CN115164975A

CN115164975A - Motion detection method and device and intelligent equipment

Info

Publication number: CN115164975A
Application number: CN202110369003.2A
Authority: CN
Inventors: 付晓葆; 周涛
Original assignee: Anhui Huami Information Technology Co Ltd
Current assignee: Anhui Huami Information Technology Co Ltd
Priority date: 2021-04-06
Filing date: 2021-04-06
Publication date: 2022-10-11

Abstract

The disclosure relates to a motion detection method, a motion detection device and intelligent equipment, wherein the method comprises the following steps: judging whether the intensity of the positioning signal of the carried first sensing hardware is lower than a threshold value or not; if yes, acquiring the moving step number of the target user in the preset time period, which is acquired by the second sensing hardware, and calculating the moving step frequency of the target user in the preset time period based on the moving step number; and determining a movement stride corresponding to the calculated movement stride frequency based on a first mathematical relationship between the movement stride frequency and the movement stride of the target user, and calculating the movement distance of the target user in the preset time period based on the movement stride and the movement step number. According to the technical scheme, when the sensing hardware is abnormal, the calculated data is used for replacing the data corresponding to the abnormal sensing hardware, the integrity and accuracy of the data are guaranteed, and the user experience is improved.

Description

Motion detection method and device and intelligent equipment

Technical Field

The present disclosure relates to the field of motion detection technologies, and in particular, to a motion detection method and apparatus, and an intelligent device.

Background

The six-minute walk test is a sport test applied in clinic, and the evaluation of the cardiopulmonary function of a subject is realized by testing the distance that the subject walks at the fastest speed within six minutes.

Along with intelligent equipment's such as smart bracelet, intelligent wrist-watch and smart mobile phone popularization, when carrying out six minutes walking experiments, the testee can not accomplish the experiment under medical personnel's auxiliary measurement, just can calculate the distance of walking through the GPS locate function that intelligent equipment possesses.

However, the GPS signal is easily affected by the environment, and is often not received stably indoors, and is easily affected by tall buildings and trees outdoors. Therefore, when the GPS signal is not good, it is difficult to accurately calculate the distance traveled.

Disclosure of Invention

In view of this, the present disclosure provides a motion detection method, a motion detection apparatus, and an intelligent device, which determine motion data of a target user based on a learned mathematical relationship by using data acquired by sensing hardware, so as to solve a problem that the motion data cannot be acquired when the sensing hardware does not meet a detection requirement in motion detection.

According to a first aspect of an embodiment of the present disclosure, a motion detection method is provided, where the method includes:

judging whether the intensity of the positioning signal of the first sensing hardware is lower than a threshold value; the positioning signal is used for determining the moving distance of the target user in the preset time period;

if the intensity of the positioning signal is lower than a threshold value, acquiring the moving step number of the target user in the preset time period, which is acquired by the second sensing hardware, and calculating the moving step frequency of the target user in the preset time period based on the moving step number;

and determining a movement stride corresponding to the calculated movement stride frequency based on a first mathematical relationship between the movement stride frequency and the movement stride of the target user, and calculating a movement distance of the target user within the preset time period based on the movement stride and the movement step number.

Optionally, the method further includes:

judging whether the acquisition precision of the second sensing hardware is lower than a threshold value;

if the acquisition precision is lower than a threshold value, acquiring the moving distance of the target user in the preset time period, which is determined by the first sensing hardware, and calculating the average moving speed of the target user in the preset time period based on the moving distance;

and determining the moving step frequency corresponding to the calculated average moving speed based on a second mathematical relationship between the average moving speed and the moving step frequency of the target user, and calculating the moving step number of the target user in the preset time period based on the moving step frequency.

Optionally, the positioning signal comprises a satellite positioning signal; the first sensing hardware includes a satellite positioner.

Optionally, the satellite locator comprises a GPS locator.

Optionally, the second sensing hardware comprises an acceleration sensor; alternatively, the second sensing hardware includes a gyroscope and an acceleration sensor.

Optionally, the acceleration sensor includes an ACC three-axis acceleration sensor.

Optionally, the first mathematical relationship is determined by fitting data based on sample data corresponding to a plurality of sample time periods, where the sample data includes moving stride frequency data and moving stride data of the target user in the sample time periods;

the second mathematical relation is determined by data fitting based on sample data corresponding to a plurality of sample time periods, wherein the sample data comprises average moving speed data and moving step frequency data of the target user in the sample time periods;

optionally, the sample period includes that the intensity of the positioning signal of the first sensing hardware is not lower than a threshold; and the acquisition precision of the second sensing hardware is not lower than the period of a threshold value.

Optionally, the smart device comprises a wearable device.

Optionally, the wearable device includes a smart band for performing motion detection.

