CN114694799A - Data processing method and device, electronic equipment and storage medium - Google Patents

Data processing method and device, electronic equipment and storage medium Download PDF

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Publication number
CN114694799A
CN114694799A CN202210348562.XA CN202210348562A CN114694799A CN 114694799 A CN114694799 A CN 114694799A CN 202210348562 A CN202210348562 A CN 202210348562A CN 114694799 A CN114694799 A CN 114694799A
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China
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data
user
sensor data
riding
motion
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CN202210348562.XA
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Chinese (zh)
Inventor
陈佳洲
赵威
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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Priority to CN202210348562.XA priority Critical patent/CN114694799A/en
Publication of CN114694799A publication Critical patent/CN114694799A/en
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02438—Detecting, measuring or recording pulse rate or heart rate with portable devices, e.g. worn by the patient
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1118—Determining activity level
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14542—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring blood gases
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
    • A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A61B5/681—Wristwatch-type devices
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/18—Stabilised platforms, e.g. by gyroscope

Abstract

The embodiment of the application discloses a data processing method and device, electronic equipment and a storage medium. The electronic equipment keeps consistent with the motion state of the riding equipment, and obtains sensor data of the motion sensor; then, optimizing the sensor data to obtain optimized sensor data; and finally, carrying out data analysis on the optimized sensor data to obtain the pedaling frequency of the user during riding. Therefore, the pedaling frequency of the user riding the riding device can be accurately detected through the mobile device of the user.

Description

Data processing method and device, electronic equipment and storage medium
Technical Field
The present application relates to the field of computer technologies, and in particular, to a data processing method, an electronic device, and a storage medium.
Background
The riding is a fitness mode capable of exercising body, but some riding devices are not provided with professional sensors which are unique to the riding devices in a gymnasium, such as bicycles, and a user cannot know the self pedaling frequency when riding with the riding devices.
Disclosure of Invention
The embodiment of the application provides a data processing method, device electronic equipment and a storage medium. The data processing method can accurately detect the pedaling frequency of the user during riding through the mobile equipment of the user.
In a first aspect, an embodiment of the present application provides a data processing method, which is applied to an electronic device, where the electronic device includes a motion sensor, and a motion state of the electronic device is consistent with a motion state of a riding device, and the method includes:
acquiring sensor data of a motion sensor;
optimizing the sensor data to obtain optimized sensor data;
and carrying out data analysis on the optimized sensor data to obtain the pedaling frequency of the user when riding the riding device.
In a second aspect, an embodiment of the present application provides a data processing apparatus, which is applied to an electronic device, where the electronic device includes a motion sensor, and a motion state of the electronic device is consistent with a motion state of a riding device, and the data processing apparatus includes:
an acquisition module for acquiring sensor data of the motion sensor;
the optimization module is used for optimizing the sensor data to obtain optimized sensor data;
and the analysis module is used for carrying out data analysis on the optimized sensor data to obtain the pedaling frequency of the user when riding the riding device.
In a third aspect, an embodiment of the present application provides an electronic device, including: a memory storing executable program code, a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the steps in the data processing method provided in the embodiment of the application.
In a fourth aspect, an embodiment of the present application provides a storage medium, where the storage medium stores multiple instructions, and the instructions are suitable for being loaded by a processor to perform steps in a data processing method provided in the embodiment of the present application.
In the embodiment of the application, the electronic equipment keeps consistent with the motion state of the riding equipment, and the sensor data of the motion sensor is acquired; then, optimizing the sensor data to obtain optimized sensor data; and finally, performing data analysis on the optimized sensor data to obtain the pedaling frequency of the user when riding the riding device. Therefore, the pedaling frequency of the user riding the riding device can be accurately detected through the mobile device of the user.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a first flowchart of a data processing method according to an embodiment of the present disclosure.
Fig. 2 is a second flowchart of the data processing method according to the embodiment of the present application.
Fig. 3 is a first schematic diagram of sensor data provided by an embodiment of the present application.
FIG. 4 is a second schematic diagram of sensor data provided by embodiments of the present application
Fig. 5 is a schematic diagram of data analysis of optimized sensor data according to an embodiment of the present application.
Fig. 6 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application.
Fig. 7 is a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The riding is a fitness mode capable of exercising body, but some riding devices are not provided with professional sensors which are unique to the riding devices in a gymnasium, such as bicycles, and a user cannot know the self pedaling frequency when riding with the riding devices.
