CN114578367B - Real-time motion trail monitoring method, device, equipment and readable storage medium - Google Patents
Real-time motion trail monitoring method, device, equipment and readable storage medium Download PDFInfo
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- G01S15/50—Systems of measurement, based on relative movement of the target
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/88—Sonar systems specially adapted for specific applications
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Abstract
The application relates to a real-time motion trail monitoring method, a device, equipment and a readable storage medium, which relate to the technical field of motion monitoring, wherein point cloud data is obtained in real time from a point cloud database through a rotation vector sent by a wearable device, the distance between an intelligent terminal and the wearable device is measured by utilizing frequency modulation continuous wave ultrasonic dynamic ranging, and real-time real position coordinates of a joint are obtained through prediction through a hidden Markov model based on the point cloud data, the distance between the intelligent terminal and the wearable device and instantaneous acceleration information sent by the wearable device, so that the monitoring and tracking of the motion trail of the joint are realized. According to the application, the real-time monitoring and tracking of the joint movement are realized through the intelligent terminal and the wearable equipment, the use cost can be effectively reduced, the movement track is monitored through the rotation vector information and the acceleration information acquired by the wearable equipment, and the accuracy of the movement track monitoring can be effectively ensured.
Description
Technical Field
The present application relates to the field of motion monitoring technologies, and in particular, to a method, an apparatus, a device, and a readable storage medium for monitoring a motion track in real time.
Background
Along with the increasingly development of economy, the health care consciousness is gradually enhanced while the living standard of people is improved. For example, more and more people gradually walk out of the house actively and select proper exercise to build up body. However, during exercise, people tend to cause various muscle and bone related health problems due to irregular exercise postures, and in more serious cases, incorrect exercise can injure viscera. Therefore, it is very important to correct irregular postures of people when exercising through a reasonable exercise monitoring method.
In the related art, the monitoring of motion gestures and trajectories is often realized by building a skeletal model of a human body. However, most of the high-precision real-time modeling methods in the prior art need to be implemented based on an image or video, which means that the monitoring of the human body posture and trajectory needs to depend on the quality of the image or video, in other words, the quality of the photographing device. Therefore, although the image or video-based method can have enough accuracy and real-time performance on the premise of high-quality images or high-definition videos, it has the problem of high cost, and it is obviously impractical for outdoor sporters to arrange a trackable high-cost shooting device for real-time tracking during the sports; in addition, the high-cost photographing device cannot obtain a high-definition picture under the condition of poor light, so that it is obvious that the high-precision real-time modeling method realized based on the image or the video has the problem of being greatly influenced by environmental factors (such as light, temperature, humidity and the like), and further the modeling result may have errors.
Disclosure of Invention
The application provides a method, a device, equipment and a readable storage medium for monitoring a real-time motion trail, which are used for solving the problems of high cost and great influence by environmental factors in the related technology.
In a first aspect, a method for monitoring a real-time motion trajectory is provided, including the following steps:
Receiving a first rotation vector and first instantaneous acceleration information of a first joint at a T moment, which are sent by wearable equipment;
Searching a corresponding first position set from a preset point cloud database based on the first rotation vector, wherein the preset point cloud database comprises a mapping relation between the rotation vector and the position set, and the position set comprises a plurality of preset position coordinates corresponding to the joints;
Acquiring a second position set of a first joint at a T-1 moment, and screening a first point cloud state space from a preset point cloud database according to the second position set and the first position set, wherein the first point cloud state space comprises a plurality of point cloud states, and each point cloud state consists of preset position coordinates in the first position set and preset position coordinates in the second position set;
Calculating the distance between the intelligent terminal and the wearable equipment at the T moment based on the frequency modulation continuous wave ranging to obtain a first distance;
Respectively acquiring a second distance between the intelligent terminal and the wearable device at the T-1 moment, a third distance between the intelligent terminal and the wearable device at the T-2 moment, a first actual position coordinate of the first joint at the T-1 moment and a second actual position coordinate of the first joint at the T-2 moment;
Calculating the position coordinate of the intelligent terminal at the T moment based on the first distance, the second distance, the third distance, the first actual position coordinate and the second actual position coordinate;
And inputting the first instantaneous acceleration information, the first cloud state space and the position coordinate of the intelligent terminal at the T moment into a hidden Markov model to obtain a third actual position coordinate of the first joint at the T moment.
In some embodiments, before the step of receiving the first rotation vector and the first instantaneous acceleration information of the first joint at the T-th moment sent by the wearable device, the method further includes:
Modeling arm movement based on the D-H model to obtain a connecting rod model;
Inputting a plurality of joint angles on an arm to the connecting rod model to obtain a plurality of rotation vectors;
And creating a mapping relation between each rotation vector and a corresponding position set comprising a plurality of preset position coordinates, and obtaining a point cloud database.