According to a second aspect of the embodiments of the present disclosure, a motion detection apparatus is provided, which is applied to an intelligent device, where the intelligent device is equipped with a first sensing hardware for determining a moving distance of a target user within a preset time period; the second sensing hardware is used for acquiring the moving steps of the target user in the preset time period; the device comprises:

the first judging unit is used for judging whether the intensity of the positioning signal of the first sensing hardware is lower than a threshold value or not; wherein the positioning signal is used for determining the moving distance of the target user in the preset time period;

the first calculating unit is used for acquiring the moving step number of the target user in the preset time period, which is acquired by the second sensing hardware, when the intensity of the positioning signal is lower than a threshold value, and calculating the moving step frequency of the target user in the preset time period based on the moving step number;

and a second calculating unit, configured to determine, based on a first mathematical relationship between a movement stride frequency and a movement stride of the target user, a movement stride corresponding to the calculated movement stride frequency, and calculate, based on the movement stride and the movement step number, a movement distance of the target user within the preset time period.

Optionally, the apparatus further comprises:

the second judging unit is used for judging whether the acquisition precision of the second sensing hardware is lower than a threshold value;

a third calculating unit, configured to, when the acquisition accuracy is lower than a threshold, obtain a moving distance of the target user within the preset time period, where the moving distance is determined by the first sensing hardware, and calculate an average moving speed of the target user within the preset time period based on the moving distance;

and the fourth calculating unit is used for determining the moving step frequency corresponding to the calculated average moving speed based on a second mathematical relation between the average moving speed and the moving step frequency of the target user, and calculating the moving step number of the target user in the preset time period based on the moving step frequency.

According to a third aspect of an embodiment of the present disclosure, a smart device is provided, including:

the first sensing hardware is used for determining the moving distance of the target user in a preset time period;

the second sensing hardware is used for acquiring the moving steps of the target user in the preset time period;

a processor and a memory for storing processor-executable instructions;

wherein the processor is configured to perform the method of any one of the first aspect of the above embodiments.

According to a fourth aspect of embodiments of the present disclosure, a computer-readable storage medium is proposed, on which a computer program is stored, wherein the program is configured to implement the method of any one of the first aspect of the embodiments when executed by a processor.

The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:

according to the embodiment, the motion detection is carried out on the target user through the plurality of sensors carried by the intelligent equipment, when one sensor does not meet the motion detection requirement, the motion data obtained by other sensors is calculated on the basis of the mathematical relationship which is fitted in advance according to the sample data of the target user, and the motion data corresponding to the sensor which does not meet the motion detection requirement is calculated. According to the technical scheme, when the sensing hardware is abnormal, the calculated data is used for replacing the data corresponding to the abnormal sensing hardware, the integrity and accuracy of the data are guaranteed, and the user experience is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

FIG. 1 is a flow chart illustrating a method according to an exemplary embodiment;

FIG. 2 is a diagram illustrating a hardware architecture of a smart device in accordance with an exemplary embodiment;

FIG. 3 is a hardware block diagram of a smart bracelet shown in accordance with an exemplary embodiment;

FIG. 4 is an architecture diagram illustrating a motion detection system built based on the smart device described above, according to an exemplary embodiment;

FIG. 5 is a block diagram illustrating a motion detection apparatus in accordance with an exemplary embodiment;

FIG. 6 illustrates an embodiment of a smart device, according to an exemplary embodiment;

FIG. 7 is a schematic block diagram illustrating an apparatus for motion detection in accordance with an exemplary embodiment;

fig. 8 is a block diagram illustrating an apparatus for motion detection according to an exemplary embodiment.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below do not represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure. The word "if," as used herein, may be interpreted as "at \8230; \8230when" or "when 8230; \823030when" or "in response to a determination," depending on the context.

Along with the development of the miniaturization of the sensing hardware, the sensing hardware which can be carried by the intelligent equipment is more and more, and the functions which can be realized are also more and more.

For example, a GPS locator can be mounted on the smart bracelet, and the moving distance of the user when wearing the bracelet can be calculated through GPS locating signals; also can carry on an acceleration sensor on intelligent bracelet, the acceleration data when passing through the collection user and removing determines user's removal step number.

For the intelligent device for motion detection, the accuracy of motion detection can be improved by improving the precision of sensing hardware or adding the sensing hardware for auxiliary detection.

For example, a three-axis acceleration sensor and a gyroscope can be mounted on the smart bracelet, the acceleration data of the user in the three-dimensional direction, which is acquired by the three-axis acceleration sensor, is combined with the angular velocity acquired by the gyroscope to determine the posture of the user when the user moves, and the statistics of the moving steps is more accurate by eliminating data irrelevant to the step statistics, such as bending, turning and the like.

However, limited by the influence of the positioning signal strength or the reliability of the sensing hardware, when the smart device is used for motion detection, the motion data may be deviated or even lost.

For example, due to the influence of terrain, in the indoor, underground or places covered by tall buildings, the GPS positioning signal is unstable and even cannot be received, and it is difficult to calculate the accurate moving distance.

In practical application, when a six-minute walking test is performed, if a GPS positioning signal of a carried intelligent device is not good and cannot meet stable test requirements, the moving distance acquired by the test is inaccurate, and the cardiopulmonary function of a user cannot be accurately evaluated.