In the prior art, a professional sensor can be arranged on a frame or a pedal to detect the pedaling frequency of a user, but the sports equipment has the defects of high price, complex assembly and disassembly and the like.
In order to solve the technical problem, in the embodiment of the application, the step frequency of the user during riding is detected by using the mobile device of the user, for example, the step frequency of the user during riding is detected by using a smart watch or a smart phone worn by the user. Therefore, when a user rides anywhere and anytime, the pedaling frequency of the user during riding can be acquired.
The embodiment of the application provides a data processing method and device, electronic equipment and a storage medium. The following are detailed below.
Referring to fig. 1, fig. 1 is a first flow chart of a data processing method according to an embodiment of the present disclosure.
110. Sensor data of a motion sensor is acquired.
In the embodiment of the application, when a user rides, the motion state of the user is consistent with that of the riding device, and the motion states of the electronic device worn by the user and the riding device are also consistent. The electronic equipment obtains sensor data through the motion sensor of the electronic equipment, and therefore motion conditions of the user during riding are analyzed through the sensor data, and the pedaling frequency of the user during riding the riding equipment is determined.
For example, the electronic device is a smart watch, a smart phone and other devices worn by the user, when the user rides, the motion state of the electronic device is consistent with that of the riding device, and then the electronic device can analyze the motion condition of the user according to the sensor data acquired by the electronic device, so that the pedaling frequency of the user when riding the riding device is obtained.
In some embodiments, the electronic device includes multiple types of motion sensors, such as acceleration sensors, gyroscopes, and the like. The electronic device can acquire sensor data corresponding to the user during riding through the sensor.
For example, the electronic device may obtain acceleration data corresponding to different times as sensor data, and use gyroscope data corresponding to different times as the sensor data.
In some embodiments, the electronic device may also acquire physiological data of the user, such as heart rate, blood oxygen saturation, etc., which may be acquired by a sensor of the electronic device worn by the user, such as a heart rate sensor, blood oxygen sensor, etc.
120. And optimizing the sensor data to obtain the optimized sensor data.
In some embodiments, before optimizing the sensor data to obtain the optimized sensor data, the electronic device may further determine a motion state of the user, so as to determine whether the user is in a riding state.
The electronic device can also determine whether the user is in a riding state according to the physiological data and the sensor data of the user.
For example, the physiological data may be a heart rate, the sensor data may be an acceleration, and the electronic device may first determine whether the acceleration is within a preset acceleration interval, and if the acceleration is within the preset acceleration interval, it indicates that the user may be in a riding state, but although the acceleration is within the preset acceleration interval, the user may be a vehicle such as an electric vehicle or a motorcycle.
In order to further judge whether the user is in a riding state, the electronic equipment judges whether the heart rate of the user exceeds a preset heart rate, if the heart rate of the user exceeds the preset heart rate, the user is indicated to be in motion at the moment, and if the user is in a preset acceleration interval in combination with acceleration, the user is indicated to be riding, for example, the user is riding a bicycle.
And when the user is in the riding state, optimizing the sensor data to obtain the optimized sensor data.
If the user is not in the riding state, at this time, the electronic device may continue to acquire corresponding sensor data using the motion sensor, thereby determining whether the user is in the riding state at the next time or for the next time period.
In the process of riding by a user, the action signal is transmitted to the electronic equipment through the body due to the existence of the body of the user and the left-right and up-down swing signals of the user in cooperation with the feet when the user treads, but due to the vibration of the bicycle on the ground and the shaking of the bicycle, the sensor data acquired by the electronic equipment has noise signals and interference signals.
In some embodiments, the electronic device may optimize the sensor data after acquiring the sensor data, thereby obtaining optimized sensor data. For example, the electronic device may delete useless data in the sensor data, such as some null values or abnormal values, so as to obtain optimized sensor data. Or to filter noise and interference signals in the sensor data.
In some embodiments, before optimizing the sensor data to obtain the optimized sensor data, the electronic device may further set a plurality of preset frequency ranges corresponding to the sensor data. The preset frequency ranges are used for performing the subsequent filtering processing on the sensor data, for example, the preset frequency ranges include [0.5,1.0], [0.9,1.4], [1.3,1.8], and there is a corresponding overlap portion between the preset frequency ranges, because the filter has passband attenuation at the edge of the frequency range, and in order to ensure that there is a normal filtering signal in the full frequency band, there needs to be a partial overlap between two adjacent frequency ranges.
