KR20150071729A - The Classifying and Counting Algorithm for Real-time Walk/Run Exercise based on An Acceleration Sensor - Google Patents
The Classifying and Counting Algorithm for Real-time Walk/Run Exercise based on An Acceleration Sensor Download PDFInfo
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- KR20150071729A KR20150071729A KR1020130152672A KR20130152672A KR20150071729A KR 20150071729 A KR20150071729 A KR 20150071729A KR 1020130152672 A KR1020130152672 A KR 1020130152672A KR 20130152672 A KR20130152672 A KR 20130152672A KR 20150071729 A KR20150071729 A KR 20150071729A
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Description
The present invention relates to an apparatus and a method for real-time motion measurement using a three-axis acceleration sensor, and more particularly to a motion measurement apparatus and method for measuring motion of an aerobic motion in daily life, (Assist, close, and landing) by analyzing the acceleration sensor signal to simplify the operation to assist and close. Then, the acceleration sensor signal is smoothed for ease of signal detection for simplified walking / From the smoothed results, we define criteria of frequency measurement along with criteria of motion classification for walking / running, and compare and analyze user 's test information and real - time exercise information on walking / running based on defined criteria, And the number of exercises for each exercise was measured and the number of calories consumed according to the number of walking / Linked to more of a real-time exercise measuring apparatus and method using a three-axis acceleration sensor to calculate the exact quantity of exercise by.
The incidence of obesity worldwide is increasing every year due to recent improvement in living standards and decrease in physical activity.
Because obesity is a cause of various adult diseases, regular health care in daily life is recommended as an effective method for prevention and treatment.
Recently, various researches on aerobic exercise measurement for walking / running which can easily manage health in everyday life based on a smart phone capable of computing with portable have been actively carried out.
In these studies, a small acceleration sensor using micro electro mechanical system (MEMS) technology is mainly used. Data obtained from sensors are combined with user information to calculate activity amount, Is used as information.
In particular, walking / running in human behavior is used to measure health status such as exercise amount measurement and body mass index (BMI) calculation. In walking or running, accurate step count detection is useful for exercise history or calorie calculation It is used as data.
In general, when an acceleration sensor is used, a specific motion pattern of a person for walking / running is defined, and motion classification or frequency measurement is performed based on the motion pattern.
However, even in the same operation, the acceleration sensor has a disadvantage in that the result is different depending on the noise of the signal or the state of the road surface.
First, the signal vector magnitude (SVM) related techniques based on the sum of the accelerations of all the axes of the three-axis acceleration sensor are used to detect the greatest motion during the walking / Threshold value to recognize a noise-robust operation pattern.
However, the methods using the SVM as a threshold value have a disadvantage in that they can not detect the operations that do not reach the threshold value by only checking the size of the instantaneous movement of the walking / running.
Next, techniques to apply the HA (Heuristic Algorithm) and GMM (Gaussian Mixture Model) to the SVM have been developed to supplement the disadvantages of the threshold of the SVM and to classify the motion composed of similar motion in more detail. However, although these techniques improve the accuracy of motion classification and frequency measurement by compensating for the disadvantages of existing SVMs, there is a problem that the computation becomes complicated and difficult to implement in real time.
Therefore, in this paper, we propose an algorithm that can simplify the walking / running configuration to two stages, ie, close and stop, and classify the number of movements in real time.
SUMMARY OF THE INVENTION The present invention has been made to solve the conventional problems as described above, and has the following objectives.
The present invention analyzes accelerometer signal signals for the walking / running configuration operations (assist, hold and landing) based on an acceleration sensor (one axis) for accurate exercise measurement of daily aerobic exercise, And smoothing of the acceleration sensor signal for the ease of signal detection for the simplified walking / running, and then, based on the smoothed result, the criteria of the motion classification for the walking / Based on the defined and defined criteria, the user's test information and the real-time exercise information on walking / running were compared and analyzed, and the number of exercise for each exercise was classified and the walking / Acceleration time using a 3-axis acceleration sensor that can calculate more accurate momentum by associating with calorie consumption according to frequency It is aimed to provide a dynamic measurement apparatus and method.