For another example, when the intelligent device is damaged or the precision of the sensing hardware is reduced due to external force impact, the intelligent device cannot accurately acquire the moving steps of the user because the sensing hardware cannot acquire data or the acquired data has a large deviation.

Therefore, in order to accurately acquire the motion data of the user, when the sensing hardware is abnormal and cannot meet the motion detection requirement, the relevant motion data can still be acquired through other modes.

In view of this, the present disclosure provides a motion detection method applied to an intelligent device, which calculates motion data corresponding to a target user when a sensor is abnormal, based on a variable relationship fitted to the motion data of the target user, which is established in advance.

In implementation, a smart device (such as a smart bracelet or a smart phone) may carry first sensing hardware (such as a satellite positioner, which may calculate a moving distance of a user within a certain time period by receiving a satellite positioning signal) for determining a moving distance of a target user within a preset time period, and second sensing hardware (such as an acceleration sensor, which may calculate a moving step number of the user within a certain time period by a change of acceleration of the user) for acquiring the moving step number of the target user within the preset time period.

The intelligent device can judge whether the intensity of the positioning signal of the first sensing hardware is lower than a threshold value or not while acquiring data acquired by each sensing hardware.

When the intelligent device judges that the intensity of the positioning signal of the first sensing hardware is lower than the threshold value, the moving step number of the target user in the preset time period, which is acquired by the second sensing hardware, can be acquired, and the moving step frequency of the target user in the preset time period is calculated based on the moving step number;

for example, when it is determined that the strength of the positioning signal of the GPS locator is lower than the threshold and cannot meet the requirement for calculating the distance, the moving step count of the target user within six minutes acquired by the second sensing hardware is acquired, and the moving step frequency of the target user within six minutes is calculated based on the moving step count.

After the movement stride frequency is calculated, a movement stride corresponding to the calculated movement stride frequency may be determined based on a first mathematical relationship between the movement stride frequency and the movement stride of the target user, and a movement distance of the target user within a preset period of time may be calculated based on the movement stride and the number of movement steps.

The first mathematical relation can be determined by data fitting based on sample data corresponding to a plurality of sample time periods, wherein the sample data comprises moving stride frequency data and moving stride data of a target user in the sample time periods;

for example, the mobile stride frequency data and the mobile stride data, which are acquired by the intelligent device at different time intervals and are specific to the same user, may be used as sample data, and a variable relationship may be obtained through data fitting; and calculating a moving stride corresponding to the moving stride frequency of the target user within six minutes according to the variable relation, and calculating the moving distance of the target user within six minutes based on the moving stride and the moving steps.

In the technical scheme, a plurality of sensors carried by intelligent equipment are used for detecting the motion of a target user, when one sensor does not meet the motion detection requirement, the motion data obtained by other sensors is calculated based on a mathematical relation fitted according to sample data of the target user in advance, and the motion data corresponding to the sensor which does not meet the motion detection requirement is calculated. According to the technical scheme, when the sensing hardware is abnormal, the calculated data is used for replacing the data corresponding to the abnormal sensing hardware, the integrity and accuracy of the data are guaranteed, and the user experience is improved.

The embodiments of the present disclosure are explained in detail below.

Referring to fig. 1, fig. 1 is a flowchart illustrating a motion detection method according to an exemplary embodiment, and as shown in fig. 1, the method is applied to an intelligent device and includes the following steps:

step 101: judging whether the intensity of the positioning signal of the first sensing hardware is lower than a threshold value; wherein the positioning signal is used for determining the moving distance of the target user in the preset time period;

step 102: if the intensity of the positioning signal is lower than a threshold value, acquiring the moving step number of the target user in the preset time period, which is acquired by the second sensing hardware, and calculating the moving step frequency of the target user in the preset time period based on the moving step number;

step 103: and determining a movement stride corresponding to the calculated movement stride frequency based on a first mathematical relationship between the movement stride frequency and the movement stride of the target user, and calculating the movement distance of the target user in the preset time period based on the movement stride and the movement step number.

The intelligent equipment is provided with first sensing hardware for determining the moving distance of a target user in a preset time period; and the second sensing hardware is used for acquiring the moving step number of the target user in the preset time period.

The intelligent device can comprise intelligent devices in any shapes and shapes; for example, a smart watch, smart bracelet, smart glasses, or other form of wearable device; or portable electronic devices such as smart phones and smart tablets.

In an illustrated embodiment, the smart device may be a smart band for performing motion detection.

Referring to fig. 2, fig. 2 is a hardware structure diagram of an intelligent device according to an exemplary embodiment of the present disclosure.

As shown in fig. 2, in the present disclosure, the smart device may specifically carry a processor, a first sensing hardware, and a second sensing hardware.

The processor may be connected to the first sensing hardware and the second sensing hardware at the same time, and the specific connection manner is not described in detail in the disclosure.

The first sensing hardware is used for determining the moving distance of the target user in a preset time period; for example, taking a smart bracelet for a six-minute walking test as an example, the moving distance of the target user within a preset time period is determined, specifically, the walking distance of the subject within six minutes is determined.