The electronic equipment can extract sub-sensor data with preset duration according to the sensor data, then determine a preset frequency range to which the sub-sensor data belongs, and finally perform filtering processing on the sub-sensor data according to the preset frequency range to which the sub-sensor data belongs to obtain optimized sensor data.
For example, the electronic device can acquire sub-sensor data with a preset duration of about 5-7 seconds in the sensor data, and at the same time, the electronic device can determine a preset frequency range to which the sub-sensor data belong, then determine an upper limit frequency and a lower limit frequency corresponding to the preset frequency range to which the sub-sensor data belong, and then filter the sub-sensor data according to the upper limit frequency and the lower limit frequency, so as to obtain optimized sensor data. For example, in the process of filtering the sub-sensor data, the high-frequency noise signal and the gravity signal in the sub-sensor data can be filtered out, so that the optimized sensor data can be obtained.
130. And carrying out data analysis on the optimized sensor data to obtain the pedaling frequency of the user when riding the riding device.
After the optimized sensor data is obtained, there is no noise signal in the optimized sensor data. The electronic device can directly perform data analysis on the optimized sensor data, so that the corresponding step frequency of the user is obtained.
In some embodiments, the optimized sensor data includes acceleration data and gyroscope data, the optimized sensor data includes gyroscope data and/or acceleration data, and the electronic device may perform data analysis on the acceleration data and/or the gyroscope data to obtain a corresponding cadence of the user.
The electronic equipment can determine the treading times within the preset duration according to the optimized sensor data; and determining the pedaling frequency corresponding to the user according to the preset time length and the pedaling frequency.
For example, in the acceleration signal, if the absolute value of the acceleration signal at a certain time exceeds the preset threshold corresponding to the acceleration signal, it indicates that the acceleration data at that time is valid data, and the number of steps is counted.
For example, in the gyroscope data, if the absolute value of the numerical value corresponding to the gyroscope data at a certain time exceeds the preset threshold value corresponding to the gyroscope data, it indicates that the gyroscope data at the certain time is valid data, and the number of treading times is counted.
Within the preset duration, the electronic device can determine the corresponding treading times according to the acceleration signal and/or the gyroscope signal, and finally, the treading frequency corresponding to the user can be obtained by dividing the treading times by the preset duration.
In some embodiments, after obtaining the pedaling frequency of the user, the electronic device inputs the pedaling frequency and the physiological data of the user into the motion analysis model, and outputs a corresponding motion scheme of the user.
For example, the electronic device may input the user's pedaling frequency and the user's heart rate into the motion analysis model, and the motion analysis model calculates a user's pedaling frequency interval, heart rate interval, acceleration interval, speed interval, and the like in the future time according to the user's heart rate and the user's pedaling frequency. Or the motion analysis model may input a specific cadence, speed, etc. corresponding to the user at a future time.
In the embodiment of the application, the electronic equipment keeps consistent with the motion state of the riding equipment, and the sensor data of the motion sensor is acquired; then, optimizing the sensor data to obtain optimized sensor data; and finally, performing data analysis on the optimized sensor data to obtain the pedaling frequency of the user when riding the riding device. Therefore, the pedaling frequency of the user riding the riding device can be accurately detected through the mobile device of the user.
For more detailed understanding of the data processing method provided in the embodiment of the present application, please continue to refer to fig. 2, where fig. 2 is a second flow chart of the data processing method provided in the embodiment of the present application, and the data processing method may include the following steps:
201. sensor data of a motion sensor and physiological data of a user are acquired.
In some embodiments, the sensor data and the physiological data of the user may be acquired by an electronic device, such as a smart watch, a smart phone, etc., worn by the user.
For example, the acceleration sensor and the gyroscope may be used to respectively acquire acceleration data and gyroscope data, the heart rate sensor may be used to acquire the heart rate of the user, and the blood oxygen sensor may be used to acquire the blood oxygen of the user.
It should be noted that, when the user is in the riding state, the motion states of the electronic device and the riding device are kept consistent, for example, when the user wears the smart watch and the smart phone to ride a bicycle, the motion states of the smart watch and the smart phone are consistent with the motion state of the bicycle.
202. Determining whether the user is in a riding state according to the physiological data and the sensor data.
In some embodiments, the physiological data may include heart rate and the sensor data may include acceleration.
The electronic device may first determine whether the acceleration is within a preset acceleration interval, and if the acceleration is within the preset acceleration interval, it indicates that the user may be in a riding state.
