CN107095401B

CN107095401B - Intelligent spire lamella

Info

Publication number: CN107095401B
Application number: CN201610100136.9A
Authority: CN
Inventors: 许润民
Original assignee: Shenzhen No Net Technology Co Ltd
Current assignee: Shenzhen No Net Technology Co Ltd
Priority date: 2016-02-23
Filing date: 2016-02-23
Publication date: 2019-01-01
Anticipated expiration: 2036-02-23
Also published as: CN107095401A

Abstract

The present invention provides a kind of intelligent spire lamella, microchip is provided in the intelligent spire lamella, the microchip includes: receiving module, for receiving the first exercise data in the case where determining current state to need to carry out the matched setting state of exercise data；Comparison module, for first exercise data compared with the second exercise data being locally stored carries out motion feature, to be determined matching degree；Statistical module, for determining highest first exercise data of matching degree and the second exercise data according to matching degree result；And exercise data statistics is carried out according to highest first exercise data of the matching degree and the second exercise data.Through the invention, it solves the problems, such as effectively record effective achievement of multiple sportsmen when more people move at present.

Description

Intelligent wrist strap

Technical Field

The invention relates to intelligent equipment, in particular to an intelligent wrist strap for basketball sports.

Background

Basketball is one of the most extensive sports in the world, and the daily exercise of amateurs, and the training and match at ordinary times of basketball clubs or professional teams all hope to record the sports information of players so as to provide reference for the follow-up sports.

With the development of intelligent device technology, the technology is gradually introduced into basketball sports. For example, patent application publication No. CN104043237A discloses a basketball shot determination system for use with a basketball hoop and a portable electronic device that includes a processing unit, a memory, and an output device, the system including a basketball, a plurality of sensors carried by the basketball, and a non-transitory computer readable medium. The medium contains code to direct a processor to obtain a plurality of attributes of a basketball shot directed toward a basketball rim. The plurality of attributes are sensed by a plurality of sensors or derived from signal outputs of the plurality of sensors. The code also directs the processor to determine whether the shot is an in-shot by comparing attributes of the shot to one or more predetermined signature features of an in-shot, and present an output to a person based on the determination of whether the shot is an in-shot.

However, this technique can only record the state information of the basketball and the single-player sports information, and cannot effectively record the effective scores of a plurality of players during the sports of a plurality of players.

Disclosure of Invention

The invention aims to provide an intelligent wrist strap for basketball sports, and the intelligent wrist strap is used for solving the problem that the effective scores of a plurality of players cannot be effectively recorded when a plurality of players play.

According to an aspect of the present invention, there is provided a smart wristband having a microchip disposed therein, the microchip including:

the device comprises a receiving module, a judging module and a judging module, wherein the receiving module is used for receiving first motion data under the condition that the current state is determined to be a set state needing motion data matching, and the first motion data is motion data of a moving object obtained by a sensor arranged in the moving object;

and the comparison module is used for comparing the motion characteristics of the first motion data with the second motion data to determine the matching degree, wherein the second motion data is the motion data of the wearer obtained by a sensor arranged on the intelligent wrist strap.

In a second aspect of the present invention, there is provided a smart wristband, wherein a microchip is disposed, the microchip including:

the motion recognition module is used for recognizing whether a preset motion occurs according to obtained second motion data, wherein the second motion data are wearer motion data obtained through a sensor arranged in the intelligent wristband;

the motion data sending module is used for sending the second motion data to a moving object when a preset motion action is identified;

the receiving module is used for receiving effective action identification information sent by the moving object, wherein the identification information is that the moving object compares a first motion data with second motion data from one or more intelligent wristbands, selects a second motion data with the best matching degree and sends the second motion data to the corresponding intelligent wristbands, and the first motion data is obtained through a sensor arranged in the moving object;

the data recording module is used for storing the action identification information; and

a display module for displaying the action identification information received by the receiving module

In a third aspect of the present invention, there is provided a smart wristband, wherein a microchip is provided, the microchip comprising:

the device comprises a first receiving module, a second receiving module and a control module, wherein the first receiving module is used for receiving first motion data from a moving object, and the first motion data is obtained through a sensor arranged in the moving object;

a comparison module for comparing the first motion data with the second motion data to determine the matching degree,

the motion data sending module is used for sending the second motion data of which the matching degree reaches a preset threshold value to the moving object;

the second receiving module is used for receiving effective action identification information sent by the moving object, wherein the identification information is sent to the corresponding intelligent wrist strap after the moving object compares the first motion data with second motion data from one or more intelligent wrist straps and selects one second motion data with the best matching degree;

the data recording module is used for storing the action identification information;

and the display module is used for displaying the action identification information received by the receiving module.

As a second aspect of the present invention, there is provided a smart wristband, wherein a microchip is provided in the smart wristband, the microchip including:

Further, the moving object is a basketball, and the predetermined motion action includes at least one of shooting, passing and dribbling.

Further, the second motion data comprises at least one of: the smart wristband maintains data, runs data and projects data; wherein the holding data of the smart wristband comprises: acceleration average value, acceleration maximum value, acceleration minimum value and acceleration peak time; the operation data of the intelligent wrist strap comprises action time and action times; the projection data of the smart wristband comprises: acceleration average, acceleration maximum, acceleration minimum, and projected time.

Further, the state identification module identifies the predetermined motion action according to the following method:

shooting: the angular velocity w <0 and is maintained for a period of time not shorter than T1; the angular speed w changes from negative to positive and is maintained for a time not shorter than T2; at the critical point where the angular velocity changes from negative to positive, the rotation angle of the wristband is within a range, i.e., 0.2s < T1<0.8s, 0s < T2<0.2s, -80 degrees < roll <0 degrees;

dribbling: if the module angular speed is identified to be in a positive and negative alternate relation, the module pitch angle change amplitude is basically 90 degrees.

The invention also provides an intelligent wrist strap, which is characterized in that a microchip is arranged in the intelligent wrist strap, and the microchip comprises:

Further, the moving object is a basketball, and the predetermined motion action is at least one of shooting, passing, cricket, holding, goal, attack, and dribbling.

Further, the second motion data comprises at least one of: the smart wristband maintains data, runs data and projects data;

wherein the holding data of the smart wristband comprises: acceleration average value, acceleration maximum value, acceleration minimum value and acceleration peak time; the operation data of the intelligent wrist strap comprises action time and action times; the projection data of the smart wristband comprises: acceleration average, acceleration maximum, acceleration minimum, and projected time.

Further, the state identification module identifies the predetermined motion action according to the following algorithm:

According to the scheme of the invention, the sensors are arranged in the moving object and the intelligent wearable devices, and the motion data of the moving object and the motion data of the plurality of wearers of the intelligent wearable devices are collected in time; determining the sender of the motion action and the corresponding motion data thereof through the comparison of the motion data of the sender and the motion data of the motion action; through statistics of the sports data, the sports characteristics of a plurality of athletes can be acquired when a plurality of people take sports, the effective scores of the plurality of athletes are effectively recorded, and reference and basis are further provided for subsequent sports training. The method comprises the steps of arranging corresponding sensors in intelligent wristbands worn by a basketball and a plurality of basketball players, collecting current movement data of the basketball and current movement data of the basketball players, further determining a current sender of actions of the basketball through comparison, counting movement data of the basketball in the field of movement to obtain movement characteristics of the basketball, such as scores, backboards, attack assistance, snap-off, caps, hit rate and the like, and further performing targeted training on the basketball according to the movement characteristics. Therefore, the scheme of the invention solves the problem that the effective scores of a plurality of players cannot be effectively recorded when a plurality of players move at present.