In one embodiment, the first sensing hardware may specifically include a satellite positioner.

The satellite positioner may be a GPS positioner, and the specific position of the GPS positioner is calculated by measuring distances from a plurality of satellites at known positions to the GPS positioner, and then the moving distance is further calculated.

Of course, the satellite positioner may be a beidou positioner, a galileo positioner, etc., and those skilled in the art may select the above-mentioned satellite positioner according to the requirements, which is not limited in the present disclosure.

In another embodiment shown, the positioning signal may specifically include a base station positioning signal; the first sensing hardware may specifically comprise a base station locator.

The second sensing hardware is used for acquiring the moving steps of the target user in a preset time period; for example, taking a smart bracelet for motion detection as an example, the number of moving steps of a target user in a preset time period is collected, specifically, the number of steps of a subject walking in six minutes is collected.

In one embodiment, the second sensing hardware may specifically include an acceleration sensor; alternatively, the second sensing hardware may specifically include a gyroscope and an acceleration sensor.

The acceleration sensor may be an ACC triaxial acceleration sensor, and the number of steps taken by the user is determined by acquiring the change of acceleration data expressed in the three-dimensional direction as the number of steps of the user changes.

Of course, the acceleration sensor may also be a single-axis acceleration sensor or a dual-axis acceleration sensor, and those skilled in the art can select the acceleration sensor according to the requirement, which is not limited in the present disclosure.

In addition, a gyroscope can be added to acquire the angular speed of the user; the acceleration data in the three-dimensional direction is combined with the angular velocity data, and the posture of the user during moving is accurately determined, so that the statistics of the moving steps is more accurate.

It should be noted that, the installation positions of the first sensing hardware and the second sensing hardware on the smart device are not particularly limited in this disclosure, and in practical applications, the installation positions may be customized according to the specific shape and actual requirements of the smart device;

for example, referring to fig. 3, taking the smart bracelet for performing motion detection as an example, the first sensing hardware and the second sensing hardware may be installed inside the bracelet, and are used for determining a moving distance of a wearer within a preset time period and collecting a moving step number of the wearer within the preset time period.

In practical applications, the first sensing hardware may be a satellite positioner; the second sensing hardware may specifically be an acceleration sensor, or a gyroscope and an acceleration sensor.

For example, in one illustrated embodiment, the first sensing hardware may specifically be a GPS locator, and the smart device may determine a moving distance of the target user within a preset time period through the GPS locator; the second sensing hardware may specifically be an ACC triaxial acceleration sensor, and the smart device may acquire the number of movement steps of the target user in the preset time period through the ACC triaxial acceleration sensor.

With continued reference to fig. 2, in the present disclosure, the hardware installed in the smart device may include hardware such as a display screen, communication hardware, and a speaker, in addition to the processor, the first sensing hardware, and the second sensing hardware.

The following takes the hardware architecture shown in fig. 2 as an example, and the technical solution of the present disclosure is described in detail through a specific embodiment.

In the disclosure, the smart device may be provided with a display screen capable of outputting the motion data acquired by the sensing hardware and the motion data calculated by the processor; it may also be provided with a communication hardware for communicating with the outside by means of a wireless and/or wired connection; the device can also be provided with a loudspeaker for broadcasting the motion detection result and prompting the user.

When the intelligent device is in use and the intensity of the positioning signal of the first sensing hardware is not lower than the threshold value, the first sensing hardware can be used for determining the moving distance of the target user in a preset time period; when the acquisition precision of the second sensing hardware is not lower than the threshold, the second sensing hardware can be used for acquiring the moving steps of the target user in a preset time period.

For example, when the strength of the positioning signal of the GPS locator is not lower than the threshold, the GPS locator may calculate the moving distance of the target user within a preset time period by receiving the positioning signal of the satellite; when the sampling precision of the ACC triaxial acceleration sensor is not lower than the threshold value, the ACC triaxial acceleration sensor can determine the moving steps of the target user in a preset time period through the collected acceleration data.

In practical application, when the intelligent device is used, the information such as the timestamp, the strength of the positioning signal, the corresponding position and the like recorded by the first sensing hardware in each positioning can be stored in the intelligent device, the moving distance can be calculated according to the change of the position, and the numerical value of the moving distance can be output through the display screen.

Similarly, when the intelligent device is used, the information such as the timestamp recorded in each sampling of the second sensing hardware and the acquired data can be stored in the intelligent device, the moving step number can be calculated according to the acquired data, and the moving step number value can be output through the display screen.

How the display of the motion data is performed is not particularly limited in this disclosure. Besides the moving distance and the moving step number output by the display screen, the moving distance and the moving step number can be output to other equipment with a display function to be displayed through communication hardware of the intelligent equipment.

For example, the display device having a wireless and/or wired connection relationship with the smart device outputs the received moving distance and moving steps determined by the smart device to the display screen for displaying.

In addition, how to store the motion detection data of the user is not particularly limited in the present disclosure. In addition to being stored in the intelligent device, the stored motion detection data can be uploaded to a server side for storage and/or backup through communication hardware of the intelligent device.