In order to further judge whether the user is in a riding state, the electronic equipment judges whether the heart rate of the user exceeds a preset heart rate, if the heart rate of the user exceeds the preset heart rate, the user is indicated to be moving at the moment, and in combination with the fact that the acceleration is in a preset acceleration interval, the user is indicated to be riding, namely the user is riding a bicycle.
In some embodiments, the electronic device may also determine whether the user is in a cycling state through a variety of physiological data and a variety of sensor data. Such as physiological data including heart rate and blood oxygen, and sensor data including acceleration data and gyroscope data.
The electronic device may determine whether the user is in the fast moving state according to the acceleration data and the gyroscope data, for example, may determine whether the user is in the fast moving state according to a preset acceleration interval in which the acceleration data is located and an angle change condition corresponding to the gyroscope data.
If the user is in a rapid movement state, whether the body of the user is in a movement state is determined according to the heart rate and the blood oxygen, for example, when the heart rate exceeds a preset heart rate threshold value, and the blood oxygen saturation is in a preset blood oxygen saturation interval, the body of the user is in movement, and the user is in a riding state.
The above is merely an example, and the electronic device may also adopt other ways to determine whether the user is in the riding state. For example, the motion state of the user may be determined by a motion condition input by the user, or by analyzing sensor data according to some neural network model.
When the user is not in the riding state, step 203 is entered. When the user is in the riding state, step 204 is entered.
203. And if the user is not in the riding state, stopping acquiring the pedaling frequency of the user.
If the user is not in the riding state, the pedaling frequency of the user does not need to be continuously acquired, and the electronic equipment does not need to make suggestions on the motion condition of the user.
204. And if the user is in the riding state, determining a plurality of preset frequency ranges corresponding to the sensor data.
If the user is in the riding state, it is indicated that the pedaling frequency of the user needs to be detected, and before the pedaling frequency of the user is determined according to the sensor data, the electronic device may determine a plurality of preset frequency ranges corresponding to the sensor data.
The preset frequency ranges are used for performing the subsequent filtering processing on the sensor data, for example, the preset frequency ranges include [0.5,1.0], [0.9,1.4], [1.3,1.8], and there is a corresponding overlap portion between the preset frequency ranges, because the filter has passband attenuation at the edge of the frequency range, and in order to ensure that there is a normal filtering signal in the full frequency band, there needs to be a partial overlap between two adjacent frequency ranges.
205. And extracting sub-sensor data with preset duration from the sensor data.
In some embodiments, the electronic device may analyze the user's pedaling frequency by using the sub-sensor data within a preset time duration, for example, taking 5 to 7 seconds before the current time as the preset time duration, and meanwhile, obtain the sub-sensor data corresponding to the preset time duration from the sensor data.
In some embodiments, the preset duration may be user-defined, such as a user inputting a user-defined preset duration on the electronic device.
In some embodiments, the electronic device may determine a preset frequency range to which the sub-sensor data in a preset duration belongs, for example, the electronic device may determine the preset frequency range to which the sensor signal belongs through a classification algorithm, where the classification algorithm may include a naive bayes algorithm, a decision tree algorithm, and the like.
For example, the electronic device may set different tags for a plurality of preset frequency ranges, determine the tag to which the sub-sensor data belongs through a classification algorithm, and use the preset frequency range corresponding to the tag as the preset frequency range to which the sub-sensor data belongs.
206. And filtering the sub-sensor data according to the preset frequency range of the sub-sensor data to obtain optimized sensor data.
In some embodiments, the electronic device may determine an upper limit frequency and a lower limit frequency corresponding to a preset frequency range to which the sub-sensor data belongs, and then filter the sub-sensor data according to the upper limit frequency and the lower limit frequency, so as to obtain the optimized sensor data. For example, in the process of filtering the sub-sensor data, the high-frequency noise signal and the gravity signal in the sub-sensor data can be filtered out, so that the optimized sensor data can be obtained.
Referring to fig. 3, fig. 3 is a first schematic diagram of sensor data according to an embodiment of the present disclosure.
As shown in fig. 3, where the horizontal axis is time and the vertical axis is acceleration.
The sub-sensor data is acceleration data, and the electronic equipment can filter the sub-sensor data according to a preset frequency range to which the sub-sensor data belongs, so that high-frequency noise signals and gravity signals in the sub-sensor data are removed, and the filtered sub-sensor data, namely the optimized sensor data, is obtained.
Referring to fig. 4, fig. 4 is a second schematic diagram of sensor data according to an embodiment of the present disclosure.