In some embodiments, the model parameters in the hidden markov model include an initial probability distribution, a state transition probability distribution, and an output probability distribution, wherein a probability expression of the state transition probability distribution includes instantaneous acceleration information, position coordinates of the intelligent terminal, and a distance between the intelligent terminal and the wearable device, and the output probability distribution is 1.
In some embodiments, the wearable device includes an inertial sensor for acquiring a first rotation vector of the first joint at a T-th moment and an acceleration sensor for acquiring first instantaneous acceleration information of the first joint at the T-th moment.
In a second aspect, a real-time motion trajectory monitoring device is provided, including:
The receiving unit is used for receiving a first rotation vector and first instantaneous acceleration information of a first joint at a T moment, which are sent by the wearable equipment;
The searching unit is used for searching a corresponding first position set from a preset point cloud database based on the first rotation vector, the preset point cloud database comprises a mapping relation between the rotation vector and the position set, and the position set comprises a plurality of preset position coordinates corresponding to the joint;
The generating unit is used for acquiring a second position set of the first joint at the T-1 moment, and screening a first point cloud state space from a preset point cloud database according to the second position set and the first position set, wherein the first point cloud state space comprises a plurality of point cloud states, and each point cloud state consists of preset position coordinates in the first position set and preset position coordinates in the second position set;
The distance measuring unit is used for calculating the distance between the intelligent terminal and the wearable equipment at the T moment based on the frequency modulation continuous wave distance measurement to obtain a first distance;
The acquisition unit is used for respectively acquiring a second distance between the intelligent terminal and the wearable device at the T-1 moment, a third distance between the intelligent terminal and the wearable device at the T-2 moment, a first actual position coordinate of the first joint at the T-1 moment and a second actual position coordinate of the first joint at the T-2 moment;
The calculating unit is used for calculating the position coordinate of the intelligent terminal at the T moment based on the first distance, the second distance, the third distance, the first actual position coordinate and the second actual position coordinate;
The prediction unit is used for inputting the first instantaneous acceleration information, the first cloud state space and the position coordinate of the intelligent terminal at the T moment into the hidden Markov model to obtain the third actual position coordinate of the first joint at the T moment.
In some embodiments, the apparatus further comprises a creation unit for:
Modeling arm movement based on the D-H model to obtain a connecting rod model;
Inputting a plurality of joint angles on an arm to the connecting rod model to obtain a plurality of rotation vectors;
And creating a mapping relation between each rotation vector and a corresponding position set comprising a plurality of preset position coordinates, and obtaining a point cloud database.
In some embodiments, the model parameters in the hidden markov model include an initial probability distribution, a state transition probability distribution, and an output probability distribution, wherein a probability expression of the state transition probability distribution includes instantaneous acceleration information, position coordinates of the intelligent terminal, and a distance between the intelligent terminal and the wearable device, and the output probability distribution is 1.
In some embodiments, the wearable device includes an inertial sensor for acquiring a first rotation vector of the first joint at a T-th moment and an acceleration sensor for acquiring first instantaneous acceleration information of the first joint at the T-th moment.
In a third aspect, a real-time motion profile monitoring apparatus is provided, comprising: the system comprises a memory and a processor, wherein at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor so as to realize the real-time motion trail monitoring method.
In a fourth aspect, a computer readable storage medium is provided, the computer storage medium storing a computer program which, when executed by a processor, implements the aforementioned method of real-time motion profile monitoring.
The technical scheme provided by the application has the beneficial effects that: the real-time monitoring and tracking of the joint movement can be realized, the use cost is effectively reduced, and the influence of external environment factors on the acquisition accuracy of the steering amount information and the acceleration information is reduced, so that the accuracy of the movement track monitoring is effectively ensured.
The application provides a real-time motion trail monitoring method, a device, equipment and a readable storage medium, wherein point cloud data is obtained in real time from a point cloud database through a rotation vector sent by a wearable device, the distance between an intelligent terminal and the wearable device is measured by utilizing frequency modulation continuous wave ultrasonic dynamic ranging, and real-time real position coordinates of a joint are obtained through prediction through a hidden Markov model based on the point cloud data, the distance between the intelligent terminal and the wearable device and instantaneous acceleration information sent by the wearable device, so that the monitoring and tracking of the motion trail of the joint are realized. Therefore, the application can realize real-time monitoring and tracking of the joint movement only through one intelligent terminal and one wearable device, does not need to arrange a trackable high-cost shooting device, effectively reduces the use cost, realizes the monitoring of the movement track through the rotation vector information and the acceleration information acquired by the wearable device, can effectively reduce the influence of external environment factors on the rotation quantity information and the acceleration information acquisition precision, and further can effectively ensure the accuracy of the movement track monitoring.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a real-time motion trail monitoring method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a real-time motion trail monitoring device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The embodiment of the application provides a real-time motion trail monitoring method, a device, equipment and a readable storage medium, which can solve the problems of high cost and great influence by environmental factors in the related technology.
