KR20170084643A - Motion analysis appratus and method using dual smart band - Google Patents
Motion analysis appratus and method using dual smart band Download PDFInfo
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- KR20170084643A KR20170084643A KR1020160003918A KR20160003918A KR20170084643A KR 20170084643 A KR20170084643 A KR 20170084643A KR 1020160003918 A KR1020160003918 A KR 1020160003918A KR 20160003918 A KR20160003918 A KR 20160003918A KR 20170084643 A KR20170084643 A KR 20170084643A
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- G06K9/00342—
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
- A63B24/0003—Analysing the course of a movement or motion sequences during an exercise or trainings sequence, e.g. swing for golf or tennis
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
- A63B24/0003—Analysing the course of a movement or motion sequences during an exercise or trainings sequence, e.g. swing for golf or tennis
- A63B24/0006—Computerised comparison for qualitative assessment of motion sequences or the course of a movement
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B71/00—Games or sports accessories not covered in groups A63B1/00 - A63B69/00
- A63B71/06—Indicating or scoring devices for games or players, or for other sports activities
- A63B71/0619—Displays, user interfaces and indicating devices, specially adapted for sport equipment, e.g. display mounted on treadmills
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B2220/00—Measuring of physical parameters relating to sporting activity
- A63B2220/80—Special sensors, transducers or devices therefor
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- Physical Education & Sports Medicine (AREA)
- Engineering & Computer Science (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- User Interface Of Digital Computer (AREA)
Abstract
The motion analyzing apparatus for collecting motion data from the dual smart bands worn in both arms of the motion analysis subject according to the embodiment of the present invention and analyzing the motion data may classify at least one section corresponding to a preset section in the motion data And a section analyzer for generating a motion analysis result for the section, a visualization unit for visualizing a motion analysis result generated by the motion analysis unit, and a visualization unit for visualizing the motion analysis result generated by the predetermined section, A database unit for storing the motion analysis result, and a controller for controlling operations of the components of the motion analysis apparatus.
Description
More particularly, the present invention relates to a motion analysis method using a dual smart band, and more particularly, to a motion analysis method using a dual smart band by using two smart bands having a three-axis gyro sensor, To a motion analysis apparatus and method capable of collecting data, dividing each section of the motion from the motion-related data, and analyzing the motion by each section.
This patent discloses two smart bands capable of collecting motion information, and a technique for analyzing and visualizing motion using the same.
Various studies have been conducted to improve the user's skill and motion in the sports field. However, there is a limitation in precisely comparing and analyzing the user's three-dimensional motion data by intervals. Therefore, there is a need for a technique for detecting a section of three-dimensional motion data and analyzing motion for each section.
SUMMARY OF THE INVENTION The present invention provides a dual smart band capable of recognizing a user's motion for motion analysis of a user.
It also provides a method and apparatus for analyzing a user's motion using a dual smart band.
The present invention also provides a dual smart band capable of analyzing swing motion by extracting characteristics of rotational motion of both arms and wrists of a user from motion related data collected from two smart bands, and a motion analysis method and apparatus using the same will be.
The present invention also provides a user-customized motion analysis method and apparatus using user's body information and corresponding standard motion analysis data.
A motion analysis apparatus according to an embodiment of the present invention collects motion data from dual smart bands worn on two different body parts of a motion analysis subject and analyzes the motion data, A motion analyzer including a section classifier for classifying at least one section corresponding to the set section and a section analyzer for generating a motion analysis result for the section; a visualization unit for visualizing a motion analysis result generated at the motion analyzer; A database unit for storing the predetermined section, the motion data, or the motion analysis result, and a controller for controlling operations of the components of the motion analysis apparatus.
The motion analyzer may further include a statistical analyzer for generating a statistical result using the motion analysis result.
The motion analyzer may further include a comparison / analysis unit for comparing the motion analysis result with predetermined motion data to generate a comparison analysis result.