Drawings

FIG. 1 is a flow chart illustrating steps of a method for statistics of athletic data according to a first embodiment of the present invention;

FIG. 2 is a flow chart illustrating the steps of a method for statistics of athletic data according to a second embodiment of the present invention;

FIG. 3 is a flow chart illustrating the steps of a method for statistics of athletic data according to a third embodiment of the present invention;

FIG. 4 is a diagram illustrating an example of the acceleration of a basketball during flight;

FIG. 5 is a diagram illustrating an example of the acceleration of a basketball during the holding of the basketball;

FIG. 6 is a diagram illustrating an example of the acceleration of a basketball during a dribbling process;

FIG. 7 is a schematic diagram illustrating an example of the change in angular velocity of a basketball during a basketball shot;

FIG. 8 is a diagram illustrating an example of the acceleration of a basketball during a pass of the basketball;

FIG. 9 is a diagram illustrating an example of the acceleration of a basketball during a shot and goal of the basketball;

FIG. 10 is a schematic diagram of an example of the change in angular velocity of the wristband during dribbling;

FIG. 11 is a schematic view showing an example of a change in the pitch angle of the wrist band during dribbling;

FIG. 12 is a schematic view of an example of the change in angular velocity of the wristband during a shot;

FIG. 13 is a schematic view showing an example of a change in the pitch angle of the wrist band during shooting;

fig. 14 is a flowchart illustrating steps of a method for statistics of athletic data according to a fourth embodiment of the present invention;

fig. 15 is a block diagram of a smart wristband according to a fifth embodiment of the present invention;

fig. 16 is a block diagram of a smart wristband according to a sixth embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more clear. The technical solutions of the present invention will be described clearly and completely below, and it should be apparent that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without any inventive step, are within the scope of protection of the present invention.

Example one

Referring to fig. 1, a flowchart illustrating steps of a motion data statistics method according to a first embodiment of the present invention is shown.

The motion data statistical method of the embodiment comprises the following steps:

step S101: and receiving first motion data when the current state is determined to be a set state needing motion data matching.

The first motion data are motion data of a moving object obtained through a sensor arranged in the moving object, or motion data of wearers of a plurality of wearers obtained through a plurality of sensors arranged in the intelligent wearable device. In the present application, "a plurality" means two or more.

Trigger conditions may be set for the athletic data statistical scenario, such as a set state that requires athletic data matching. The setting state can be set by a person skilled in the art according to actual requirements, for example, the setting state is started after receiving a certain instruction, and for example, the moving object is in a certain running state, such as a holding state and a projection state, and for example, the setting state can be set in real time by default.

Whether the first motion data is the motion data of the moving object or the motion data of the wearer depends on the implementation party of the motion data statistical method, and if the implementation party is the intelligent wearable device party, the first motion data is the motion data of the moving object; if the implementation party is a moving object party, the first motion data is the motion data of the wearer.

Step S102: and comparing the motion characteristics of the first motion data with the second motion data stored locally to determine the matching degree.

Wherein the degree of matching indicates a degree of matchable of the first motion data and the second motion data. When the first motion data is motion data of a moving object, the second motion data is motion data of wearers of a plurality of wearers; when the first motion data is wearer motion data of a plurality of wearers, the second motion data is the moving object motion data.

Step S103: determining first motion data and second motion data with the highest matching degree according to the matching degree result; and carrying out motion data statistics according to the first motion data and the second motion data with the highest matching degree.

According to the embodiment, the sensors are arranged in the moving object and the intelligent wearable devices, and the motion data of the moving object and the motion data of the plurality of wearers of the intelligent wearable devices are collected in time; determining the sender of the motion action and the corresponding motion data thereof through the comparison of the motion data of the sender and the motion data of the motion action; through statistics of the sports data, the sports characteristics of a plurality of athletes can be acquired when a plurality of people take sports, the effective scores of the plurality of athletes are effectively recorded, and reference and basis are further provided for subsequent sports training. Taking basketball as an example, a corresponding sensor is arranged in an intelligent wrist strap worn by a basketball and a plurality of basketball players, the current movement data of the basketball and the current movement data of the plurality of basketball players are collected, the current sender of the basketball action is determined through comparison, the movement data of the basketball in the sports field is counted to obtain the movement characteristics of the basketball, such as scoring, backboard, attack assistance, breaking, cap covering, hit rate and the like, and the players can be trained in a targeted manner according to the movement characteristics. It is thus clear that through this embodiment, solved at present can't effectively record the problem of a plurality of sportsman's effective score when many people move.

Example two

Referring to fig. 2, a flowchart illustrating steps of a motion data statistics method according to a second embodiment of the invention is shown.

step S201: and detecting the current state, and determining whether the current state is a set state needing motion data matching.

The detection of the current state can be carried out in real time, can also be carried out at intervals of a certain time, can also be carried out when the change of the motion data meets a certain condition, and can determine the current state through the motion data.

Optionally, in this embodiment, the setting state that requires the motion data matching includes: a hold state, a run state, or a project state. The holding state, the running state or the projection state has a relatively obvious motion state identification function, wherein the holding state is used for indicating that the moving object is in a state controlled by a wearer of the intelligent wearable device, such as a basketball holding state of a basketball or a football; the running state is used for indicating that the moving object is in a state of being separated from the control of a wearer of the intelligent wearable device, such as a dribbling state of basketball or football; the projection state is used for indicating that the moving object is in a state of being projected by a wearer of the intelligent wearable device, such as a shooting state of a basketball or a shooting state of a football.

Step S202: and receiving first motion data when the current state is determined to be a set state needing motion data matching.

The first motion data are motion data of a moving object obtained through a sensor arranged in the moving object, or motion data of wearers of a plurality of wearers obtained through a plurality of sensors arranged in the intelligent wearable device.

Preferably, the first motion data and the second motion data each comprise at least one of: the maintenance data, the operational data, and the projection data. Namely: when the first motion data comprises hold data, and/or run data, and/or projection data, correspondingly, the second motion data comprises corresponding hold data, and/or run data, and/or projection data.

Wherein,

the maintaining of the data includes: the acceleration average value of the moving object, the acceleration maximum value of the moving object, the acceleration minimum value of the moving object, the acceleration peak time of the moving object, the acceleration average value of the intelligent wearable device, the acceleration maximum value of the intelligent wearable device, the acceleration minimum value of the intelligent wearable device, and the acceleration peak time of the intelligent wearable device. That is, when the first motion data includes the acceleration average value of the moving object, the acceleration maximum value of the moving object, the acceleration minimum value of the moving object, and the acceleration peak time of the moving object, correspondingly, the second motion data includes the acceleration average value of the smart wearable device, the acceleration maximum value of the smart wearable device, the acceleration minimum value of the smart wearable device, and the acceleration peak time of the smart wearable device. Or vice versa.

The operational data includes: the action time and the action times of the moving object and the action time and the action times of the intelligent wearable device. Namely: when the first motion data comprise the motion time and the motion times of the moving object, the second motion data comprise the motion time and the motion times of the intelligent wearable device. Or vice versa.

