Method and device for determining exercise consumed energy, electronic equipment and storage medium

Abstract

The invention provides a method and device for determining exercise energy consumption, electronic equipment and a storage medium, and relates to the technical field of wearable equipment. Comprising the following steps: acquiring an electromyographic signal corresponding to a target object collected by wearable equipment; determining motion data of the target object according to the electromyographic signal; and according to the motion data, determining energy consumed by motion of the target object. Therefore, the motion data of the target object in the motion process can be determined based on the electromyographic signals of the target device in the motion process, and then the energy consumed by motion can be accurately determined based on the motion data.

Description

Technical Field

[0001] The present disclosure relates to the technical field of wearable devices, and in particular to a method, device, electronic device and storage medium for determining energy consumption during exercise. Background Art

[0002] Nowadays, with the continuous development of technology, wearable devices have gradually entered people's lives. At present, in some fields that require intelligence, wearable devices are constantly expanding. For example, the currently popular smart watches and some other wearable devices are showing up in people's lives.

[0003] For example, there are many sensors in smart watches that can collect physiological signals of the human body. The watch obtains the human body's sports health information by decoding these physiological signals. Among the indicators of exercise, energy consumption has become an object of increasing concern. Therefore, how to accurately determine the energy consumed by exercise has become an urgent problem to be solved. Summary of the invention

[0004] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0005] The first embodiment of the present disclosure provides a method for determining exercise energy consumption, including:

[0006] Obtaining electromyographic signals corresponding to the target object collected by the wearable device;

[0007] Determining motion data of the target object according to the electromyographic signal;

[0008] The energy consumed by the movement of the target object is determined according to the movement data.

[0009] The second aspect of the present disclosure provides a device for determining exercise energy consumption, including:

[0010] An acquisition module is used to acquire the electromyographic signal corresponding to the target object collected by the wearable device;

[0011] A first determination module, used to determine the motion data of the target object according to the electromyographic signal;

[0012] The second determination module is used to determine the energy consumed by the movement of the target object according to the movement data.

[0013] The third aspect embodiment of the present disclosure proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for determining exercise energy consumption proposed in the first aspect embodiment of the present disclosure is implemented.

[0014] The fourth aspect embodiment of the present disclosure proposes a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for determining exercise energy consumption proposed in the first aspect embodiment of the present disclosure is implemented.

[0015] The method, device, electronic device and storage medium for determining exercise energy consumption provided by the present disclosure have the following beneficial effects:

[0016] In the disclosed embodiment, the electromyographic signal corresponding to the target object collected by the wearable device can be first obtained, and then the motion data of the target object can be determined based on the electromyographic signal, and finally the energy consumed by the target object during the motion can be determined based on the motion data. Thus, the motion data of the target object during the motion can be determined based on the electromyographic signal of the target device during the motion, and the energy consumed by the motion can be accurately determined based on the motion data.

[0017] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0019] Figure 1 A flowchart of a method for determining energy consumption during exercise provided by an embodiment of the present disclosure;

[0020] Figure 2 A schematic flow chart of a method for determining energy consumption during exercise provided by another embodiment of the present disclosure;

[0021] Figure 3 A schematic flow chart of a method for determining energy consumption during exercise provided by another embodiment of the present disclosure;

[0022] Figure 4 A schematic diagram of the structure of a device for determining exercise energy consumption provided by another embodiment of the present disclosure;

[0023] Figure 5 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0024] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0025] In the related art, regarding the determination of exercise energy, most of the algorithms on the market are for aerobic exercise scenarios, especially indoor running, outdoor running, cycling and other scenarios. There is a serious lack of estimation algorithms for energy consumption of anaerobic exercise (lifting dumbbells, squats, etc.) to scientifically estimate the energy consumption of anaerobic exercise. During aerobic exercise, heart rate and energy consumption are highly correlated, so the energy consumption during aerobic exercise can be estimated using heart rate. The heart rate and oxygen consumption in anaerobic exercise show a poor correlation. Therefore, the present disclosure proposes a method for determining the energy consumed by exercise, which determines the motion data of the target object within a preset time period through the electromyographic signals collected by a wearable device, and then based on the motion data, the energy consumed during anaerobic exercise can be accurately determined.