The motion analysis apparatus according to an embodiment of the present invention may further include an input unit for receiving motion analysis subject information including body information of the motion analysis subject, position information on the dual smart band, or analysis subject motion information, The motion analyzer may generate a customized analysis result according to the characteristics of the body information of the motion analysis subject.
A method of analyzing motion data according to an exemplary embodiment of the present invention is a method for analyzing motion data collected from dual smart bands worn on two different body parts of a subject of motion analysis using a motion analyzer, The method comprising the steps of: collecting motion data from the dual smart bands worn on two different body parts; classifying at least one motion section corresponding to a predetermined motion section in the motion data; And visualizing the motion analysis result generated by the motion analysis unit and displaying the result on a screen.
The method may further include performing filtering to remove noise from the motion data.
The method may further include generating a statistical result using the motion analysis result.
The method may further include setting a comparison target motion data by a user, and comparing the motion analysis result and the comparison target motion data to generate a comparison analysis result.
The method may further include receiving motion analysis subject information including body information of the motion analysis subject, location information of the dual smart band, or analysis subject motion information, and performing a customized analysis according to characteristics of the body information of the motion analysis subject And generating a result.
A motion analysis program according to an embodiment of the present invention is a motion analysis program stored in a recording medium and analyzing motion data collected from dual smart bands worn on two different body parts of a motion analysis subject, An instruction set for collecting motion data from the dual smart bands worn on two different body parts of the motion analysis subject to be executed; a command set for classifying at least one motion section corresponding to a preset motion section in the motion data; A set of instructions for generating motion analysis results for each of the motion sections, and a set of instructions for visualizing the motion analysis results and displaying them on the screen.
The rotational motion analyzing apparatus according to an embodiment of the present invention collects three-dimensional rotational motion data from a dual smart band that is worn on both arms of a motion analysis subject and includes a three-axis acceleration sensor, a three-axis gyro sensor, and a three- And a rotation motion analyzer for analyzing a rotation motion of the motion analysis subject using the three-dimensional rotation motion data, the apparatus comprising: a motion data collection unit for collecting rotation motion data from the dual smart band; And a motion analyzer for generating an analysis result for each motion section, wherein the motion analyzer comprises a quantizer transformer for transforming the rotational motion data into a quaternion, a motion detector for detecting motion using the quaternion, The rotation change of the arm and the rotation change data of both wrists A normalization unit for performing normalization based on the rotation change of both the arms and the rotation change data of both wrists with respect to the data before the motion generation to generate rotation change data of the normalized arms and both wrists; A section detector for detecting the rotation motion section corresponding to a predetermined motion section from the rotation change data of the normalized arms and both wrists and a rotation motion analysis section for each rotation motion section to generate analysis results for each rotation motion section And a section analyzing section.
The motion analyzer may further include a statistical analysis unit for providing a statistical analysis result using the analysis results of the rotational motion segments and a comparison and analysis unit for providing a result of a comparison analysis of standard motion data with the analysis results of the rotational motion segments can do.
A method for analyzing rotational motion data according to an embodiment of the present invention is a method for analyzing rotational motion data from a dual smart band that is worn on both arms of a motion analysis subject using a motion analyzer and includes a 3-axis acceleration sensor, a 3-axis gyro sensor, and a 3-axis compass sensor A method for analyzing collected three-dimensional rotational motion data, the method comprising: collecting rotational motion data from the dual smart band; converting the rotational motion data to a quaternion; detecting motion using the quaternion; Calculating a rotation change of both arms and a rotation change data of both wrists using a quaternion, performing a normalization based on the rotation change of both the arms and the rotation change data of both wrists with respect to the data before the movement, Generating rotation change data of both arms and both wrists, Detecting a rotational motion section corresponding to a predetermined motion section from rotational change data of the arm and both wrists, and generating a motion analysis result by performing rotational motion analysis for each rotational motion section.
The method may further include providing a statistical analysis result using the analysis result of each rotation motion section and providing a result of comparing analysis results of the rotation motion section with standard motion data.
