CN112349381A - Calorie calculation method, device, wearable equipment and storage medium - Google Patents
Calorie calculation method, device, wearable equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a calorie calculation method and device, wearable equipment and a storage medium, and relates to the field of intelligent wearable equipment. The method comprises the steps of obtaining personal information and motion data of a wearer of the wearable device, detecting whether the wearable device is in a training mode or not, obtaining a detection result, determining a calorie calculation model according to the motion data and the detection result, and calculating a calorie consumption value of the wearer according to the personal information, the motion data and the calorie calculation model. According to the embodiment of the invention, the calorie calculation model is selected according to the exercise data of the wearer and the detection result of whether the wearable device is in the training mode, and then the calorie consumption value is calculated based on the selected calorie calculation model, the personal information of the wearer and the exercise data, so that various exercise conditions of the wearer can be considered in a refining manner, and the calculation accuracy of the calorie consumption value is effectively improved.
Description
Technical Field
The invention relates to the field of intelligent wearable equipment, in particular to a calorie calculation method and device, wearable equipment and a storage medium.
Background
With the development of wearable devices, wearable devices have become increasingly popular with users. Many users use wearable devices to monitor physical health conditions, such as monitoring in terms of heart rate, sleep, calorie consumption, and the like.
The calorie is a unit for measuring the heat consumption of human activities, the existing wearable device does not have the condition of directly measuring the heat, generally, a calorie consumption value is simply estimated by utilizing the sensor data in the device and the input personal information of a user, so that the calculation of the calorie consumption value is inaccurate, and the real energy consumption condition is difficult to reflect.
Disclosure of Invention
In view of the above, the present invention provides a calorie calculation method, a calorie calculation apparatus, a wearable device, and a storage medium, so as to improve the calculation accuracy of a calorie consumption value.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
in a first aspect, an embodiment of the present invention provides a calorie calculation method applied to a wearable device, where the method includes:
acquiring personal information and motion data of a wearer of the wearable device;
detecting whether the wearable equipment is in a training mode or not to obtain a detection result;
determining a calorie calculation model according to the exercise data and the detection result;
calculating a calorie expenditure value of the wearer from the personal information, the exercise data and the calorie calculation model.
In an alternative embodiment, the method further comprises:
if the wearable device is in the training mode, performing heart rate measurement in real time;
if the wearable device is not in the training mode, performing one-time heart rate measurement at intervals of first preset time, wherein the measurement duration of the one-time heart rate measurement is second preset time.
In an alternative embodiment, the motion data includes an acceleration value change, a heart rate value, an acceleration integrated value, a step number, and a motion speed, and the personal information includes a weight, a height, an age, and a sex of the wearer.
In an alternative embodiment, the step of determining a calorie calculation model based on the exercise data and the detection result includes:
if the detection result represents that the wearable equipment is in a training mode, determining that a calorie calculation model is a first calculation model;
or if the detection result indicates that the wearable device is not in the training mode, determining that the calorie calculation model is a first calculation model if the motion grade corresponding to the acceleration value variation is larger than a first preset value and the heart rate grade corresponding to the heart rate value is larger than or equal to a second preset value;
the step of calculating a calorie expenditure value of the wearer from the personal information, the exercise data and the calorie calculation model comprises:
inputting the personal information and the heart rate value into the first calculation model, and determining model parameters of the first calculation model according to the gender in the personal information and the heart rate grade corresponding to the heart rate value;
and calculating the calorie consumption value of the wearer according to the heart rate value, the model parameters of the first calculation model and the weight, the height and the age in the personal information.
In an alternative embodiment, the step of determining a calorie calculation model based on the exercise data and the detection result includes:
if the detection result indicates that the wearable device is not in the training mode, the motion grade corresponding to the acceleration value variation is larger than a first preset value, and the heart rate grade corresponding to the heart rate value is smaller than a second preset value, determining that the calorie calculation model is a second calculation model;
or if the detection result indicates that the wearable device is not in the training mode, the exercise grade is greater than the first preset value, and the step number is smaller than a third preset value, determining that the calorie calculation model is a second calculation model;
the step of calculating a calorie expenditure value of the wearer from the personal information, the exercise data and the calorie calculation model comprises:
inputting the personal information and the acceleration cumulative value into the second calculation model, and determining model parameters of the second calculation model according to the gender in the personal information and the acceleration cumulative value;
and calculating the calorie consumption value of the wearer according to the model parameters of the second calculation model, the weight, the height and the age in the personal information.
In an alternative embodiment, the step of determining a calorie calculation model based on the exercise data and the detection result includes:
if the detection result indicates that the wearable device is not in the training mode, the motion grade corresponding to the acceleration value variation is larger than a first preset value, and the step number is larger than or equal to a third preset value, determining that the calorie calculation model is a third calculation model;
the step of calculating a calorie expenditure value of the wearer from the personal information, the exercise data and the calorie calculation model comprises:
inputting the personal information, the acceleration cumulative value and the movement speed into the third calculation model, and determining model parameters of the third calculation model according to the gender in the personal information and the acceleration cumulative value;
and calculating the calorie consumption value of the wearer according to the model parameters of the third calculation model, the movement speed, the weight, the height and the age in the personal information.
In an alternative embodiment, the method further comprises:
and calculating the calorie consumption value in unit time according to the calorie consumption value of the wearer, and if the calorie consumption value in unit time is smaller than a fourth preset value and the exercise data meets preset conditions, correcting the calorie consumption value of the wearer.
In a second aspect, an embodiment of the present invention provides a calorie calculation apparatus applied to a wearable device, where the apparatus includes:
the information acquisition module is used for acquiring personal information and motion data of a wearer of the wearable device;
the detection module is used for detecting whether the wearable equipment is in a training mode or not and obtaining a detection result;
the decision-making module is used for determining a calorie calculation model according to the movement data and the detection result;
a calculation module for calculating a calorie expenditure value of the wearer from the personal information, the exercise data and the calorie calculation model.