Fig. 1 is a real-time motion trail monitoring method provided by the embodiment of the application, which comprises the following steps:
Step S10: receiving a first rotation vector and first instantaneous acceleration information of a first joint at a T moment, which are sent by wearable equipment;
Further, the wearable device comprises an inertial sensor and an acceleration sensor, wherein the inertial sensor is used for acquiring a first rotation vector of the first joint at the T moment, and the acceleration sensor is used for acquiring first instantaneous acceleration information of the first joint at the T moment.
Further, before the step of receiving the first rotation vector and the first instantaneous acceleration information of the first joint at the T-th moment sent by the wearable device, the method further includes:
Modeling arm movement based on the D-H model to obtain a connecting rod model;
Inputting a plurality of joint angles on an arm to the connecting rod model to obtain a plurality of rotation vectors;
And creating a mapping relation between each rotation vector and a corresponding position set comprising a plurality of preset position coordinates, and obtaining a point cloud database.
Step S20: searching a corresponding first position set from a preset point cloud database based on the first rotation vector, wherein the preset point cloud database comprises a mapping relation between the rotation vector and the position set, and the position set comprises a plurality of preset position coordinates corresponding to the joints;
Step S30: acquiring a second position set of a first joint at a T-1 moment, and screening a first point cloud state space from a preset point cloud database according to the second position set and the first position set, wherein the first point cloud state space comprises a plurality of point cloud states, and each point cloud state consists of preset position coordinates in the first position set and preset position coordinates in the second position set;
step S40: calculating the distance between the intelligent terminal and the wearable equipment at the T moment based on the frequency modulation continuous wave ranging to obtain a first distance;
Step S50: respectively acquiring a second distance between the intelligent terminal and the wearable device at the T-1 moment, a third distance between the intelligent terminal and the wearable device at the T-2 moment, a first actual position coordinate of the first joint at the T-1 moment and a second actual position coordinate of the first joint at the T-2 moment;
step S60: calculating the position coordinate of the intelligent terminal at the T moment based on the first distance, the second distance, the third distance, the first actual position coordinate and the second actual position coordinate;
step S70: and inputting the first instantaneous acceleration information, the first cloud state space and the position coordinate of the intelligent terminal at the T moment into a hidden Markov model to obtain a third actual position coordinate of the first joint at the T moment.
Further, the model parameters in the hidden Markov model include an initial probability distribution, a state transition probability distribution and an output probability distribution, wherein the probability expression of the state transition probability distribution comprises instantaneous acceleration information, position coordinates of the intelligent terminal and a distance between the intelligent terminal and the wearable device, and the output probability distribution is 1.
The application can realize real-time monitoring and tracking of joint movement only through one intelligent terminal and one wearable device, does not need to arrange a trackable high-cost shooting device, effectively reduces the use cost, realizes the monitoring of movement tracks through the rotation vector information and the acceleration information acquired by the wearable device, can effectively reduce the influence of external environment factors on the rotation quantity information and the acceleration information acquisition precision, and further can effectively ensure the accuracy of movement track monitoring.
Exemplarily, with the great popularity of mobile wearable devices, wearable devices are becoming hot in areas such as medical, recreational, security, and fitness; the intelligent wearable device can provide high-precision activity data, and provides a plurality of convenient key technologies for life of human beings, namely an action sensing technology of the intelligent wearable device, which can monitor action data of a person during walking, running and riding by means of an action recognition technology, and monitor behavior actions of the person by means of an artificial intelligence method, so that irregular action problems generated during movement are reminded, and health potential safety hazards generated by daily accumulation and monthly accumulation are avoided. The wearable devices widely used at present comprise a smart wristband, a smart bracelet, a smart waistband and the like, behavior actions of a person are monitored mainly by acquiring action data of different parts of the body of the person, and the monitoring of the action actions by using a single wearable device is a further development trend. In this embodiment, the rotation vector information and the instantaneous acceleration information for monitoring the real-time motion track can be obtained through the wearable device, wherein the wearable device can be an intelligent wristband, an intelligent waistband, or other wearable devices capable of realizing the collection of the rotation vector information and the instantaneous acceleration information, so that the specific selection of the wearable device can be determined according to the actual requirements, and the method is not limited.
In this embodiment, the creation of the point cloud database is performed before the rotation vector information and the instantaneous acceleration information are acquired by the wearable device. Taking a wearable device as an intelligent bracelet as an example, an upper arm and a forearm as a connecting rod model, and a standard D-H model (Denavit-Hartenberg model is widely used in robot kinematics, which represents a very simple method for modeling a connecting rod and a joint of a robot and can be used for representing transformation in any coordinate) for modeling: the method comprises the steps of establishing a connecting rod coordinate system through four parameters of a connecting rod length L i, a connecting rod offset d i, a torsion angle alpha i and a joint angle theta i, and establishing a homogeneous transformation matrix A i of adjacent connecting rods as shown in a formula (1) after two translations and two rotations.