The rotational motion data analysis program according to the embodiment of the present invention is stored in a recording medium and is worn in both arms of a motion analysis subject and is collected from a dual smart band including a 3-axis acceleration sensor, a 3-axis gyro sensor, and a 3-
A motion analysis system according to an embodiment of the present invention includes a three-axis gyro sensor, a three-axis acceleration sensor, and a three-axis compass sensor. The dual motion smart analysis system includes a dual smart band A motion analyzer including a section classifier for classifying at least one section corresponding to a predetermined section in the rotational motion data and a section analyzer for generating a motion analysis result for the section; And a visualization unit for visualizing the analysis result.
The smart band and the motion analysis method using the same according to the embodiment of the present invention can collect and analyze the trajectory information of both hands and arms by using the triaxial acceleration sensor, the triaxial gyro center and the triaxial compass sensor.
The smart band and the motion analysis method using the same according to the embodiment of the present invention can perform the motion analysis of the motion based on the values of the movements of both hands and both arms and further can perform the swing rhythm analysis for each swing interval .
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS In order to more fully understand the drawings recited in the detailed description of the present invention, a detailed description of each drawing is provided.
1 is a schematic diagram of a motion analysis system in accordance with an embodiment of the present invention.
2 is a functional block diagram of the smart band shown in FIG.
3 is a functional block diagram of the motion analysis apparatus shown in FIG.
4 is a flowchart illustrating a motion analysis method according to an embodiment of the present invention.
5 is a flowchart illustrating a rotational motion analysis method according to another embodiment of the present invention.
FIG. 6 is a diagram showing golf swing motions to be analyzed using the motion analysis apparatus according to an embodiment of the present invention, which are divided by intervals.
FIG. 7 is an exemplary graph of results of performing segmental analysis of golf swing motion using quaternion transform.
It is to be understood that the specific structural or functional description of embodiments of the present invention disclosed herein is for illustrative purposes only and is not intended to limit the scope of the inventive concept But may be embodied in many different forms and is not limited to the embodiments set forth herein.
The embodiments according to the concept of the present invention can make various changes and can take various forms, so that the embodiments are illustrated in the drawings and described in detail herein. It should be understood, however, that it is not intended to limit the embodiments according to the concepts of the present invention to the particular forms disclosed, but includes all modifications, equivalents, or alternatives falling within the spirit and scope of the invention.
The terms first, second, etc. may be used to describe various elements, but the elements should not be limited by the terms. The terms may be named for the purpose of distinguishing one element from another, for example, without departing from the scope of the right according to the concept of the present invention, the first element may be referred to as a second element, The component may also be referred to as a first component.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, the terms "comprises" or "having" and the like are used to specify that there are features, numbers, steps, operations, elements, parts or combinations thereof described herein, But do not preclude the presence or addition of one or more other features, integers, steps, operations, components, parts, or combinations thereof.
Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the meaning of the context in the relevant art and, unless explicitly defined herein, are to be interpreted as ideal or overly formal Do not.
Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings attached hereto.
Hereinafter, a motion analysis system capable of analyzing motion and coaching according to a point or an interval according to an embodiment of the present invention will be described in detail with reference to FIG. 1 to FIG.
Figure 1 illustrates a
2 is a functional block diagram of the
Referring to FIG. 2, the
As used herein, the term "minus" may mean a functional and structural combination of hardware for carrying out the technical idea of the present invention and software for driving the hardware. For example, the 'minus sign' may refer to a logical unit of a predetermined code and a hardware resource for the predetermined code to be executed, and does not necessarily mean a code physically connected or a kind of hardware.
The
The
The
The
The
The
The compass sensor digitizes the azimuth angle according to the movement of the user of the
The
The
The
3 is a functional block diagram of the
Referring to FIG. 3, the
The
The
According to an embodiment, the
The
The
The motion analyzing apparatus may further include a filtering unit (not shown) for preprocessing the collected motion data. The noise of the motion-related data collected using the filtering unit is removed, and the smoothing process is performed.