In one implementation, for the motion detection data stored in the smart device, mathematical fitting may be performed using a plurality of data as sample data to obtain a variable relationship in the motion data.

For example, several time periods may be selected as sample time periods, data corresponding to the sample time periods is used as sample data, the moving step frequency and the moving step width are used as variables, and mathematical fitting is performed based on the known sample time periods and the known sample data to obtain a variable relationship.

To improve the accuracy of the mathematical fit, the sample period may be determined as a period in which neither the first sensing hardware nor the second sensing hardware is anomalous.

For example, the sample period may include the strength of the positioning signal of the first sensing hardware not being below a threshold; and the acquisition accuracy of the second sensing hardware is not lower than the time period of the threshold value.

Of course, the above-described fitted variable relationship is such that, when the user changes with respect to the user corresponding to the motion detection data, the variable relationship should be fitted again based on the motion detection data of the new user.

In one implementation, when the user uses the smart device, the user identity can be bound and switched. For example, different users may be distinguished by adding a user identity.

Wherein, when the user is binding the identity, relevant information such as height, weight, age and the like can be input.

In addition, the method for binding and switching the identity of the user is not limited in this disclosure, and those skilled in the art may select the method according to the specific use mode of the intelligent device.

Correspondingly, when the data is stored in the intelligent device or the server, different user data can be stored separately according to different user identities, and a variable relation based on the fitting of the motion detection data of the user is determined for each user.

Specifically, in one embodiment shown, a first mathematical relationship may be established between the movement stride frequency and the movement stride of the target user; the first mathematical relationship can be determined by data fitting based on sample data corresponding to a plurality of sample time periods, wherein the sample data comprises moving step frequency data and moving step data of a target user in the sample time periods;

of course, a second mathematical relationship between the average moving speed of the target user and the moving stride frequency may be established; performing data fitting determination on the second mathematical relationship based on sample data corresponding to a plurality of sample time periods, wherein the sample data comprises average moving speed data and moving step frequency data of the target user in the sample time periods;

wherein to improve the accuracy of the established mathematical relationship, in one illustrated embodiment, the sample period may be determined to include the strength of the positioning signal of the first sensing hardware not being below the threshold; and the acquisition accuracy of the second sensing hardware is not lower than the time period of the threshold value.

In addition, in the present disclosure, data fitting determination may be performed by the processor of the smart device according to sample data corresponding to a plurality of sample periods stored on the smart device; the server side can also receive the motion detection data sent by the intelligent equipment, perform data fitting determination on sample data corresponding to a plurality of sample time periods, and then issue the mathematical relationship to the intelligent equipment. The present disclosure is not limited thereto, and those skilled in the art can select the configuration according to the hardware of the smart device.

When the intelligent equipment carries out motion detection and data recording, whether each sensing hardware is in a normal working state or not can be judged, and whether the intensity of a positioning signal of the first sensing hardware is lower than a threshold value or not and whether the acquisition precision of the second sensing hardware is lower than the threshold value or not are included.

When the sensing hardware is lower than the threshold value, the sensing hardware is determined to be in an abnormal state, and at the moment, the sensing hardware cannot acquire motion detection data or the motion detection data has overlarge deviation.

When the intensity of the positioning signal of the first sensing hardware is lower than a threshold value, determining that the first sensing hardware is in an abnormal state; and acquiring the moving step number of the target user in the preset time period, which is acquired by the second sensing hardware, and calculating the moving step frequency of the target user in the preset time period based on the moving step number.

For example, when a six-minute walking test is performed, if the indoor GPS positioning signal is weak and is lower than the threshold required by the test, the accurate walking distance cannot be calculated by the GPS positioning signal; acquiring the moving step number of the tested person in six minutes, which is acquired by an ACC triaxial acceleration sensor, and calculating the moving step frequency of the tested person in six minutes based on the moving step number; assuming that the number of moving steps of the subject in six minutes is 900 steps, the moving step frequency of the subject is 150 steps/minute.

After the movement stride frequency of the target user within the preset time period is calculated, a movement stride corresponding to the calculated movement stride frequency may be determined based on a first mathematical relationship between the movement stride frequency and the movement stride of the target user, and the movement distance of the target user within the preset time period may be calculated based on the movement stride and the movement step number.

For example, after the movement stride frequency of the subject is calculated, the movement stride corresponding to the calculated movement stride frequency may be determined based on the mathematical relationship between the movement stride frequency and the movement stride corresponding to the subject; assuming that, based on the mathematical relationship, when the moving stride frequency of the subject is 150 steps/minute, the determined moving stride is 80cm; the moving distance of the human subject within six minutes is calculated to be 720m according to the moving step frequency of the human subject of 150 steps/minute and the moving step number of 900 steps.