Wherein the joint angles θ i include five, which represent shoulder flexion/extension, abduction/adduction and adduction/abduction, and elbow flexion/extension and forearm adduction/abduction, respectively. According to the embodiment, mapping between wrist joint rotation vector information and position information is established by widely traversing all motion states, namely, each rotation vector has a plurality of preset position coordinates corresponding to the rotation vector, and the plurality of preset coordinates form a position set, so that one rotation vector has a position set with a mapping relation with the position set, and the position set is stored to form a point cloud database for point cloud motion monitoring.
In addition, the two position sets corresponding to the two rotation vectors can form a point cloud state space, the point cloud state space contains a plurality of point cloud states, and each point cloud state is composed of preset position coordinates in the two position sets. For example, the rotation vector F corresponds to a position set F ', and the position sets F' = { F '1,F′2},F′1 and F' 2 each represent a position coordinate corresponding to the rotation vector F; the rotation vector M corresponds to a set of positions M ', and the set of positions M' = { M '1,M′2,M′3},M′1、M′2 and M' 3 each represent a position coordinate corresponding to the rotation vector M; then the point cloud state space S＝{(F′1,M′1),(F′1,M′2),(F′1,M′3),(F′2,M′1),(F′2,M′2),(F′2,M′3)}, composed of the rotation vector F and the rotation vector M includes six point cloud states ：(F′1,M′1)、(F′1,M′2)、(F′1,M′3)、(F′2,M′1)、(F′2,M′2)、(F′2,M′3).
Therefore, the rotation vector information and the instantaneous acceleration information of each joint can be respectively acquired through the inertial sensor and the acceleration sensor which are arranged in the intelligent bracelet, and the monitoring of the motion track of the human body is realized based on the rotation vector information and the instantaneous acceleration information, wherein the acceleration information acquired by the intelligent bracelet in real time can be used as the observed quantity of the hidden Markov model to predict the motion position, and further the predicted position triplet is output, so that high-cost shooting equipment is not required to be arranged, and a series of adverse effects caused by factors such as light rays, motion distance, sports ground and the like can be effectively solved.
In addition, the embodiment also uses the method of FMCW (Frequency Modulated Continuous Wave frequency modulated continuous wave) ultrasonic dynamic ranging and the point cloud prediction algorithm to be mixed on the basis of the point cloud data formed by the intelligent bracelet, so as to restrict the point cloud data space, and the motion trail of the arm can be monitored and tracked in real time only by one intelligent terminal and one intelligent bracelet worn on the wrist under the condition of not increasing the use cost of a user.
The FMCW ultrasonic ranging is a continuous wave with frequency variation emitted in a sweep frequency period, and the echo and the emitted signal reflected by the object have certain frequency difference, so that the distance information between the target and the sound wave signal generating source can be obtained by measuring the frequency difference. Because the difference frequency signal is low in frequency, relatively simple hardware processing can be used, which is suitable for data acquisition and digital signal processing; and because FMCW ultrasonic ranging is transmitted and received, there is no ranging blind area that pulse radar exists in theory, and the average power of the transmitted signal is equal to the peak power, only a device with small power is needed to realize, thus reducing the probability of intercepted interference.
The FMCW ultrasonic ranging principle will be further explained below.
Let f s(t)、fe (t) denote the frequency variation functions of the transmitted signal and the received signal, respectively, and assuming that the relative velocity v r is 0, the following relationship exists between the upper and lower edges of the signal:
fe(t)＝fs(t-τ) (3)
Where f 0 denotes the lowest frequency, f c denotes the frequency bandwidth of the FMCW wave, t c denotes the period of the FMCW wave, t denotes the time, and τ denotes the time difference from the departure to the reception of the sound;
the frequency difference function f b (t) exists between f s(t)、fe (t):
fb(t)＝fs(t)-fe(t) (4)
let the signal be a cosine wave, then the time domain changes as follows:
in equation (5), u s (t) is a transmit signal function, Representing the amplitude of the transmitted signal, f s (t) representing the frequency of the transmitted signal, phi s representing the phase difference of the transmitted signal;
the received signal changes in the time domain as follows:
in the formula (6), u e (t) represents a received signal function, Representing the amplitude of the received signal, f e (t) representing the frequency of the received signal, phi e representing the phase difference of the received signal.
Based on the formulae (4) to (6), it is possible to obtain:
Then, u e (t) is multiplied by u s (t), resulting in a mixed wave u m (t):
As can be seen from equation (8), the frequency of the resulting mixed wave Is related only to f b (t), while the relative distance/>, is to be determinedC represents the speed of sound waves, f c represents the frequency bandwidth of FMCW waves, t c represents the period of FMCW waves, where f b (t) can be obtained by low-pass filtering of the mixed wave u m (t) using fourier transform and the relative distance R is proportional to f b (t), from which it can be seen that the relative distance R can be calculated by the mixed wave.
Therefore, the embodiment calculates the distance between the intelligent terminal and the wearable device based on the frequency modulation continuous wave ranging, and constrains the point cloud data space through the distance. The intelligent terminal can be a smart phone or a tablet, and the specific selection of the intelligent terminal can be determined according to actual requirements and is not limited herein. In this embodiment, an intelligent terminal is taken as an example of a smart phone, and ultrasonic waves are sent between the smart phone and a smart bracelet to realize position ranging between two devices.