In a third aspect, an embodiment of the present invention provides a wearable device, including a processor and a memory, where the memory stores a computer program, and the processor implements the method of any one of the foregoing embodiments when executing the computer program.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method of any one of the foregoing embodiments.
According to the calorie calculation method, the device, the wearable device and the storage medium provided by the embodiment of the invention, the personal information and the motion data of the wearer of the wearable device are obtained, whether the wearable device is in the training mode or not is detected, the detection result is obtained, the calorie calculation model is determined according to the motion data and the detection result, and the calorie consumption value of the wearer is calculated according to the personal information, the motion data and the calorie calculation model. According to the embodiment of the invention, the calorie calculation model is selected according to the exercise data of the wearer and the detection result of whether the wearable device is in the training mode, and then the calorie consumption value is calculated based on the selected calorie calculation model, the personal information of the wearer and the exercise data, so that various exercise conditions of the wearer can be considered in a refining manner, and the calculation accuracy of the calorie consumption value is effectively improved.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 shows a block diagram of a wearable device provided in an embodiment of the present invention;
FIG. 2 is a flow chart diagram illustrating a calorie calculation method provided by an embodiment of the present invention;
FIG. 3 is a schematic flow chart diagram illustrating a calorie calculation method according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart diagram illustrating a calorie calculation method according to an embodiment of the present invention;
FIG. 5 is a schematic flow chart diagram illustrating a calorie calculation method according to an embodiment of the present invention;
FIG. 6 is a functional block diagram of a calorie calculation apparatus according to an embodiment of the present invention;
fig. 7 is a diagram showing another functional block diagram of the calorie calculation apparatus provided in the embodiment of the present invention.
Icon: 100-a wearable device; 600-calorie calculation device; 110-a memory; 120-a processor; 130-a sensor; 140-a display unit; 150-an input unit; 610-an information acquisition module; 620-a detection module; 630-a decision module; 640-a calculation module; 650-a correction module; 141-a display panel; 151-touch panel; 152-other input devices.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It is noted that relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The calorie calculation method provided by the embodiment of the invention can be applied to terminal equipment such as mobile phones, tablet computers, Personal Digital Assistants (PDAs), wearable equipment and the like.
By way of example and not limitation, when the terminal device is a wearable device, the wearable device may be a smart wearable product such as a smart watch, a smart bracelet, and the like. Fig. 1 is a block diagram of a wearable device 100 according to an embodiment of the present invention. The wearable device 100 includes components such as a memory 110, a processor 120, a sensor 130, a display unit 140, an input unit 150, and the like. The memory 110, the processor 120, the sensor 130, the display unit 140, and the input unit 150 are electrically connected to each other directly or indirectly to realize data transmission or interaction.
The memory 110 is used to store programs or data. The Memory 110 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 120 is used to read/write data or programs stored in the memory 110 and perform corresponding functions. For example, the processor 120 implements the calorie calculation method disclosed in the embodiment of the present invention by executing the computer program stored in the memory 110.
The sensor 130 may include an acceleration sensor, a heart rate sensor, or the like. The acceleration sensor can detect the magnitude of acceleration in all directions (three axes, six axes and the like); the heart rate sensor can detect the heart rate of the human body movement by utilizing a PPG (photoplethysmography) technology to obtain a heart rate value. Optionally, the sensor 130 may further include other sensors such as a gyroscope and a light sensor, which are not described herein.
The display unit 140 may be used to display information input by or provided to the user and various menus of the wearable device 100. The Display unit 140 may include a Display panel 141, and the Display panel 141 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.
The input unit 150 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the wearable device 100. The input unit 150 may include a touch panel 151 and other input devices 152. The touch panel 151, also referred to as a touch screen, may collect a touch operation performed by a user on or near the touch panel 151 (e.g., an operation performed by the user on or near the touch panel 151 using any suitable object such as a finger or a stylus pen), and drive a corresponding connection device according to a preset program. Alternatively, the touch panel 151 may include two parts of a touch detection device 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 120, and can receive and execute commands sent by the processor 120. In addition, the touch panel 151 may be implemented in various types, such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. Other input devices 152 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.
Alternatively, the touch panel 151 may cover the display panel 141, and when the touch panel 151 detects a touch operation on or near the touch panel 151, the touch panel is transmitted to the processor 120 to determine the type of the touch event, and then the processor 120 provides a corresponding visual output on the display panel 141 according to the type of the touch event. Although in fig. 1, the touch panel 151 and the display panel 141 are two separate components to implement the input and output functions of the wearable device 100, in some embodiments, the touch panel 151 and the display panel 141 may be integrated to implement the input and output functions of the wearable device 100.
It should be understood that the structure shown in fig. 1 is merely a schematic diagram of the structure of the wearable device 100, and the wearable device 100 may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, and the computer program can implement the calorie calculating method disclosed in the embodiments of the present invention when executed by the processor 120.
Fig. 2 is a schematic flow chart of a calorie calculation method according to an embodiment of the present invention. It should be noted that the calorie calculation method according to the embodiment of the present invention is not limited by fig. 2 and the following specific sequence, and it should be understood that in other embodiments, the sequence of some steps in the calorie calculation method according to the present invention may be interchanged according to actual needs, or some steps in the calorie calculation method may be omitted or deleted. The calorie calculation method is applicable to the wearable device 100 described above, and the specific process shown in fig. 2 will be described in detail below.
Step S201, personal information and motion data of a wearer of the wearable device are acquired.