In order to accomplish the above object, the present invention is implemented by the following embodiments.
The present invention relates to an acceleration sensor unit (110) for measuring an axial acceleration;
A control unit (120) for determining whether or not to perform walking or running using the measured data of the acceleration sensor unit and calculating the number of times of exercise; And
And a display unit (130) for displaying data received from the control unit.
The present invention has the following effects with the above-described configuration.
The present invention relates to an apparatus and method for real-time motion measurement using a three-axis acceleration sensor, which comprises a walking / running configuration operation (assist, stop and landing) based on an acceleration sensor (single axis) And then performs the smoothing of the acceleration sensor signal for the ease of signal detection for the simplified walking / running, and performs the walking / running from the smoothed result We define the criteria of frequency measurement with the criteria of Korean motion classification and compare the test information and real time exercise information about walking / running based on the defined criteria and classify the walking / The number of exercise cycles was measured and correlated with the calorie consumption according to the number of walking / It is possible to calculate the amount of the liquid.
It also provides users with reliability, convenience and efficiency.
BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 shows a schematic configuration of the present invention. Fig.
2 is a schematic flow diagram of a motion measurement method according to an embodiment of the present invention.
Fig. 3 is a view showing an embodiment for sorting the action of walking or running; Fig.
4 is a graphical representation of the measured acceleration sensor signal according to FIG.
FIG. 5 is a diagram of the graph of FIG. 4 illustrating the simplification of the implementation steps according to an embodiment of the present invention. FIG.
FIG. 6 illustrates a leveling step of an embodiment of the present invention in accordance with an embodiment of the present invention; FIG.
FIG. 7 is a graph schematically illustrating the results of performing the simplification and leveling steps of the measured acceleration sensor signal according to the embodiment of FIG. 3 according to an embodiment of the present invention; FIG.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The applicants will now describe in detail the construction of the present embodiments with reference to the accompanying drawings.
Detailed descriptions of well-known functions and constructions that may be unnecessarily obscured by the gist of the present invention will be omitted.
The present invention relates to an acceleration sensor unit (110) for measuring a uniaxial acceleration; A control unit (120) for determining whether or not to perform walking or running using the measured data of the acceleration sensor unit and calculating the number of times of exercise; And a
The acceleration sensor part measures the acceleration in a direction perpendicular to the ground.
The acceleration sensor unit includes a wired or wireless communication device and transmits data measured by the sensor to the control unit.
The control unit includes a wired or wireless communication device capable of receiving data of the acceleration sensor unit.
The controller includes storage means for storing data of the acceleration sensor unit in real time or for a predetermined period of time.
And the control unit is provided with a connection interface for transmitting the stored data to the outside.
The control unit includes a database engine to convert the data of the acceleration sensor unit into a DB in real time.
The display unit includes a touch screen having a touch function, and an application for controlling the control unit using the touch screen is incorporated.
The display unit displays any one or more of the measured value of the acceleration sensor, whether or not the robot is walking or running, and the number of times of exercise, from the control unit.
A real-time motion measurement method using an axial acceleration sensor
Connecting an acceleration sensor (S100);
Determining whether the test information is present (S110);
If test information is present, starting a movement (S120);
Collecting an acceleration sensor signal (S130);
Analyzing the acceleration sensor signal (S140);
Determining a kind of exercise of walking or running and detecting the number of times of exercise (S150);
A step S160 of displaying the discriminated kind of exercise and the number of exercises thereof; And
And selecting the end of the exercise (S170).
At this time, the step of determining whether or not the test information is present (S110)
Collecting an acceleration sensor signal for a walking or running motion in the absence of test information (S180);
Analyzing the collected acceleration sensor signal (S181); And
And registering the analyzed data as test information (S182).