Finally, relevant information acquired through the smart bracelet and the smart phone is required to be input into a hidden Markov model to predict the track of human body movement.
Wherein the sequence of states randomly generated by the hidden Markov chain in the hidden Markov model is referred to as a state sequence; each state has an output, thereby producing an observable array of random numbers, known as an observation array, and the observation sequence can also be used as a time series sequence. The hidden markov model is determined by an initial probability distribution, a state transition probability distribution, and an output probability distribution, i.e., λ= (a, B, pi); wherein λ represents a hidden Markov model, A is a state transition probability matrix, B is an output probability matrix, and pi is an initial state probability vector. A. B and pi are referred to as the three elements of the hidden markov model.
The hidden Markov model is mainly used for solving the following three basic problems:
1) Probability calculation problem: given the model λ= (a, B, pi) and the observed sequence o= (O 1,o2,…,oT), the probability P (o|λ) of the occurrence of the sequence O under the model λ is calculated.
2) Learning problem: knowing the observation sequence o= (O 1,o2,…,oT), estimating the parameters of the model λ= (a, B, pi), maximizing the probability of observing the sequence under this model, which usually uses a method of maximum likelihood estimation to estimate the parameters.
3) Prediction problems, also known as decoding problems: knowing the model λ= (a, B, pi) and the observation sequence o= (O 1,o2,…,oT), the hidden state sequence with the highest probability of being able to output the observation sequence is solved.
The application mainly relates to the prediction problem related to hidden Markov, whereas the Viterbi (Viterbi algorithm) algorithm is the current mainstream prediction algorithm, and because the dynamic programming thought is used, the defect of greedy algorithm can be solved, and the state sequence with the highest probability can be effectively obtained in practice, so that the prediction algorithm with the highest use frequency is used at present. Therefore, the embodiment will largely use the viterbi algorithm to infer the position of the limb during the exercise in the exercise monitoring, thereby describing the exercise track and providing reliable data basis for the exercise normalization evaluation.
Specifically, three positions with continuous time intervals can be used as one point cloud state of the joint, at this time, the acceleration of the joint in the point cloud state can be calculated, the acceleration value is used as an observed quantity, and the state transition probability is properly designed, so that the most likely position point can be estimated by using the viterbi algorithm.
The Viterbi algorithm has the time complexity of O (|S| 2 T), S is the size of a state space, and T is the size of a time sequence; assuming that the state is a triplet of positions, then s=n 3, and the time complexity of the viterbi algorithm is O (N 6 T). Therefore, in order to reduce the time complexity of the viterbi algorithm, it is necessary to limit the continuity of the positions within the state, to put the output probability into the state transition probability for calculation, and to reduce the complexity of S so that the final time complexity can be reduced to O (N 3 T). The specific implementation is as follows:
According to the abstract function lambda= (A, B, pi) of the hidden Markov model, the initial probability distribution, transition probability distribution and output probability distribution of the hidden state need to be known, and the Viterbi algorithm needs to acquire an observable time sequence; the present embodiment uses acceleration information as observation output, and uses displacement amount of a period of time DeltaT as hidden state (i.e. point cloud state), namely Indicating the position from time T-1/>Position by time T/>I represents any state, wherein the time interval between the time T-1 and the time T can be set to 0.05s.
For an initial probability distribution: since the initial position is not clear, a uniform distribution can be simply adopted for all the states. When t=1, the state space is N 2, but the range of motion of the joint is limited to a very limited space due to the speed, and the higher the sampling rate, the smaller the space. Thus, the state space can be reduced to αn 2, where α is much smaller than 1, then:
For a state transition probability distribution: the state transition probability of transition from state i to state j corresponding to time T to time t+1 is represented by pr= (state j|statei; T, t+1); the probability consists of three parts: the first part, the motion trajectory is continuous first, that is, there must be continuity between state i and state j, that is, the end of state i is the start of state j, and this continuity property can reduce the spatial complexity from O (N 4 T) to O (N 3 T), so the following function can be used to represent this relationship:
Wherein I represents a 0-1 probability distribution, The position corresponding to the end point of the state i at the time T is shown.
The second part, unlike the conventional viterbi algorithm, directly introduces the observed quantity into the state transition probability distribution, that is, the acceleration is represented by the position information in the state, that is, the predicted acceleration value accel i,j, and calculates the difference between the predicted acceleration and the instantaneous acceleration actually measured by the smart band, and the probability of the second part can be expressed as:
Where σ is a constant, acel observe represents the actual measured instantaneous acceleration of the smart band.
Third part, for new state j 2, its positionMust belong to the Set T+1 of locations that can be queried from the point cloud database, namely:
the final state transition probability distribution can be expressed as:
For the output probability distribution: since the present embodiment introduces the observed amount to the state probability distribution part, the output probability can be set to 1.
