KR20180099177A - SNOWBOARD SELF-TRAINING SYSTEM USING IoT - Google Patents

Abstract

According to an embodiment of the present invention, a snowboard self-training system using an IoT technology comprises: a sub-control unit which measures the speed of a snowboarder who is sliding down along a slope based on sensed air pressure, provides an over-speed alarm when the measured speed exceeds a predetermined speed limit, and plays a voice file; and a main control unit which analyzes snowboarder′s pose based on motion information of a sensed snowboard, pressure information imposed on sensed snowboarder′s feet, and speed information received from the sub-control unit, identifies snowboarder′s habit or problems of the pose, provides self-training information to the snowboarder through the sub-control unit, estimates a current location based on signals from a plurality of beacons installed on the slope, and provides information about the speed, pose, current location coordinates, tumbling, and slope information to a cloud server. The snowboard self-training system can increase snowboarder′s skill and safety.

Description

SNOWBOARD SELF-TRAINING SYSTEM USING IOT BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a snowboard self-

The present invention relates to a snowboard self-training system utilizing the Internet of things.

Snowboarding refers to the use of large ski-type equipment that can ride on snow or snow-covered slopes using the equipment. One board per board is available and can be enjoyed on the slopes of the ski slopes. The method of burning is similar to skiing, but the two feet are tied to one plate and may be a bit more dangerous than skiing.

On the other hand, as the 'snowboard', one of the winter sports, is activated, it is required to correct the posture for prevention of safety accidents and snowboarders. In addition, as the number of skiers decreases every year, new fun factors are needed to increase the number of passengers. There is therefore a need for a solution that improves the safety of snowboarders on the ski slope and provides new fun for experienced skiers to improve their skills.

Patent Document 1 discloses an interactive bodily-feeling virtual reality snowboard training system utilizing a 5 DOF actuator.

KR 10-2016-0082658 A

A problem to be solved by the present invention is to provide a ski resort culture by improving speed and safety through speed measurement, posture analysis and self-training, thereby providing a safe skiing culture and providing an entertainment factor to a skilled person through a recording and ranking system. And to provide a snowboard self-training system utilizing the Internet.

According to an aspect of the present invention, there is provided a snowboard self-training system using the Internet,

A sub-controller for measuring a speed at which the snowboarder descends on the slope based on the sensed air pressure, providing an overspeed alarm when the measured speed exceeds a predetermined speed limit, and reproducing the voice file; And

By analyzing the posture of the snowboarder based on the motion information of the sensed snowboard, the pressure information applied to the foot of the sensed snowboarder, and the speed information received from the sub-control unit, And provides the self-training information to the snowboarder through the sub-control unit, estimates a current position based on a signal from a plurality of beacons installed in the slope, and calculates a current position, a velocity, an attitude, a current position coordinate, And a main control unit for providing information to the cloud server.

In the snowboard self-training system using the Internet of objects according to an embodiment of the present invention, the sub-

An air pressure sensor for sensing the air pressure;

An audio file reproducing unit for reproducing the audio file;

A first communication unit for communicating with the main control unit;

And a speed over alarm signal is provided to the audio file playback unit when the measured speed exceeds a predetermined speed limit, A first controller for providing the self-training information received from the main controller to the audio file player; And

And a speaker for outputting the audio reproduced by the audio file reproducing unit.

In addition, in the snowboard self-training system utilizing the Internet of things according to an embodiment of the present invention,

A motion sensor for sensing motion information of the snowboard;

A pressure sensor for sensing a pressure applied to both feet of the snowboarder;

A second communication unit for communicating with the sub-control unit;

A third communication unit for communicating with the cloud server;

A button unit for receiving mode selection and emergency alarm input from the snowboarder; And

Analyzing the posture of the snowboarder based on the motion information of the sensed snowboard, the pressure information applied to the feet of the sensed snowboarder, and the speed information received from the sub-control unit, Finds a problem, provides self-training information to the snowboarder through the sub-controller, estimates a current position based on a signal from a plurality of beacons installed in the slope, and calculates a current position, And a second controller for providing the slope information to the cloud server through the third communication unit and processing the mode selection or emergency alarm input.

In addition, in the snowboard self-training system utilizing the Internet of objects according to an embodiment of the present invention, the main controller compares the appropriate slope of the snowboard with the current slope based on the motion information, The problem of the habit and posture of the problem can be grasped.

In addition, in the snowboard self-training system using the Internet of objects according to an embodiment of the present invention, the main controller may triangulate a Received Signal Strength Indication (RSSI) value by scanning three or more beacon signals, The current position coordinates can be obtained using the printing technique.

In the snowboard self-training system using the Internet of objects according to an embodiment of the present invention, the motion sensor includes a 9-axis motion sensor incorporating a gyro sensor, an acceleration sensor, and a geomagnetic sensor,

The motion sensor senses the tilt and the rotation angle of the snowboard,

Wherein the ROLL value of the motion sensor indicates a slope slope, the PITCH value of the motion sensor indicates a slope of the snowboard, and the YAW value of the motion sensor indicates a rotation angle of the snowboard Lt; / RTI >

According to another aspect of the present invention, there is provided a snowboard self-training system using the Internet, wherein the pressure sensor senses a pressure acting on a pair of shin and calf portions of the snowboard,

The main control unit may measure the external pressure of the snowboard, which is applied to the snowboard based on the pressure applied to the shin and calf portions of the feet of the sensed snowboarder.

Also, in the snowboard self-training system using the Internet of objects according to an embodiment of the present invention, the sub control unit and the main control unit provide speed data and a command message through low power Bluetooth (BLE) pairing Can receive.