And analyzing the acceleration sensor signal
Dividing the acceleration sensor signal into the assist close, the take I, the take II and the landing motion;
Dividing the landing motion by the method of Equation (2) in the divided acceleration sensor signals;
And simplifying the divided landing operation by the method of Equation (1) to include the landing operation between the end point of the landing operation and the start point of the assist close, to simplify the assist closing and the landing operation.
And the step of discriminating the kind of exercise of walking or running and detecting the number of times of exercise
The simplified acceleration sensor signal is leveled by the method of Equation (3), and the area S MoT of the positive acceleration sensor signal from the leveled acceleration sensor signal Or an area S MoB of a negative acceleration sensor signal;
Determining a walking or running motion by the method of Equation (4);
A step of comparing the value of the acceleration sensor signal in the area S and the leveling XAoB accelerometer signals to determine the walk or run - the positive (+) of the acceleration sensor signal values of the area S XAoT, the negative input to the test information (); And
Detecting the number of walking or running motions based on one pair of positive and negative acceleration sensor signals in the acceleration sensor signals performed in Equations (1), (2), and (3) Further comprising.
The test information collects the acceleration sensor signal for the walking or running motion for a predetermined period of time in advance by the user before the exercise and obtains the acceleration sensor signal by using Equation (1), Equation (2) and Equation (3) The sum of the positive values S XAoT or the sum of the negative values S XAoB is further included.
More detailed description is as follows.
As shown in FIG. 3, the walking / running is constituted by the assist closing, the landing, and the landing operation on the basis of one leg (right or left), and an acceleration sensor signal in the direction perpendicular to the paper Signal) appears as shown in FIG.
FIG. 4 shows signals for two walks / runs with reference to the right foot (sensor attached to the ankle) and signals for assist close, hold and landing in sequential walk / run (walk / run) As shown in Fig.
In this case, the task classification is divided into two stages, and the operation is classified because it has a signal section having different characteristics in the operation.
Increased positive attitude
Increase in the number of consonants
Increased positive attitude
Increase in the number of consonants
As a result, it can be seen from FIG. 4 and [Table 1] that the characteristics of the arc section for the walking / running configuration are very similar, while the magnitude of the momentum (acceleration) Different features can also be identified.
Therefore, in the proposed method, the size of the acceleration for the walking / running configuration is defined as the classification criterion of the two movements.
First, the walking / running movement consists of consecutive repetitions of assist closing, landing and landing movements.
Among them, helpless and involuntary movements play a key role in the walking / running that advances the entire body, and landing movements connect the individual movements and assist closing movements. Therefore, it is possible to redefine the walking / running movement by simplifying the walking / jumping movement into two actions, that is, the closing operation and the closing movement, including the ending point of the landing operation and the starting point of the assist closing. The signal for the simplified walk / run can be generated as shown in FIG. 3 through the following equations (1) and (2).
here,
a = Acceleration sensor signal
x = Walking or running segment of the run
S (t x ) = Sum of acceleration sensor signal a during time t in operating section x
here,
a = Acceleration sensor signal
T = Threshold for signal detection of landing motion
cnt = Number of times the action is performed
In Equation (1), S ( t X ) represents the sum of the acceleration signal a returned by the sensor during the time t in the operation section X , and T represents a threshold for signal detection of the landing operation. In this case, X in Eq. (1) can be help close, I, II and landing, and cnt in Eq. (2) indicates the number of times the corresponding operation is performed.
Therefore, if the signal for the landing motion period is detected through Equation (2), then the signal region of the landing motion and the landing motion is linked to the signaling region of the landing motion through Equation (1) can do.
Also, T is also used as a reference of a filter for detecting noise signals having different acceleration directions intermittently generated in the operation section X. The detected noise signal is subjected to a sign change in the same direction as the acceleration direction of the corresponding operation section And converts it into a signal of the operation section X.