Then, predicted state states T-1 and state T corresponding to time T-1 are obtained from the viterbi algorithm of the hidden markov model, respectively, where state T-1 represents the predicted state value at time T-1 and state T represents the predicted state value at time T. Thus, only one triplet, i.e. < loc T-2,locT-1,locT >, can be obtained from two consecutive state spaces, where loc T-2、locT-1 and loc T represent the actual position coordinates of the T-2, T-1, T-time inodes, respectively; meanwhile, the distances dist T-2、distT-1 and dist T between the smart bracelet and the smart phone at the T-2, T-1 and T moments can be obtained from an FMCW ranging algorithm; then according to the known six information, an ellipsoidal equation can be solved, and the position coordinates of the smart phone under the motion monitoring coordinate system can be obtainedAnd in the Viterbi prediction algorithm at time T+1, the calculation can be performedAnd/>In/>To constrain the point cloud space by adding a fourth part to the state transition probabilities in the hidden markov model:
Wherein, Represents time T+1,/>State/>To/>Distance of (2), i.eEpsilon represents the minimum error allowed.
Thus, the state transition probability can be rewritten as follows:
it can be seen that the positions of the joints can be calculated according to the hidden Markov model and the Viterbi algorithm, so that the motion trail can be described.
The present embodiment will be further explained below.
In the embodiment, original data are collected at a fixed frequency of 200Hz, such as a rotation vector Rot and an instantaneous acceleration information Acc of a wrist joint at a moment T on an intelligent bracelet, and a data flow Q= { Rot, acc } after 50Hz pretreatment is output by a mean value downsampling method during data pretreatment; and in the preprocessing stage, a low-pass filter is used for noise reduction treatment of the data. Secondly, inquiring in a preset point cloud database through the acquired rotation vector Rot, finding out a corresponding position set, and acquiring a wrist joint position set at the moment T-1 from the preset point cloud database; and acquiring a point cloud state space matched with the rotation vector Rot from a preset point cloud database according to the two position sets, and inputting the point cloud state space into a hidden Markov model.
Then calculating the distance between the smart phone and the smart band at the T moment by double-transmitting and double-receiving FMCW ultrasonic frequency band sound waves between the smart band and the smart phone; according to the embodiment, the time difference from sending to receiving of the FMCW sounds in two different frequency bands is calculated through a unimodal monitoring method, so that clock errors between two different intelligent devices are compensated, meanwhile, the Doppler effect generated due to movement is compensated through selecting a triangular waveform, and the distance is finally solved.
Finally, combining the point cloud states obtained at the time T-1 and the time T-2 with distance information of distance measurement, calculating the position coordinates of the smart phone at the time T, introducing acceleration information as observed quantity when the probability of each state is calculated in each hidden Markov model prediction iteration, and finally predicting the most possible state at the time T, thereby obtaining the most possible position coordinates of the wrist joint at the time T, and finally carrying out motion track monitoring and tracking in real time; the position coordinate of the smart phone at the T moment can be used as the point cloud space constraint at the next iteration.
The traditional exercise monitoring methods mainly adopt vision-based data or use sensor data worn at a plurality of positions of a human body, the methods have high limit and strict requirements on places, and the ordinary people cannot meet the rigid requirements of exercise monitoring when exercising, so that the usability is low. The intelligent wearable device and the intelligent mobile phone which are worn on the wrist of the human body are only used, so that the motion trail of the human body can be monitored in real time on the premise of not adding the use cost of the user, the user can monitor at any time and any place, the limitation of places is avoided, and the usability is high. In addition, the clock synchronization of the two intelligent devices is realized by introducing the two ultrasonic sound sources without adding any hardware cost and software optimization, so that the application is ensured to be used immediately after being opened, and the purposes of high usability and strong usability are achieved; meanwhile, by means of acquiring inertial sensing data and ultrasonic ranging, the inertial sensing data and the ultrasonic ranging are mixed with an improved hidden Markov model, and finally, the real-time track tracking error with the accuracy of 7-10cm can be achieved.
The embodiment of the application also provides a real-time motion trail monitoring device, which comprises:
The receiving unit is used for receiving a first rotation vector and first instantaneous acceleration information of a first joint at a T moment, which are sent by the wearable equipment;
The searching unit is used for searching a corresponding first position set from a preset point cloud database based on the first rotation vector, the preset point cloud database comprises a mapping relation between the rotation vector and the position set, and the position set comprises a plurality of preset position coordinates corresponding to the joint;
The generating unit is used for acquiring a second position set of the first joint at the T-1 moment, and screening a first point cloud state space from a preset point cloud database according to the second position set and the first position set, wherein the first point cloud state space comprises a plurality of point cloud states, and each point cloud state consists of preset position coordinates in the first position set and preset position coordinates in the second position set;
The distance measuring unit is used for calculating the distance between the intelligent terminal and the wearable equipment at the T moment based on the frequency modulation continuous wave distance measurement to obtain a first distance;
The acquisition unit is used for respectively acquiring a second distance between the intelligent terminal and the wearable device at the T-1 moment, a third distance between the intelligent terminal and the wearable device at the T-2 moment, a first actual position coordinate of the first joint at the T-1 moment and a second actual position coordinate of the first joint at the T-2 moment;
The calculating unit is used for calculating the position coordinate of the intelligent terminal at the T moment based on the first distance, the second distance, the third distance, the first actual position coordinate and the second actual position coordinate;
The prediction unit is used for inputting the first instantaneous acceleration information, the first cloud state space and the position coordinate of the intelligent terminal at the T moment into the hidden Markov model to obtain the third actual position coordinate of the first joint at the T moment.