The projection data includes: the average value of the acceleration of the moving object, the maximum value of the acceleration of the moving object, the minimum value of the acceleration of the moving object, the projection time of the moving object, the average value of the acceleration of the intelligent wearable device, the maximum value of the acceleration of the intelligent wearable device, the minimum value of the acceleration of the intelligent wearable device, and the projection time of the intelligent wearable device. Namely: when the first motion data includes an acceleration average value of the animal body, an acceleration maximum value of the moving object, an acceleration minimum value of the moving object, and a projection time of the moving object, the second motion data includes: the average value of the acceleration of the intelligent wearable device, the maximum value of the acceleration of the intelligent wearable device, the minimum value of the acceleration of the intelligent wearable device and the projection time of the intelligent wearable device. Or vice versa.

Step S203: and comparing the motion characteristics of the first motion data with the second motion data stored locally to determine the matching degree.

When the first motion data are motion data of a moving object, the second motion data are motion data of wearers of a plurality of wearers; when the first motion data is the wearer motion data of a plurality of wearers, the second motion data is the motion data of a moving object.

The method specifically comprises the following steps:

respectively comparing the acceleration average value of the moving object, the acceleration maximum value of the moving object, the acceleration minimum value of the moving object and the acceleration peak time of the moving object with the corresponding acceleration average value of the intelligent wearable device, the acceleration maximum value of the intelligent wearable device, the acceleration minimum value of the intelligent wearable device and the acceleration peak time of the intelligent wearable device; determining the matching degree of the first motion data and the second motion data according to each comparison result;

and/or the presence of a gas in the gas,

respectively comparing the action time and the action times of the moving object with the action time and the action times of the corresponding intelligent wearable equipment; determining the matching degree of the first motion data and the second motion data according to each comparison result;

and/or the presence of a gas in the gas,

respectively comparing the acceleration average value of the animal body, the acceleration maximum value of the moving object, the acceleration minimum value of the moving object and the projection time of the moving object with the corresponding acceleration average value of the intelligent wearable device, the acceleration maximum value of the intelligent wearable device, the acceleration minimum value of the intelligent wearable device and the projection time of the intelligent wearable device; and determining the matching degree of the first motion data and the second motion data according to each comparison result.

Step S204: determining first motion data and second motion data with the highest matching degree according to the matching degree result; and carrying out motion data statistics according to the first motion data and the second motion data with the highest matching degree.

It should be noted that, when the first motion data is motion data of a moving object and the second motion data is motion data of wearers of multiple wearers, that is, when the implementation side of the method of this embodiment is the intelligent wearable device side, the first motion data is compared with the locally stored second motion data in motion characteristics to determine the matching degree; determining the first motion data and the second motion data with the highest matching degree according to the matching degree result comprises the following steps: comparing the motion characteristics of the first motion data with second motion data stored locally to obtain the matching degree of the first motion data and the second motion data, and determining whether the obtained matching degree meets the set matching degree; if yes, the second motion data and the corresponding identification of the intelligent wearable device are sent to the moving object, and the first motion data and the second motion data with the highest matching degree are determined by the moving object. When the moving object determines that the matching degree of the first motion data and the second motion data is the highest, certain information or instructions can be sent to the smart wristband. After the smart wristband receives the information or the instruction, the received first motion data and the locally stored second motion data are determined to be data with the highest matching degree, and motion data statistics is carried out according to the first motion data and the second motion data. In most ball games, the ratio of ball to player is typically 1: n, namely, 1 moving object and N wearing equipment exist, under the condition, the matching degree is obtained by the moving object, and then the first motion data and the second motion data with the highest matching degree are determined, so that the data processing efficiency and speed are greatly improved.

When the first motion data is the motion data of the wearers of the multiple wearers, and the second motion data is the motion data of the moving object, that is, when the implementation party of the method of the embodiment is the moving object party, the motion characteristics of the first motion data and the second motion data stored locally are compared to determine the matching degree; determining the first motion data and the second motion data with the highest matching degree according to the matching degree result comprises the following steps: the moving object compares the received first motion data with second motion data stored locally in the moving object to obtain a plurality of matching degrees; first motion data and second motion data having the highest degree of matching are determined from the plurality of degrees of matching. By the method, data transmission and interaction can be reduced, and data transmission burden is relieved.

When the exercise data statistics is performed according to the first exercise data and the second exercise data with the highest matching degree, exercise characteristic data (such as scores, backboards, attack assistance, snap-off, caps, hit rate and the like) can be obtained according to the first exercise data and the second exercise data with the highest matching degree; and counting the motion characteristic data, sending the motion characteristic data to the moving object and the intelligent wearable device, and uploading the motion characteristic data to the mobile terminal through the moving object and the intelligent wearable device. By uploading the data to the mobile terminal, sharing and displaying of the data can be performed.

According to the embodiment, the sensors are arranged in the moving object and the intelligent wearable devices, and the motion data of the moving object and the motion data of the plurality of wearers of the intelligent wearable devices are collected in time; determining the sender of the motion action and the corresponding motion data thereof through the comparison of the motion data of the sender and the motion data of the motion action; through statistics of the sports data, the sports characteristics of a plurality of athletes can be acquired when a plurality of people take sports, the effective scores of the plurality of athletes are effectively recorded, and reference and basis are further provided for subsequent sports training. It is thus clear that through this embodiment, solved at present can't effectively record the problem of a plurality of sportsman's effective score when many people move.

The exercise data statistics plan of the present invention will be described below by taking basketball exercise as an example. It will be appreciated by those skilled in the art that the principles and features of the embodiments shown in examples three and four may be applied to other similar sports, such as football, volleyball, baseball, softball, football, hockey, golf, tennis, badminton, table tennis, etc.

Referring to fig. 3, a flow chart of steps of a motion data statistical method according to a third embodiment of the present invention is shown.

In this embodiment, take the moving object as the basketball, and the intelligent wearing equipment as the wrist strap. Be provided with in basketball and intelligent wearing equipment and carry out corresponding data processing's device, for example microprocessor or microchip etc. for the convenience of description, all take the chip as the example in this application. The basketball chip arranged in the basketball is used for identifying the states of the basketball, such as flying, holding the basketball, shooting and grabbing the backboard; the wrist strap chip arranged in the wrist strap is used for identifying the state of the arm of the player, such as dribbling, shooting, basketball shooting and the like. The basketball chip and the wrist strap chip can alternatively set a mode matching function so as to match the basketball state with the wrist strap state of the player, find the player causing each basketball state, and respectively count the data (such as scores, hit rates, backboards and the like) of the single player. Of course, the basketball chip and the wristband chip may also be configured with a mode matching function, and one may be in an active state and the other in an inactive state at the same time, or may be configured to switch between active states when needed.

In addition, still be provided with the basketball sensor in the basketball, it is the sensor of encapsulation inside the basketball, can include: a three-axis acceleration sensor, and a three-axis gyroscope sensor. The three-axis acceleration sensor can acquire the acceleration of the basketball under the three-dimensional coordinate system, and the three-axis gyroscope sensor can acquire the rotation angular velocity of the basketball under the three-dimensional coordinate system. Of course, when necessary, those skilled in the art may also set other sensors, such as a three-axis magnetometer and a pressure gauge, where the three-axis magnetometer can acquire the magnetic field strength of the basketball under the three-dimensional coordinate system, and the pressure gauge can acquire the air pressure of the basketball.

