CN117084671B - Motion evaluation system based on gyroscope signals - Google Patents
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Abstract
The invention provides a motion estimation system based on gyroscope signals, comprising: the data acquisition module is used for acquiring user information and motion information, wherein the motion information is acquired through a gyroscope; the data preprocessing module is used for preprocessing the data of the user information and the motion information; the data classification module is used for classifying the motion information according to the motion type; the model deployment module is used for reconstructing the motion process of the user according to the motion information; and the data feedback module is used for carrying out motion quality assessment according to the motion information. According to the gyroscope signal-based motion evaluation system, the risk of overextension is prevented through the gyroscope, the in-place rate of motion is improved, the motion smoothness and balance of a user are analyzed, and the risk of motion is prevented.
Description
Technical Field
The invention relates to the technical field of biomedical engineering, in particular to a motion evaluation system based on gyroscope signals.
Background
In competitive sports, rehabilitation and daily exercise processes, correct exercise postures can help us to better evaluate exercise levels and correct wrong exercise postures in time to protect our bodies. In China, conscious people actively participating in sports fitness are increasing, home fitness becomes a new sports fitness scene since 2020, 46.2% of interviewees prefer home fitness, and people under 60 years old prefer indoor fitness, and women prefer home fitness or indoor stadium fitness compared with men. Meanwhile, 68.5% of adult body-building people in conscious active participation have been subjected to scientific body-building instruction, and 44.5% of elderly body-building people have been subjected to scientific body-building instruction. For the scientific guidance of indoor sports, the method is helpful for standardizing the actions of sporters, avoiding injuries caused by nonstandard actions, accurately evaluating the sports level and timely feeding back to users.
A large number of technical solutions are based on artificial intelligence motion tracking and computer vision algorithms, which on the one hand strongly depend on video image information, which greatly limits the use scenarios of these techniques, which can only be used indoors, while outdoors and in combination with related intelligent devices lack imaginative space. Meanwhile, the solution is high in cost, a set of tracking algorithm is needed to track joints and limbs of a human body, a large amount of data set training is needed, and a great amount of data is needed to analyze. In addition, key problems of large inter-class variance, large intra-class variance, high concealment, accurate identification of redundant background information in the large collar of video information and the like exist between motion actions.
The usual assessment of sports relies on the assessment of professional athletes and doctors of professional rehabilitation medicine on the one hand, while new intelligent analysis also requires a lot of equipment to be worn, in professional test sites and mainly for analysis of video information. The conventional analysis method requires a large amount of professionals and professional equipment, and is relatively expensive and unsuitable for popularization.
Disclosure of Invention
In view of the above, the present invention provides a motion estimation system based on gyroscope signals to solve the above-mentioned problems.
The invention provides a motion evaluation system based on gyroscope signals, which comprises: the data acquisition module is used for acquiring user information and motion information, wherein the motion information is acquired through a gyroscope; the data preprocessing module is used for preprocessing the data of the user information and the motion information; the data classification module is used for classifying the motion information according to the motion type; the model deployment module is used for reconstructing the motion process of the user according to the motion information; and the data feedback module is used for carrying out motion quality assessment according to the motion information.
In another implementation of the invention, the gyroscopes include an upper limb gyroscope, a lower limb gyroscope, a head gyroscope, and a torso gyroscope.
In another implementation of the present invention, the gyroscope includes at least one of a six-axis sensor and a nine-axis sensor.
In another implementation of the present invention, the motion information includes position information, angle information, acceleration, and gyro flag information of the gyro during motion.
In another implementation manner of the invention, the data feedback module is further used for judging the standard degree of the motion action of the user according to the position information of the gyroscope in the motion process.
The invention provides a motion estimation system based on a gyroscope signal. The beneficial effects are as follows: by using the gyroscope, the actions can be analyzed on the personal terminal intelligent machine and the movements can be evaluated, so that the dependence on professional athletes and doctors in professional rehabilitation and medical treatment is reduced. In the system, the risk of overextension can be prevented through gyroscope information, the exercise obtaining rate is improved, the smoothness and balance of the exercise of a user are analyzed, and the risk of exercise is prevented.