Therefore, the application can realize real-time monitoring and tracking of the joint movement only through one intelligent terminal and one wearable device, does not need to arrange a trackable high-cost shooting device, effectively reduces the use cost, realizes the monitoring of the movement track through the rotation vector information and the acceleration information acquired by the wearable device, can effectively reduce the influence of external environment factors on the rotation quantity information and the acceleration information acquisition precision, and further can effectively ensure the accuracy of the movement track monitoring.
Further, the apparatus further comprises a creation unit for:
Modeling arm movement based on the D-H model to obtain a connecting rod model;
Inputting a plurality of joint angles on an arm to the connecting rod model to obtain a plurality of rotation vectors;
And creating a mapping relation between each rotation vector and a corresponding position set comprising a plurality of preset position coordinates, and obtaining a point cloud database.
Further, the model parameters in the hidden Markov model include an initial probability distribution, a state transition probability distribution and an output probability distribution, wherein the probability expression of the state transition probability distribution comprises instantaneous acceleration information, position coordinates of the intelligent terminal and a distance between the intelligent terminal and the wearable device, and the output probability distribution is 1.
Further, the wearable device comprises an inertial sensor and an acceleration sensor, wherein the inertial sensor is used for acquiring a first rotation vector of the first joint at the T moment, and the acceleration sensor is used for acquiring first instantaneous acceleration information of the first joint at the T moment.
It should be noted that, for convenience and brevity of description, the specific working process of the above-described apparatus and units may refer to the corresponding process in the foregoing embodiment of the real-time motion track monitoring method, which is not described herein again.
The apparatus provided by the above embodiments may be implemented in the form of a computer program that is executable on a real-time motion profile monitoring device as shown in fig. 2.
The embodiment of the application also provides a real-time motion trail monitoring device, which comprises: the system comprises a memory, a processor and a network interface which are connected through a system bus, wherein at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor so as to realize all or part of the steps of the real-time motion trail monitoring method.
Wherein the network interface is used for network communication, such as sending assigned tasks, etc. It will be appreciated by persons skilled in the art that the architecture shown in fig. 2 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
The Processor may be a CPU, but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL processors, DSPs), application SPECIFIC INTEGRATED Circuits (ASICs), field programmable gate arrays (FieldProgrammable GATE ARRAY, FPGA) or other programmable logic devices, discrete gate or transistor logic discrete hardware components, etc. A general purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like, that is a control center of a computer device, with various interfaces and lines connecting various parts of the entire computer device.
The memory may be used to store computer programs and/or modules, and the processor implements various functions of the computer device by running or executing the computer programs and/or modules stored in the memory, and invoking 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, application programs required for at least one function (such as a video playing function, an image playing function, etc.), and the like; the storage data area may store data (such as video data, image data, etc.) created according to the use of the cellular phone, etc. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart memory card (SMART MEDIA CARD, SMC), secure Digital (SD) card, flash memory card (FLASH CARD), at least one disk storage device, flash memory device, or other volatile solid state storage device.
The embodiment of the application also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, all or part of the steps of the real-time motion trail monitoring method are realized.
The foregoing embodiments of the present application may be implemented in whole or in part by computer program instructions for implementing the relevant hardware, and the computer program may be stored in a computer readable storage medium, where the computer program when executed by a processor may implement the steps of the methods described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-Only memory (ROM), a random access memory (Random Access memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, server, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing is only a specific embodiment of the application to enable those skilled in the art to understand or practice the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The real-time motion track monitoring method is characterized by comprising the following steps of:
Receiving a first rotation vector and first instantaneous acceleration information of a first joint at a T moment, which are sent by wearable equipment;
Searching a corresponding first position set from a preset point cloud database based on the first rotation vector, wherein the preset point cloud database comprises a mapping relation between the rotation vector and the position set, and the position set comprises a plurality of preset position coordinates corresponding to the joints;
Acquiring a second position set of a first joint at a T-1 moment, and screening a first point cloud state space from a preset point cloud database according to the second position set and the first position set, wherein the first point cloud state space comprises a plurality of point cloud states, and each point cloud state consists of preset position coordinates in the first position set and preset position coordinates in the second position set;
Calculating the distance between the intelligent terminal and the wearable equipment at the T moment based on the frequency modulation continuous wave ranging to obtain a first distance;
Respectively acquiring a second distance between the intelligent terminal and the wearable device at the T-1 moment, a third distance between the intelligent terminal and the wearable device at the T-2 moment, a first actual position coordinate of the first joint at the T-1 moment and a second actual position coordinate of the first joint at the T-2 moment;
Calculating the position coordinate of the intelligent terminal at the T moment based on the first distance, the second distance, the third distance, the first actual position coordinate and the second actual position coordinate;
And inputting the first instantaneous acceleration information, the first cloud state space and the position coordinate of the intelligent terminal at the T moment into a hidden Markov model to obtain a third actual position coordinate of the first joint at the T moment.