The wearable equipment of intelligence wrist strap for the assembly on sportsman's body is provided with sportsman's sensor in the intelligence wrist strap, include: a three-axis acceleration sensor and a three-axis gyroscope sensor. The three-axis acceleration sensor can acquire the acceleration of the wrist strap in the three-dimensional coordinate system, and the three-axis gyroscope sensor can acquire the rotation angular velocity of the wrist strap in the three-dimensional coordinate system. Optionally, other devices may be provided by those skilled in the art according to actual needs, such as a three-axis magnetometer for acquiring the magnetic field strength of the wrist band in the three-dimensional coordinate system, a pressure gauge for acquiring the air pressure applied to the wrist band, a vibrator for prompting the technical statistics result specific to the player by vibration, a display screen for displaying the technical statistics result of the player, a buzzer for prompting the technical statistics result specific to the player by sounding, and the like. The embodiments of the present invention are not limited in this regard.

Based on this, the motion data statistical method of the present embodiment includes:

step S301: basketball chip and wrist strap chip correspond the motion state of discerning basketball and sportsman respectively.

In this embodiment, the basketball movement status includes: flying, holding, dribbling, shooting, passing, goal, shooting no-go, attack-aid, and cricket.

The following describes recognition of a movement state in basketball movement.

(1) Flight state

Detecting the flight status of a basketball is the basis of the entire program, and it is only possible to identify a shot, pass, dribble, etc. if it is known whether the basketball is flying or not. When the flight state of the basketball is detected, at least one characteristic of the basketball in the flight process can be extracted, for example: the basketball does free-falling body movement and rotates at a constant speed when flying.

The basketball keeps rotating at a constant speed in the flying process, and the acceleration collected by the sensor keeps unchanged. If a (t) is that the acceleration sensor of the three-axis acceleration sensor records the acceleration of the basketball at the time t, and in a time range not shorter than the time T (fly), if the acceleration variation is smaller than a certain value, namely | a (t) -a (t-1) | < M, the basketball is judged to be in a flying state. Preferably, 0.2s < t (fly) <2.0s, 0.1g < M <0.3 g. Where s represents "seconds", g is the unit of acceleration, and 1g is equal to about 9.8m/s 2.

Figure 4 is a diagram illustrating an example of the acceleration of a basketball as it flies. It can be seen from the figure that:

when t <396.5 s: the basketball is in the hand of the person, and the acceleration fluctuates up and down;

when 396.5s < t <396.9 s: the ball flies in the air, the ball body rotates at a constant speed, and the acceleration of the basketball keeps constant;

when t >397s, the basketball touches the object, the flight stops, and the acceleration spikes.

Where t denotes time and s denotes "second".

(2) Holding state of the ball

The pass in the basketball game is actually formed by the alternate change of the ball holding, flying and holding, so the detection of the ball holding state is the basic and important detection. If a (t) is the acceleration of the basketball recorded by the triaxial acceleration sensor at the time t, and if the variation of the acceleration acquired by the triaxial acceleration sensor of the basketball is larger than a certain value within a range not shorter than the time t (hold), namely | a (t) -a (t-1) | > M, the basketball is determined to be in the "ball holding" state. Preferably, T (hold) >0.2s, M >0.3 g. Where s represents "seconds", g is the unit of acceleration, and 1g is equal to about 9.8m/s 2.

Figure 5 is a diagram illustrating an example of the acceleration of a basketball as the basketball is held. It can be seen from the figure that:

when t <395.7 s: the basketball flies and is not held by people;

when 395.7s < t <396.5 s: in the ball holding process, the acceleration of the basketball is unstable and changes smoothly;

when t >396.5 s: the basketball leaves the hands and enters a flying state.

Where t denotes time and s denotes "second".

(3) Dribbling state

When the basketball continuously appears in the following states in sequence, the basketball is judged to be in a dribbling state: a "ball holding" state, wherein the time of the ball holding state is shorter than T1; a "flight" state, and the flight state is shorter in time than T2; a "crash" condition (brief acceleration spike, as shown in fig. 6) and the crash condition is shorter in time than T3; a "flight" state, and the flight state is shorter in time than T4; a "ball holding" state, and the time of the ball holding state is shorter than T5. Preferably 0.1s < T1<1.0s, 0.1s < T2<1.0s, 0s < T3<0.2s, 0.1s < T4<1.0s, 0.1s < T5<1.0 s. Where s represents "second".

When the basketball state appears as follows: when the ball holding state is determined, the dribbling state can be determined. Figure 6 is a diagram illustrating an example of the acceleration of a basketball during a dribble of the basketball. It can be seen from the figure that:

t <0.3 s: holding the ball, the acceleration is unstable;

0.3s < t <0.41 s: flying, the acceleration is constant;

0.41s < t <4.9 s: bouncing, the acceleration spikes;

4.9s < t <5.6 s: flying, the acceleration is constant;

t >5.6 s: holding the ball and the acceleration is unstable.

Where t denotes time and s denotes "second".

The frequency and the strength of dribbling can be calculated by utilizing the characteristic data in dribbling, thereby being helpful for training.

(4) State of shooting

When the following characteristics continuously appear in the angular velocity acquired by the gyroscope of the basketball, the basketball is judged to be in a shooting state:

the player lifts the basketball, the angular speed collected by the gyroscope is characterized by increasing firstly and then decreasing, the peak value of the peak is at least larger than W1, the basketball is almost still when the process is finished, and the angular speed of the module is smaller than W2; the basketball is thrown by the player, and the angular speed collected by the gyroscope rises; the basketball flies to the ring and enters into a flying state. Wherein 200deg/s < W1<400deg/s, 50deg/s < W2<180deg/s, deg/s being units of angular velocity (degrees per second).

The shooting action is decomposed and can be found to be divided into three steps:

the first step is as follows: lifting the ball to the highest point;

the second step is that: the ball is thrown, a feature of the process being available at this step;

the third step: the basketball flies to the basket.

Figure 7 is a diagram illustrating an example of the change in angular velocity of a basketball during a basketball shot. It can be seen from the figure that:

when 326.8s < t <327.5 s: players lift the ball over the top of the head: the basketball firstly rotates in an accelerating way, then rotates in a decelerating way and finally stays at the top of the head;

when 327.5s < t <327.7 s: throwing out the ball;

when t >327.7 s: the basketball flies to the basket and enters into a flying state.

Where t denotes time and s denotes "second".

(5) Pass state

When the acceleration of the basketball continuously shows the following characteristics, the basketball is judged to be in a pass state: a section of "ball holding" state; a "flight" state; a section of "ball holding" state.

When passing, the basketball moves in the process of 'holding the ball-' flying- 'catching the ball'. Figure 8 is a diagram illustrating an example of the acceleration of a basketball as it passes. It can be seen from the figure that:

when t <107.1 s: holding the ball, the acceleration is unstable;

when 107.1s < t <107.8 s: flying, the acceleration is constant;

when t >107.8 s: the acceleration is unstable when the ball is caught.

Where t denotes time and s denotes "second".

(6) Goal state

When the acceleration of the basketball continuously shows the following characteristics, the basketball is judged to be in a goal state: a "flight" state; acceleration fluctuates continuously, | a (T) -a (T-1) | > A, and the duration T is within a certain range, namely T (min) < T (max); a flight condition. Preferably, 0.2g < a <10g, 0.01s < t (min) <0.1s, 0.1s < t (max) <0.4 s. Wherein g is the unit of acceleration, 1g is equal to about 9.8m/s 2, and s represents "seconds".