Drawings
Fig. 1 is a schematic diagram of a motion estimation system based on gyroscope signals according to the present invention.
Fig. 2 is a schematic diagram of an acquisition flow of the data acquisition module of the present invention.
Fig. 3 is a schematic process flow diagram of the data preprocessing module of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a schematic diagram of a motion estimation system 100 based on a gyroscope signal according to an embodiment of the present invention, as shown in fig. 1, the embodiment mainly includes:
the data acquisition module 101 is configured to acquire user information and motion information, where the motion information is acquired through a gyroscope.
Illustratively, the user information mainly includes gender, age, height, weight, arm span, thigh length, forearm length, thigh length, calf length, and the like of the reference user. As shown in fig. 2, the motion information is collected and transmitted by a collection device worn during motion, and the collection device can be a kinematic data gyroscope with a sampling rate of 40Hz. The motion information has standard motion starting and ending time, which is beneficial to setting standardized data.
And the data preprocessing module 102 is used for preprocessing the data of the user information and the motion information.
Illustratively, as shown in fig. 3, the gyroscope position information in the motion information is preprocessed, the data is segmented by a sliding window method (overlap), and in order to avoid predicting existing data by using future data, the training set and the test set can be distinguished by adopting the sliding window method for the data with time sequence characteristics, such as related information of the gyroscope.
Further, the motion information is subjected to data filtering processing, and the data is processed by using median filtering and Butterworth filtering, so that the popcorn noise and the noise with higher frequency in the data are removed. The cut-off frequency is normalized to ωn= (2ωc)/fs using a butterworth filter of fourth order, where fs is the sampling frequency and Wc is the cut-off frequency.
Preferably, the analysis of the variance of the gyroscope is performed on the gyroscope data, and the analysis of the variance of the algorithm is one of the most commonly used gyroscope noise identification methods at present, and the variance of the algorithm is essentially a process of measuring the stability of a signal in the whole time period by solving the variance form of adjacent time periods in the whole information acquisition process.
It should be understood that, because the analysis of the Allan variance is to finely divide the power spectrum by adjusting the bandwidth of the Allan variance filter, a plurality of different types of random process errors can be identified, and each error coefficient can be quantitatively separated, and the method has the advantages of simple operation on algorithm, convenient calculation and obvious gyro noise identification.
Preferably, the zero offset caused by the noise component is calculated, a certain data is collected, and an average value is obtained, and the average value is the zero offset. And (3) performing gyro calibration based on zero offset caused by noise components, wherein the gyro calibration refers to removing zero offset. The noise component contained in the gyro mainly comprises: angle random walk, correlated noise, velocity random walk, zero bias instability in gyroscopes during task execution is relatively small, so the zero bias from the previous gyroscopic calibration can be considered a constant.
Preferably, the motion information is extracted by features, and the original data is directly input into a classifier for classification, or classified based on time data statistics, and can be divided into a mean value, a median, a lower quartile, an upper quartile, a quartile difference, a standard deviation, a variance, a discrete coefficient, kurtosis, skewness, zero crossing times, average passing times, shannon entropy and the like.
And the data classification module 103 is used for classifying the motion information according to the motion type.
By way of example, data classification may use SVM classifiers that are linear kernel functions, as well as multi-layer perceptron (MLP) classifiers. The accuracy of the two classifiers can reach 85%, the MLP classifier with two hidden layers can be selected, the number of neurons of the input layer is the characteristic number in the training set, namely the length of each row, the number of neurons of the output layer is the target classification number, namely the number of neurons of the hidden layers is preset to be 100 and 10, and the actions are required to be identified.
The model deployment module 104 is configured to reconstruct a motion process of the user according to the motion information.
For example, in the recognition task, the movement process of the athlete is reconstructed using the previous avatar, and the number of movements is recorded, and by re-demonstrating the movement process by the avatar, data of the corresponding sensor can be transmitted in real time, so that the previously constructed avatar moves together with the user, which requires higher signal transmission rate and data processing capability.
