CN112987926B - Method and system for reconstructing volley gesture track - Google Patents
Method and system for reconstructing volley gesture track Download PDFInfo
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Abstract
The invention relates to the technical field of data processing, in particular to a volley gesture track reconstruction method and a volley gesture track reconstruction system, wherein the method comprises the following steps: acquiring six-axis motion data of a gesture captured by a six-axis sensor, and determining whether the six-axis motion data belongs to a preset gesture motion library, wherein the six-axis motion data comprises acceleration data and gyroscope data; when the six-axis motion data are determined to belong to a preset gesture action library, carrying out noise reduction and filtering pretreatment on the six-axis motion data by adopting a Mallat algorithm to obtain pretreated six-axis motion data; performing feature extraction on the preprocessed six-axis motion data to obtain gesture motion features; according to the gesture motion characteristics, the track size of the gesture motion is reconstructed, and the method has higher stability and reliability in coarse-grained volley gesture track size reconstruction.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a volley gesture track reconstruction method and system.
Background
The ubiquitous environment is an environment with computing and communication capabilities, wherein an information space and a physical space are fused. In a general environment, the calculation is centered on people, man-machine interaction is similar to a natural communication mode between people, equipment for calculation is ubiquitous, the equipment is integrated into the living environment of people, and required services can be conveniently provided for people.
The volley gestures are non-contact air gestures which enable a user to operate in a free-hand mode, and are essentially a natural man-machine interaction mode which does not bring any inconvenience to the user gesture interaction. For the reconstruction of the volley gesture track, in the prior art, a strapdown inertial navigation algorithm is generally adopted to reconstruct the motion track determined by the MPU9250 sensor. This solution has the following drawbacks:
the strapdown inertial navigation algorithm mainly utilizes data collected by a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer in an MPU6050 or an MPU9250 to perform operation, firstly, acceleration data under a carrier coordinate system (a carrier is an inertial sensor on a hand) is converted into acceleration data under a world coordinate system, referring to fig. 1, in the world coordinate system, an X axis points to a geographical east, a Y axis points to geographical north, a Z axis points to the sky, and the three axes are vertical to each other; and then calculating acceleration data of an X axis, a Y axis and a Z axis in a world coordinate system, and performing secondary integration on the acceleration data to obtain the displacement of the volley gesture in the air. However, due to the influence of various factors, the measured displacement is noisy, and particularly for inertial sensor equipment with low precision, large displacement deviation still exists even if various processing methods are adopted, so that reconstruction of the size of the volley gesture track under the head-up view angle based on the method is extremely unstable and unreliable.
Therefore, a scheme is urgently needed to be provided to solve the problem of instability of the strapdown inertial navigation algorithm to the low-precision inertial sensor-based volley gesture track reconstruction.
Disclosure of Invention
The present invention is directed to a method and a system for reconstructing a volley gesture trajectory, so as to solve one or more technical problems in the prior art and provide at least one useful choice or creation condition.
In order to achieve the above object, the present invention provides the following technical solutions:
a volley gesture trajectory reconstruction method, the method comprising the steps of:
acquiring six-axis motion data of a gesture captured by a six-axis sensor, and determining whether the six-axis motion data belongs to a preset gesture motion library, wherein the six-axis motion data comprises acceleration data and gyroscope data;
when the six-axis motion data are determined to belong to a preset gesture action library, carrying out noise reduction and filtering pretreatment on the six-axis motion data by adopting a Mallat algorithm to obtain pretreated six-axis motion data;
performing feature extraction on the preprocessed six-axis motion data to obtain gesture motion features;
and reconstructing the track size of the gesture action according to the gesture motion characteristics.
Further, the six-axis sensor is a low-precision sensor MPU6050.