Whether the basketball is shot or not can be identified by capturing the feature data of the interaction between the basketball and the net through the net. Fig. 9 is a diagram illustrating an example of the acceleration of a basketball during a shot and goal of the basketball. It can be seen from the figure that:

when t <393 s: a ball holding process;

when 393s < t <394 s: the basketball leaves the hands and flies to the basket ring, and the state is a flying state;

when 394s < t <394.2 s: the basketball interacts with the basketry and the net;

when t >394.2 s: the basketball flies off the ring.

Where t denotes time and s denotes "second".

(7) Non-advance state of shooting

When the basketball detects the following states, the basketball is judged not to be shot: shooting state; the shooting of the basketball is not in the state of goal.

(8) Attack-assisting status

The assistant attack is a special pass, the essential characteristics of which are the same as the pass, and the difference is that if the receiver of the pass takes the ball quickly after receiving the ball, the player who delivers the pass successfully carries out one assistant attack.

When the basketball first appears in the passing state and then appears in the goal state, the basketball is judged to be helpful for attacking once. Specifically, basketball continues to appear in several states: holding the ball by the player A; player a passes the ball to player B; shooting a basketball by the player B; player B goals.

(9) State of basketball

The cricket refers to a basketball which flies off the ring after being kicked by a certain ball player after the basketball is not in the first shooting. When the basketball recognizes a "no-go" shot, if the basketball is caught by a player, a cricket is generated.

When the basketball continuously appears in the following states, the basketball is judged to be in the backboard ball state: shooting state; the shooting state is not advanced; a ball holding state.

It should be noted that the shooting position can also be determined by the above states and data.

When the "shot" status is identified, the basketball can also identify the location where the player shot, requiring three conditions: the flight speed of the basketball; basketball flight time; the angle of the shot.

Firstly, multiplying the flight speed of the basketball by the flight time, wherein the product is the distance between a shooting point and a basketball hoop; then calculating the arctangent values of the acceleration of the basketball on the horizontal plane in the x-axis direction and the acceleration of the basketball in the y-axis direction during shooting, namely the shooting angle; finally, the shooting position can be uniquely determined according to the shooting distance and the shooting angle. Therefore, the distance between the shooting point and the ring is measured through the flight speed and the time of the basketball, and the shooting angle is added, so that the shooting position can be uniquely determined.

In this embodiment, the motion state of the wristband includes: dribbling and shooting.

The following describes recognition of a motion state of a wrist band (player) in motion.

(1) Dribbling state

Dribbling is an important action in basketball sports, and the characteristics of dribbling by players (dribbling frequency, strength) are the basis for matching the sports data of the subsequent basketball and players.

When the wrist strap has the following rules, the wrist strap is judged to be in a dribbling state: the angular velocity acquired by the gyroscope of the wrist strap is in a positive-negative alternating relation, the wave crest represents downward shooting, and the wave trough represents the process that the basketball rebounds and returns to the hand to lift the hand; the pitch angle of the wrist band: the arm swings up and down, and the change range of the corresponding pitch angle is about 90 degrees.

Fig. 10 is a schematic diagram showing an example of the change in angular velocity of the wrist band during a dribbling process, in which the abscissa represents time and the ordinate represents angular velocity. Fig. 11 shows a schematic diagram of an example of the change in the pitch angle of the wrist strap during dribbling, where the abscissa represents time and the ordinate represents angle.

(2) State of shooting

In the shooting process, the angular speed and the pitch angle of the wrist strap are regularly changed. When the wrist strap has the following rules, the wrist strap is judged to be in a shooting state: the angular velocity w <0 and is maintained for a period of time not shorter than T1; the angular speed w changes from negative to positive and is maintained for a time not shorter than T2; at the critical point where the angular velocity changes from negative to positive, the rotation angle of the wrist band should be within a certain range, i.e., roll (min) < roll (max). Preferably, 0.2s < T1<0.8s, 0s < T2<0.2s, -80 degrees < roll <0 degrees.

Here, the positive-negative relationship is related to the wearing direction of the smart wearable device, and if the positive-negative relationship is inverted in the wearing mode in the opposite direction, then: when the wrist strap has the following rules, the wrist strap is judged to be in a shooting state: angular velocity w >0 and is maintained for a time not shorter than T1; the angular speed w changes from positive to negative and is maintained for a time not shorter than T2; at the critical point where the angular velocity changes from positive to negative, the rotation angle of the wrist band should be within a certain range, i.e., roll (min) < roll (max). Preferably, 0.2s < T1<0.8s, 0s < T2<0.2s, 0 degree < roll <80 degrees.

Fig. 12 is a diagram showing an example of the change in the angular velocity of the wrist band during a shot, in which the abscissa represents time and the ordinate represents the angular velocity. Fig. 13 is a schematic diagram showing an example of the change in the pitch angle of the wrist band during a shot, where the abscissa represents time and the ordinate represents angle. It can be seen from the figure that:

when 336.8s < t <337.5 s: in the hand lifting process, the angular speed is less than 0, and the pitch angle rises;

when t >337.5 s: hand-out process, angular velocity >0.

Where t denotes time and s denotes "second".

Step S302: the basketball chip judges whether the current motion state is a set state needing motion data matching or not; if yes, go to step S303; if not, the process returns to step S301.

When the basketball chip detects that the basketball chip is in a set state (including any one of shooting, dribbling and holding), a search mode is automatically started, and wristbands matched with the state are searched. For example: when the basketball detects that the basketball is in a shooting state, the basketball searches for a shooting person matched with the basketball, and updates data records of the shooting person, such as shooting number, hit rate and the like.

Step S303: the basketball chip establishes Bluetooth communication with all the wrist strap chips and sends the motion data to each wrist strap chip.

In this embodiment, adopt the bluetooth communication mode between basketball chip and the wrist strap chip. Basketball, as a system center, can communicate with a plurality of wristbands in a two-way mode.

As described in the previous description of the exercise state, when the basketball is in a different state, the exercise data sent to the wristband is also different.

Step S304: the wrist strap chip matches the received motion data with the motion data of the wrist strap chip, and the successfully matched wrist strap chip establishes Bluetooth communication with the basketball chip and sends a matching result to the basketball chip.

When the basketball starts a search mode, the basketball sends certain motion data of the self state, such as acceleration, angular velocity and the like, to all wrist bands, after the wrist bands receive the motion data, the motion data are matched with the motion data of the wrist bands, whether the motion data of the wrist bands are matched with the motion data of the basketball is judged, the result is fed back to the basketball, and the fed back data carry the identification of the wrist bands so that the basketball can distinguish the data of different wrist bands.

When the basketball is in the ball holding state, in order to identify the current 'ball holder', the movement data acquired by the basketball chip and the wrist strap chip are respectively and independently analyzed, and the movement data of the two chips are required to be matched. For example, a golfer within a time frame (T1, T2) may be determined according to the following data characteristics: average acceleration values of basketball and wristband chips, a _ mean (ball), a _ mean (wrist); the maximum acceleration values of the basketball and wrist strap chips, a _ max (ball), a _ max (christ); the minimum acceleration values of basketball and wristband chips, a _ min (ball), a _ min (christ); the peak time T _ peak (ball), T _ peak (wrist) of the basketball chip and the wrist strap chip. Here, "ball" means basketball data, and "wrist" means wristband data. If basketball and the above data feature of wrist strap chip possess certain degree of matching, then judge that the sportsman who wears this wrist strap is the sportsman who holds the ball.