In the snowboard self-training system using the Internet of objects according to an embodiment of the present invention, the mode may be a free mode, a pendulum mode, a turn mode, and a record mode Lt; / RTI >

The pre-mode is a basic mode in which it is recommended to avoid overspeed through a real-time speed check and confirms that the snowboard has fallen once per second, and analyzes the data for the last three seconds when the snowboarder falls,

The pendulum mode is a mode for the beginner to perform a pendulum operation which is the basic operation of the snowboard. The snowboarder determines a posture once every 5 seconds at the time of boarding,

The turn mode is an intermediate mode for performing a turn operation, which is one of the operations of the snowboard. The turn mode analyzes the PICTCH value, the YAW value, and the pressure value of the pressure sensor of the motion sensor every 0.2 seconds, Speed, tilt, and foot pressure,

The record mode may be a mode for evaluating the skill by sharing and comparing the slope information used for one time with the snowboarder without real-time training function.

In addition, in the snowboard self-training system utilizing the Internet of objects according to an embodiment of the present invention, the main control unit provides a self-training function,

The self-

A snowboard edge determination function for determining a direction of the snowboard according to whether the edge is used or not based on the values sensed by the pressure sensor and the motion sensor,

A balance discriminating function for comparing a safety slope set for each slope with a current slope to determine a problem of the posture of the snowboarder,

A fall determination function for determining whether the snowboarder has fallen due to the roll (roll) value of the snowboard and determining the number and timing of the snowboarder,

A safe speed alarm function for providing a voice alarm when the recommended limit speed of the slope usage is set or higher, and

And a voice support function for providing a voice alarm when the safe speed is exceeded.

According to another aspect of the present invention, there is provided a snowboard self-training system using the Internet, wherein the snowboarder is provided with a recording function based on information stored in the cloud server through a predetermined application,

The recording function includes:

A recent record checking function for checking the latest data of each member measured in the above four modes, a speed, an attitude,

Full history check that checks all data stored by each member, checks individual improvements such as rating and number of falls,

A rating function that scales from 0 to 5 points through speed, attitude, and number of falls; and

A ranking function for dividing the ranking among the SLBs by the slope of the information measured through the record mode by dividing the ranking into three types of distinction, maximum speed, average speed, and rating.

According to another aspect of the present invention, there is provided a snowboard self-training system using the Internet, wherein the snowboarder is provided with a tracking function based on information stored in the cloud server through a predetermined application,

The copper wire trace function is a function to map copper wire coordinates by mapping the time-based coordinates to the slope image,

The main control unit may provide the current position to the central management server when the snowboarder presses the emergency alarm button.

In the snowboard self-training system using the Internet, the air pressure sensor is mounted on the helmet of the snowboarder, the motion sensor is mounted on the snowboard, Can be mounted on the binding.

According to the snowboard self-training system using the Internet of the object according to the embodiment of the present invention, safe skiing culture can be created by improving the skill and safety through speed measurement, posture analysis and self-training, To provide an entertainment element to the skilled person.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a view illustrating snowboarding equipment and hardware for explaining a snowboard self-training system utilizing the Internet of objects according to an embodiment of the present invention; FIG.
2 is a block diagram of a snowboard self-training system utilizing the Internet of Things according to an embodiment of the present invention;
3 is a diagram illustrating an exemplary configuration of a snowboard self-training system utilizing the Internet of Things according to an embodiment of the present invention.
4 is a diagram for explaining a triangulation technique;
5 is a diagram for explaining a fingerprint technique;
FIG. 6 is a view for explaining the operation of a snowboard self-training system utilizing the Internet of Things according to an embodiment of the present invention; FIG.
Figure 7 illustrates an exemplary algorithm of a training mode;
8 illustrates an exemplary balance analysis algorithm;
9 is a view showing a main screen of a web application;
FIG. 10 shows a membership management screen of the web application, FIG. 10A shows a membership registration screen, FIG. 10B shows a login screen, FIG. 10C shows a profile screen, and FIG.
11 is a view showing a ranking screen of a web application;
FIG. 12 shows an integrated information screen of a web application. FIG. 12A shows a community screen, FIG. 12B shows a ski area information screen, and FIG. 12C shows a snowboard education screen.
13 is a view showing a main screen of an Android application;
FIG. 14 is a diagram showing a member management screen of the Android application. FIG. 14A is a member registration screen, FIG. 14B is a login screen, and FIG. 14C is a device registration screen.
15A and 15B illustrate a recording and ranking screen of the Android application. FIG. 15A shows a recent recording screen, FIG. 15B shows a copper screen, FIG. 15C shows an entire recording screen, and FIG. 15D shows a ranking screen.
16 is a graph showing a velocity accuracy graph.

BRIEF DESCRIPTION OF THE DRAWINGS The objectives, specific advantages and novel features of the present invention will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which: FIG.

Prior to that, terms and words used in the present specification and claims should not be construed in a conventional and dictionary sense, and the inventor may properly define the concept of the term in order to best explain its invention Should be construed in accordance with the principles and the meanings and concepts consistent with the technical idea of the present invention.

It should be noted that, in the present specification, the reference numerals are added to the constituent elements of the drawings, and the same constituent elements are assigned the same number as much as possible even if they are displayed on different drawings.