The motion analysis apparatus may further include an input unit (not shown) for receiving motion analysis object information. The motion analysis target information may be inherent information of a motion analysis subject including body information, age, or gender of a motion analysis subject, body part information worn by a dual smart band, or motion information to be analyzed.
The
The
Although not shown in the drawings, the
The
Unlike the present embodiment, the
Unlike the present embodiment, at least one of the plurality of
Alternatively, the
Hereinafter, a motion analysis method using the motion analysis system according to an embodiment of the present invention will be described in detail with reference to FIG.
4 is a flowchart illustrating a motion analysis method using the motion analysis system shown in FIG.
First, device information and motion-related data are collected from at least two smart bands 100 (S110) and stored. Preferably, the motion-related data measured from the two smart bands are collected. Specifically, the motion analyzing apparatus includes first device information including information on a part of the body from a first smart band worn on a part of the body of the motion analysis subject, and first unit information including information on a
In this case, when the
Next, the collected motion related data is filtered (S120). Specifically, noise is removed from the motion-related data collected from motion sensors of a plurality of smart bands using the noise removing filter, and smoothing processing is performed. A gravity value filtering process may be further included to remove the gravity value from the data collected from the gyro filter.
Depending on the embodiment, some of the filtering steps may be handled directly in the
Next, the motion is classified according to preset sections (S130). The motion section classification step may include a process of extracting feature points (or points) of a motion and a process of extracting a motion section having a feature point of the motion as a start point or an end point. The motion section that connects the minutiae points of the motion and the different minutiae points may be preset by an administrator, a user, or a motion analysis subject. In addition, the motion section classification step may further include a step of extracting a lower motion section of the motion section. The motion section classification can be performed using, for example, a DTW (Dynamic Time Warping) algorithm.
Next, motion for each section is analyzed (S140). The analysis result of the current motion of the motion analysis subject can be provided. Also, in the case of the motion repeating the same operation, the statistical result can be provided to the motion of the motion analysis subject from the past certain point to the present motion. Also, it is possible to provide a result of comparing and analyzing the standard motion information for the motion, the current motion information of the motion analysis subject or the motion statistical data of the motion analysis subject. It is possible to compare the motion information of the user with the motion information of the standard, thereby helping to perform more accurate motion.
Next, the motion analysis result is visualized and displayed on the screen (S150). So that it can be easily recognized by the user, and thus the posture of each section can be easily calibrated.
Next, the motion analysis result can be used to provide coaching for the motion.
Hereinafter, the rotating motion analysis method will be described in detail with reference to FIG. FIG. 5 is a flowchart illustrating a rotational motion analysis method according to another embodiment of the present invention.
First, the rotation motion data is collected from the dual
Next, the motion data is transformed into a quaternion (S231). More specifically, acceleration data and gyroscope data (pitch, yaw, roll rotation angle) collected from a dual smart band including a 9-axis motion sensor are converted into quaternions using the correlation of data.
In the case of motion based on rotation, it is important to make the rotation change accurate. When the rotation information is generated using the Euler formula, a gimbal lock phenomenon may occur in which the calculated values of the two axes of the three-dimensional rotation are superimposed on one axis. In this embodiment, rotation change information is extracted by using quaternion transformation to minimize such burden lock phenomenon. Figure 7 (a) shows an exemplary result graph of performing the quaternion transform.
Next, motion is detected using a quaternion (S233). Specifically, we compute the sum of the differences of each element in a continuous quaternion. The cumulative histogram is calculated in units of a predetermined time (for example, 0.5 second), and when the value of the cumulative histogram is larger than the threshold value T, it is determined that the motion has occurred.
Next, the rotation change value of the arm and wrist is calculated using the quaternion transformation (S235). FIG. 7 (b) shows an exemplary graph of the rotational variation data extracted using the quaternion transform.
Next, all the rotation change values based on the arm and wrist rotation values with respect to the values before motion generation are normalized (S237). The motion of the smart band is detected by applying a threshold value for the amount of change in acceleration and the amount of change in the amount of rotation during a unit time (for example, 0.5 second), and normalization is performed for each rotation based on the average of the rotation information before motion do. By performing normalization, it is possible to facilitate the calculation and visualization of the angular change and the motion change that occur later from the angle of the starting point of motion.
