leg lifting height, quadriceps femoris forward tilting angle, ankle joint rotation angle, toe ground clearance or standing exercise duration; the target muscle group is a lower limb muscle group.
7. The method of claim 1, wherein the myoelectrical parameters include frequency and duration.
8. The rehabilitation training scheme determining system is characterized by comprising a training image extracting module, an action parameter generating module, an myoelectricity parameter collecting module, a user characteristic generating module and a training scheme determining module;
the training image extraction module is used for collecting training videos containing the target user when the target user performs the rehabilitation training, and extracting a plurality of training images from the training videos;
the action parameter generation module is used for carrying out skeleton key point identification on each training image by utilizing a human skeleton key point detection algorithm to obtain the position information of each skeleton key point, and then determining the action parameters of each skeleton key point of the target user according to the position information of each skeleton key point in each training image and the acquisition time of each training image;
the myoelectricity parameter acquisition module is used for acquiring myoelectricity parameters of each target muscle group of the target user;
the user characteristic generating module is used for generating target user characteristics of the target user according to the action parameters of each skeletal key point and the myoelectricity parameters of each target muscle group;
The training scheme determining module is used for comparing the target user characteristics with the standard user characteristics and determining the training scheme of the target user when the next rehabilitation training is carried out according to the comparison result.
9. A computer device, comprising: a processor, a memory and a bus, said memory storing machine readable instructions executable by said processor, said processor and said memory communicating via said bus when the computer device is running, said machine readable instructions when executed by said processor performing the steps of the rehabilitation training protocol determination method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the rehabilitation training regimen determination method according to any one of claims 1 to 7.
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