In the present embodiment, the personal information of the wearer may be understood as physical characteristic information of the wearer, and the wearer may input own personal information by using the input unit 150, or may input own personal information by using a device (e.g., a mobile phone) wirelessly connected to the wearable device 100. The wearable device 100 can calculate the motion data of the wearer according to the signals collected by the sensors 130 such as the acceleration sensor, the heart rate sensor, the gyroscope, etc. and the personal information of the wearer.
Step S202, whether the wearable device is in the training mode is detected, and a detection result is obtained.
In this embodiment, the wearer may choose to turn on the training mode or not to turn on the training mode as desired.
In step S203, a calorie calculation model is determined according to the exercise data and the detection result.
In this embodiment, when the wearer is in different exercise types, the exercise data corresponding to the wearer is different, so by setting different calorie calculation models corresponding to different exercise data and detection results, after obtaining the exercise data and the detection result of whether the wearable device 100 is in the training mode, a reasonable calorie calculation model can be selected for calculating the calorie consumption value, thereby realizing a refined consideration of various exercise conditions of the wearer.
Step S204, calculating the calorie consumption value of the wearer according to the personal information, the exercise data and the calorie calculation model.
In this embodiment, after the calorie calculation model is determined, the current calorie expenditure value of the wearer may be calculated based on the calorie calculation model, the personal information of the wearer, and the exercise data.
As can be seen, in the calorie calculation method provided in the embodiment of the present invention, the personal information and the exercise data of the wearer of the wearable device 100 are obtained, whether the wearable device 100 is in the training mode is detected, the detection result is obtained, the calorie calculation model is determined according to the exercise data and the detection result, and the calorie consumption value of the wearer is calculated according to the personal information, the exercise data and the calorie calculation model. Since the calorie calculation model is selected according to the exercise data of the wearer and the detection result of whether the wearable device 100 is in the training mode, and then the calorie consumption value is calculated based on the selected calorie calculation model, the personal information of the wearer and the exercise data, various exercise conditions of the wearer can be considered in a refining manner, and the calculation accuracy of the calorie consumption value is effectively improved.
Optionally, the wearable device 100 may select to turn on the training mode or not to turn on the training mode according to actual needs, and measure the heart rate in real time in the training mode, or intermittently in the non-training mode, so as to save power. Based on this, the wearable device 100 performs a corresponding heart rate detection function according to the selection result of the user, as shown in fig. 3, the calorie calculation method may further include:
step S301, if the wearable device is in the training mode, performing heart rate measurement in real time, and if the wearable device is not in the training mode, performing one-time heart rate measurement at intervals of first preset time, wherein the measurement duration of the one-time heart rate measurement is second preset time.
That is, when the wearer selects to turn on the training mode, the wearable device 100 will be in the training mode, turn on the real-time heart rate detection function, and measure the heart rate value of the wearer in real time; when the wearer chooses not to turn on the training mode, the wearable device 100 is not in the training mode, and the heart rate detection function is performed once every first preset time interval in order to save power. In one example, the first preset time may be 5-10 minutes, and the second preset time may be set to 30 s.
Step S301 may be executed before or after any one of steps S201 to S204, or may be executed simultaneously with any one of steps S201 to S204, which is not limited in this embodiment.
Alternatively, in the present embodiment, the motion data of the wearer may include an acceleration value change amount, an acceleration integrated value, a heart rate value, a step number, a motion speed (running speed), and the like, and the personal information of the wearer may include information of the weight, height, age, sex, and the like of the wearer.
During implementation, a time length can be preset as a time period for calculating the algorithm once, and the sensitivity of motion data acquisition and the algorithm performance can be balanced in the time period. That is, the wearable device 100 acquires the motion data of the wearer during the current time period T, and then calculates the calorie consumption value of the wearer during the current time period T. Optionally, the current time period T may be between 30s and 60s, e.g. 40s, 45s, 50s, etc.
The variation of the acceleration value can be understood as a difference between the acceleration value at the starting time and the acceleration value at the ending time of the current time period T, in this embodiment, the movement grades can be divided according to the variation of the acceleration value, each movement grade corresponds to one or more movement types, and the larger the variation of the acceleration value is, the more violent the movement of the wearer is. In one example, six motion levels may be divided according to the acceleration value variation, and the correspondence relationship between the motion levels, the acceleration value variation, and the motion types may refer to table 1.
TABLE 1
| Grade of movement | Acceleration value variation (m/s)2) | Type of sport |
| Level 0 | Δa＜5 | Sleeping or sitting still |
| Level 1 | 5≤Δa＜10 | Riding vehicle |
| Stage 2 | 10≤Δa＜15 | Riding sports |
| Grade 3 | 15≤Δa＜25 | Walking movement |
| 4 stage | 25≤Δa＜45 | Fast walking exercise |
| Grade 5 | Δa≥45 | Running exercise |
As can be seen from Table 1, when the variation of the acceleration value is Δ a < 5m/s2When the user is in a sleep state, the corresponding motion grade is 0 grade, the acceleration sensor is in a static state, and the motion type corresponding to the wearer can be sleep or sitting; when the variation delta a of the acceleration value satisfies 5m/s2≤Δa＜10m/s2When the user is in a riding mode, the corresponding movement grade is 1 grade, and the corresponding movement type of the wearer is riding trafficTools (e.g., riding a bus, taxi, etc.); when the acceleration value variation delta a satisfies 10m/s2≤Δa＜15m/s2When the user moves, the corresponding movement grade is 2 grade, and the movement type corresponding to the wearer is riding movement (such as riding a bicycle, an exercise bicycle or a spinning bicycle); when the acceleration value variation delta a satisfies 15m/s2≤Δa＜25m/s2When the user moves, the corresponding movement grade is 3 grades, and the movement type corresponding to the wearer is walking movement (such as walking, walking slowly, climbing mountains, descending mountains and the like); when the acceleration value variation delta a satisfies 25m/s2≤Δa＜45m/s2When the user moves, the corresponding movement grade is 4 grades, and the movement type corresponding to the wearer is fast walking movement (such as heel-and-toe walking, jogging and the like); when the variation delta a of the acceleration value is more than or equal to 45m/s2The corresponding exercise level is 5, and the type of exercise corresponding to the wearer is running (e.g., running, playing football, playing basketball, etc.).