Further, the performing noise reduction and filtering preprocessing on the six-axis motion data by using a Mallat algorithm to obtain preprocessed six-axis motion data includes:
determining wavelet basis and wavelet decomposition level number N, and performing wavelet decomposition on the six-axis motion data to obtain wavelet coefficients of each level;
determining the threshold of each level of wavelet coefficient through a threshold selection criterion, and respectively acting the threshold of each level of wavelet coefficient on each level of wavelet coefficient based on a threshold function to obtain the wavelet coefficient subjected to threshold quantization;
and (4) performing inverse transformation on the wavelet coefficient subjected to the threshold quantization action to obtain the preprocessed six-axis motion data.
Further, the threshold of the wavelet coefficients of each level is determined by multiple tests, the threshold function is a hard threshold function, and the calculation formula of the threshold function is as follows:
Further, the feature extraction is performed on the six-axis motion data after the preprocessing, so as to obtain gesture motion features, and the method comprises the following steps:
carrying out normalization processing on the preprocessed six-axis motion data to obtain normalized data;
and extracting gesture motion characteristics from the normalized data, wherein the gesture motion characteristics comprise time domain characteristics and frequency domain characteristics.
Further, the time-domain feature includes at least one of the following feature information: maximum, minimum, mean, variance in the time domain;
the frequency domain features include at least one of the following feature information: average frequency, root mean square frequency, peak frequency, sum frequency, standard deviation, variance in the frequency domain.
Further, the reconstructing the trajectory size of the gesture motion according to the gesture motion characteristics specifically includes:
inputting the training set into a machine learning model for training to obtain a gesture recognition model; the training set comprises pre-acquired gesture motion characteristic samples;
and inputting the gesture motion characteristics into the gesture recognition model, and finishing track size reconstruction according to the obtained size of the volley track.
A computer readable storage medium having stored thereon a volley gesture trajectory reconstruction program which, when executed by a processor, implements the steps of the volley gesture trajectory reconstruction method as described in any one of the above.
A volley gesture trajectory reconstruction system, the system comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement any of the above volley gesture trajectory reconstruction methods.
The invention has the beneficial effects that: the invention discloses a method and a system for reconstructing a volley gesture track, wherein six-axis motion data are captured by a six-axis sensor of a low-precision inertial sensor, and are subjected to noise reduction and filtering pretreatment by adopting a Mallat algorithm to obtain the pretreated six-axis motion data; the wavelet threshold denoising method based on the Mallat algorithm has the advantages of low entropy, multi-resolution, decorrelation, flexibility and the like, finally feature extraction is carried out on the preprocessed six-axis motion data to obtain gesture motion features, and the track size of gesture motion is reconstructed according to the gesture motion features. The method has higher stability and reliability in coarse-grained volley gesture track size reconstruction.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a volley gesture trajectory reconstruction method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of wavelet decomposition of the six-axis motion data based on the Mallat algorithm in the embodiment of the present invention.
Detailed Description
The conception, specific structure and technical effects of the present application will be described clearly and completely with reference to the following embodiments and the accompanying drawings, so that the purpose, scheme and effects of the present application can be fully understood. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Referring to fig. 1, as shown in fig. 1, a volley gesture trajectory reconstruction method provided in an embodiment of the present application includes the following steps:
s100, acquiring six-axis motion data of a gesture motion captured by a six-axis sensor, and determining whether the six-axis motion data belongs to a preset gesture motion library, wherein the six-axis motion data comprises acceleration data and gyroscope data;
s200, when the six-axis motion data belong to a preset gesture action library, performing noise reduction and filtering pretreatment on the six-axis motion data by adopting a Mallat algorithm to obtain pretreated six-axis motion data;
step S300, performing feature extraction on the preprocessed six-axis motion data to obtain gesture motion features;
and S400, reconstructing the track size of the gesture motion according to the gesture motion characteristics.
The gesture motion library is used for reconstructing the trajectory size of the gesture in the air, and the preset gesture motion library refers to the same preset gesture but different in gesture amplitude. In some exemplary embodiments, the gesture motion is set as a horizontal line motion, the horizontal line motion with the amplitude of 10cm, 20cm, 30cm, 40cm, 50cm and 60cm is performed respectively (the gesture shown here is only an example, and the method is not only applicable to such gestures), then in the motion process, the six-axis sensor generates motion data according to the captured gesture motion, and sends the motion data to an upper computer (a terminal with a data processing function) at a set frequency for processing, and finally, the trajectory size of the gesture motion is reconstructed. The gesture motion in step S400 is a gesture motion determined to be preset according to the six-axis motion data.