The smart device may match the calculated movement distance with a simple determination table shown in table 1 below to determine a determination result of the six-minute walking test, where table 1 is shown below:

walking distance of six minutes	Level of cardiopulmonary function
		Less than 150m	Severe functional insufficiency
150～425m	Moderate dysfunction
		426～550m	Mild functional insufficiency
Greater than 550m	Normal function of the device

TABLE 1

In practical application, the intelligent device can output the walking distance and the test result of the six-minute walking test of the testee through the display screen and/or the loudspeaker; furthermore, the intelligent device can also output related suggestions according to the test results.

In one embodiment, when the acquisition precision of the second sensing hardware is lower than a threshold value, determining that the second sensing hardware is in an abnormal state; and acquiring the moving distance of the target user in the preset time period, which is determined by the first sensing hardware, and calculating the average moving speed of the target user in the preset time period based on the moving distance.

For example, when a user walks, if the ACC triaxial acceleration sensor is abnormal, the deviation value of the data is too large, and the acquisition precision is lower than a threshold value, the ACC triaxial acceleration sensor cannot accurately acquire the number of moving steps of the user within a preset time period; assuming that the GPS locator calculates the moving distance of the user to be 720m in 6 minutes, the average moving speed of the user during 6-minute walking is 7.2km/h.

After the average moving speed of the target user within the preset time period is calculated, a moving step frequency corresponding to the calculated average moving speed may be determined based on a second mathematical relationship between the average moving speed of the target user and the moving step frequency, and the number of moving steps of the target user within the preset time period may be calculated based on the moving step frequency.

For example, after the average moving speed of the user is calculated, the moving step frequency corresponding to the calculated average moving speed may be determined based on the mathematical relationship between the average moving speed corresponding to the user and the moving step frequency; assuming that the average moving speed of the user is 7.2km/h based on the mathematical relationship, the determined moving step frequency is 150 steps/min; the number of steps moved by the user in 6 minutes is calculated to be 900 steps according to the moving step frequency of the user of 150 steps/minute and the moving time length of 6 minutes.

In the technical scheme, a plurality of sensors carried by intelligent equipment are used for detecting the motion of a target user, when a certain sensor does not meet the motion detection requirement, the motion data obtained by other sensors is calculated based on a mathematical relationship fitted according to sample data of the target user in advance, and the motion data corresponding to the sensor which does not meet the motion detection requirement is calculated. According to the technical scheme, when the sensing hardware is abnormal, the calculated data is used for replacing the data corresponding to the abnormal sensing hardware, the integrity and accuracy of the data are guaranteed, and the user experience is improved.

Please refer to fig. 4, fig. 4 is an architecture diagram of a motion detection system built based on the above smart device according to the present disclosure.

As shown in fig. 4, the motion detection system may include a server and an intelligent device communicating with the server.

When the intelligent device is used, the user can perform identity binding or replace identity information stored on the intelligent device. After determining the identity of the user, the smart device may bind the data for subsequent motion detection with the identity of the user. A person skilled in the art may refer to the description of the previous embodiment for a specific binding process, which is not described in detail in this embodiment.

When a user moves in the detection process, the intelligent device can determine the moving distance of a target user in a preset time period through the carried first sensing hardware, and records information such as a timestamp, the strength of a positioning signal and a corresponding position which are recorded when the first sensing hardware is positioned every time; the moving steps of the target user in a preset time period can be collected through the second sensing hardware, and meanwhile, information such as a timestamp and collected data recorded during each sampling of the second sensing hardware is recorded. A person skilled in the art can refer to the description of the previous embodiment for a specific process of acquiring motion data by sensing hardware, and details are not described in this embodiment.

After the intelligent device obtains the motion detection data of the target user, the motion detection data corresponding to the identity can be transmitted to the server according to the identity bound to the target user; and the server stores the data into a database according to the user identity.

After receiving the data, the server may perform mathematical fitting based on the motion data of the user to determine a mathematical relationship for the user; and send the mathematical relationship to the smart device. A person skilled in the art may refer to the description of the previous embodiment in the specific mathematical relationship establishing process, which is not described in detail in this embodiment.

The intelligent device can judge the working state of the sensing hardware in the motion detection process, and when the state of one sensing hardware is determined to be abnormal, the intelligent device acquires the motion detection data determined by other sensing hardware; and calculating the motion data corresponding to the abnormal state period of the sensing hardware based on the received mathematical relation from the server. For a specific process of determining the sensing hardware state abnormality, a person skilled in the art may refer to the description of the previous embodiment, and details are not described in this embodiment.

Therefore, when the sensing hardware is abnormal, the calculated data is used for replacing the data corresponding to the abnormal sensing hardware, the integrity and the accuracy of the data are guaranteed, and the user experience is improved.

Corresponding to the above embodiment of the motion detection method, the present disclosure also provides an embodiment of a motion detection apparatus.