For example, if the following four conditions are simultaneously satisfied: | a mean (ball) -a mean (wrist) | < a 1; | a _ max (ball) -a _ max (wrist) | < a 2; | a _ min (ball) -a _ min (wrist) | < a 3; l T _ peak (ball) -T _ peak (wrist) l < a 4; the data for the basketball chip and the wristband chip are deemed to match. Wherein A1 is 1.5g, preferably 1.0g, and more preferably 0.5 g; a2 is 2.0g, preferably 1.2g, still more preferably 0.6 g; a3 is 2.0g, preferably 1.2g, still more preferably 0.6 g; a4 is 0.8s, preferably 0.5s, more preferably 0.3 s. "|" indicates absolute value, g is the unit of acceleration, 1g is equal to about 9.8m/s ^2, and s indicates "second".

When the basketball is in the dribbling state or when the wrist strap is in the dribbling state, the up and down movement rules of the basketball and the wrist strap are matched, and the dribbling player in a time range (T1, T2) can be judged according to the following data characteristics: (T1, T2) recording the time Ti _ ball, Ti _ wirst of each racket ball obtained by the basketball chip and the wristband chip, wherein i is 1, 2, … N, and N is the smaller of N _ ball and N _ wrist; and (T1, T2) recording the times N _ ball, N _ wrist of the shots obtained by the basketball chip and the wrist strap chip. The wristband wearer is considered to be dribbling if both of the following conditions are met: l N ball-N wrist l < N1, Sum (| Ti ball-Ti wrist l) N2.

When the basketball is in the dribbling state or when the wristband is in the shooting state, the basketball never leaves the player's hands during the shooting process. The movement rules of the movement data obtained by the basketball chip and the wrist strap chip are strictly matched. In the time period corresponding to the shooting action, the shooting player can be judged according to the following data characteristics: the average acceleration values of the basketball chip and the wrist strap chip are a _ mean (ball), a _ mean (wrist); the maximum acceleration values a _ max (ball), a _ max (christ) of the basketball chip and the wrist strap chip; the minimum acceleration values of the basketball chip and the wrist strap chip, a _ min (ball), a _ min (christ); the time T _ up (ball), T _ up (wrist) when the basketball chip and the wrist strap chip raise hands. The above description of "ball" means basketball data, and the above description of "wrist" means wristband data.

If the basketball chip and the data characteristics of the wrist strap chip have a certain matching degree, the player wearing the wrist strap is judged to be the shooting player. For example, if | a mean (ball) -a mean (wrist) | < a 1; | a _ max (ball) -a _ max (wrist) | < a 2; | a _ min (ball) -a _ min (wrist) | < a 3; t _ up (ball) -T _ up (wrist) | < a 4; the data for the basketball chip and the wristband chip are deemed to match. Wherein: a1 is 1.5g, preferably 1.0g, still more preferably 0.5g g; a2 is 2.0g, preferably 1.2g, still more preferably 0.6 g; a3 is 2.0g, preferably 1.2g, still more preferably 0.6 g; a4 is 0.8s, preferably 0.5s, particularly preferably 0.3 s. "|" indicates absolute value, g is the unit of acceleration, 1g is equal to about 9.8m/s ^2, and s indicates "second".

Step S305: the basketball chip selects the best matching wristband from the one or more matching wristbands received.

Furthermore, the basketball chip can inform the best matching wrist strap of the matching result, and the best matching wrist strap carries out movement data statistics according to the first movement data and the second movement data.

Step S306: bluetooth communication is established between the basketball chip and the mobile terminal, and the motion characteristic data and the matching result are sent to the mobile terminal.

The exercise characteristic data may be the result of processing and counting the exercise data, such as the number of shots, the number of backboards, the number of attacks, the hit rate, etc.

In this embodiment, the mobile terminal is a mobile phone, and one basketball can communicate with a plurality of mobile phones. The basketball transmits the data on the field to the mobile phone through the Bluetooth and displays the data on the mobile phone. Wherein, the data that basketball transmitted to the cell-phone can include: the corresponding shooting number, backboard number, attack assisting number, hit rate, shooting position of each shooting and the like of each wrist strap.

In addition, the basketball has the function of storing besides transmitting data, when the basketball cannot search the mobile phone nearby, the data can be stored in the local basketball, and after the mobile phone is found again, the untransmitted data can be transmitted to the mobile phone.

Step S307: and the mobile terminal uploads the received motion state data to the cloud terminal through a mobile network.

The mobile terminals can share data through the cloud, and even if a certain player does not carry a mobile terminal such as a mobile phone when playing a ball, the data of the player can be transmitted to the cloud through the mobile terminals of other players and can be synchronized to the mobile terminal of the player.

Step S308: and the cloud carries out the next processing.

This processing includes, but is not limited to: and storing data, counting and analyzing the motion characteristic data of each player, counting and analyzing the motion characteristic data of the whole player, and giving a training suggestion according to a set model, wherein the invention is not limited to the above.

Through this embodiment, not only can provide technical statistics service for every sportsman on the scene, can also assist sportsman's scientific training, improve the competitive level, also can note sportsman's daily motion information, improve the physical quality. Moreover, a large amount of motion data which cannot be obtained manually is obtained through electronic equipment, so that the labor cost is saved; furthermore, through an internet platform, sports enthusiasts from all over the world can share the sports experience on the internet, compare sports data and improve the use experience.

Example four

Referring to fig. 14, a flowchart illustrating steps of a motion data statistics method according to a fourth embodiment of the present invention is shown.

The present embodiment is still based on the description of the basketball and the wrist band in the third embodiment, and the various movement states of the basketball and the wrist band.

step S401: basketball chip and wrist strap chip correspond the motion state of discerning basketball and sportsman respectively.

In this embodiment, the basketball movement status includes: flying, holding, dribbling, shooting, passing, goal, shooting no-go, attack-aid, and cricket; the motion state of the wrist band includes: dribbling and shooting. The specific description of each motion state is as described in example three.

Step S402: the wrist strap chip judges whether the current motion state is a set state needing motion data matching or not; if yes, go to step S403; if not, the process returns to step S401.

Wherein the set state comprises a shooting state or a dribbling state.

Step S403: the wrist strap chip establishes Bluetooth communication with the basketball chip and sends the motion data to the basketball chip.

When the wrist strap is in different states, the sports data sent to the basketball is different.

Step S404: the basketball chip matches the received motion data of one or more wrist strap chips with the motion data of the basketball chip in sequence, and the optimal matching wrist strap is screened out.

The basketball chip may refer to the matching process in step S304 in the third embodiment for sequentially matching the received motion data of one or more wristband chips with the motion data of the basketball chip, which is not described herein again.

Step S405: bluetooth communication is established between the basketball chip and the mobile terminal, and the motion characteristic data and the matching result are sent to the mobile terminal, preferably comprising the best matching wrist strap.

Step S406: and the mobile terminal uploads the received motion state data to the cloud terminal through a mobile network.

Step S407: and the cloud carries out the next processing.

It should be noted that, the wrist strap also can be through bluetooth with the data transmission of self for mobile terminal like the cell-phone, every sportsman's wrist strap all can be provided with the memory, stores own data, can transmit in real time for own cell-phone, and the data of transmission include: the corresponding shooting number, backboard number, attack assisting number, hit rate, shooting position of each shooting and the like of the wrist strap.

EXAMPLE five

Referring to fig. 15, a block diagram of a smart wristband according to a fifth embodiment of the present invention is shown.