2. The method for monitoring a real-time motion trajectory according to claim 1, further comprising, before the step of receiving the first rotation vector and the first instantaneous acceleration information of the first joint at the T-th time transmitted by the wearable device:
Modeling arm movement based on the D-H model to obtain a connecting rod model;
Inputting a plurality of joint angles on an arm to the connecting rod model to obtain a plurality of rotation vectors;
And creating a mapping relation between each rotation vector and a corresponding position set comprising a plurality of preset position coordinates, and obtaining a point cloud database.
3. The method for monitoring a real-time motion trajectory according to claim 1, wherein: the model parameters in the hidden Markov model comprise initial probability distribution, state transition probability distribution and output probability distribution, wherein the probability expression of the state transition probability distribution comprises instantaneous acceleration information, position coordinates of the intelligent terminal and the distance between the intelligent terminal and the wearable device, and the output probability distribution is 1.
4. The method for monitoring a real-time motion trajectory according to claim 1, wherein: the wearable device comprises an inertial sensor and an acceleration sensor, wherein the inertial sensor is used for acquiring a first rotation vector of a first joint at a T moment, and the acceleration sensor is used for acquiring first instantaneous acceleration information of the first joint at the T moment.
5. A real-time motion trajectory monitoring device, comprising:
The receiving unit is used for receiving a first rotation vector and first instantaneous acceleration information of a first joint at a T moment, which are sent by the wearable equipment;
The searching unit is used for searching a corresponding first position set from a preset point cloud database based on the first rotation vector, the preset point cloud database comprises a mapping relation between the rotation vector and the position set, and the position set comprises a plurality of preset position coordinates corresponding to the joint;
The generating unit is used for acquiring a second position set of the first joint at the T-1 moment, and screening a first point cloud state space from a preset point cloud database according to the second position set and the first position set, wherein the first point cloud state space comprises a plurality of point cloud states, and each point cloud state consists of preset position coordinates in the first position set and preset position coordinates in the second position set;
The distance measuring unit is used for calculating the distance between the intelligent terminal and the wearable equipment at the T moment based on the frequency modulation continuous wave distance measurement to obtain a first distance;
The acquisition unit is used for respectively acquiring a second distance between the intelligent terminal and the wearable device at the T-1 moment, a third distance between the intelligent terminal and the wearable device at the T-2 moment, a first actual position coordinate of the first joint at the T-1 moment and a second actual position coordinate of the first joint at the T-2 moment;
The calculating unit is used for calculating the position coordinate of the intelligent terminal at the T moment based on the first distance, the second distance, the third distance, the first actual position coordinate and the second actual position coordinate;
The prediction unit is used for inputting the first instantaneous acceleration information, the first cloud state space and the position coordinate of the intelligent terminal at the T moment into the hidden Markov model to obtain the third actual position coordinate of the first joint at the T moment.
6. The real-time motion trajectory monitoring device of claim 5, further comprising a creation unit for:
Modeling arm movement based on the D-H model to obtain a connecting rod model;
Inputting a plurality of joint angles on an arm to the connecting rod model to obtain a plurality of rotation vectors;
And creating a mapping relation between each rotation vector and a corresponding position set comprising a plurality of preset position coordinates, and obtaining a point cloud database.
7. The real-time motion profile monitoring device of claim 5, wherein: the model parameters in the hidden Markov model comprise initial probability distribution, state transition probability distribution and output probability distribution, wherein the probability expression of the state transition probability distribution comprises instantaneous acceleration information, position coordinates of the intelligent terminal and the distance between the intelligent terminal and the wearable device, and the output probability distribution is 1.
8. The real-time motion profile monitoring device of claim 5, wherein: the wearable device comprises an inertial sensor and an acceleration sensor, wherein the inertial sensor is used for acquiring a first rotation vector of a first joint at a T moment, and the acceleration sensor is used for acquiring first instantaneous acceleration information of the first joint at the T moment.
9. A real-time motion trajectory monitoring device, comprising: a memory and a processor, the memory having stored therein at least one instruction that is loaded and executed by the processor to implement the real-time motion profile monitoring method of any one of claims 1 to 4.
10. A computer-readable storage medium, characterized by: the computer readable storage medium stores a computer program which, when executed by a processor, implements the real-time motion profile monitoring method of any one of claims 1 to 4.
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