Referring to fig. 5, fig. 5 is a block diagram of a motion detection apparatus according to an exemplary embodiment of the present disclosure, which is applied to an intelligent device equipped with a first sensing hardware for determining a moving distance of a target user within a preset time period; the second sensing hardware is used for acquiring the moving steps of the target user in the preset time period; the device comprises:

a first determining unit 501, configured to determine whether the strength of the positioning signal of the first sensing hardware is lower than a threshold; wherein the positioning signal is used for determining the moving distance of the target user in the preset time period;

a first calculating unit 502, configured to, when the intensity of the positioning signal is lower than a threshold, obtain the number of moving steps of the target user in the preset time period, which is acquired by the second sensing hardware, and calculate a moving step frequency of the target user in the preset time period based on the number of moving steps;

a second calculating unit 503, configured to determine a movement stride corresponding to the calculated movement stride based on a first mathematical relationship between the movement stride and the movement stride of the target user, and calculate a movement distance of the target user within the preset time period based on the movement stride and the movement step number.

Optionally, the apparatus further comprises:

Optionally, the satellite locator comprises a GPS locator.

Optionally, the second sensing hardware comprises an acceleration sensor; alternatively, the first and second liquid crystal display panels may be,

the second sensing hardware includes a gyroscope and an acceleration sensor.

Optionally, the first mathematical relationship is determined by data fitting based on sample data corresponding to a plurality of sample time periods, where the sample data includes moving stride frequency data and moving stride data of the target user in the sample time periods;

and performing data fitting determination on the second mathematical relationship based on sample data corresponding to a plurality of sample time periods, wherein the sample data comprises average moving speed data and moving step frequency data of the target user in the sample time periods.

Optionally, the sample period includes that the intensity of the positioning signal of the first sensing hardware is not lower than a threshold; and the acquisition precision of the second sensing hardware is not lower than the time period of the threshold value.

Optionally, the smart device comprises a wearable device.

Optionally, the wearable device includes a smart band or a smart watch for performing motion detection.

With regard to the apparatus in the above embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments of the related method, and will not be described in detail here.

For the device embodiment, since it basically corresponds to the method embodiment, reference may be made to the partial description of the method embodiment for relevant points. The above-described embodiments of the apparatus are merely illustrative, wherein the modules described as separate parts may or may not be physically separate, and the parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. One of ordinary skill in the art can understand and implement it without inventive effort.

Referring to fig. 6, the present disclosure further provides an embodiment of an intelligent device, corresponding to the above method embodiment.

As shown in fig. 6, the smart device includes:

a processor and a memory for storing processor-executable instructions; wherein the processor and the memory are typically interconnected by an internal bus. The processor may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute a relevant program to implement the technical solution provided by the embodiments of the present disclosure.

In this embodiment, the processor is caused to, by reading and executing machine-executable instructions stored by the memory corresponding to control logic for motion detection:

judging whether the intensity of the positioning signal of the first sensing hardware is lower than a threshold value; wherein the positioning signal is used for determining the moving distance of the target user in the preset time period;

In this embodiment, the processor is further caused to:

if the acquisition precision is lower than a threshold value, acquiring the movement distance of the target user in the preset time period, which is determined by the first sensing hardware, and calculating the average movement speed of the target user in the preset time period based on the movement distance;

In this embodiment, the positioning signal comprises a satellite positioning signal; the first sensing hardware includes a satellite positioner. Specifically, the satellite positioner comprises a GPS positioner.

In this embodiment, the second sensing hardware includes an acceleration sensor; alternatively, the second sensing hardware includes a gyroscope and an acceleration sensor. Specifically, the acceleration sensor includes an ACC three-axis acceleration sensor.

In this embodiment, the smart device comprises a wearable device. Specifically, the wearable device comprises a smart bracelet for performing motion detection.

In this embodiment, the first mathematical relationship is determined by fitting data based on sample data corresponding to a plurality of sample time periods, where the sample data includes moving stride frequency data and moving stride data of the target user in the sample time periods;

specifically, the sample period includes that the intensity of the positioning signal of the first sensing hardware is not lower than a threshold value; and the acquisition precision of the second sensing hardware is not lower than the period of a threshold value.

Embodiments of the present disclosure further provide a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps in the method according to any one of the above embodiments.

Referring to fig. 7, the present disclosure also provides a schematic block diagram of an apparatus 700 for motion detection, corresponding to the above method embodiment.

For example, apparatus 700 may be a smart phone, smart bracelet, smart watch, game console, tablet device, medical device, fitness device, personal digital assistant, and the like.

As shown in fig. 7, the apparatus 700 may include one or more of the following components: processing component 702, memory 704, power component 706, multimedia component 708, audio component 710, input/output (I/O) interface 712, sensor component 714, and communications component 716.

The processing component 702 generally controls overall operation of the device 700, such as operations associated with display, data communication, detection operations, and recording operations. The processing components 702 may include one or more processors 720 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 702 may include one or more modules that facilitate interaction between the processing component 702 and other components. For example, the processing component 702 may include a multimedia module to facilitate interaction between the multimedia component 708 and the processing component 702.

The memory 704 is configured to store various types of data to support operations at the apparatus 700. Examples of such data include instructions for any application or method operating on device 700, motion detection data, notifications, and so forth. The memory 704 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

The power supply component 706 provides power to the various components of the device 700. The power components 706 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the apparatus 700.