In order to simplify the signal detection for the operation period since the signal of the simplified walk / run motion has an irregular waveform, the signal is leveled as shown in FIG. 6 through the following equation (3).
here,
a = Acceleration sensor signal
x = Walking or running segment of the run
a aver = the value obtained by dividing the sum of the acceleration sensor signals of Equation (1) by the time t x as the average acceleration in the exercise section x of walking or running
In equation (3), a aver is the value obtained by dividing the sum of the acceleration signals of equation (1) by time t x and represents the average acceleration in the operating section X , and the other variables are the same as in equation (1).
In the leveled acceleration signal, a walk / run can be detected based on a pair of positive and negative bar graphs. The area of the detected bar graph can be determined by the following equation (4) have.
here,
X = Walking or running exercise
S XAoT ( average of top ) = the area of the acceleration sensor signal of positive (+) value entered in the test information
S XAoB ( average of bottom ) = area of the negative acceleration sensor signal input to the test information
S MoT ( Measurement of top ) = Area of acceleration sensor signal of positive (+) value from leveled acceleration sensor signal
S MoB ( average of bottom ) = area of the acceleration sensor signal of negative (-) value from the leveled acceleration sensor signal
In equation (4), X can be walk and run, and S XAoT ( Average of Top ) and S XAoB (Average of Bottom ) represents the average area of the positive and negative bar graphs based on the signal that the user pre-tested for motion X based on the signal. And Measurement of S MoT Top ) and S MoB ( Measurement of Bottom ) represents the area of the positive and negative bar graphs calculated from the equations (1) to (3) based on the signal obtained by the user in real time.
As a result, Equation (4) compares the similarity of the user's walking / raising signal obtained in advance with the test obtained in advance and the similarity of the user's signal acquired in real time. .
In order to verify the performance of the real-time walking / running motion classification and counting algorithm based on the acceleration sensor (one axis) proposed in the present invention, a mobile application capable of interlocking with the acceleration sensor based on the Android programming in the development environment as shown in [Table 2] Respectively.
The processing procedure of the developed mobile application shown in FIG. 2 is as follows, and the proposed classification and count detection algorithm of walking / running motion can be adaptively used by the user.
First, the application checks whether test information of the user exists. The test information is obtained by preliminarily collecting the acceleration sensor signal before the user starts the exercise and calculating S XAoT and S XAoB in Equation (4) for walking / running, respectively.
In this case, if the test information does not exist, the user causes the acceleration sensor signal for walking / running to be collected for a predetermined period of time before the exercise. From the collected data, the equation (1) 4) S XAoT and S XAoB are calculated and stored.
Next, while the user performs the actual walking / running motion, the application analyzes the acceleration sensor signal coming in real time, calculates S MoT and S MoB of equation (4), assigns the calculated result to equation (4) And the number of times
In the experiment of the proposed algorithm, 5 adults in their 20s and 20s participated in the experiment. The evaluation was performed by randomly crossing the walking / running and performing 100 exercises. Respectively.
At this time, the acceleration sensor was attached on the ankle of the experimenter.
7 shows an example of a process of calculating test information on an operator's walking.
First, (i) shows the acceleration sensor signal obtained from the walking motion for about 20 seconds, and (ii) shows the result of performing leveling on (i) by applying Equation (3).
(Iii) shows the results of simplifying the walking exercise by applying the equations (1) and (2) to (ii) And shows the result of converting the acceleration sensor signal of the walking motion into a uniformized waveform by performing leveling.
At this time, applied to the positive and negative bars to calculate the average area for the graph, and possible to calculate the S walking AoT and S walking AoB of formula (4), the acceleration sensor signals obtained in the run the same procedure, movement of the (iv) and it is possible to calculate the S and S AoT run run AoB.
At this time, the leveling process of (ii) is actually unnecessary, but was performed to improve the readability of the signal.
Similarly, we can calculate the S MoT and S MoB of Equation (4) by obtaining the acceleration sensor signal of the user performing real-time motion and performing the process of Equation (1) to Equation (3) By substituting the equation (4) for the similarity of the walking / running, it is possible to discriminate the current user's current exercise.
