CN111166345A - Three-dimensional motion detection method, device and equipment and readable storage medium - Google Patents
Three-dimensional motion detection method, device and equipment and readable storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a readable storage medium for detecting three-dimensional motion, wherein the method comprises the following steps: receiving motion signals generated when joints and/or muscles of a human body move; and identifying the motion state of the received motion signal, and determining the current three-dimensional motion state of the user. Thus, the received motion signal is identified by an identification algorithm, which is relatively easy to implement compared to conventional gyroscope and accelerometer based algorithms.
Description
Technical Field
The invention relates to the technical field of intelligent monitoring, in particular to a method, a device and equipment for detecting three-dimensional motion and a readable storage medium.
Background
Most of the existing methods for detecting three-dimensional motion monitor the posture and motion of limbs based on a gyroscope sensor and an acceleration sensor, and the existing technologies have the defects that the existing algorithm based on the gyroscope sensor and the acceleration sensor is complex and the realization process is difficult.
Disclosure of Invention
The embodiment of the invention provides a method, a device and equipment for detecting three-dimensional motion and a readable storage medium, wherein the three-dimensional motion state of a user is determined by using a recognition algorithm, and compared with the traditional algorithm based on a gyroscope and an accelerometer, the scheme is relatively easy to realize.
One aspect of the present invention provides a method for detecting three-dimensional motion, including: receiving motion signals generated when joints and/or muscles of a human body move; and identifying the motion state of the received motion signal, and determining the current three-dimensional motion state of the user.
In an embodiment, the motion state recognition of the received motion signal to determine the current three-dimensional motion state of the user includes: and identifying the motion state by taking the received motion signal as the input of the classification model, and determining the current three-dimensional motion state of the user.
In an embodiment, the motion state recognition of the received motion signal to determine the current three-dimensional motion state of the user includes: generating a data waveform map according to the received motion signal; judging whether the generated data oscillogram is matched with a preset oscillogram or not; and if the generated data oscillogram is judged to be matched with the preset oscillogram, determining the current three-dimensional motion state of the user.
In an embodiment, after determining the three-dimensional motion state of the user at the time, the method further comprises: recording the time length of the user in the three-dimensional motion state or the number of times of repeating the three-dimensional motion state in a set time period; and determining whether to generate warning information corresponding to the three-dimensional motion state or not according to the recorded duration or times and a preset value corresponding to the three-dimensional motion state, and informing the warning information to the user.
In one embodiment, the informing the user of the warning information includes: and transmitting the generated warning information to a mobile terminal, and indicating the mobile terminal to inform the user of the warning information.
In another aspect, the present invention provides an apparatus for detecting three-dimensional motion, the apparatus comprising: the motion signal receiving module is used for receiving motion signals generated when joints and/or muscles of a human body move; and the motion state identification module is used for identifying the motion state of the received motion signal and determining the current three-dimensional motion state of the user.
In an implementation manner, the motion state identification module is specifically configured to: and identifying the motion state by taking the received motion signal as the input of the classification model, and determining the current three-dimensional motion state of the user.
In an implementation manner, the motion state identification module is further specifically configured to: generating a data waveform map according to the received motion signal; judging whether the generated data oscillogram is matched with a preset oscillogram or not; and if the generated data oscillogram is judged to be matched with the preset oscillogram, determining the current three-dimensional motion state of the user.
In another aspect, the present invention provides a three-dimensional motion detection apparatus, including: the stress sensor patches are arranged at the bending part or the muscle stretching part of the human joint and used for detecting motion signals of the human joint or the muscle; the data collection device is connected with the stress sensor patches through a human body wire, and is used for receiving motion signals detected by the stress sensor patches and carrying out three-dimensional motion state identification according to the received motion signals; and the system is also used for carrying out data interaction with the mobile equipment according to the motion signal and the recognized three-dimensional motion state.
Another aspect of the invention provides a computer-readable storage medium comprising a set of computer-executable instructions which, when executed, perform a method of detecting three-dimensional motion as described in any one of the above.
In the embodiment of the invention, firstly, a motion signal generated when a human joint and/or muscle moves is received, wherein the receiving frequency can be real-time receiving or intermittent receiving; the motion signal includes information of motion direction, motion time, and the like.
And then, carrying out motion state identification on the received motion signal so as to determine the current three-dimensional motion state of the user.
Thus, the received motion signal is identified by an identification algorithm, which is relatively easy to implement compared to conventional gyroscope and accelerometer based algorithms.