As shown in fig. 4, in which the horizontal axis is time and the vertical axis is angular velocity.
The sub-sensor data is gyroscope data, and the electronic equipment can filter the sub-sensor data according to a preset frequency range to which the sub-sensor data belongs, so that high-frequency noise signals and gravity signals in the sub-sensor data are removed, and the filtered sub-sensor data, namely the optimized sensor data, is obtained.
207. And determining the treading times of the user within the preset time according to the optimized sensor data.
The optimized sensor data includes acceleration data and gyroscope data.
For example, in the acceleration data, if the absolute value of the numerical value corresponding to the acceleration at a certain time exceeds the preset threshold value corresponding to the acceleration, the acceleration at that time is valid data, and the number of steps is counted.
For example, in the gyroscope data, if the absolute value of the numerical value corresponding to the gyroscope data at a certain time exceeds the preset threshold value corresponding to the gyroscope data, it indicates that the gyroscope data at the certain time is valid data, and the number of treading times is counted.
In some embodiments, the electronic device may screen the gyroscope data and the acceleration data for target data that meets a preset condition; determining a plurality of moments corresponding to the target data, and determining the number of different moments in the plurality of moments; the number of different moments is determined as the number of treading.
The preset condition may be that the gyroscope data must be greater than a corresponding preset threshold, and the acceleration data must be greater than a corresponding preset threshold.
Specifically, referring to fig. 5, fig. 5 is a schematic diagram of data analysis performed on optimized sensor data according to an embodiment of the present disclosure.
When the optimized sensor data includes gyroscope data and acceleration data, as shown in fig. 5, in the gyroscope data and the acceleration data corresponding to the bounding box 1, a numerical absolute value of the acceleration data in the bounding box 1 exceeds a preset threshold value of 0.25, and a numerical absolute value of the gyroscope data does not exceed a corresponding preset threshold value of 0.05, at this time, because the acceleration data is obvious, the gyroscope data and the acceleration data corresponding to the bounding box 1 are counted as the number of steps. Alternatively, the acceleration data is determined as the target data by counting the time corresponding to the acceleration data.
In the gyroscope data and the acceleration data corresponding to the delineation frame 2, wherein the numerical absolute value of the acceleration data in the delineation frame 2 exceeds a preset threshold value of 0.25, the numerical absolute value of the gyroscope data exceeds a corresponding preset threshold value of 0.05, and the gyroscope data and the acceleration data are obvious, the gyroscope data and the acceleration data corresponding to the delineation frame 2 are counted into the treading times. Alternatively, the acceleration data and the gyro data are determined as target data by taking the time corresponding to the acceleration data and the gyro data.
In the gyroscope data and the acceleration data corresponding to the delineation frame 3, the numerical absolute value of the acceleration data in the delineation frame 3 does not exceed the preset threshold value of 0.25, and the numerical absolute value of the gyroscope data does not exceed the corresponding preset threshold value of 0.05, at this time, because the gyroscope data is obvious, the corresponding gyroscope data and acceleration data in the delineation frame 3 are counted into the treading times. Or, the time corresponding to the gyroscope data is counted, and the gyroscope data is determined as target data.
After the time corresponding to each target data is acquired, the electronics can determine the number of different times in a plurality of times, and then determine the number of different times as the number of treading times.
By the method, data analysis is carried out on the optimized sensor data in the whole preset time length, so that the corresponding treading times of the user in the preset time length are determined.
208. And determining the pedaling frequency corresponding to the user according to the preset time length and the pedaling frequency.
In some embodiments, the electronic device may determine the number of steps per second by dividing the number of steps by the preset time length, and then obtain the frequency of steps corresponding to the user by multiplying the number of steps per second by 60.
209. And inputting the pedaling frequency and the physiological data of the user into the motion analysis model, and outputting a motion scheme corresponding to the user.
In some embodiments, before inputting the pedaling frequency and the physiological data of the user into the motion analysis model and outputting the motion scheme corresponding to the user, the electronic device may further obtain historical motion power corresponding to the user in a historical time period, and use the historical motion power as an output quantity of the basic model; acquiring historical stepping frequency, historical speed and historical acceleration corresponding to a user in a historical time period, and taking the historical stepping frequency, the historical speed and the historical acceleration as input quantities of a basic model; and training the basic model according to the input quantity and the output quantity to obtain a motion analysis model.