The accumulated acceleration value may be understood as an accumulated value of the sum of accelerations in the current time period T. Taking a three-axis acceleration sensor as an example, the resultant acceleration
x, y and z are coordinate values of the three-axis acceleration sensor in the three-axis direction, and the accumulated value of the total acceleration a in the time period T is the acceleration accumulated value A, namely
The heart rate value is understood in the present exemplary embodiment as the average value of the heart rate over the current time period T, i.e. the heart rate value
hr represents the heart rate value detected at a time within the current time period T. Wearable device 100 heart rate value based on acquisition
And maximum heart rate hrmaxRatio of (b) < hr >stageA division of heart rate classes, each heart rate etc. may be madeA level may correspond to a heart rate type. As an example, maximum Heart Rate hrmax220-age, heart rate rating, heart rate value of wearer
And maximum heart rate hrmaxRatio of (b) < hr >stageAnd heart rate type, can be referred to table 2.
TABLE 2
It should be understood that, since the wearable device 100 in the training mode may perform the heart rate measurement in real time, and the heart rate measurement is performed at intervals when the wearable device 100 is not in the training mode, there may be a case where the obtained motion data of the wearer in the current time period T does not include the heart rate value when the wearable device 100 is not in the training mode, depending on whether the wearable device 100 performs the heart rate measurement in the current time period T.
In the present embodiment, the step number refers to the total step number generated in the current time period T
It can be obtained by comprehensive calculation according to data collected by sensors 130 such as an acceleration sensor and a gyroscope.
The speed of movement is the speed at which the wearer walks within the current time period T, i.e. the speed of movement
Dist denotes the distance, stride, the wearer has walked during the current time period TiThe stride of the wearer is calculated, and the specific calculation method of the stride is not limited in this embodiment, and for example, the calculation may be performed according to personal information of the wearerAnd (4) calculating.
In this embodiment, a plurality of calorie calculation models may be provided for the wearable device 100 to select, for example, the plurality of calorie calculation models includes a first calculation model, a second calculation model, and a third calculation model, and after obtaining personal information and exercise data of the wearer and a detection result of whether the wearable device 100 is in the training mode, the wearable device 100 may select a reasonable calorie calculation model from the plurality of calorie calculation models based on the exercise data and the detection result to calculate a calorie consumption value, wherein when the wearable device 100 is in the training mode or a heart rate level in the non-training mode (i.e., not in the training mode) meets a setting condition, the calorie consumption value may be calculated based on the personal information, the heart rate value, and the first calculation model of the wearer; when the wearable device 100 is in the non-training mode, the heart rate level does not meet the set condition, or the wearable device is in the non-training mode, the heart rate value is not obtained, and the step number does not reach the set step number, the calorie consumption value can be calculated based on the personal information of the wearer, the acceleration cumulative value and the second calculation model; when the wearable device 100 is in the non-training mode and the heart rate value is not acquired and the number of steps reaches the set number of steps, the calorie consumption value may be calculated based on the personal information of the wearer, the accumulated acceleration value, the movement speed, and the third calculation model. Next, a specific selection strategy of the calorie calculation model and a specific calculation manner of the calorie consumption value will be described.
Optionally, in an embodiment, the step S203 may include: if the detection result represents that the wearable equipment is in a training mode, determining that the calorie calculation model is a first calculation model; or if the detection result indicates that the wearable device is not in the training mode, determining that the calorie calculation model is a first calculation model when the motion grade corresponding to the acceleration value variation is larger than a first preset value and the heart rate grade corresponding to the heart rate value is larger than or equal to a second preset value; the step S204 may include: inputting the personal information and the heart rate value into a first calculation model, and determining model parameters of the first calculation model according to the gender in the personal information and the heart rate grade corresponding to the heart rate value; calculating the calorie consumption value of the wearer according to the heart rate value, the model parameters of the first calculation model and the weight, the height and the age in the personal information.
Optionally, the model parameters of the first calculation model include a feature coefficient vector and a first intercept, and the first calculation model is: kcalA＝CijX+bijWherein C ═ C1,c2,c3,c4) Representing a feature coefficient vector, c1、c2、c3、c4Representing an element in a feature coefficient vector, X ═ X1,x2,x3,x4)TRepresenting a feature vector, x1Representing heart rate value, x2、x3、x4Respectively representing the weight, height and age of the wearer, b representing the first intercept, i representing the gender, j representing the heart rate rating corresponding to the heart rate value.