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The above and other objects, features and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:
in the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
Fig. 1 is a schematic flow chart illustrating an implementation of a method for detecting three-dimensional motion according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a three-dimensional motion detection apparatus according to an embodiment of the present invention;
FIG. 3 is a waveform diagram of data obtained by lifting and lowering an arm according to a method for detecting three-dimensional motion of the present invention;
fig. 4 is a schematic diagram of a stress sensor patch attached to a shoulder in a three-dimensional motion detection device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in 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 a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart illustrating an implementation of a method for detecting three-dimensional motion according to an embodiment of the present invention;
as shown in fig. 1, an aspect of the present invention provides a method for detecting three-dimensional motion, where the method includes:
and 102, identifying the motion state of the received motion signal, and determining the current three-dimensional motion state of the user.
In the embodiment, when a human body moves, the relevant joints and muscles of the human body can be bent or stretched, and by utilizing the characteristic, one or more sensors for detecting joint or muscle movement signals are arranged at the joints or muscles of the user, so that the movement state of the user at the moment can be judged according to the movement signals.
Firstly, receiving a motion signal generated when a joint and/or muscle of a human body moves, wherein the receiving frequency can be real-time receiving or intermittent receiving; the motion signal includes information of motion direction, motion time, and the like.
And then, carrying out motion state identification on the received motion signal so as to determine the current three-dimensional motion state of the user.
Thus, the received motion signal is identified by an identification algorithm, which is relatively easy to implement compared to conventional gyroscope and accelerometer based algorithms.
In one embodiment, the motion state recognition of the received motion signal to determine the current three-dimensional motion state of the user includes:
and identifying the motion state by taking the received motion signal as the input of the classification model, and determining the current three-dimensional motion state of the user.
In this embodiment, one of the specific processes in step 102 is: and identifying the motion state by taking the received motion signal as the input of the classification model, and determining the current three-dimensional motion state of the user. The classification model is preferably a classification model based on an SVM algorithm, and the classification model needs to be trained in advance, wherein the training process is roughly as follows:
the user repeatedly does the same action for N times or repeatedly does the action for a period of time to obtain training data of a plurality of motion signals, the training data are used as input of the classification model to be trained to obtain a motion state output result, the output result and the training labels are used for adjusting weight information in the classification model through a loss function until the error between the output result and the training labels is within an allowable range, and then the classification model can be obtained.
In one embodiment, the motion state recognition of the received motion signal to determine the current three-dimensional motion state of the user includes:
generating a data waveform map according to the received motion signal;
judging whether the generated data oscillogram is matched with a preset oscillogram or not;
and if the generated data oscillogram is judged to be matched with the preset oscillogram, determining the current three-dimensional motion state of the user.
In this embodiment, another specific process of step 102 is: a data waveform map is generated based on the received motion signal. For example, when a user walks, the degree of knee flexion of the user changes with time, and when the user walks in each step, a data waveform diagram can be generated according to the motion signal, and the data waveform diagram can refer to fig. 3, where fig. 3 is a data waveform diagram of the arm being lifted and lowered in the method for detecting three-dimensional motion according to the embodiment of the present invention.
Then, carrying out similarity matching on the generated data oscillogram and a plurality of preset oscillograms in the system, and selecting the oscillogram with the highest matching degree from the oscillograms so as to determine the three-dimensional motion state corresponding to the oscillogram as the current three-dimensional motion state of the user;
alternatively, the generated data waveform map may be used as an input to a trained convolutional neural network, and the three-dimensional motion state corresponding to the motion signal may be recognized by the convolutional neural network.
In an embodiment, after determining the three-dimensional motion state of the user at the time, the method further includes:
103, recording the time length of the user in the three-dimensional motion state or the number of times of repeating the three-dimensional motion state in a set time period;
and 104, determining whether to generate warning information corresponding to the three-dimensional motion state or not according to the recorded duration or times and a preset value corresponding to the three-dimensional motion state, and informing a user of the warning information.
In this embodiment, after determining the current three-dimensional motion state of the user, the method further includes:
recording the time length of the user in a three-dimensional motion state (such as head-down, standing, lying, and the like), wherein the time length record is mostly used when using the classification model for identification; or the number of times a three-dimensional motion state (such as knee motion, shoulder motion, etc.) is repeated within a set period of time, the number of times being recorded for most uses when using pattern recognition.
Then, according to the recorded duration or times and a preset value corresponding to the three-dimensional motion state, whether warning information corresponding to the three-dimensional motion state is generated or not is determined, and the warning information is informed to a user; the method specifically comprises the following steps: the system stores preset values corresponding to various three-dimensional motion states, such as knee motion, shoulder motion, long-time standing time, sedentary time and the like.