In a preferred embodiment, the six-axis sensor is a low-precision sensor MPU6050.
As shown in fig. 2, in a preferred embodiment, in step S200, the performing denoising and filtering preprocessing on the six-axis motion data by using the Mallat algorithm to obtain preprocessed six-axis motion data includes:
step S210, determining wavelet basis and wavelet decomposition level number N, and performing wavelet decomposition on the six-axis motion data to obtain wavelet coefficients of each level;
in this step, the Wavelet basis is preferably a multi-bezier Wavelet basis (Daubechies-Wavelet), which has good regularity and orthogonality, and the number of vanishing moments increases with the increase of the number of decomposition levels.
Determining wavelet basis and wavelet decomposition level number N by combining qualitative analysis and quantitative analysis, in some embodiments, performing wavelet decomposition on the six-axis motion data by using a wavelet filter, wherein the number of layers is four layers of decomposition, fig. 2 is a structural diagram form of performing three-level wavelet decomposition on the six-axis motion data based on a Mallat algorithm, and decomposing the six-axis motion data into a low-frequency coefficient and a high-frequency coefficient based on a Mallat algorithm, and the structural diagram form has S = d1+ d2+ d3+ d4+ a4, wherein S represents the six-axis motion data, a1, a2, a3, and a4 all represent the low-frequency coefficient, and d1, d2, d3, and d4 all represent the high-frequency coefficient.
Step S220, determining the threshold of each level of wavelet coefficient through a threshold selection criterion, and respectively acting the threshold of each level of wavelet coefficient on each level of wavelet coefficient based on a threshold function to obtain the wavelet coefficient subjected to threshold quantization;
and step S230, performing inverse transformation on the wavelet coefficient subjected to the threshold quantization function to obtain preprocessed six-axis motion data.
In the embodiment provided by the invention, the threshold value and the threshold value function of each level of wavelet coefficient are required to be selected, the wavelet basis is determined through the threshold value, and the decomposition level number is determined through the threshold value function. In a preferred embodiment, the threshold of each level of wavelet coefficient is determined by multiple tests, the threshold function is a hard threshold function, and the calculation formula of the threshold function is as follows:
The invention applies the Mallat algorithm to carry out denoising and filtering preprocessing, and the Mallat algorithm unifies the wavelet transformation theory so that the wavelet transformation theory becomes feasible in calculation. The wavelet threshold denoising method can better separate noise and useful signals in wavelet coefficients, and comprises the steps of respectively processing parts of wavelet coefficients larger than or smaller than a certain threshold in each level of coefficients obtained after wavelet decomposition, and then performing wavelet reconstruction to obtain denoised signals. Because the output data of the MEMS sensor is non-stationary and non-linear, the invention adopts the noise reduction method to carry out noise reduction processing on the six-axis motion data, compared with the traditional filtering and noise reduction method, the wavelet threshold noise reduction based on the Mallat algorithm has the advantages of low entropy, multi-resolution, decorrelation, flexibility and the like, and has great advantages in the aspects of signal noise reduction and processing of the MEMS sensor.
In a preferred embodiment, the step S300 includes:
step S310, carrying out normalization processing on the preprocessed six-axis motion data to obtain normalized data;
preferably, the normalization is performed by using a dispersion normalization (min-max-normalization), and the data after the normalization process is between (0 and 1), so that the subsequent feature extraction is easy to perform. The normalized formula is:
wherein X is any kind of data in the six-axis motion data, xmin is the minimum value of the kind of data, xmax is the maximum value of the kind of data,
in order to normalize the data after the data normalization process, in this embodiment, each piece of data is processed in this manner.
Step S320, extracting gesture motion characteristics from the normalized data, wherein the gesture motion characteristics comprise time domain characteristics and frequency domain characteristics.