When the wearable device 100 is detected to be in the training mode, the first calculation model under the current motion condition can be determined to be a reasonable calorie calculation model, and the heart rate value corresponding to the current time period is used
And personal information (i.e. weight, height, age, sex) of the wearer into the first calculation model KcalA＝CijX+bij(ii) a When the wearable device 100 is not in the training mode, firstly, whether a triggering condition of calorie calculation is achieved is judged (that is, whether the motion grade corresponding to the variation of the acceleration value is greater than a first preset value), and if the triggering condition is not achieved, the wearer is in a sleeping or sitting state currently, the calculation and analysis of the calorie are not required; if the exercise level is greater than the first preset value (for example, 1, or a specific value may be set according to actual needs), further determining whether a heart rate value exists in the current exercise data, if the heart rate value exists and the heart rate level corresponding to the heart rate value is greater than or equal to the second preset value (for example, 1, or a specific value may be set according to actual needs), determining that the first calculation model is a reasonable calorie calculation model under the current exercise condition, and determining that the heart rate value corresponding to the current time period is the reasonable calorie calculation model
And personal information (i.e. weight, height, age, sex) of the wearer into the first calculation model KcalA＝CijX+bij。
The heart rate value corresponding to the current time period
And personal information (i.e. weight, height, age, sex) of the wearer into the first calculation model KcalA＝CijX+bijThen, it is first necessary to determine the sex and heart rate values of the wearer
The corresponding heart rate level determines the model parameters of the first computational model (i.e. the eigen coefficient vector C)ijAnd a first section distance bij). In one example, i ═ 0 may be set to indicate female, i ═ 1 may indicate male, and j may be set to correspond to heart rate levels (e.g., 1, 2, 3, 4, 5), so that the specific values of i and j may be used to determine the corresponding feature coefficient vector CijAnd a first section distance bijAccording to the heart rate value
And the weight, height and age of the wearer can determine the characteristic vector X ═ X1,x2,x3,x4)TTherefore, Kcal is usedA＝CijX+bijThe calorie consumption value can be calculated.
Optionally, in another embodiment, the step S203 may include: if the detection result indicates that the wearable device is not in the training mode, the motion grade corresponding to the acceleration value variation is larger than a first preset value, and the heart rate grade corresponding to the heart rate value is smaller than a second preset value, and determining that the calorie calculation model is a second calculation model; or if the detection result indicates that the wearable device is not in the training mode, the exercise grade is greater than the first preset value, and the step number is smaller than the third preset value, determining that the calorie calculation model is the second calculation model. The step S204 may include: inputting the personal information and the acceleration integrated value into a second calculation model, and determining model parameters of the second calculation model according to the gender and the acceleration integrated value in the personal information; and calculating the calorie consumption value of the wearer according to the model parameters of the second calculation model, the weight, the height and the age in the personal information. The third preset value may be the lowest number of steps of one minute of continuous normal walking, and generally the number of steps of normal walking per minute of an adult is 50-100 steps.
Optionally, the model parameters of the second calculation model include the consumption coefficient, the second intercept, and the respective weights of the height, the weight, and the age of the wearer, and the second calculation model is: kcalB＝α·BEE，BEE＝w1·weight+w2·height+w3Age + b', where BEE represents the basal energy expenditure, α represents the expenditure coefficient, weight, height and age represent the weight, height and age, respectively, of the wearer, w1、w2、w3Respectively representing the weight corresponding to the height, the weight and the age of the wearer, and b' represents a second intercept; w is a1、w2、w3B' is determined by the gender of the wearer, and a is determined by the accumulated acceleration value.
In this embodiment, when it is detected that the wearable device 100 is not in the training mode, first, it is determined whether a trigger condition for calorie calculation is met, and if the trigger condition is not met, it indicates that the wearer is currently in a sleeping or sitting state, the calculation and analysis of calories are not required; if the exercise grade is larger than the first preset value, whether a heart rate value exists in the current exercise data is further judged, if the heart rate value exists and the heart rate grade corresponding to the heart rate value is smaller than the second preset value, the second calculation model under the current exercise condition can be determined to be a reasonable calorie calculation model, and the acceleration cumulative value A corresponding to the current time period and the personal information (namely weight, height, age and gender) of the wearer are input into the second calculation model KcalB＝α·BEE，BEE＝w1·weight+w2·height+w3Age + b'; if no heart rate value exists, further judging whether the number of the steps of the exercise reaches a third preset valueSetting the value (for example, 50 steps), if the number of steps is less than a third preset value, determining that the second calculation model is a reasonable calorie calculation model under the current exercise condition, and inputting the acceleration cumulative value A corresponding to the current time period and the personal information (namely, the weight, the height, the age and the sex) of the wearer into the second calculation model KcalB＝α·BEE，BEE＝w1·weight+w2·height+w3·age+b′。
The acceleration integrated value corresponding to the current time period and the personal information (weight, height, age, sex) of the wearer are input into the second calculation model KcalB＝α·BEE，BEE＝w1·weight+w2·height+w3Age + b', it is necessary to determine the consumption coefficient α from the cumulative acceleration value A and the corresponding w from the sex of the wearer1、w2、w3B' in determining alpha, w1、w2、w3B', calculating the basic energy consumption BEE of the wearer according to the weight, height and age of the wearer, and further calculating the basic energy consumption BEE of the wearer according to KcalBThe calorie consumption value is calculated as α · BEE.
As an example, the correspondence relationship between the acceleration integrated value a and the consumption coefficient α may refer to table 3.
TABLE 3
| Acceleration integrated value A | Coefficient of consumption alpha |
| A＜400 | 1.1 |
| 400≤A＜600 | 1.2 |
| 600≤A＜1000 | 1.3 |
| 1000≤A＜2700 | 1.4 |
| A≥2700 | 1.5 |
Optionally, in another embodiment, the step S203 may include: if the detection result indicates that the wearable device is not in the training mode, the motion grade corresponding to the acceleration value variation is larger than the first preset value, and the step number is larger than or equal to a third preset value, determining that the calorie calculation model is a third calculation model; the step S204 may include: inputting the personal information, the acceleration cumulative value and the movement speed into a third calculation model, and determining model parameters of the third calculation model according to the gender and the acceleration cumulative value in the personal information; and calculating the calorie consumption value of the wearer according to the model parameters, the movement speed, the weight, the height and the age in the personal information of the third calculation model.