Also, the terms "first", "second", "one side", "other side", etc. are used to distinguish one element from another, It is not.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS In the following description of the present invention, a detailed description of known arts which may unnecessarily obscure the gist of the present invention will be omitted.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

As winter sports, called 'snowboard', is activated, the need for posture correction for prevention of safety accidents and for snowboarders is increasing. Therefore, it is necessary to improve the safety through speed measurement through velocity measurement, emergency response through position tracking, helmet wear and binding tightness check, and development of posture and alarm algorithm in education side, It is necessary to provide self-training services that can be improved, and in the interest of fun, it is necessary to identify, share, compete and provide new fun elements while measuring the slope. Accordingly, a solution that improves the safety of snowboarders in the ski area and provides new enjoyment to the skilled user for the beginner.

In the snowboard self-training system utilizing the Internet of objects according to the embodiment of the present invention, sensors are attached to equipment used in a snowboard, and data is collected and stored in a database, analyzed and processed, To create various services.

That is, the snowboard self-training system utilizing the Internet of the object according to the embodiment of the present invention is a solution that combines IT technology with sports called 'snowboard', and as shown in FIG. 1, the snowboard, helmet, It is a solution to measure various information such as balance and speed by attaching sensors and gateways to other equipments. The snowboard self-training system using the Internet according to an embodiment of the present invention improves the skill through self-training and improves safety, thereby providing a safe skiing culture and providing various entertainment elements.

2 is a block diagram of a snowboard self-training system that utilizes the Internet of Things according to an embodiment of the present invention.

Referring to FIG. 2, a snowboard self-training system using the Internet of the object according to an embodiment of the present invention measures the speed at which a snowboarder, a user of a snowboard, descends on a slope based on sensed air pressure, A sub-control unit 200 for providing an overspeed alarm when the measured speed exceeds a predetermined speed limit, and for reproducing an audio file, and a sub-control unit 200 for applying the motion information of the sensed snowboard to the foot of the user of the sensed snowboard Analyzing the posture of the snowboarder based on the pressure information and the speed information received from the sub-control unit to identify the problem of the habit or posture of the snowboarder and provide the self-training information to the snowboarder through the sub- , Estimates the current position based on signals from a plurality of beacons 206_1 to 206_n provided on the slope, Three, and a main control unit 202 for providing the present position coordinates, and falling, and slope information of the server cloud 204. The

The sub-control unit 200 includes an air pressure sensor 208 for sensing the air pressure, a voice file reproduction unit 210 for reproducing the voice file, a first communication unit 216 for communicating with the main control unit 202, ), Measures the speed at which the snowboarder descends on the slope based on the air pressure sensed by the air pressure sensor (208), and outputs an overspeed alarm signal to the voice file A first control unit for providing the audio file playback unit 212 with the audio file playback unit 212 and providing the audio file playback unit 212 with the self-training information received from the main control unit 202, And a speaker 210 for outputting a sound.

The main controller 202 includes a motion sensor 218 for sensing motion information of the snowboard, a pressure sensor 220 for sensing pressure applied to both feet of the snowboarder, A third communication unit 224 for communicating with the cloud server 204, a button unit 228 for receiving mode selection and emergency alarm input from the snowboarder, By analyzing the posture of the snowboarder based on the motion information of the sensed snowboard, the pressure information applied to both feet of the sensed snowboarder, and the speed information received from the sub-controller 200, And provides the self-training information to the snowboarder through the sub-control unit 200. Based on the signal from the plurality of beacons 206_1 to 206_n installed on the slope, And provides slope information to the cloud server 204 via the third communication unit 224, and transmits the mode selection or emergency alarm input through the third communication unit 224, And a second control unit 226 for processing.

The main controller 202 compares the appropriate slope of the snowboard with the current slope based on the motion information to identify problems of the habit or the posture of the snowboarder and scans three or more beacon signals for RSSI Signal Strength Indication) values are triangulated, and the current position coordinates are obtained using the fingerprint technique.

The motion sensor 218 includes a 9-axis motion sensor incorporating a gyro sensor, an acceleration sensor, and a geomagnetic sensor. The motion sensor 218 senses the tilt and the rotation angle of the snowboard. The motion sensor 218 The YOL value of the motion sensor 218 indicates the slope of the slope of the snowboard 218, the pitch of the motion sensor 218 indicates the slope of the snowboard, Represents the rotation angle of the board.

The pressure sensor 220 senses the pressure acting on the shin and the calf portions of the feet of the snowboarder and the main controller 202 controls the pressure applied to the shin and calf portions of the both feet of the sensed snowboarder The external pressure of the snowboard is applied to the snowboard.

The sub control unit 200 and the main control unit 202 exchange speed data and command messages through low-power Bluetooth (BLE) pairing

The mode includes a free mode, a pendulum mode, a turn mode, and a record mode. The free mode is a basic mode, And the reason for the fall is analyzed. The pendulum mode is a mode in which the pendulum operation, which is the basic operation of the snowboard, is performed for a beginner Mode is a mode in which a snowboarder identifies a posture by identifying the posture once every 5 seconds at the time of boarding, and the turn mode is an intermediate mode for performing a turn operation, which is one of the operations of the snowboard, The PICTCH value and the YAW value of the motion sensor 218 and the pressure value of the pressure sensor 220 are analyzed at intervals of 0.2 second to analyze the rotational speed, tilt, and foot pressure to identify problems Mode and the record mode is a mode to evaluate the ability by using the slope information for the one without training in real time, and shared function comparisons snowboarders.

The main control unit 202 provides a self-training function, and the self-training function measures the direction of the snowboard according to whether the edge is used or not, based on the values sensed by the pressure sensor 220 and the motion sensor 218 Determination of the snowboard edge function, comparing the safety slope set for each slope with the current slope to determine the problem of the posture of the snowboard, a function of determining the balance, and determining whether the snowboarder has fallen through the ROLL value of the snowboard A fall detection function for detecting the number of times and timing, a safety speed alarm function for providing a voice alarm when the recommended limit speed of the slope is set or higher, and a voice alarm For example.