Next, a motion section corresponding to each section preset by the user or the like is detected from the rotation variation data of the normalized arms and both wrists (S239). For example, it is possible to determine a characteristic point in the rotational motion, and to extract at least one or more intervals connecting two different points using the determined point. In addition, the motion section classification step may further include a step of extracting a lower motion section of the motion section.
That is, the motion analysis method according to the present invention can classify the sections into the middle class motion according to the large class motion and the sub class motion into the low class motion which means the lower motion period again.
The major motion of the present invention may be any one of golf swing motion, baseball swing motion, baseball (pitcher) swing motion, or swing motion, for example, In the case of motion, the top section, the impact section in the top, and the finishing section in the impact are subdivided into motion sections in the address. In addition, each of the sub-classified motion can be divided into sub-classified motions. When the sub-classified motion is impacted in the top, the top preserving period and other intervals can be subdivided motion subdivisions. The middle classification motion section and the small classification motion section according to the large classification motion may be changed according to the setting of the user or the manager.
FIG. 6 illustrates points and segments preset by a user or the like when the large category motion is golf swing motion. Referring to FIG. 6, a starting point at which a motion occurs is set as an address (P1) of the golf swing (hereinafter referred to as a "first point"). A point at which the arm angle change is maximum from the first point is set as a top point P2 (hereinafter, referred to as a 'second point'). The point at which the rotation change of the wrist becomes '0' from the second point, that is, the impact point is set to the third point P3. At this time, when there is no point where the rotation change becomes '0', the third point P3 is determined as the point where the rotation change of the wrist is the minimum value, that is, the end point of the down swing (Down Swing). A point at which the rotation change of the wrist is maximum from the third point P3 is set as a finishing point P4 (hereinafter referred to as a fourth point).
Next, the back swing section from the first point to the second point is set as the first section I1, and the down swing section from the second point to the third point is set as the second section I2 ). And the follow through section from the third point to the fourth point is set as the third section I3.
Each of the sub-classified motion sections may further detect a sub-section (a sub-classified motion section) according to a setting of an administrator or a user. For example, in the second period, a region corresponding to 10% of the second period from the second point may be set as the top sustaining period I2S1, and then detected in the motion classifying step.
For accurate motion analysis at each of the points, a section from each point to a point reaching a certain reference (for example, a predetermined time, etc.) can be additionally set.
Next, the rotation motion per section is analyzed (S240). In other words, motion analysis of the change of rotational motion of both arms and two wrists is performed for each section.
For example, the shaking motion of the swing posture can be analyzed using the arm rotation change data in the top maintaining section I2S1. In addition, the rotation angle of the wrist at the third point (or the impact point) can be analyzed to analyze the motion of the headers such as opening and closing.
In addition, rhythm information of the swing (rotation) motion can be provided using the time difference of each section.
Next, the motion analysis result can be used to provide coaching for the motion.
The above-described motion analysis method can be implemented in a general-purpose digital computer that can be created as a program that can be executed by a computer and operates the program using a computer-readable recording medium.
Specifically, a motion analysis program for analyzing motion data collected from a dual smart band stored on a recording medium and worn on different parts of a body of a motion analysis subject, said program comprising: An instruction set for collecting motion data from the dual smart bands worn on the at least one smart band, a command set for classifying at least one motion section corresponding to a predetermined motion section in the motion data, A set of instructions, and a set of instructions for visualizing and displaying the motion analysis results on the screen.