The electronic device can input the user's pedaling frequency and the user's heart rate into the motion analysis model, and the motion analysis model calculates the user's pedaling frequency interval, heart rate interval, acceleration interval, speed interval, etc. in the future time according to the user's heart rate and the user's pedaling frequency. Or the motion analysis model may input a specific cadence, speed, etc. corresponding to the user at a future time.
In some embodiments, after the electronic device obtains the step frequency corresponding to the user, the electronic device may calculate the exercise power corresponding to the user according to the heart rate of the user, then determine whether the step frequency is matched with the exercise power, and if the step frequency is not matched with the exercise power, it indicates that the user needs to adjust the step frequency of the user, and at this time, the electronic device may output a corresponding step frequency scheme, thereby prompting the user to ride according to the corresponding step frequency scheme. Therefore, the user can have more reasonable pedaling frequency when riding, and a better exercise effect is achieved.
In the embodiment of the application, the electronic device acquires sensor data of the motion sensor and physiological data of a user, then determines whether the user is in a riding state according to the physiological data and the sensor data, and if the user is in the riding state, determines a plurality of preset frequency ranges corresponding to the sensor data. And extracting sub-sensor data with preset duration according to the sensor data, and then carrying out filtering processing on the sub-sensor data according to a preset frequency range to which the sub-sensor data belongs to obtain optimized sensor data. And finally, determining the treading frequency within the preset time according to the optimized sensor data, determining the treading frequency corresponding to the user according to the preset time and the treading frequency, inputting the treading frequency and the physiological data of the user into the motion analysis model, and outputting the motion scheme corresponding to the user.
Therefore, when the user rides, the pedaling frequency of the user is accurately acquired through the electronic equipment, and after the pedaling frequency of the user is acquired, a more optimal pedaling frequency scheme can be recommended to the user, so that the user can achieve a better exercise effect when riding.
Referring to fig. 6, fig. 6 is a schematic diagram of a first structure of a data processing apparatus according to an embodiment of the present application. The data processing apparatus can be applied to an electronic device including a sensor, the electronic device is consistent with the motion state of a riding device, and the data processing apparatus 300 can include:
an obtaining module 310 is configured to obtain sensor data of the motion sensor.
The obtaining module 310 is further configured to obtain physiological data of the user; determining whether the user is in a riding state according to the physiological data and the sensor data.
The obtaining module 310 is further configured to determine whether the sensor data includes an acceleration, and the physiological data includes a heart rate, and determine whether the acceleration is within a preset acceleration interval; if the acceleration is within the preset acceleration interval, judging whether the heart rate exceeds a preset heart rate threshold value; and if the heart rate exceeds the preset heart rate threshold value, indicating that the user is in a riding state.
And the optimizing module 320 is configured to optimize the sensor data to obtain optimized sensor data.
The optimization module 320 is further configured to determine a plurality of preset frequency ranges corresponding to the sensor data before optimizing the sensor data to obtain the optimized sensor data.
The optimization module 320 is further configured to extract sub-sensor data of a preset duration from the sensor data; determining a preset frequency range to which the sub-sensor data belongs; and filtering the sub-sensor data according to the preset frequency range of the sub-sensor data to obtain optimized sensor data.
And the analysis module 330 is configured to perform data analysis on the optimized sensor data to obtain a pedaling frequency of the user when riding the riding device.
The analysis module 330 is further configured to determine, according to the optimized sensor data, a number of steps of the user within a preset duration; and determining the pedaling frequency corresponding to the user according to the preset time length and the pedaling frequency.
The analysis module 330 is further configured to screen target data meeting a preset condition from the gyroscope data and the acceleration data; determining a plurality of moments corresponding to the target data, and determining the number of different moments in the plurality of moments; the number of different moments is determined as the number of treading.
The analysis module 330 is further configured to input the user's cadence and physiological data into the motion analysis model, and output a motion scheme corresponding to the user.
The analysis module 330 is further configured to obtain historical motion power corresponding to the user in a historical time period before inputting the pedaling frequency and the physiological data of the user into the motion analysis model and outputting the motion scheme corresponding to the user, and use the historical motion power as an output quantity of the basic model; acquiring historical stepping frequency, historical speed and historical acceleration corresponding to a user in a historical time period, and taking the historical stepping frequency, the historical speed and the historical acceleration as input quantities of a basic model; and training the basic model according to the input quantity and the output quantity to obtain a motion analysis model.