The extraction of the gesture motion features refers to selecting feature values according to the characteristics of data for classification selection of a subsequent machine learning model. In the embodiment of the present invention, using ten features of the normalized data, as shown in fig. 2, the normalized data is extracted in the time domain and the frequency domain, respectively, where the time domain refers to a time series signal, and the frequency domain refers to a frequency signal obtained by fourier transforming the time domain signal.
In a preferred embodiment, the time-domain feature comprises at least one of the following feature information: maximum, minimum, mean, variance in the time domain;
the frequency domain features include at least one of the following feature information: mean frequency, root mean square frequency, peak frequency, sum frequency, standard deviation, variance in the frequency domain.
In a preferred embodiment, the step S400 specifically includes:
inputting the training set into a machine learning model for training to obtain a gesture recognition model; the training set comprises pre-acquired gesture motion characteristic samples;
and inputting the gesture motion characteristics into the gesture recognition model, and completing track size reconstruction according to the obtained size of the volley track.
In order to solve the problem of instability of reconstruction of the flight gesture track based on a low-precision inertial sensor in the prior art, the coarse-grained track reconstruction method utilizes a machine learning model (such as a support vector machine, a decision tree, a random forest and other models) to carry out coarse-grained track reconstruction on the flight gesture track, so that the track reconstruction has higher stability and reliability; in the embodiment, a plurality of different gesture motion characteristic samples are imported into a machine learning model for training, so that the model can learn different sizes of the trajectories of the volley gestures; the number of the gesture motion characteristic samples is set according to actual conditions, and it can be understood that when the number of the gesture motion characteristic samples is larger, the obtained gesture recognition model is more accurate.
In some exemplary embodiments, the gesture motion is set as horizontal traverse motions, and horizontal traverse motions with amplitudes of 10cm, 20cm, 30cm, 40cm, 50cm and 60cm are respectively made (the gesture shown here is only an example, and the method is not only applicable to such gestures), and the horizontal traverse dimensions of 10cm, 20cm, 30cm, 40cm, 50cm and 60cm can be effectively resolved through a trained model through experimental verification. According to the method, the size of the trajectory of the gesture in the air can be stably identified, illustratively, the gesture displaces 18cm in the air, the trajectory size of the volley gesture is identified to be any value from 16cm to 20cm through the method disclosed by the embodiment, the method disclosed by the invention belongs to coarse-grained volley gesture trajectory size reconstruction, and the method has higher stability, feasibility and reliability.
Corresponding to the method in fig. 1, an embodiment of the present invention further provides a computer-readable storage medium, where a volley gesture trajectory reconstruction program is stored on the computer-readable storage medium, and when executed by a processor, the volley gesture trajectory reconstruction program implements the steps of the volley gesture trajectory reconstruction method according to any one of the above embodiments.
Corresponding to the method in fig. 1, an embodiment of the present invention further provides a volley gesture trajectory reconstruction system, where the system includes:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is enabled to implement the volley gesture trajectory reconstruction method according to any one of the above embodiments.
The contents in the method embodiments are all applicable to the system embodiments, the functions specifically implemented by the system embodiments are the same as those in the method embodiments, and the beneficial effects achieved by the system embodiments are also the same as those achieved by the method embodiments.
The Processor may be a Central-Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application-Specific-Integrated-Circuit (ASIC), a Field-Programmable Gate array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor, etc., and the processor is a control center of the volley gesture trajectory reconstruction system and connects various parts of the whole apparatus operable by the volley gesture trajectory reconstruction system by using various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement the various functions of the volley gesture trajectory reconstruction system by running or executing the computer programs and/or modules stored in the memory and invoking the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart-Media-Card (SMC), a Secure-Digital (SD) Card, a Flash-memory Card (Flash-Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
While the description of the present application has been made in considerable detail and with particular reference to a few illustrated embodiments, it is not intended to be limited to any such details or embodiments or any particular embodiments, but it is to be construed that the present application effectively covers the intended scope of the application by reference to the appended claims, which are interpreted in view of the broad potential of the prior art. Further, the foregoing describes the present application in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial changes from the present application, not presently foreseen, may nonetheless represent equivalents thereto.