The snowboarder is provided with a recording function based on the information stored in the cloud server 204 through a predetermined application, and the recording function includes a function of checking the latest data per member measured in the four modes, , A recent history check function to check whether a user has fallen down, a user to check all data stored by each member, an overall record check function to check individual improvements such as a rating and a number of falls, a score of 0 to 5 And a ranking function for dividing the ranking among the snowboarders for each slope among the information measured through the record mode into three types of distinction, maximum speed, average speed, and rating.

The snowboarder is provided with a copper line tracking function based on information stored in the cloud server 204 through a predetermined application, and the copper line tracking function is a function of drawing a copper line by mapping coordinates, which are positioned by time, to a slope image, When the snowboarder presses the emergency alarm button included in the button unit 228, the main control unit 202 provides the current position to the central management server. In this embodiment, the central management server may be integrated into the cloud server 204. [

In the snowboard self-training system using the Internet, according to an embodiment of the present invention, the air pressure sensor 208 is mounted on a helmet of a snowboarder, the motion sensor 218 is mounted on the snowboard, The pressure sensor 220 is mounted on the binding.

In the snowboard self-training system using the Internet, according to an embodiment of the present invention, the air pressure sensor 208, the motion sensor 218, and the pressure sensor 220 are used to measure the velocity, the slope of the snowboard, , And it is recommended to maintain the self-training service and safe speed by using the collected speed and balance information to enhance the safety. The collected sensor data is accumulated in the database for each member, and the personal log information can be checked through the web application driven by the web browser and the application operating in the operating system of the smart device.

Particularly, in the snowboard self-training system using the Internet of things according to the embodiment of the present invention, there is a big difference in the existing system and the speed measurement method. The other system measures the velocity using GPS, but in the present invention, the velocity is measured using the air pressure sensor 208. This can consider a narrower skiing environment than overseas, and has the great advantage of measuring speed even in a room where GPS can not be used. In addition, the snowboard self-training system utilizing the Internet of objects according to an embodiment of the present invention provides a self-training function capable of improving the skill of a snowboarder by storing, analyzing and processing data collected through a sensor.

FIG. 3 is a diagram illustrating an exemplary configuration of a snowboard self-training system using the Internet of Things according to an embodiment of the present invention.

Referring to FIG. 3, a snowboard self-training system using the Internet of objects according to an embodiment of the present invention includes a sub-controller 300, a main controller 302, and a cloud server 304.

Arduino Nano is used as the sub control unit 300 and RaspberryPi3 is used as the main control unit 302. The cloud server 304 includes the web server 306 and the database 308 And reference numeral 310 denotes a web application driven by a web browser or an application operating in an operating system of a smart device. In an embodiment of the present invention, users using snowboarders and snowboards are used in the same sense.

sensor

MPXV7002DP is used as the air pressure sensor 208, which measures the air velocity, i.e., the air pressure, in the Adinonano 300 and converts it to a velocity.

As the motion sensor 218, a 9-axis motion sensor MPU-9250 is used, and the slope of the snowboard, the rotation angle, and the slope of the snowboard are measured in the Raspberry Pipe 3 (302).

FSR-406 is used as the pressure sensor 220. The pressure applied to the feet of the raspberry pie 3 302 is measured to measure the user's external pressure applied to the snowboard.

Communication

Bluetooth 4.0 (BLE): The Adinano Nano 300 transmits the measured velocity data to the Raspberry Pi 3 302 through the HM-10 module, a BLE module. In addition, the raspberry pyramid 3 302 scans the beacons (206_1 to 206_n in FIG. 2) and tracks the position.

Wi-Fi: Raspberry P 3 (302) transmits data collected using the RESTful API to the cloud server 304.

SPI: Raspberry Pi 3 (302) can not read analog values. Therefore, the value can be read using the analog-to-digital converter (MCP3008), which uses SPI communication.

I2C: The data of the motion sensor (218, MPU-9250) is transmitted through I2C communication.

Voice support

Analysis of posture information data collected in real time to support voice service suitable for users. The DFPlayer mini module, which is an MP3 player module connected to the AIDA Nano 300, executes an audio file according to an algorithm designed in each mode.

cloud  server

The WAS (Tomcat 7.0) and the DBMS (MySQL) 308 are installed in the Amazon Web Services (AWS) cloud server 304 to process the requests of the clients (Raspberry 3 302 or application 310) And stores and manages the data.

The Web server 306 separates the model (Model (JavaBeans), View (JSP), and Controller (Servlet)) using the Spring Framework (Log-in server, data processing server, and moving-line calculation server) which manage the three kinds of logic.

The web server 306 manages the member information and encrypts it using the SHA256 hash algorithm. It distinguishes users according to client's request and displays log data. Log data is expressed in recent records, total records, ranking, copper lines and graphs.

The Web server 306 stores the log data received from the RaspberryPipe 3 302 and identifies the data by mapping the ID and the hardware serial number. It offers two different resolutions for mobile and web browsers, and the details are similar.

Database

The database 308 uses MySQL, and the myBatis framework is used for smooth communication between the web server 306 and the database 308. The database 308 has three tables: an account table, a speed table, and an axis table. Manage the query statement in DBMapper.xml.

application

(1) Web application

You can check your personal log data via a web browser. Member management, equipment registration, records, and integrated information. The responsive web was implemented using a bootstrap template.