Also, there is provided a rotational motion analysis program for analyzing three-dimensional rotational motion data collected from a dual smart band, which is worn on both arms of a motion analysis subject and includes a three-axis acceleration sensor, a three-axis gyro sensor, and a three- The program includes a set of instructions for collecting rotational motion data from the dual smart bands executed in a computing system, a set of instructions for converting the rotational motion data into quaternions, a set of instructions for detecting motions using the quaternions, A command set for calculating rotation change of both arms and rotation change data of both wrists, normalization based on the rotation change of both the arms and the rotation change data of both wrists with respect to the data before the movement, A command for generating rotation change data of both wrists A set of instructions for detecting a rotational motion section corresponding to a predetermined motion section from the rotation change data of the normalized arms and both wrists and a command for generating a motion analysis result by performing rotational motion analysis for each of the rotational motion sections / RTI >
The motion analysis program is stored on the recording medium and may be stored in a storage medium such as a magnetic storage medium (e.g., ROM, floppy disk, hard disk, etc.), an optical reading medium (e.g., CD ROM, Media. In addition, the recording medium may be distributed and distributed to a network-connected computer system so that a computer-readable instruction set can be stored and executed in a distributed manner.
The block diagrams disclosed herein may be construed to those skilled in the art to conceptually represent circuitry for implementing the principles of the present invention. Likewise, any flow chart, flow diagram, state transitions, pseudo code, etc., may be substantially represented in a computer-readable medium to provide a variety of different ways in which a computer or processor, whether explicitly shown or not, It will be appreciated by those skilled in the art.
While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, the true scope of the present invention should be determined by the technical idea of the appended claims.
10: Motion Analysis System
100: Smart Band 200: Motion Analysis Device
Claims (16)
A motion analyzer including a section classifier for classifying at least one section corresponding to a predetermined section in the motion data, and a section analyzer for generating a motion analysis result for the section;
A visualization unit for visualizing the motion analysis result generated by the motion analysis unit;
A database for storing the predetermined section, the motion data, or the motion analysis result; And
And a control unit for controlling operations of the components of the motion analysis apparatus.
The motion analyzer may include:
And a statistical analysis unit for generating a statistical result using the motion analysis result.
The motion analyzer may include:
And a comparison and analysis unit for comparing the motion analysis result with preset motion data to generate a comparison analysis result.
Further comprising an input unit for receiving motion analysis subject information including body information of the motion analysis subject, location information on the dual smart band, or analysis subject motion information,
The motion analyzer may include:
And generates a customized analysis result according to the characteristics of the body information of the motion analysis subject.
Collecting motion data from the dual smart bands worn on two different body parts of the motion analysis subject;
Classifying at least one motion section corresponding to a predetermined motion section in the motion data;
Generating motion analysis results for each of the motion segments; And
And visualizing the motion analysis result generated by the motion analysis unit and displaying the result on a screen.
And performing filtering to remove noise of the motion data.
And generating a statistical result using the motion analysis result.
Setting a comparison target motion data by a user; And
And comparing the motion analysis result and the comparison target motion data to generate a comparison analysis result.
Receiving the motion analysis subject information including the body information of the motion analysis subject, the position information of the dual smart band, or the analysis subject motion information; And
And generating customized analysis results according to the characteristics of the body information of the motion analysis subject.
A set of instructions for collecting motion data from the dual smart bands worn on two different body parts of the motion analysis subject;
A command set for classifying at least one motion section corresponding to a preset motion section in the motion data;
A command set for generating a motion analysis result for each of the motion sections; And
And a command set for visualizing the motion analysis result and displaying the result on a screen.
A motion data collection unit for collecting the rotation motion data from the dual smart band; And
And a motion analyzer for generating an analysis result for each of the rotational motion segments for the rotational motion,
The motion analyzer
A quantizer for converting the rotation motion data into a quaternion;
A motion detector for detecting motion using the quaternion;
A rotation change calculation unit for calculating rotation change of both arms and rotation change data of both wrists using the quaternion;
A normalization unit for performing normalization based on the rotation change of both arms and the rotation change data of both wrists with respect to the data before motion generation to generate rotation change data of normalized arms and both wrists;
An interval detector for detecting the rotation motion interval corresponding to a preset motion interval from the rotation change data of the normalized arms and both wrists; And
And a section analyzer for performing a rotational motion analysis for each of the rotational motion sections to generate an analysis result for each of the rotational motion sections.