The multimedia component 708 includes a screen that provides an output interface between the device 700 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.

The audio component 710 is configured to output and/or input audio signals. For example, audio component 710 includes a Microphone (MIC) configured to receive external audio signals when apparatus 700 is in an operating mode, such as a recording mode and a speech recognition mode. The received audio signal may further be stored in the memory 704 or transmitted via the communication component 716. In some embodiments, audio component 710 further includes a speaker for outputting audio signals.

The I/O interface 712 provides an interface between the processing component 702 and peripheral interface modules, which may be click wheels, buttons, and the like. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 714 includes one or more sensors for providing status assessment of various aspects of the apparatus 700. For example, sensor assembly 714 may detect an open/closed state of device 700, the relative positioning of components, such as a GPS locator and an ACC triaxial acceleration sensor of device 700, sensor assembly 714 may also detect a change in position of device 700 or a component of device 700, the presence or absence of user contact with device 700, orientation or acceleration/deceleration of device 700, and a change in temperature of device 700. The sensor assembly 714 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 714 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 714 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 716 is configured to facilitate wired or wireless communication between the apparatus 700 and other devices. The device 700 may access a wireless network based on a communication standard, such as WiFi,2G or 3g,4glte, 5G NR, or a combination thereof. In an exemplary embodiment, the communication component 716 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 716 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the apparatus 700 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the methods described in any of the above embodiments.

In an exemplary embodiment, a non-transitory computer readable storage medium comprising instructions, such as the memory 704 comprising instructions, executable by the processor 720 of the apparatus 700 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

Fig. 8 is a block diagram illustrating an apparatus 800 for motion detection in accordance with an example embodiment. For example, the apparatus 800 may be provided as a server. Referring to fig. 8, the apparatus 800 includes a processing component 822, which further includes one or more processors and memory resources, represented by memory 832, for storing instructions, such as applications, that may be executed by the processing component 822. The application programs stored in memory 832 may include one or more modules that each correspond to a set of instructions. Further, the processing component 822 is configured to execute instructions to perform the methods of 8230, 8230

The device 800 may also include a power component 826 configured to perform power management of the device 800, a wired or wireless network interface 850 configured to connect the device 800 to a network, and an input/output (I/O) interface 858. The apparatus 800 may operate based on an operating system stored in the memory 832, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, or the like.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice in the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It will be understood that the present disclosure is not limited to the precise arrangements that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A motion detection method is applied to intelligent equipment and is characterized in that the intelligent equipment is provided with first sensing hardware for determining the moving distance of a target user in a preset time period; the second sensing hardware is used for acquiring the moving steps of the target user in the preset time period; the method comprises the following steps:

and determining a movement stride corresponding to the calculated movement stride frequency based on a first mathematical relationship between the movement stride frequency and the movement stride of the target user, and calculating the movement distance of the target user in the preset time period based on the movement stride and the movement step number.

2. The method of claim 1, further comprising:

3. The method of claim 1, the positioning signal comprising a satellite positioning signal; the first sensing hardware includes a satellite positioner.

4. The method of claim 3, the satellite locator comprising a GPS locator.

5. The method of claim 1, the second sensing hardware comprising an acceleration sensor; alternatively, the first and second electrodes may be,

the second sensing hardware includes a gyroscope and an acceleration sensor.

6. The method of claim 5, the acceleration sensor comprising an ACC triaxial acceleration sensor.

7. The method of claim 2, the first mathematical relationship being determined by data fitting based on sample data corresponding to a number of sample time periods, the sample data comprising movement stride frequency data and movement stride data for the target user over the sample time periods;

8. The method of claim 7, the sample period comprising a strength of a localization signal of the first sensing hardware not being below a threshold; and the acquisition precision of the second sensing hardware is not lower than the time period of the threshold value.

9. The method of any of claims 1-8, the smart device comprising a wearable device.

10. The method of claim 9, the wearable device comprising a smart bracelet or smart watch for motion detection.

11. A motion detection device is applied to intelligent equipment and is characterized in that the intelligent equipment is provided with first sensing hardware for determining the moving distance of a target user in a preset time period; the second sensing hardware is used for acquiring the moving steps of the target user in the preset time period; the device comprises:

12. The apparatus of claim 11, the apparatus further comprising:

the third calculating unit is used for acquiring the moving distance of the target user in the preset time period, which is determined by the first sensing hardware, when the acquisition precision is lower than a threshold value, and calculating the average moving speed of the target user in the preset time period based on the moving distance;

a fourth calculating unit, configured to determine a moving step frequency corresponding to the calculated average moving speed based on a second mathematical relationship between the average moving speed and the moving step frequency of the target user, and calculate a moving step number of the target user in the preset time period based on the moving step frequency.

13. A smart device, comprising:

the first sensing hardware is used for determining the moving distance of a target user in a preset time period;

the second sensing hardware is used for collecting the moving steps of the target user in the preset time period;

a processor and a memory for storing processor-executable instructions;

wherein the processor is configured to perform the method of any of the preceding claims 1-10.

14. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 10.