In the embodiment of the application, the electronic device obtains the sensor data of the motion sensor by keeping the motion state of the riding device consistent with that of the electronic device 310; then, the optimization module 320 optimizes the sensor data to obtain optimized sensor data; finally, the analysis module 330 performs data analysis on the optimized sensor data to obtain the pedaling frequency of the user when riding the riding device. Therefore, the pedaling frequency of the user when riding the riding device can be accurately detected through the mobile device of the user.
Accordingly, an electronic device may include, as shown in fig. 7, a memory 401 having one or more computer-readable storage media, an input unit 402, a display unit 403, a sensor 404, a processor 405 having one or more processing cores, and a power supply 406. Those skilled in the art will appreciate that the electronic device configuration shown in fig. 7 does not constitute a limitation of the electronic device and may include more or fewer components than shown, or some components may be combined, or a different arrangement of components. Wherein:
the memory 401 may be used to store software programs and modules, and the processor 405 executes various functional applications and data processing by operating the software programs and modules stored in the memory 401. The memory 401 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the electronic device, and the like. Further, the memory 401 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 401 may further include a memory controller to provide the processor 405 and the input unit 402 with access to the memory 401.
The input unit 402 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control. In particular, in one particular embodiment, input unit 402 may include a touch-sensitive surface as well as other input devices. The touch-sensitive surface, also referred to as a touch display screen or a touch pad, may collect touch operations by a user (e.g., operations by a user on or near the touch-sensitive surface using a finger, a stylus, or any other suitable object or attachment) thereon or nearby, and drive the corresponding connection device according to a predetermined program. Alternatively, the touch sensitive surface may comprise two parts, a touch detection means and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 405, and receives and executes commands sent from the processor 405. In addition, touch sensitive surfaces may be implemented using various types of resistive, capacitive, infrared, and surface acoustic waves. The input unit 402 may include other input devices in addition to a touch-sensitive surface. In particular, other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.
The display unit 403 may be used to display information input by or provided to a user and various graphical user interfaces of the electronic device, which may be made up of graphics, text, icons, video, and any combination thereof. The Display unit 403 may include a Display panel, and optionally, the Display panel may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch-sensitive surface may overlay the display panel, and when a touch operation is detected on or near the touch-sensitive surface, the touch operation is transmitted to the processor 405 to determine the type of touch event, and then the processor 405 provides a corresponding visual output on the display panel according to the type of touch event. Although in FIG. 7 the touch-sensitive surface and the display panel are two separate components to implement input and output functions, in some embodiments the touch-sensitive surface may be integrated with the display panel to implement input and output functions.
The electronic device may also include at least one sensor 404, such as a light sensor, motion sensor, and other sensors. In particular, the light sensor may include an ambient light sensor that may adjust the brightness of the display panel according to the brightness of ambient light, and a proximity sensor that may turn off the display panel and/or the backlight when the electronic device is moved to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally, three axes), detect the magnitude and direction of gravity when the motion sensor is stationary, and can be used for applications (such as horizontal and vertical screen switching, related games, magnetometer attitude calibration) for recognizing the attitude of an electronic device, vibration recognition related functions (such as pedometer and tapping), and the like; as for other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which may be further configured to the electronic device, detailed descriptions thereof are omitted.
The processor 405 is a control center of the electronic device, connects various parts of the whole electronic device by using various interfaces and lines, performs various functions of the electronic device and processes data by operating or executing software programs and/or modules stored in the memory 401 and calling data stored in the memory 401, thereby performing overall monitoring of the electronic device. Optionally, processor 405 may include one or more processing cores; preferably, the processor 405 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 405.
The electronic device also includes a power source 406 (e.g., a battery) for powering the various components, which may preferably be logically coupled to the processor 405 via a power management system to manage charging, discharging, and power consumption management functions via the power management system. The power supply 406 may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.
Although not shown, the electronic device may further include a camera, a bluetooth module, and the like, which are not described in detail herein. Specifically, in this embodiment, the processor 405 in the electronic device loads the computer program stored in the memory 401, and the processor 405 implements various functions by loading the computer program:
acquiring sensor data of a motion sensor;
optimizing the sensor data to obtain optimized sensor data;
and carrying out data analysis on the optimized sensor data to obtain the pedaling frequency of the user when riding the riding device.
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.
To this end, embodiments of the present application provide a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any data processing method provided by the embodiments of the present application. For example, the instructions may perform the steps of:
acquiring sensor data of a motion sensor;
optimizing the sensor data to obtain optimized sensor data;
and carrying out data analysis on the optimized sensor data to obtain the pedaling frequency of the user when riding the riding device.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
Wherein the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
Since the instructions stored in the storage medium can execute the steps in any data processing method provided in the embodiments of the present application, beneficial effects that can be achieved by any data processing method provided in the embodiments of the present application can be achieved, which are detailed in the foregoing embodiments and will not be described herein again.