And comparing the recorded duration or times with a preset value, and determining whether to send out warning information to a user according to a comparison result. For example, it is determined that the number of times of knee movement of the user exceeds a preset value (e.g., 1000 times) of knee movement stored in the system, which indicates that the user has an excessively long travel path, and the system generates warning information corresponding to the knee movement (e.g., "please rest properly"); for another example, if the shoulder movement of the user is judged to be less than the shoulder movement times (such as 50 times) pre-stored in the system, warning information corresponding to the shoulder movement is generated (such as 'please add more times, which is beneficial to health'); for example, if the user is in a sedentary state exceeding the stored sedentary preset value (e.g. 120 minutes), an alarm message corresponding to sedentary is generated (e.g. "please stand up activity"); after the warning information is generated, the warning information is informed to the user.
In one embodiment, the method for informing the user of the warning message includes:
and transmitting the generated warning information to the mobile terminal, and indicating the mobile terminal to inform the user of the warning information.
In this embodiment, after the warning information is generated, a specific process of "informing the user of the warning information" is as follows: the warning information is sent to a mobile terminal of the user through a network, specifically, the warning information can be in an application program in the mobile terminal, the user is informed through the mobile terminal, and the mobile terminal can be a mobile phone, a tablet and the like.
Fig. 2 is a schematic structural diagram of a three-dimensional motion detection device according to an embodiment of the present invention.
As shown in fig. 2, an aspect of the present invention provides a three-dimensional motion detection apparatus, including:
a motion signal receiving module 201, configured to receive a motion signal generated when a joint and/or a muscle of a human body moves;
and the motion state identification module 202 is configured to perform motion state identification on the received motion signal, and determine a current three-dimensional motion state of the user.
In this embodiment, first, the motion signal receiving module 201 receives a motion signal generated when a joint and/or a muscle of a human body moves, wherein the receiving frequency may be real-time receiving or intermittent receiving; the motion signal includes information of motion direction, motion time, and the like.
Then, the motion state recognition module 202 performs motion state recognition on the received motion signal, so as to determine the current three-dimensional motion state of the user.
Thus, the received motion signal is identified by an identification algorithm, which is relatively easy to implement compared to conventional gyroscope and accelerometer based algorithms.
In an implementation, the motion state identification module 202 is specifically configured to:
and identifying the motion state by taking the received motion signal as the input of the classification model, and determining the current three-dimensional motion state of the user.
In this embodiment, the motion state identification module 202 is specifically configured to: and identifying the motion state by taking the received motion signal as the input of the classification model, and determining the current three-dimensional motion state of the user. The classification model is preferably a classification model based on an SVM algorithm, and the classification model needs to be trained in advance, wherein the training process is roughly as follows:
the user repeatedly does the same action for N times or repeatedly does the action for a period of time to obtain training data of a plurality of motion signals, the training data are used as input of the classification model to be trained to obtain a motion state output result, the output result and the training labels are used for adjusting weight information in the classification model through a loss function until the error between the output result and the training labels is within an allowable range, and then the classification model can be obtained.
In an implementation, the motion state identification module 202 is further specifically configured to:
generating a data waveform map according to the received motion signal;
judging whether the generated data oscillogram is matched with a preset oscillogram or not;
and if the generated data oscillogram is judged to be matched with the preset oscillogram, determining the current three-dimensional motion state of the user.
In this embodiment, the motion state identification module 202 is further specifically configured to: a data waveform map is generated based on the received motion signal. For example, when a user walks, the degree of knee flexion of the user changes with time, and a data waveform map can be generated according to the motion signal every step.
Then, carrying out similarity matching on the generated data oscillogram and a plurality of preset oscillograms in the system, and selecting the oscillogram with the highest matching degree from the oscillograms so as to determine the three-dimensional motion state corresponding to the oscillogram as the current three-dimensional motion state of the user;
alternatively, the generated data waveform map may be used as an input to a trained convolutional neural network, and the three-dimensional motion state corresponding to the motion signal may be recognized by the convolutional neural network.
One aspect of the present invention provides a three-dimensional motion detection apparatus, the apparatus comprising:
the stress sensor patches are arranged at the bending part or the muscle stretching part of the human joint and used for detecting motion signals of the human joint or the muscle;
the data collection device is connected with the stress sensor patches through a human body wire and is used for receiving the motion signals detected by the stress sensor patches and carrying out three-dimensional motion state identification according to the received motion signals; and the system is also used for carrying out data interaction with the mobile equipment according to the motion signal and the recognized three-dimensional motion state.
In this embodiment, the stress sensor patch is preferably skin-like in color and soft in texture, and the shape and size thereof can be adjusted according to actual conditions. The stress sensor patches are arranged at the bending part or the stretching part of the muscle of the human body joint, and a plurality of sensors are generally arranged at the same human body joint or muscle to detect the motion signals of the appointed human body joint or muscle so as to judge the motion state of the user according to the plurality of motion signals. The joints are not limited to cervical vertebrae, spine, lumbar vertebrae, shoulder/elbow joints, knuckle joints, knee joints, and the like, and the muscles include those having a relatively large deformation amount of the human body, such as biceps brachii and the like. Fig. 4 is a schematic view of an installation of a stress sensor patch, where fig. 4 is a schematic view of a stress sensor patch attached to a shoulder in a three-dimensional motion detection device according to an embodiment of the present invention.