(2) Android applications

It is an application for Android mobile for increasing the convenience of users in the ski area. It performs a somewhat simpler operation than the web application, and it can check the membership management and the recent and total records. We developed a hybrid application using Web view.

function

Software function

(1) Membership management function: Membership function to store new member information in database membership information table, login function to confirm membership information for application use, and data collected from hardware by mapping member ID and hardware information A device registration function for distinguishing, a profile function for briefly confirming personal information and records, and an encryption function for encrypting personal information using the SHA256 hash algorithm.

(2) Mode: It includes a free mode, a pendulum mode, a turn mode, and a record mode.

The pre-mode is the basic mode. If it falls down, it will analyze the posture, point out the problem, and recommend speeding through real-time speed check.

Pendulum mode is a beginner mode for performing the pendulum operation, which is the basic operation of the snowboard. It identifies the posture once every 5 seconds and grasps the problem of posture in real time.

The turn mode is an intermediate mode for performing a turn operation, one of the operations of the snowboard, and analyzes the rotational speed, the inclination, and the foot pressure to identify the problem.

The record mode is a mode for evaluating the performance by sharing and comparing the slope information used for one time without using the real time training function among the users.

(3) Self-training: includes board edge discrimination, balance discrimination, fall discrimination, safety speed, and voice support function.

The board edge determination function uses the values measured by the pressure sensor (FSR-406) and the motion sensor (MPU-9250) to determine the direction of the user depending on whether the edge is used or not.

The balance discrimination function compares the safety slope set for each slope with the current slope to determine the problem of the user's attitude.

The fall determination function determines whether the user has fallen through the roll value of the snowboard to determine the number and timing of the fall.

The safe speed function sets the recommended speed limit for the use of the slope and provides a voice alarm if the speed limit is exceeded.

The voice assist function provides voice alarm when the balance is judged to be over the problem of the attitude and the safe speed is exceeded.

(4) Recording: includes recent record confirmation, full record confirmation, rating, and ranking function.

The recent record check function checks the latest data for each member measured in 4 modes, and confirms the speed, posture, and fall time by time.

The full history check function checks all data stored for each member and checks individual improvements such as ratings and number of falls.

The rating function scales from 0 to 5 points based on speed, stance, and number of falls.

The ranking function distinguishes the ranking among the users according to the slope among the information measured through the record mode, and distinguishes the ranking by the highest speed, the average speed, and the rating point.

(5) User location: Includes copper trace and emergency alarm function.

The copper line tracking function scans the beacon BLE signal and measures it using triangulation and fingerprinting. Map the time-based coordinates to the slope image to draw the copper line.

Emergency alarm function responds quickly by giving alarm to central management room through emergency alarm operation when emergency situation occurs to user.

(6) Providing information: Includes skiing information, educational information, and community functions.

The ski resort information providing function can easily check the information of the domestic ski area.

The educational information providing function provides a comprehensive video on snowboard education registered on YouTube.

The community feature works with cafes, Facebook, and YouTube to share information and personal log records.

Hardware Features

The sensor performs speed measurement, tilt measurement, and pressure measurement functions. Measuring 9-axis motion information from the motion sensor (MPU-9250), measuring the air pressure from the air pressure sensor (MPXV7002DP), converting it to velocity, Lt; / RTI >

The button unit 228 includes a mode button and an emergency alarm button. When the emergency button is pressed for 5 seconds or more in relation to the emergency alarm button, The alarm function is activated.

In connection with the voice file execution function, the audio file stored in the SD card is executed using the DFPlayer mini in the AIDA Nano (300).

Beacon Signal Scan the BLE signal of the beacon every 0.1 second in relation to the scan function.

The Rasberry Pie 3 302 and the Arduino Nano 300 are in a BLE communication and the Raspberry Pie 3 302 and the Rasberry Pie 3 302 communicate with each other through the WAS and Wi- It performs SPI and I2C communication with the sensor.

The case is made of a waterproof and resilient silicone case.

The invention In the embodiment  Snowboarding using Internet self training  System behavior

1. Raspberry pie 3 (202, 302)

(1) Function

- Posture Analysis: Analyze the posture using motion information and pressure information collected from motion sensor (218, MPU-9250) and pressure sensor (220, FSR-406). Compares the appropriate slope with the current slope to determine the user's habit or posture problem.

- Mode Algorithm Implementation: Performs the algorithm designed in 4 modes. Each algorithm has different logic such as attitude analysis timing and alarm timing.

- Positioning: The second communication unit 222, which is a Bluetooth chip, is incorporated in the Raspberry Pi 3 (202, 302). Three or more beacon signals are scanned to triangulate the RSSI value and the accuracy is increased using the fingerprint technique.

- Gateway: transmits the speed, attitude, coordinates, tilt, and slope information to the cloud servers 204 and 304 using Wi-Fi communication and RESTfulAPI at Raspberry Pi 3 202 and 302.

(2) Positioning technique

- Kalman Filter: The accuracy of the signal is very important for positioning, and Kalman Filter is used as a method to remove noise.

- Triangulation Technique: As shown in FIG. 4, the coordinates are obtained through a distance between three or more reference points and a point. You can get the coordinates by finding the intersection by drawing a circle with the distance from the three reference points to the radius.

Fingerprint Technique: As shown in FIG. 5, the fingerprint technique generates a table by measuring RSSI values for each location in an actual environment. The received beacon signal can be scanned to find a point located in comparison with the dictionary table. In an embodiment of the present invention, the RSSI value received for each beacon is compared with the table value to obtain the cost, and the coordinates of the point having the lowest cost are obtained.