Optionally, the model parameters of the third calculation model include consumption coefficient, second intercept and weight corresponding to height, weight and age of the wearer, and the third calculation model is KcalC＝α·BEE+(a1·KPH3+a2·KPH2+a3·KPH+a4)*weight，BEE＝(w1·weight+w2·height+w3Age + b'), where BEE represents the base energy consumption, α represents the consumption coefficient, a1、a2、a3、a4All are preset constants, KPH is the movement speed, weight, height and age respectively represent the weight, height and age of the wearer, w1、w2、w3Respectively representing the weight corresponding to the height, the weight and the age of the wearer, and b' represents a second intercept; w is a1、w2、w3、b′Based on the gender determination of the wearer, α is determined from the acceleration integrated value.
It can be understood that the third calculation model actually introduces the feature of the movement velocity KPH on the basis of the second calculation model, so that the motion velocity KPH is related to alpha and w1、w2、w3The determination of b' can refer to the description of the aforementioned second calculation model, and is not described herein again.
Next, a flow of the calorie calculation method provided by the embodiment of the present invention is described based on fig. 4. As shown in fig. 4, the calorie calculation method includes the steps of:
step S401, personal information and motion data of a wearer of the wearable device are acquired.
Step S402, whether the wearable device is in the training mode or not is detected, and a detection result is obtained.
If the detection result indicates that the wearable device 100 is in the training mode, step S403 is executed; if the detection result indicates that the wearable device 100 is not in the training mode, step S404 is executed.
Step S403, inputting the personal information and the heart rate value into the first calculation model, and calculating to obtain the calorie consumption value of the wearer.
And S404, judging whether the motion grade corresponding to the acceleration value variation is larger than a first preset value or not.
If the exercise level is less than or equal to 1, returning to the step S401, and not performing the analysis and calculation of calorie consumption; if the motion level is greater than 1, step S405 is performed.
Step S405, it is determined whether a heart rate value exists in the motion data.
If the heart rate value exists, executing step S406; if no heart rate value exists, go to step S407.
Step S406, whether the heart rate grade corresponding to the heart rate value is larger than or equal to a second preset value is judged.
If the heart rate grade is greater than or equal to the second preset value, executing step S403; if the heart rate level is less than 1, step S408 is performed.
Step 407, determining whether the step number is smaller than a third preset value.
If the number of steps is less than the third preset value, executing step S408; if the number of steps is greater than or equal to the third preset value, step S409 is executed.
In step S408, the personal information and the integrated acceleration value are input to the second calculation model, and the calorie consumption value of the wearer is calculated.
Step S409, inputting the personal information, the accumulated acceleration value, and the movement speed into the third calculation model, and calculating the calorie consumption value of the wearer.
Therefore, according to the calorie calculation method provided by the embodiment of the invention, the selection of the calorie calculation model is performed by judging whether the wearable device 100 is in the training mode or not and combining the characteristics of the exercise grade corresponding to the variation of the acceleration value, the heart rate grade corresponding to the heart rate value, the step number and the like, so that the calorie consumption is calculated by using the reasonable calorie calculation model, the calculation accuracy of the calorie consumption is effectively improved, and the energy consumption condition of a wearer can be reflected more truly.
Alternatively, in order to further improve the calculation accuracy of calorie consumption, the calorie consumption value may be corrected after the calorie consumption value is calculated. Referring to fig. 5, the calorie calculation method may further include:
step S501, calculating the calorie consumption value in unit time according to the calorie consumption value of the wearer, and if the calorie consumption value in unit time is smaller than a fourth preset value and the exercise data meets preset conditions, correcting the calorie consumption value of the wearer.
Optionally, the preset condition may be: the motion grade corresponding to the variation of the acceleration value is larger than a first preset value, and the heart rate grade corresponding to the heart rate value is larger than or equal to a second preset value, or the motion grade corresponding to the variation of the acceleration value is larger than the first preset value, no heart rate value exists in the motion data, and the number of steps is larger than or equal to a third preset value.
In this embodiment, the calorie consumption value of the wearer may be the calorie consumption value of the wearer during the current time period. For example, if the exercise level is greater than the first preset value and the heart rate level is greater than or equal to the second preset value, or if the exercise level is greater than the first preset value and the number of steps is greater than or equal to the third preset value, the calorie consumed per minute should be at least 1 (i.e., the fourth preset value), then after the calorie consumed value of the wearer in the current time period is calculated, the calorie consumed per minute may be calculated according to the calorie consumed value in the current time period, and if the calorie consumed per minute is less than 1, the calorie consumed per minute may be set to 1 to correct the calorie consumed per minute in the current time period, thereby improving the accuracy of calculating the calorie consumption.
It should be noted that, in practical applications, the calorie consumption values calculated in each time period T may be added up and displayed by the display unit 140 of the wearable device 100. Alternatively, the ratio may be 24: 00 carries out data zero clearing, and carries out accumulation from 0 again the next day, thereby realizing that the calorie consumption value of the user is continuously accumulated for 24 hours.
In order to perform the corresponding steps in the above embodiments and various possible manners, an implementation manner of the calorie calculation apparatus is given below. Referring to fig. 6, a functional block diagram of a calorie calculating apparatus 600 according to an embodiment of the present invention is shown. It should be noted that the basic principle and the generated technical effect of the calorie calculating device 600 provided by the present embodiment are the same as those of the above embodiments, and for the sake of brief description, no part of the present embodiment is mentioned, and reference may be made to the corresponding contents in the above embodiments. The calorie calculation apparatus 600 includes: an information acquisition module 610, a detection module 620, a decision module 630 and a calculation module 640.