The intelligent wrist strap of this embodiment is provided with the microchip, and this microchip includes:

a receiving module 501, configured to receive first motion data when it is determined that the current state is a set state in which motion data matching is required, where the first motion data is motion data of a moving object obtained by a sensor disposed in the moving object;

a comparison module 502, configured to compare motion characteristics of the first motion data with locally stored second motion data, and determine a matching degree, where the second motion data is wearer motion data of a wearer obtained through a sensor disposed in the intelligent wearable device;

the statistic module 503 is configured to perform motion data statistics according to the first motion data and the second motion data when it is determined that the first motion data and the second motion data are data with the highest matching degree.

The motion data statistical device of the embodiment is used for realizing the corresponding motion data statistical method in the foregoing method embodiment, and has the beneficial effects of the corresponding method embodiment. According to the embodiment, the sensors are arranged in the basketball and the intelligent wearable devices, and the movement data of the basketball and the movement data of the plurality of wearers of the intelligent wearable devices are collected in time; determining the sender of the motion action and the corresponding motion data thereof through the comparison of the motion data of the sender and the motion data of the motion action; through statistics of the sports data, the sports characteristics of a plurality of athletes can be acquired when a plurality of people take sports, the effective scores of the plurality of athletes are effectively recorded, and reference and basis are further provided for subsequent sports training. It is thus clear that through this embodiment, solved at present can't effectively record the problem of a plurality of sportsman's effective score when many people move.

EXAMPLE six

Referring to fig. 16, a block diagram of a smart wristband according to a sixth embodiment of the present invention is shown.

the receiving module 601 is configured to receive first motion data when it is determined that the current state is a set state in which motion data matching is required, where the first motion data is motion data of a moving object obtained by a sensor disposed in the moving object;

the comparison module 602 is configured to compare motion characteristics of the first motion data with locally stored second motion data to determine a matching degree, where the second motion data is wearer motion data of a wearer obtained through a sensor disposed in the intelligent wearable device;

the statistical module 603 is configured to perform motion data statistics according to the first motion data and the second motion data when it is determined that the first motion data and the second motion data are data with the highest matching degree.

Preferably, the first motion data comprises at least one of: the method comprises the steps of maintaining data of a moving object, operating data of the moving object and projection data of the moving object; the second motion data comprises at least one of: the method comprises the following steps of maintaining data of the intelligent wearable device, operating data of the intelligent wearable device and projection data of the intelligent wearable device;

wherein,

the holding data of the moving object includes: the acceleration average value of the moving object, the acceleration maximum value of the moving object, the acceleration minimum value of the moving object and the acceleration peak time of the moving object; the data that keep of intelligence wearing equipment includes: the method comprises the following steps of obtaining an acceleration average value of the intelligent wearable device, an acceleration maximum value of the intelligent wearable device, an acceleration minimum value of the intelligent wearable device and acceleration peak time of the intelligent wearable device;

the operation data of the moving object comprises: the action time and the action times of the moving object; the operating data of intelligence wearing equipment includes: the action time and the action times of the intelligent wearable equipment;

the projection data of the moving object includes: the average value of the acceleration of the moving object, the maximum value of the acceleration of the moving object, the minimum value of the acceleration of the moving object and the projection time of the moving object; the projection data of intelligent wearing equipment includes: the average value of the acceleration of the intelligent wearable device, the maximum value of the acceleration of the intelligent wearable device, the minimum value of the acceleration of the intelligent wearable device and the projection time of the intelligent wearable device.

Preferably, the comparing module 602 includes:

the first comparison module 6021 is configured to compare the acceleration average value of the moving object, the acceleration maximum value of the moving object, the acceleration minimum value of the moving object, and the acceleration peak time of the moving object with the corresponding acceleration average value of the intelligent wearable device, the acceleration maximum value of the intelligent wearable device, the acceleration minimum value of the intelligent wearable device, and the acceleration peak time of the intelligent wearable device, respectively; determining the matching degree of the first motion data and the second motion data according to each comparison result;

and/or the presence of a gas in the gas,

the second comparison module 6022 is configured to compare the motion time and the motion frequency of the moving object with the motion time and the motion frequency of the corresponding intelligent wearable device, respectively; determining the matching degree of the first motion data and the second motion data according to each comparison result;

and/or the presence of a gas in the gas,

the third comparison module 6023 is configured to compare the average acceleration value of the animal body, the maximum acceleration value of the moving object, the minimum acceleration value of the moving object, and the projection time of the moving object with the corresponding average acceleration value of the intelligent wearable device, the maximum acceleration value of the intelligent wearable device, the minimum acceleration value of the intelligent wearable device, and the projection time of the intelligent wearable device, respectively; and determining the matching degree of the first motion data and the second motion data according to each comparison result.

Preferably, the first comparing module 6021 is configured to compare the absolute value of the difference between the average value of the acceleration of the basketball and the average value of the acceleration of the smart wristband, with more than 0g and less than 1.5g, preferably with more than 0g and less than 1.0g, and more preferably with more than 0g and less than 0.5 g; when the absolute value of the difference between the maximum acceleration value of the basketball and the maximum acceleration value of the smart wristband is greater than 0g and less than 2.0g, preferably greater than 0g and less than 1.2g, more preferably greater than 0g and less than 0.6g, the absolute value of the difference between the minimum acceleration value of the basketball and the minimum acceleration value of the smart wristband is greater than 0g and less than 2.0g, preferably greater than 0g and less than 1.2g, more preferably greater than 0g and less than 0.6g, the absolute value of the difference between the acceleration peak time of the basketball and the acceleration peak time of the smart wristband is greater than 0s and less than 0.8s, preferably greater than 0s and less than 0.5s, more preferably greater than 0s and less than 0.3s, it is determined that the first motion data and the second motion data are matched;

and/or the presence of a gas in the gas,

a second comparison module 6022, configured to determine that the first motion data matches the second motion data when a sum of absolute values of differences between the action time of the basketball and the action time of the smart band is greater than 0s and less than 10s, preferably greater than 0s and less than 5s, and an absolute value of a difference between the action frequency of the basketball and the action frequency of the smart band is greater than 0s and less than 5 times, preferably greater than 0s and less than 3 s;

and/or the presence of a gas in the gas,

a third comparing module 6023 for comparing the absolute value of the difference between the average value of the acceleration of the basketball and the average value of the acceleration of the smart band with the absolute value of the difference between the maximum value of the acceleration of the basketball and the maximum value of the acceleration of the smart band greater than 0g and less than 1.5g, preferably greater than 0g and less than 1.0g, more preferably greater than 0g and less than 1.2g, more preferably greater than 0g and less than 0.6g, the absolute value of the difference between the minimum value of the acceleration of the basketball and the minimum value of the acceleration of the smart band greater than 0g and less than 2.0g, preferably greater than 0g and less than 1.2g, more preferably greater than 0g and less than 0.6g, the absolute value of the difference between the shooting time of the basketball and the shooting time of the smart band greater than 0s and less than 0.8s, preferably greater than 0s and less than 0.5s, more preferably greater than 0s and less than 0.3s, determining that the first motion data and the second motion data match. Wherein g represents acceleration units, 1g is equal to about 9.8m/s 2, and s represents time units in seconds.