The foregoing detailed description is directed to a data processing method, an apparatus, an electronic device, and a storage medium provided in the embodiments of the present application, and specific examples are applied in the present application to explain the principles and implementations of the present application, and the descriptions of the foregoing embodiments are only used to help understand the method and the core ideas of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims (11)

1. A data processing method is applied to electronic equipment, the electronic equipment comprises a motion sensor, the electronic equipment is consistent with the motion state of riding equipment, and the method comprises the following steps:
acquiring sensor data of the motion sensor;
optimizing the sensor data to obtain optimized sensor data;
and performing data analysis on the optimized sensor data to obtain the pedaling frequency of the user when riding the riding device.
2. The data processing method of claim 1, wherein the optimizing the sensor data to obtain optimized sensor data comprises:
extracting sub-sensor data with preset duration from the sensor data;
determining a preset frequency range to which the sub-sensor data belongs;
and carrying out filtering processing on the sub-sensor data according to the preset frequency range to which the sub-sensor data belongs to obtain the optimized sensor data.
3. The data processing method of claim 2, wherein the performing data analysis on the optimized sensor data to obtain a pedaling frequency of a user while riding the riding device comprises:
determining the treading times of the user within a preset time according to the optimized sensor data;
and determining the pedaling frequency of the user when riding the riding device according to the preset duration and the pedaling frequency.
4. The data processing method of claim 3, wherein the optimized sensor data comprises gyroscope data and acceleration data, and the determining the number of steps of the user within a preset time period according to the optimized sensor data comprises:
screening target data meeting preset conditions from the gyroscope data and the acceleration data;
determining a plurality of moments corresponding to the target data, and determining the number of different moments in the plurality of moments;
and determining the number of different moments as the treading times.
5. The data processing method of claim 1, wherein prior to said optimizing the sensor data to obtain optimized sensor data, the method further comprises:
acquiring physiological data of a user;
determining whether the user is in a riding state according to the physiological data and the sensor data;
and if the user is in the riding state, optimizing the sensor data to obtain the optimized sensor data.
6. The data processing method of claim 5, wherein the sensor data comprises acceleration, the physiological data comprises heart rate, and the determining whether the user is in a cycling state from the physiological data and the sensor data comprises:
judging whether the acceleration is in a preset acceleration interval or not;
if the acceleration is within a preset acceleration interval, judging whether the heart rate exceeds a preset heart rate threshold value;
and if the heart rate exceeds a preset heart rate threshold value, determining that the user is in a riding state.
7. The data processing method of claim 5, wherein after the data analysis of the optimized sensor data results in a pedaling frequency of a user while riding the cycling apparatus, the method further comprises:
and inputting the pedaling frequency of the user and the physiological data into a motion analysis model, and outputting a motion scheme corresponding to the user.
8. The data processing method of claim 7, wherein prior to the inputting the user's cadence and the physiological data into a motion analysis model and outputting a corresponding motion profile for the user, the method further comprises:
acquiring historical motion power corresponding to the user in a historical time period, and taking the historical motion power as the output quantity of a basic model;
acquiring historical stepping frequency, historical speed and historical acceleration corresponding to the user in the historical time period, and taking the historical stepping frequency, the historical speed and the historical acceleration as input quantities of a basic model;
and training the basic model according to the input quantity and the output quantity to obtain the motion analysis model.
9. A data processing device is applied to an electronic device, wherein the electronic device comprises a motion sensor, the electronic device is consistent with the motion state of a riding device, and the data processing device comprises:
an acquisition module for acquiring sensor data of the motion sensor;
the optimization module is used for optimizing the sensor data to obtain optimized sensor data;
and the analysis module is used for carrying out data analysis on the optimized sensor data to obtain the pedaling frequency of the user riding the riding device.
10. An electronic device, comprising:
a memory storing executable program code, a processor coupled with the memory;
the processor calls the executable program code stored in the memory to perform the steps in the data processing method according to any one of claims 1 to 8.
11. A storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the steps of the data processing method according to any one of claims 1 to 8.
CN202210348562.XA 2022-04-01 2022-04-01 Data processing method and device, electronic equipment and storage medium Pending CN114694799A (en)

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