In addition, the user's movement is identified by the indication of movement once every two seconds in the movement process, and the completion time can be set according to the original data input condition of the user aiming at different movements. The angular information and acceleration information of the gyroscope are extracted, and if the gyroscope is a nine-axis sensor, the related geomagnetic information is also required to be extracted. The relu is selected as the hidden function according to the principle of MLP after the classification method of MLP is selected, and softmax is used as the activation function at the hidden layer.
And the data feedback module 105 is used for carrying out motion quality assessment according to the motion information.
The standard degree of the action is determined by the motion position information of the gyroscope, and after the related action is identified by using an algorithm, the standard degree of the action can be compared with the standard data of the recorded professional sports coach, and can also be compared with the average value of the data or the data recorded by the user for the first time. And obtaining the limb characteristics according to the information of the limb movement, further calculating an evaluation parameter, and evaluating the movement quality according to the evaluation parameter. And comparing the preset angle information of the joint with the maximum angle of joint buckling, stretching, abduction, adduction, external rotation, horizontal adduction and horizontal abduction in the previously recorded user movement process, thereby determining whether the condition of exceeding or not in place of action occurs.
The minimum acceleration trajectory of the upper limb corresponds well to the user's extension movement, the correlation between the velocity profile and the corresponding minimum acceleration velocity profile is also used as a measure of smoothness, the measure of the number of peaks is unimodal depending on the velocity profile of the smooth movement, whereas the non-smooth movement has a higher number of velocity peaks. The spectral arc length is taken as a measure of smoothness, which depends on the change in the fourier spectrum of the motion to quantify the smoothness.
In gait literature, harmonic ratios have been used as a measure of smoothness. The Harmonic Ratio (HR), defined as the ratio of the sum of the even harmonic amplitudes to the sum of the odd harmonic amplitudes of the single stride torso acceleration, exploits the periodicity inherent in gait, mainly a measure of the two-leg gait symmetry, by dividing the data into individual strides to analyze the smoothness of the entire gait data.
The invention provides a motion estimation system based on a gyroscope signal. The beneficial effects are as follows: using gyroscopes, actions can be analyzed on a personal terminal smart phone and motion evaluated. Reduces the dependence on professional athletes and doctors of professional rehabilitation medical treatment. In the system, the risk of overextension can be prevented through gyroscope information, the exercise obtaining rate is improved, the smoothness and balance of the exercise of a user are analyzed, and the risk of exercise is prevented.
In another implementation of the invention, the gyroscopes include an upper limb gyroscope, a lower limb gyroscope, a head gyroscope, and a torso gyroscope.
Illustratively, as shown in fig. 2, the movement information is collected by wearable devices, which are divided into four categories, an upper limb gyroscope, a lower limb gyroscope, a head gyroscope, and a torso gyroscope, respectively. The upper limb gyroscopes are mainly worn on the forearm and the forearm. For the shoulder joint, the maximum angle of shoulder joint flexion, extension, abduction, adduction, abduction, horizontal adduction, horizontal abduction needs to be calculated by two motion gyroscope differences.
In another implementation of the present invention, the gyroscope includes at least one of a six-axis sensor and a nine-axis sensor.
Illustratively, at least a six-axis sensor is used, wherein the combination of six axes is a three-axis accelerometer and a three-axis gyroscope. Nine-axis sensors can be used, preferably, nine-axis sensors can be used for being matched with cloud data uploading processing, and the Beidou navigation system is matched with the Beidou navigation system to monitor the movement of the vehicle, such as walking, running, riding and the like.
In another implementation of the present invention, the motion information includes position information, angle information, acceleration, and gyro flag information of the gyro during motion.
Illustratively, the gyro flag information is used to distinguish which gyro is, and reset information may be sent at the beginning of the system according to the system requirements, reset the associated gyroscopes in order, and assign order values.