2. Arduino Nano (200, 300)

(1) Function

- Velocity measurement: Velocity is measured through the air pressure sensor (208, MPXV7002DP) and transmitted to Raspberry Pi 3 (202, 302) by Bluetooth communication every second. In the free mode, speed warning alarm is executed when speed is over 10m / s.

Voice Alarm: When receiving the voice file execution message from the Raspberry Pi 3 (202, 302), the voice file playback unit 212 executes the corresponding voice file stored in the SD card using the DFPlayer mini module.

3. Sensor

(1) Air pressure sensor (208, MPXV7002DP)

- Calibrated airspeed (CAS) process is performed as shown in Equation 1 to measure the correct value of equipment and installation error.

는 보정 속도이고,

는 피토관 튜브(pitot tube) 압력이며,

는 표준 해수면에서의 정적 공기 압력으로서 29.92126 inchHg이고,

는 표준 해수면에서의 음속으로서, 661.4788 knots이다.In the above,

Is a correction speed,

Is the pitot tube pressure,

Is 29.92126 inches Hg as the static air pressure at standard sea level,

Is the sound velocity at standard sea level, 661.4788 knots.

(2) Motion sensor (218, MPU-9250)

- A 9-axis motion sensor incorporating a gyro sensor, an acceleration sensor, and a geomagnetic sensor to measure the tilt and rotation angle of the snowboard. The roll (ROLL), pitch (PITCH), and yaw (YAW) that rotate around the X, Y, and Z axes mean the following values.

- ROLL: Slope of slope, PITCH: Slope of snowboard, Yaw: Yaw angle of snowboard

(3) Pressure sensor (208, FSR-406)

- Measure the external pressure of the user applied to the snowboard by measuring the pressure acting on the shin and calf parts of the feet.

(4) Beacon

- Use 'RECO Beacon', iBeacon solution of 'Purple's' company. At least three beacon signals are required.

4. Audio file reproduction unit 212 (DFPlayer mini module)

(1) Function

- The current SD card stores 12 speed alarms and 6 situation analysis alarms, and executes MP3 files stored on the SD card via the execution command on the Adinano Nano (200, 300).

5. Cloud servers (204, 304)

(1) use Amazon Web Services (AWS) cloud server

(2) Server

- We used Tomcat 7.0 as AWS and implemented web server based on Spring Framework.

- Receive data from Raspberry Pi 3 (202. 302) and store it in database (308).

- Processes the request of the client and fetches data from the database 308 and exposes it to the application.

(3) Database 308

- I use MySQL as the DBMS, and MyBatis as the database (DB) framework.

6. Application

(1) Android Applications

- Developed a hybrid application that combines Native and Web functions using a Web view library.

(2) Web applications

- A dashboard web page was constructed using a bootstrap template.

7. BLE pairing

- Raspberry Pi 3 (202, 302) and Arduino Nano (200, 300) send and receive velocity data and command messages in real time through BLE pairing.

8. Mode

(1) Four modes (Free, Pendulum, Turn, Record) can be set

- Free mode: It is a basic mode. It recommends a speed limit. It confirms that it falls down once per second. When a user falls down, it analyzes the data for the last 3 seconds to understand why it fell.

- Pendulum mode: It is a mode for performing the pendulum operation which is the basic operation of the board, and it is a beginner mode. The user identifies the posture once every 5 seconds when boarding and identifies the problem of posture in real time.

- Turn mode: It is a mode for performing the turn operation which is one of the operations of the snowboard, and is an intermediate mode. Analyze the PITCH value, YAW value, and pressure value every 0.2 second to understand the problem of rotational speed and slope.

- Record mode: It is a mode to evaluate the skill by sharing and comparing slope information used for one time without using real-time training function among users.

9. Analysis

- Raspberry Pi 3 (202, 302) performs an algorithm with a posture analysis function.

- Press and hold the emergency alarm button for more than 5 seconds to exit the current state and transfer the current location to the central management server.

10. Data transmission

- Sends speed, attitude, coordinates, tilt, and slope information to the cloud servers 204 and 304 using Wi-Fi communication and RESTful API at Raspberry Pi 3 202 and 302.

self training (Self-Training)

1. Training mode

In various boarding situations, it is difficult to analyze all the data in each situation. There are two special modes according to boarding ability and technique, and there are four training modes by adding basic mode and recording mode.

(1) Free mode: It is a basic mode. It recommends a speed limit. It confirms that it falls down once per second. When the user falls down, it analyzes the data for the last 3 seconds to understand why it fell.

(2) Pendulum mode: Pendulum mode, which is the basic operation of the snowboard, is a beginner mode. The user identifies the posture once every 5 seconds when boarding and identifies the problem of posture in real time.

(3) Turn mode: It is a mode for performing turn operation which is one of the operations of the snowboard, and is an intermediate mode. Analyze the PITCH value, YAW value, and pressure value every 0.2 second to understand the problem of rotational speed and slope.

(4) Record mode: It is a mode to evaluate the skill by sharing and comparing the slope information used for one time without using real-time training function among users.

FIG. 6 is a view for explaining the operation of the snowboard self-training system using the Internet of Things according to an embodiment of the present invention.

Referring to FIG. 6, in step S618, Raspberry Pie 3 600 and Arduino nano 602 perform Bluetooth Low Energy (BLE) pairing.

In step S620, the mode setting is input by the user, and in steps S622 to S628, it is determined whether the mode is the free mode, the pendulum mode, the turn mode, and the record mode.

In step S630, the start of velocity measurement is determined, and a velocity measurement start message is transmitted to the adinano nano 602. In step S632, real-time data is collected from the sensor 604 and the beacon, Measurement information is collected.