The data collection device comprises a power module, a processor module and a communication module, wherein the power module is used for supplying power to the processor module, the communication module and other electric devices, the communication module is used for carrying out data interaction with the mobile equipment, and the processor module is used for carrying out data operation. The data collection device is connected with the stress sensor patch through a human body wire and used for receiving a motion signal sent by the stress sensor patch; among them, the human body lead is preferably a skin-like, soft, biocompatible and stretchable lead.
When the stress sensor patch is used, the data collection device receives a motion signal transmitted by the stress sensor patch, the processor module identifies the motion signal by utilizing a classification algorithm or an image identification algorithm to obtain a current three-dimensional motion state of a user, records the time length for maintaining the motion state of the user or the times of repeating the motion state in a set time period, generates warning information corresponding to the motion state if the recorded time length does not meet the preset time length requirement corresponding to the motion state in the system or the recorded times do not meet the preset time requirement corresponding to the motion state in the system, transmits the warning information to the mobile equipment through the communication module, and informs the user of the warning information through the mobile equipment.
In an embodiment of the present invention, a computer-readable storage medium includes a set of computer-executable instructions that, when executed, receive a motion signal generated by a joint and/or muscle of a human body in motion; and identifying the motion state of the received motion signal, and determining the current three-dimensional motion state of the user.
Thus, the received motion signal is identified by an identification algorithm, which is relatively easy to implement compared to conventional gyroscope and accelerometer based algorithms.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered 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. A method for detecting three-dimensional motion, the method comprising:
receiving motion signals generated when joints and/or muscles of a human body move;
and identifying the motion state of the received motion signal, and determining the current three-dimensional motion state of the user.
2. The method of claim 1, wherein the performing motion state recognition on the received motion signal to determine the current three-dimensional motion state of the user comprises:
and identifying the motion state by taking the received motion signal as the input of the classification model, and determining the current three-dimensional motion state of the user.
3. The method of claim 1, wherein the performing motion state recognition on the received motion signal to determine the current three-dimensional motion state of the user comprises:
generating a data waveform map according to the received motion signal;
judging whether the generated data oscillogram is matched with a preset oscillogram or not;
and if the generated data oscillogram is judged to be matched with the preset oscillogram, determining the current three-dimensional motion state of the user.
4. The method of any one of claims 1-3, wherein after determining the current three-dimensional state of motion of the user, the method further comprises:
recording the time length of the user in the three-dimensional motion state or the number of times of repeating the three-dimensional motion state in a set time period;
and determining whether to generate warning information corresponding to the three-dimensional motion state or not according to the recorded duration or times and a preset value corresponding to the three-dimensional motion state, and informing the warning information to the user.
5. The method of claim 4, wherein said informing said user of said alert information comprises:
and transmitting the generated warning information to a mobile terminal, and indicating the mobile terminal to inform the user of the warning information.
6. An apparatus for detecting three-dimensional motion, the apparatus comprising:
the motion signal receiving module is used for receiving motion signals generated when joints and/or muscles of a human body move;
and the motion state identification module is used for identifying the motion state of the received motion signal and determining the current three-dimensional motion state of the user.
7. The apparatus according to claim 6, wherein the motion state identification module is specifically configured to:
and identifying the motion state by taking the received motion signal as the input of the classification model, and determining the current three-dimensional motion state of the user.
8. The apparatus of claim 6, wherein the motion state identification module is further specifically configured to:
generating a data waveform map according to the received motion signal;
judging whether the generated data oscillogram is matched with a preset oscillogram or not;
and if the generated data oscillogram is judged to be matched with the preset oscillogram, determining the current three-dimensional motion state of the user.
9. A three-dimensional motion detection apparatus, characterized in that the apparatus comprises:
the stress sensor patches are arranged at the bending part or the muscle stretching part of the human joint and used for detecting motion signals of the human joint or the muscle;
the data collection device is connected with the stress sensor patches through a human body wire, and is used for receiving motion signals detected by the stress sensor patches and carrying out three-dimensional motion state identification according to the received motion signals; and the system is also used for carrying out data interaction with the mobile equipment according to the motion signal and the recognized three-dimensional motion state.
10. A computer-readable storage medium comprising a set of computer-executable instructions that, when executed, perform a method of detecting three-dimensional motion as claimed in any one of claims 1 to 5.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113749652A (en) * | 2021-11-09 | 2021-12-07 | 中南大学湘雅二医院 | Muscle movement monitoring method and device |
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Application publication date: 20200519 |