The motion analyzer
A statistical analysis unit for providing a statistical analysis result by using the analysis results of the rotational motion segments; And
And a comparison and analysis unit for providing a result of a comparison and analysis of standard motion data with the results of the analysis of the rotational motion intervals.
Collecting rotational motion data from the dual smart bands;
Converting the rotation motion data into a quaternion;
Detecting a motion using the quaternion;
Calculating rotation change data of both arms and rotation change data of both wrists using the quaternion;
Generating normalized rotation change data of both arms and both wrists by performing normalization based on the rotation change of both arms and the rotation change data of both wrists with respect to the data before motion generation;
Detecting a rotation motion section corresponding to a predetermined motion section from the rotation change data of the normalized arms and both wrists; And
And performing a rotational motion analysis for each of the rotational motion sections to generate a motion analysis result.
Providing a statistical analysis result using the analysis result of each rotation motion section; And
And providing a result of a comparison and analysis of standard motion data with the results of the analysis of the rotational motion segments.
A set of instructions for collecting rotational motion data from the dual smart bands;
A set of instructions for converting the rotational motion data into quaternions;
A command set for detecting motion using the quaternion;
A command set for calculating rotation change of both arms and rotation change data of both wrists using the quaternion;
A set of instructions for performing normalization based on the rotational change of both the arms and the rotational change data of both wrists with respect to the data before the motion generation to generate rotational variation data of the normalized both arms and both wrists;
A command set for detecting a rotation motion section corresponding to a predetermined motion section from the rotation variation data of the normalized arms and both wrists; And
And performing a rotational motion analysis for each of the rotational motion sections to generate a motion analysis result.
A motion analyzer including a section classifier for classifying at least one section corresponding to a predetermined section in the rotational motion data, and a section analyzer for generating a motion analysis result for the section; and a motion analyzer And a visualization unit for visualizing the result.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| KR20200052106A (en) * | 2018-11-06 | 2020-05-14 | 부산대학교 산학협력단 | Apparatus and Method for Controlling Adaptive Threshold on Motion Sensor in Wearable Device |
| KR20200074609A (en) * | 2018-12-17 | 2020-06-25 | 이화여자대학교 산학협력단 | Supporting method and system for home fitness |
| KR102182413B1 (en) * | 2019-11-28 | 2020-11-25 | 한국전자기술연구원 | Apparatus for Behavior Recognition using Virtual Motion Generation and Method Therefor |
| KR20210041670A (en) * | 2019-10-07 | 2021-04-16 | 동서대학교 산학협력단 | Smart health gloves operating method |
| KR20220020705A (en) * | 2020-08-12 | 2022-02-21 | 임용근 | Embedded 9-Axis Gyrosensor Module Without Gimberlocks |
| KR20230032454A (en) * | 2021-08-31 | 2023-03-07 | 대구대학교 산학협력단 | Home training system using artificial intelligence |
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| KR20230005009A (en) | 2021-06-30 | 2023-01-09 | (주)티에이치케이컴퍼니 | Wearable suit with soft sensor |
| KR102429683B1 (en) * | 2021-11-01 | 2022-08-09 | 주식회사 위니드소프트 | Exercise analysis system and method using dual wearable device |
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| KR20190094881A (en) * | 2018-02-06 | 2019-08-14 | 한림대학교 산학협력단 | System of recognizing a continuous gesture |
| KR20200052106A (en) * | 2018-11-06 | 2020-05-14 | 부산대학교 산학협력단 | Apparatus and Method for Controlling Adaptive Threshold on Motion Sensor in Wearable Device |
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| KR102182413B1 (en) * | 2019-11-28 | 2020-11-25 | 한국전자기술연구원 | Apparatus for Behavior Recognition using Virtual Motion Generation and Method Therefor |
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| KR20230032454A (en) * | 2021-08-31 | 2023-03-07 | 대구대학교 산학협력단 | Home training system using artificial intelligence |
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