Edge determination is performed in step S634, balance is checked in step S636, and positioning is performed in step S638.

In step S640, it is determined whether or not the mode button has been pushed. In step S642, it is determined whether the emergency button has been pushed.

If the emergency button is pushed, the current position coordinates are transmitted to the cloud server 608 integrated with the central management room in step S650.

On the other hand, in step S652, it is determined whether a speed measurement start message has been received. When the speed measurement start message is received, the speed is measured based on the air pressure sensed by the air pressure sensor 605 in step S654.

In step S656, it is determined whether the current speed is greater than a predetermined limit speed of 10 m / s. If the current speed exceeds the predetermined limit speed of 10 m / s, the speed alarm audio file is executed in step S662.

If the current speed is less than the predetermined limit speed of 10 m / s, it is determined in step S658 whether a posture consulting voice file execution message has been received from the raspberry pie 3 600. If the voice file execution message is received, , And executes the received posture consulting voice file.

In step S660, it is determined whether or not the end of measurement has been received. When the end of measurement is received, in step S648, the stored data is combined to transmit the speed, attitude, coordinates, tilt, and slope information to the cloud server 608.

Meanwhile, the user can join the application through the application 610 and confirm the data stored in the cloud server 608.

It is determined in step S662 whether the new member is a new member, and in the case of a new member, the member joining step is executed in step S664.

If it is not a new member, login is performed in step S666, and it is determined in step S668 whether registered equipment exists. If the registered equipment exists, the data is confirmed in step S670.

Meanwhile, FIG. 7 illustrates an exemplary training mode algorithm, wherein the sensing rate is 0.1s.

2. Balance

The Raspberry Pi 3 (202, 302) measures motion information in the motion sensor (218, MPU-9250) at intervals of 0.1 second, and determines whether the edge is used through the measured data. Then, using the PITCH value indicating the slope of the snowboard, the current slope and the safety slope are compared to store the balance of the user by time. The balance method is executed according to the algorithm of each mode, and the alarm method is executed by grasping the posture problem of the user based on the stored balance information. Figure 8 illustrates an exemplary balance analysis algorithm.

Screen design

1. Web application

9 to 12, the screen configuration of the web application is divided into a main screen, a member management, a record, a ranking, and an integrated information page.

2. Android application

As shown in FIGS. 13 to 15, the screen configuration of the Android application is similar to that of the web application, and provides a simpler screen.

conclusion

1. Test results

(1) Speed Accuracy

In order to obtain the accuracy of the speed data obtained through the air pressure sensor (208, MPXV7002DP), a sensor was attached to the vehicle, and then the test was performed by comparing with the navigation GPS speed data. The error was reduced by correcting the data obtained by the sensor and showed an error of + 10 ~ -13Km / h at maximum and the error was increased at low speed. The accuracy of the average error of -2.45588 Km / h, and the absolute error of 4.602941 km / h.

Table 1 is a table comparing speed accuracy, and FIG. 16 is a graph showing speed accuracy.

2. Position accuracy

We used triangulation method and fingerprint method using RSSI value of beacon signal for position measurement. The period of the beacon signal is 660 ms, and TX = -8 dbm. The test was carried out in a 5M × 6M indoor space without any obstacles. The accuracy of the triangulation method and the fingerprint method were as follows.

accuracy Triangulation technique Fingerprint technique Error within 1M 12% 52% Error within 2M 44% 71% Error within 3M 74% 89% Error within 4M 87% 97%

In order to increase the accuracy, the beacon period is increased to 100 ms and the Kalman filter is applied to reduce the RSSI error of the beacon signal. As a result, we confirmed that the accuracy is improved.

Expected Effects and Applications

1. Expected Effect

(1) User

- It collects individual log data such as speed, circulation, and balance, and makes big data, so that user's improvement and problem can be grasped.

- Through the recording and ranking system, it enhances the entertainment elements for the experts and provides convenient educational service to the educator through attitude consulting.

- It is possible to create a safer skiing environment through posture and speed data.

- Provides comprehensive information such as the number of users in the ski area, facilities, weather, etc., and various services can be provided through one application.

(2) Operator

- Snowboards available for both beginners and experienced skiers can be rented at a premium price to create added value.

- LBS (Location Based Service) technology prevents the loss of snowboard and enables quick response of emergency patients.

2. Field of application

(1) Interlocking with general-purpose equipment

- Video and real-time information can be fused with video equipment such as GoPro, which is a popular action cam in sports such as snowboarding.

- It is difficult to use the smartphone while using the slope due to user's safety. Therefore, real-time information can be checked in conjunction with wearable devices such as Google Glass and Smart Watch.

(2) Application to other sports

- Using the air pressure sensor (208, MPXV7002DP) used in the embodiment of the present invention, speed can be measured and service can be created in various environments.

- If you simply modify the algorithm for each item, you can use ski, bike, skateboard as well as snowboard.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. It is clear that the present invention can be modified or improved.