Claims (7)
1. A volley gesture trajectory reconstruction method is characterized by comprising the following steps:
acquiring six-axis motion data of a gesture captured by a six-axis sensor, and determining whether the six-axis motion data belongs to a preset gesture motion library, wherein the six-axis motion data comprises acceleration data and gyroscope data; the preset gesture action library refers to the same preset gesture but different in gesture amplitude;
when the six-axis motion data belong to a preset gesture motion library, carrying out noise reduction and filtering pretreatment on the six-axis motion data by adopting a Mallat algorithm to obtain pretreated six-axis motion data;
performing feature extraction on the preprocessed six-axis motion data to obtain gesture motion features;
reconstructing the track size of the gesture action according to the gesture motion characteristics;
the method for carrying out noise reduction and filtering preprocessing on the six-axis motion data by adopting the Mallat algorithm to obtain the preprocessed six-axis motion data comprises the following steps:
determining wavelet basis and wavelet decomposition level number N, and performing wavelet decomposition on the six-axis motion data to obtain wavelet coefficients of each level;
determining the threshold of each level of wavelet coefficient through a threshold selection criterion, and respectively acting the threshold of each level of wavelet coefficient on each level of wavelet coefficient based on a threshold function to obtain the wavelet coefficient subjected to threshold quantization;
performing inverse transformation on the wavelet coefficient subjected to threshold quantization to obtain preprocessed six-axis motion data;
reconstructing the trajectory size of the gesture motion according to the gesture motion characteristics specifically comprises:
inputting the training set into a machine learning model for training to obtain a gesture recognition model; the training set comprises pre-acquired gesture motion characteristic samples, and a plurality of different gesture motion characteristic samples are imported into a machine learning model for training, so that the machine learning model can learn different sizes of the trajectories of the volley gestures;
and inputting the gesture motion characteristics into the gesture recognition model, and completing track size reconstruction according to the obtained size of the volley track.
2. The volley gesture trajectory reconstruction method according to claim 1, wherein the six-axis sensor is a low-precision sensor MPU6050.
3. The volley gesture trajectory reconstruction method according to claim 1, wherein the threshold of each level of wavelet coefficients is determined by multiple tests, the threshold function is a hard threshold function, and the calculation formula of the threshold function is as follows:
4. The volley gesture trajectory reconstruction method according to claim 3, wherein the step of performing feature extraction on the preprocessed six-axis motion data to obtain the gesture motion features comprises:
carrying out normalization processing on the preprocessed six-axis motion data to obtain normalized data;
and extracting gesture motion characteristics from the normalized data, wherein the gesture motion characteristics comprise time domain characteristics and frequency domain characteristics.
5. The volley gesture trajectory reconstruction method according to claim 4, wherein the time domain features comprise at least one of the following feature information: maximum, minimum, mean, variance in the time domain;
the frequency domain features include at least one of the following feature information: average frequency, root mean square frequency, peak frequency, sum frequency, standard deviation, variance in the frequency domain.
6. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, carries out the steps of the volley gesture trajectory reconstruction method according to one of claims 1 to 5.
7. A volley gesture trajectory reconstruction system, the system comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the volley gesture trajectory reconstruction method of any one of claims 1 to 5.
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| CN110163142A (en) * | 2019-05-17 | 2019-08-23 | 重庆大学 | Real-time gesture recognition method and system |
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| US9811555B2 (en) * | 2014-09-27 | 2017-11-07 | Intel Corporation | Recognition of free-form gestures from orientation tracking of a handheld or wearable device |
| WO2020042121A1 (en) * | 2018-08-30 | 2020-03-05 | Oppo广东移动通信有限公司 | Gesture recognition method, terminal, and storage medium |
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| CN110163142A (en) * | 2019-05-17 | 2019-08-23 | 重庆大学 | Real-time gesture recognition method and system |
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