The gyroscope is a device for sensing and maintaining the direction, and is designed based on the theory of the constant angular momentum. The gyroscope mainly consists of a wheel which is arranged on the axle center and can rotate, and once the rotation starts, the gyroscope has a trend of resisting the change of the direction due to the angular momentum of the wheel. Gyroscopes are used in systems for navigation, positioning, wearable device research and development, etc. The gyroscope system may use nine-axis sensors, including a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. The axis gyroscope is omnidirectional dynamic information that senses Roll (tilting right and left), pitch (tilting back and forth), and Yaw (tilting left and right), respectively. The shaft gyroscope is mainly used for judging basic information such as whether the overextension risk action is in place or not. The three-axis accelerometer is used for sensing the acceleration in the XYZ (three directions of the three-dimensional space, front, back, left, right, up, down) axis and judging the smoothness and balance of the calculated motion and the motion completion quality.
In another implementation manner of the invention, the data feedback module is further used for judging the standard degree of the motion action of the user according to the position information of the gyroscope in the motion process.
Thus, specific embodiments of the present invention have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may be advantageous.
It should be noted that all directional indicators (such as up, down, left, right, and rear … …) in the embodiments of the present invention are merely used to explain the relative positional relationship, movement conditions, etc. between the components in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indicator is correspondingly changed.
In the description of the present invention, the terms "first," "second," and the like are used merely for convenience in describing the various components or names, and are not to be construed as indicating or implying a sequential relationship, relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
It should be noted that, although specific embodiments of the present invention have been described in detail with reference to the accompanying drawings, the present invention should not be construed as limiting the scope of the present invention. Various modifications and variations which may be made by those skilled in the art without the creative effort fall within the protection scope of the present invention within the scope described in the claims.
Examples of embodiments of the present invention are intended to briefly illustrate technical features of embodiments of the present invention so that those skilled in the art may intuitively understand the technical features of the embodiments of the present invention, and are not meant to be undue limitations of the embodiments of the present invention.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (3)
1. A motion estimation system based on a gyroscope signal, comprising:
the data acquisition module is used for acquiring user information and motion information, wherein the motion information has standard motion starting and ending time, the motion information is acquired through a gyroscope, the gyroscope comprises an upper limb gyroscope, a lower limb gyroscope, a head gyroscope and a trunk gyroscope, the upper limb gyroscope is mainly worn on a forearm and a forearm, wherein the shoulder joint of a user needs to calculate the maximum angles of shoulder joint buckling, stretching, abduction, adduction, abduction, horizontal adduction and horizontal abduction through the difference value of the two motion gyroscopes;
the data preprocessing module is used for preprocessing the user information and the motion information, wherein the data preprocessing comprises the steps of dividing the motion information through a sliding window method, carrying out data filtering processing on the motion information by using median filtering and Butterworth filtering, carrying out Allan variance analysis and calculating zero offset caused by noise components of the motion information, and carrying out gyro calibration based on the zero offset;
the data classification module is used for classifying the motion information according to the motion type, wherein the classification can use an SVM classifier or an MLP classifier;
the model deployment module is used for re-demonstrating the motion process through the virtual image, transmitting the data of the corresponding sensor in real time, enabling the constructed virtual image to move along with the user, recording the motion times, and setting the completion time according to the original data input condition of the user aiming at different motions;
the data feedback module is used for judging the standard degree of the action through the motion position information of the gyroscope, comparing the standard degree of the action with the standard data of the recorded professional exercise coach or the average value of the data or the data recorded for the first time by the user after the related action is identified by using an algorithm, obtaining limb characteristics according to limb motion information in the motion information, further calculating an evaluation parameter, evaluating the motion quality according to the evaluation parameter, comparing the preset angle information of the joint with the maximum angle of joint buckling, stretching, abduction, adduction, abduction, horizontal adduction and horizontal abduction in the previously recorded user motion process, thereby determining whether the condition of overstretching or not in place occurs or not, and analyzing the smoothness of the user motion according to the correspondence between the minimum acceleration track of the upper limb and the stretching motion of the user and the correlation between the speed profile and the corresponding minimum acceleration profile.
2. The system of claim 1, wherein the gyroscope comprises at least one of a six-axis sensor and a nine-axis sensor.
3. The system of claim 1, wherein the motion information includes position information, angle information, acceleration, and gyroscope flag information of the gyroscope during motion.
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