The number of movements can be measured by detecting a pair of positive and negative bar graphs in the acceleration sensor signal of the user who performed the process up to equation (3).
In this way, it is possible to classify and measure the number of walking / running movements of the experimenters. [Table 3] shows the results of experiment evaluation of the proposed algorithm to 5 experimenters.
(Number of times)
(Number of times)
(%)
The accuracy of [Table 3] is calculated by equation (5). Experimental results show that the proposed algorithm can classify walking / running movements with high accuracy between 93% and 100% Respectively.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the inventions. Will be clear to those who have knowledge of.
100: Real-time motion measurement device using acceleration sensor
110: acceleration sensor unit 120:
130:
Claims (12)
A control unit (120) for determining whether or not to perform walking or running using the measured data of the acceleration sensor unit and calculating the number of times of exercise; And
And a display unit (130) for displaying data received from the control unit.
The acceleration sensor measures the acceleration in a direction perpendicular to the ground;
And a wired or wireless communication device is provided to transmit data measured by the sensor to the control unit.
The control unit includes a wired or wireless communication device capable of receiving data of the acceleration sensor unit;
And storage means for storing data of the acceleration sensor unit in real time or for a predetermined period of time;
And a connection interface capable of transmitting the stored data to the outside;
And a database engine is provided to convert the data of the acceleration sensor unit into a DB in real time.
Wherein the display unit is provided with a touch screen having a touch function, and an application for controlling the control unit is incorporated therein.
Wherein the display unit displays at least one of the measured value of the acceleration sensor, whether or not the robot is walking or running, and the number of times of the robot movement, from the control unit.
Determining whether the test information is present (S110);
If test information is present, starting a movement (S120);
Collecting an acceleration sensor signal (S130);
Analyzing the acceleration sensor signal (S140);
Determining a kind of exercise of walking or running and detecting the number of times of exercise (S150);
A step S160 of displaying the discriminated kind of exercise and the number of exercises thereof; And
And a step (S170) of selecting the end of the exercise.
Collecting an acceleration sensor signal for a walking or running motion in the absence of test information (S180);
Analyzing the collected acceleration sensor signal (S181); And
And registering the analyzed data as test information (S182). ≪ RTI ID = 0.0 > [10] < / RTI >
Dividing the acceleration sensor signal into the assist close, the take I, the take II and the landing motion;
Dividing the landing motion by the method of Equation (2) in the divided acceleration sensor signals;
And a step of simplifying the divided landing operation to include a landing operation between the end point of the landing operation and the start point of the landing operation by the method of Equation (1) Real - time motion measurement method using 3 - axis acceleration sensor.
The simplified acceleration sensor signal is leveled by the method of Equation (3), and the area S MoT of the positive acceleration sensor signal from the leveled acceleration sensor signal Or an area S MoB of a negative acceleration sensor signal;
Determining a walking or running motion by the method of Equation (4);
A step of comparing the value of the acceleration sensor signal in the area S and the leveling XAoB accelerometer signals to determine the walk or run - the positive (+) of the acceleration sensor signal values of the area S XAoT, the negative input to the test information (); And
Detecting the number of walking or running motions based on one pair of positive and negative acceleration sensor signals in the acceleration sensor signals performed in Equations (1), (2), and (3) Axis acceleration sensor according to the present invention.
Wherein the test information includes at least one of sex, age, and body weight of the user.
The test information collects the acceleration sensor signal for the walking or running motion for a predetermined time in advance by the user before the exercise and calculates the amount of the acceleration sensor signal by the method of Equation (1), Equation (2) and Equation (3) (+) value of the sum S XAoT or negative (-), real-time motion measurement method using the three-axis acceleration sensor, characterized in that the sum S XAoB the value is further included.
And selecting to return to the step of collecting the acceleration sensor signal.
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