It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

200: sub control unit 202: main control unit
204: Cloud servers 206_1 to 206_n: Beacons
208: air pressure sensor 210: speaker
212: audio file playback unit 214: first control unit
216: first communication unit 218: motion sensor
220: pressure sensor 222: second communication section
224: Third communication section 226: Button section
300: Arduino Nano 302: Raspberry Pie 3
304: Cloud server 306: Web server
308: Database 310: Application

Claims (13)

A sub-controller for measuring a speed at which the snowboarder descends on the slope based on the sensed air pressure, providing an overspeed alarm when the measured speed exceeds a predetermined speed limit, and reproducing the voice file; And
By analyzing the posture of the snowboarder based on the motion information of the sensed snowboard, the pressure information applied to the foot of the sensed snowboarder, and the speed information received from the sub-control unit, And provides the self-training information to the snowboarder through the sub-control unit, estimates a current position based on a signal from a plurality of beacons installed in the slope, and calculates a current position, a velocity, an attitude, a current position coordinate, And a main control unit for providing information to the cloud server. The method according to claim 1,
The sub-
An air pressure sensor for sensing the air pressure;
An audio file reproducing unit for reproducing the audio file;
A first communication unit for communicating with the main control unit;
And a speed over alarm signal is provided to the audio file playback unit when the measured speed exceeds a predetermined speed limit, A first controller for providing the self-training information received from the main controller to the audio file player; And
And a speaker for outputting a sound reproduced by the audio file reproducing unit. The method of claim 2,
The main control unit,
A motion sensor for sensing motion information of the snowboard;
A pressure sensor for sensing a pressure applied to both feet of the snowboarder;
A second communication unit for communicating with the sub-control unit;
A third communication unit for communicating with the cloud server;
A button unit for receiving mode selection and emergency alarm input from the snowboarder; And
Analyzing the posture of the snowboarder based on the motion information of the sensed snowboard, the pressure information applied to the feet of the sensed snowboarder, and the speed information received from the sub-control unit, Finds a problem, provides self-training information to the snowboarder through the sub-controller, estimates a current position based on a signal from a plurality of beacons installed in the slope, and calculates a current position, And a second controller for providing the slope information to the cloud server through the third communication unit and for processing the mode selection or the emergency alarm input. The method of claim 3,
The main control unit,
The snowboard self-training system utilizes the object Internet technology to compare the appropriate slope of the snowboard with the current slope based on the motion information to identify problems of the habit or posture of the snowboard. The method of claim 3,
The main control unit scans three or more beacon signals to triangulate Received Signal Strength Indication (RSSI) values and acquires current position coordinates using a fingerprint technique. The main control unit performs snowboard self- system. The method of claim 3,
The motion sensor includes a 9-axis motion sensor incorporating a gyro sensor, an acceleration sensor, and a geomagnetic sensor,
The motion sensor senses the tilt and the rotation angle of the snowboard,
Wherein the ROLL value of the motion sensor indicates a slope slope, the PITCH value of the motion sensor indicates a slope of the snowboard, and the YAW value of the motion sensor indicates a rotation angle of the snowboard , A snowboard self-training system utilizing the Internet technology of things. The method of claim 3,
The pressure sensor senses the pressure acting on the shin and the calf portions of the legs of the snowboarder,
The main control unit measures the external pressure of the snowboard applied to the snowboard based on the pressure applied to the shin and calf portions of the feet of the sensed snowboarder. system. The method of claim 3,
Wherein the sub control unit and the main control unit exchange speed data and command messages through low-power Bluetooth (BLE) pairing. The method of claim 3,
The mode includes a free mode, a pendulum mode, a turn mode, and a record mode,
The pre-mode is a basic mode in which it is recommended to avoid overspeed through a real-time speed check and confirms that the snowboard has fallen once per second, and analyzes the data for the last three seconds when the snowboarder falls,
The pendulum mode is a mode for the beginner to perform a pendulum operation which is the basic operation of the snowboard. The snowboarder determines a posture once every 5 seconds at the time of boarding,
The turn mode is an intermediate mode for performing a turn operation, which is one of the operations of the snowboard. The turn mode analyzes the PICTCH value, the YAW value, and the pressure value of the pressure sensor of the motion sensor every 0.2 seconds, Speed, tilt, and foot pressure,
Wherein the record mode is a mode for sharing the slope information used for one time without using the real-time training function between the snowboarders, and comparing the slope information with the snowboarders to evaluate the performance of the snowboarder. The method of claim 3,
The main control unit provides a self-training function,
The self-
A snowboard edge determination function for determining a direction of the snowboard according to whether the edge is used or not based on the values sensed by the pressure sensor and the motion sensor,
A balance discriminating function for comparing a safety slope set for each slope with a current slope to determine a problem of the posture of the snowboarder,
A fall determination function for determining whether the snowboarder has fallen due to the roll (roll) value of the snowboard and determining the number and timing of the snowboarder,
A safe speed alarm function for providing a voice alarm when the recommended limit speed of the slope usage is set or higher, and
A snowboard self-training system utilizing object-oriented Internet technology, which includes a posture problem through balance discrimination and a voice support function to provide a voice alarm when a safety speed is exceeded. The method of claim 9,
Wherein the snowboarder is provided with a recording function based on information stored in the cloud server through a predetermined application,
The recording function includes:
A recent record checking function for checking the latest data of each member measured in the above four modes, a speed, an attitude,
Full history check that checks all data stored by each member, checks individual improvements such as rating and number of falls,
A rating function that scales from 0 to 5 points through speed, attitude, and number of falls; and
A ranking function for dividing the ranking among the slobbers by the slope of the information measured through the record mode into three types of distinction, maximum speed, average speed and rating, Training system. The method of claim 11,
Wherein the snowboarder is provided with a tracing function of tracing based on information stored in the cloud server through a predetermined application,
The copper wire trace function is a function to map copper wire coordinates by mapping the time-based coordinates to the slope image,
Wherein the main control unit provides the current position to the central management server when the snowboarder presses the emergency alarm button. The method of claim 3,
Wherein the air pressure sensor is mounted on the helmet of the snowboarder, the motion sensor is mounted on the snowboard, and the pressure sensor is mounted on the binding.

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