Specifically, when the basketball is in the ball holding state, in order to identify the current "ball holder", the motion data of the two chips need to be matched in addition to the independent analysis of the motion data acquired by the basketball chip and the wrist strap chip. For example, a golfer within a time frame (T1, T2) may be determined according to the following data characteristics: average acceleration values of basketball and wristband chips, a _ mean (ball), a _ mean (wrist); the maximum acceleration values of the basketball and wrist strap chips, a _ max (ball), a _ max (christ); the minimum acceleration values of basketball and wristband chips, a _ min (ball), a _ min (christ); the peak time T _ peak (ball), T _ peak (wrist) of the basketball chip and the wrist strap chip. Here, "ball" means basketball data, and "wrist" means wristband data. If basketball and the above data feature of wrist strap chip possess certain degree of matching, then judge that the sportsman who wears this wrist strap is the sportsman who holds the ball.

If the basketball chip and the data characteristics of the wrist strap chip have a certain matching degree, the player wearing the wrist strap is judged to be the shooting player. For example, if | a mean (ball) -a mean (wrist) | < a 1; | a _ max (ball) -a _ max (wrist) | < a 2; | a _ min (ball) -a _ min (wrist) | < a 3; t _ up (ball) -T _ up (wrist) | < a 4; the data for the basketball chip and the wristband chip are deemed to match.

Preferably, the comparing module 602 compares the motion characteristics of the first motion data with the second motion data stored locally, obtains the matching degree of the first motion data and the second motion data, and determines whether the obtained matching degree meets the set matching degree; if yes, the second motion data and the corresponding identification of the intelligent wearable device are sent to the moving object, and the first motion data and the second motion data with the highest matching degree are determined by the moving object.

Preferably, the setting states requiring the motion data matching include: an operating state, or a projection state; the running state is used for indicating that the moving object is in a state of being separated from the control of a wearer of the intelligent wearable device, such as a dribbling state; the projection state is used for indicating that a moving object is in a state of being projected by a wearer of the intelligent wearable device, such as a shooting state.

Preferably, the statistic module 603, when performing the motion data statistics according to the first motion data and the second motion data: obtaining motion characteristic data according to the first motion data and the second motion data; and counting the motion characteristic data, sending the motion characteristic data to the moving object, and uploading the motion characteristic data to the mobile terminal through the moving object.

Preferably, the microchip may further comprise a memory for storing the statistical motion profile data.

The motion data statistics apparatus of this embodiment is used to implement the corresponding motion data statistics method in the foregoing multiple method embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein again.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. An intelligent wristband, wherein a microchip is disposed in the intelligent wristband, the microchip comprising:

a comparison module, configured to compare motion characteristics of the first motion data with second motion data to determine a matching degree, where the second motion data is wearer motion data obtained through a sensor provided in the smart wristband,

the first motion data comprises at least one of: the method comprises the steps of maintaining data of a moving object, operating data of the moving object and projection data of the moving object; the second motion data comprises at least one of: the smart wristband maintains data, runs data and projects data;

wherein,

the hold data of the moving object includes: the acceleration average value of the moving object, the acceleration maximum value of the moving object, the acceleration minimum value of the moving object and the acceleration peak time of the moving object; the holding data of the smart wristband comprises: acceleration average value, acceleration maximum value, acceleration minimum value and acceleration peak time;

the operation data of the moving object comprises: the action time and the action times of the moving object; the operation data of the intelligent wrist strap comprises: the action time and the action times;

the projection data of the moving object includes: the average value of the acceleration of the moving object, the maximum value of the acceleration of the moving object, the minimum value of the acceleration of the moving object and the projection time of the moving object; the projection data of the smart wristband comprises: acceleration average, acceleration maximum, acceleration minimum and throw time, the moving object is a basketball,

the comparison module comprises:

the first comparison module is used for respectively comparing the acceleration average value of the moving object, the acceleration maximum value of the moving object, the acceleration minimum value of the moving object and the acceleration peak time of the moving object with the acceleration average value, the acceleration maximum value, the acceleration minimum value and the acceleration peak time of the corresponding intelligent wrist strap; determining the matching degree of the first motion data and the second motion data according to each comparison result;

and/or the presence of a gas in the gas,

the second comparison module is used for comparing the action time and the action times of the moving object with the action time and the action times of the corresponding intelligent wrist strap respectively; determining the matching degree of the first motion data and the second motion data according to each comparison result;

and/or the presence of a gas in the gas,

the third comparison module is used for respectively comparing the acceleration average value of the animal body, the acceleration maximum value of the moving object, the acceleration minimum value of the moving object and the projection time of the moving object with the acceleration average value, the acceleration maximum value, the acceleration minimum value and the projection time of the corresponding intelligent wrist strap; determining a matching degree of the first motion data and the second motion data according to each comparison result,

the first comparison module is used for determining that the first motion data is matched with the second motion data when the absolute value of the difference between the average acceleration value of the basketball and the average acceleration value of the intelligent wrist strap is greater than 0g and less than 1.5g, the absolute value of the difference between the maximum acceleration value of the basketball and the maximum acceleration value of the intelligent wrist strap is greater than 0g and less than 2.0g, the absolute value of the difference between the minimum acceleration value of the basketball and the minimum acceleration value of the intelligent wrist strap is greater than 0g and less than 2.0g, and the absolute value of the difference between the peak acceleration time of the basketball and the peak acceleration time of the intelligent wrist strap is greater than 0s and less than 0.8 s;

and/or the presence of a gas in the gas,

the second comparison module is used for determining that the first motion data are matched with the second motion data when the sum of absolute values of differences between the action time of a basketball and the action time of the intelligent wrist strap is greater than 0s and less than 10s and the absolute value of the difference between the action times of the basketball and the action times of the intelligent wrist strap is greater than 0 times and less than 5 times;

and/or the presence of a gas in the gas,

the third comparison module is used for determining that the first motion data is matched with the second motion data when the absolute value of the difference between the average acceleration value of the basketball and the average acceleration value of the intelligent wrist strap is greater than 0g and less than 1.5g, the absolute value of the difference between the maximum acceleration value of the basketball and the maximum acceleration value of the intelligent wrist strap is greater than 0g and less than 2.0g, the absolute value of the difference between the minimum acceleration value of the basketball and the minimum acceleration value of the intelligent wrist strap is greater than 0g and less than 2.0g, and the absolute value of the difference between the projection time of the basketball and the projection time of the intelligent wrist strap is greater than 0s and less than 0.8 s;

where g represents the unit of acceleration and s represents the unit of time in seconds.

2. The smart wristband of claim 1,

the comparison module compares the motion characteristics of the first motion data and the second motion data to obtain the matching degree of the first motion data and the second motion data, and determines whether the obtained matching degree meets the set matching degree; and if so, sending the second motion data and the identification of the corresponding intelligent wrist strap to the moving object, and determining the first motion data and the second motion data with the highest matching degree by the moving object.

3. The smart wristband as recited in claim 1, wherein the setting state requiring motion data matching comprises: dribbling, holding and pitching states.

4. The smart wristband as recited in claim 1, wherein the microchip further comprises a statistics module for performing statistics of motion data based on the first motion data and the second motion data when the first motion data and the second motion data are determined to be the data with the highest degree of matching.

5. The smart wristband as recited in claim 4, wherein the statistics module, when performing motion data statistics from the first and second motion data:

obtaining motion characteristic data according to the first motion data and the second motion data;

and counting the motion characteristic data, sending the motion characteristic data to a moving object, and uploading the motion characteristic data to a mobile terminal through the moving object.

6. The smart wristband as recited in claim 5, wherein the microchip further comprises a memory; the memory is used for storing the motion characteristic data after statistics.