Alternatively, the modules may be stored in the memory 110 shown in fig. 1 in the form of software or Firmware (Firmware) or be fixed in an Operating System (OS) of the wearable device 100, and may be executed by the processor 120 in fig. 1. Meanwhile, data, codes of programs, and the like required to execute the above-described modules may be stored in the memory 110.
The information acquisition module 610 is used to acquire personal information and motion data of the wearer of the wearable device 100.
It is understood that the information obtaining module 610 may perform the step S201.
The detection module 620 is configured to detect whether the wearable device 100 is in the training mode, and obtain a detection result.
It is understood that the detecting module 620 can perform the step S202.
The decision module 630 is used to determine a calorie calculation model based on the exercise data and the detection result.
It is understood that the decision module 630 can perform the step S203.
The calculation module 640 is for calculating a calorie expenditure value of the wearer based on the personal information, the exercise data, and the calorie calculation model.
It is understood that the calculating module 640 may perform the step S204.
Optionally, the motion data includes an acceleration value change, a heart rate value, an acceleration integrated value, a step number, and a motion speed, and the personal information includes a weight, a height, an age, and a gender of the wearer. This wearable device 100 is when being in the training mode, and real-time execution heart rate is measured, when not being in the training mode, then every interval first preset time carries out once the heart rate and measures, and the measuring duration of once the heart rate is the second preset time.
In one embodiment, the decision module 630 is specifically configured to determine that the calorie calculation model is the first calculation model if the detection result indicates that the wearable device 100 is in the training mode; or if the detection result indicates that the wearable device 100 is not in the training mode, determining that the calorie calculation model is the first calculation model if the motion grade corresponding to the acceleration value variation is greater than the first preset value and the heart rate grade corresponding to the heart rate value is greater than or equal to the second preset value; the calculation module 640 is specifically configured to input the personal information and the heart rate value into the first calculation model, and determine a model parameter of the first calculation model according to a gender in the personal information and a heart rate level corresponding to the heart rate value; calculating the calorie consumption value of the wearer according to the heart rate value, the model parameters of the first calculation model and the weight, the height and the age in the personal information.
The model parameters of the first calculation model comprise a characteristic coefficient vector and a first intercept, and the first calculation model is as follows: kcalA＝CijX+bijWherein C ═ C1,c2,c3,c4) Representing a feature coefficient vector, c1、c2、c3、c4Representing an element in a feature coefficient vector, X ═ X1,x2,x3,x4)TRepresenting a feature vector, x1Representing heart rate value, x2、x3、x4Respectively representing the weight, height and age of the wearer, b representing the first intercept, i representing the gender, j representing the heart rate rating corresponding to the heart rate value.
Optionally, in another embodiment, the decision module 630 is specifically configured to determine that the calorie calculation model is the second calculation model if the detection result indicates that the wearable device 100 is not in the training mode, the exercise level corresponding to the acceleration value variation is greater than the first preset value, and the heart rate level corresponding to the heart rate value is less than the second preset value, or determine that the calorie calculation model is the second calculation model if the detection result indicates that the wearable device 100 is not in the training mode, the exercise level is greater than the first preset value, and the number of steps is less than the third preset value; the calculating module 640 is specifically configured to input the personal information and the acceleration integrated value into the second calculation model, and determine a model parameter of the second calculation model according to the gender and the acceleration integrated value in the personal information; and calculating the calorie consumption value of the wearer according to the model parameters of the second calculation model, the weight, the height and the age in the personal information.
Wherein, the model parameters of the second calculation model comprise consumption coefficient, second intercept and respective corresponding weight of height, weight and age of the wearer, and the second calculation model is as follows: kcalB＝α·BEE，BEE＝w1·weight+w2·height+w3Age + b', where BEE represents the basal energy expenditure, α represents the expenditure coefficient, weight, height and age represent the weight, height and age, respectively, of the wearer, w1、w2、w3Respectively shows the height, the weight and the like of the wearer,The weights corresponding to the ages, and b' represents a second intercept; w is a1、w2、w3B' is determined by the gender of the wearer, and a is determined by the accumulated acceleration value.
Optionally, in another embodiment, the decision module 630 is specifically configured to determine that the calorie calculation model is a third calculation model if the detection result indicates that the wearable device 100 is not in the training mode, the motion level corresponding to the acceleration value variation is greater than the first preset value, and the number of steps is greater than or equal to a third preset value; the calculating module 640 is specifically configured to input the personal information, the accumulated acceleration value, and the movement speed into a third calculating model, and determine a model parameter of the third calculating model according to the gender and the accumulated acceleration value in the personal information; and calculating the calorie consumption value of the wearer according to the model parameters, the movement speed, the weight, the height and the age in the personal information of the third calculation model.
Wherein the model parameters of the third calculation model comprise consumption coefficient, second intercept and weight corresponding to height, weight and age of the wearer, and the third calculation model is KcalC＝α·BEE+(a1·KPH3+a2·KPH2+a3·KPH+a4)*weight，BEE＝(w1·weight+w2·height+w3Age + b'), where BEE represents the base energy consumption, α represents the consumption coefficient, a1、a2、a3、a4All are preset constants, KPH is the movement speed, weight, height and age respectively represent the weight, height and age of the wearer, w1、w2、w3Respectively representing the weight corresponding to the height, the weight and the age of the wearer, and b' represents a second intercept; w is a1、w2、w3B' is determined by the gender of the wearer, and a is determined by the accumulated acceleration value.
Optionally, referring to fig. 7, the calorie calculating device 600 may further include a correcting module 650, where the correcting module 650 is configured to calculate a calorie consumption value per unit time according to the calorie consumption value of the wearer, and correct the calorie consumption value of the wearer if the calorie consumption value per unit time is less than a fourth preset value and the exercise data meets a preset condition.
It is understood that the modification module 650 may perform the step S501.
It can be seen that the calorie calculation apparatus 600 according to the embodiment of the present invention obtains personal information and exercise data of a wearer of the wearable device 100 through the information obtaining module 610, detects whether the wearable device 100 is in the training mode through the detecting module 620, obtains a detection result, the decision module 630 determines a calorie calculation model according to the exercise data and the detection result, and the calculation module 640 calculates a calorie consumption value of the wearer according to the personal information, the exercise data and the calorie calculation model. Since the calorie calculation model is selected according to the exercise data of the wearer and the detection result of whether the wearable device 100 is in the training mode, and then the calorie consumption value is calculated based on the selected calorie calculation model, the personal information of the wearer and the exercise data, various exercise conditions of the wearer can be considered in a refining manner, and the calculation accuracy of the calorie consumption value is effectively improved.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a mobile phone, a tablet computer, a wearable device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A calorie calculation method applied to a wearable device is characterized by comprising the following steps:
acquiring personal information and motion data of a wearer of the wearable device;
detecting whether the wearable equipment is in a training mode or not to obtain a detection result;
determining a calorie calculation model according to the exercise data and the detection result;
calculating a calorie expenditure value of the wearer from the personal information, the exercise data and the calorie calculation model.
2. The method of claim 1, further comprising:
if the wearable device is in the training mode, performing heart rate measurement in real time;
if the wearable device is not in the training mode, performing one-time heart rate measurement at intervals of first preset time, wherein the measurement duration of the one-time heart rate measurement is second preset time.
3. The method of claim 1, wherein the motion data includes an amount of change in acceleration values, a heart rate value, an accumulated value of acceleration, a number of steps, and a speed of motion, and the personal information includes a weight, a height, an age, and a gender of the wearer.
4. The method of claim 3, wherein the step of determining a calorie calculation model based on the exercise data and the detection comprises:
if the detection result represents that the wearable equipment is in a training mode, determining that a calorie calculation model is a first calculation model;
or if the detection result indicates that the wearable device is not in the training mode, determining that the calorie calculation model is a first calculation model if the motion grade corresponding to the acceleration value variation is larger than a first preset value and the heart rate grade corresponding to the heart rate value is larger than or equal to a second preset value;
the step of calculating a calorie expenditure value of the wearer from the personal information, the exercise data and the calorie calculation model comprises:
inputting the personal information and the heart rate value into the first calculation model, and determining model parameters of the first calculation model according to the gender in the personal information and the heart rate grade corresponding to the heart rate value;
and calculating the calorie consumption value of the wearer according to the heart rate value, the model parameters of the first calculation model and the weight, the height and the age in the personal information.
5. The method of claim 3, wherein the step of determining a calorie calculation model based on the exercise data and the detection comprises:
if the detection result indicates that the wearable device is not in the training mode, the motion grade corresponding to the acceleration value variation is larger than a first preset value, and the heart rate grade corresponding to the heart rate value is smaller than a second preset value, determining that the calorie calculation model is a second calculation model;
or if the detection result indicates that the wearable device is not in the training mode, the exercise grade is greater than the first preset value, and the step number is smaller than a third preset value, determining that the calorie calculation model is a second calculation model;
the step of calculating a calorie expenditure value of the wearer from the personal information, the exercise data and the calorie calculation model comprises:
inputting the personal information and the acceleration cumulative value into the second calculation model, and determining model parameters of the second calculation model according to the gender in the personal information and the acceleration cumulative value;
and calculating the calorie consumption value of the wearer according to the model parameters of the second calculation model, the weight, the height and the age in the personal information.
6. The method of claim 3, wherein the step of determining a calorie calculation model based on the exercise data and the detection comprises:
if the detection result indicates that the wearable device is not in the training mode, the motion grade corresponding to the acceleration value variation is larger than a first preset value, and the step number is larger than or equal to a third preset value, determining that the calorie calculation model is a third calculation model;
the step of calculating a calorie expenditure value of the wearer from the personal information, the exercise data and the calorie calculation model comprises:
inputting the personal information, the acceleration cumulative value and the movement speed into the third calculation model, and determining model parameters of the third calculation model according to the gender in the personal information and the acceleration cumulative value;
and calculating the calorie consumption value of the wearer according to the model parameters of the third calculation model, the movement speed, the weight, the height and the age in the personal information.
7. The method according to any one of claims 1-6, further comprising:
and calculating the calorie consumption value in unit time according to the calorie consumption value of the wearer, and if the calorie consumption value in unit time is smaller than a fourth preset value and the exercise data meets preset conditions, correcting the calorie consumption value of the wearer.
8. A calorie calculation device applied to wearable equipment is characterized by comprising:
the information acquisition module is used for acquiring personal information and motion data of a wearer of the wearable device;
the detection module is used for detecting whether the wearable equipment is in a training mode or not and obtaining a detection result;
the decision-making module is used for determining a calorie calculation model according to the movement data and the detection result;
a calculation module for calculating a calorie expenditure value of the wearer from the personal information, the exercise data and the calorie calculation model.
9. A wearable device comprising a processor and a memory, the memory storing a computer program that, when executed by the processor, performs the method of any of claims 1-7.
10. 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 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116665839A (en) * | 2023-06-02 | 2023-08-29 | 深圳市腾进达信息技术有限公司 | Big data regression analysis method and system based on intelligent wearable equipment |
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| CN113592205A (en) * | 2021-09-30 | 2021-11-02 | 浙江宇视科技有限公司 | Intelligent scheduling method, device, equipment and medium based on multidimensional data analysis |
| CN116665839A (en) * | 2023-06-02 | 2023-08-29 | 深圳市腾进达信息技术有限公司 | Big data regression analysis method and system based on intelligent wearable equipment |
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