[0026] The following describes the method, device, electronic device and storage medium for determining exercise energy consumption according to embodiments of the present disclosure with reference to the accompanying drawings.

[0027] Figure 1 A flowchart of a method for determining exercise energy consumption provided in an embodiment of the present disclosure.

[0028] The embodiment of the present disclosure takes the method for determining the energy consumed by exercise as an example, in which the method is configured in a device for determining the energy consumed by exercise. The device for determining the energy consumed by exercise can be applied to any electronic device so that the electronic device can perform the function of determining the energy consumed by exercise.

[0029] like Figure 1 As shown, the method for determining the exercise energy consumption may include the following steps:

[0030] Step 101: Acquire the electromyographic signal corresponding to the target object collected by the wearable device.

[0031] The wearable device may include an electromyographic signal acquisition module, which can be used to acquire electromyographic signals corresponding to the wearing part of the wearable device on the target object. Optionally, the wearable device may be a smart bracelet, a watch, a finger ring, a smart monitoring clothing, etc. This disclosure does not limit this.

[0032] Optionally, the electromyographic signal acquisition module may be a surface electromyographic sensor, which can be placed on the surface of the skin and collect electromyographic data by detecting weak electrical signals generated during muscle activity. It usually includes patch electrodes, sensor belts, and amplifiers.

[0033] The target object may be a person or an animal.

[0034] Optionally, a target object may wear one or more wearable devices. The wearing position of the wearable device may be determined according to the type of movement of the target object within a preset period of time.

[0035] Specifically, the moving limb parts corresponding to the exercise type may be determined, and then the wearable electronic device is worn on the moving limb parts corresponding to the exercise type. For example, if the exercise type is standing with both hands lifting dumbbells, the moving limb parts are both arms, and the wearable electronic device is worn on both arms.

[0036] In some embodiments, the electromyographic signal corresponding to the target object collected by the wearable device within a preset time period is obtained.

[0037] The preset time period may be a time period between the time when the target object starts to move and the current time.

[0038] Optionally, it may be determined that the start of movement of the target object is detected when a control corresponding to the start of movement in the wearable device is touched.

[0039] It is understandable that it is more resource-consuming to obtain the corresponding electromyographic signal of the target object in real time when the target object is moving or not moving, and then determine the energy consumption of the target object based on the displacement and muscle force value. This is because when the target object is not moving, the target object will not consume energy due to movement, that is, the electromyographic signal hardly changes.

[0040] Step 102: determining the motion data of the target object according to the electromyographic signal.

[0041] The exercise data may include exercise repetitions, muscle contraction strength, etc. For example, if the exercise is lifting dumbbells, the exercise repetitions may be the number of times dumbbells are lifted within a preset period of time.

[0042] Optionally, the electromyographic signal may be analyzed in the time-frequency domain to obtain the muscle contraction intensity, the number of muscle contractions, etc., wherein the number of movement repetitions is equal to the number of muscle contractions.

[0043] Step 103: determining the energy consumed by the target object's movement according to the movement data.

[0044] In the disclosed embodiment, the energy corresponding to the muscle contraction intensity in the motion data can be determined based on a predetermined mapping relationship between muscle contraction intensity and consumed energy, and then the energy corresponding to the muscle contraction intensity in a preset time period can be accumulated to obtain the energy consumed by the target object during the exercise in the preset time period.

[0045] Alternatively, if the muscle contractions corresponding to each movement of the target object within the preset time period are slightly similar, the energy corresponding to the muscle contraction intensity and the product of the number of movement repetitions within the preset time period can be determined as the energy consumed by the target object during the preset time period.

[0046] In the disclosed embodiment, the electromyographic signal corresponding to the target object collected by the wearable device within a preset period of time can be first obtained, and then the motion data of the target object within the preset period of time can be determined based on the electromyographic signal, and finally the energy consumed by the target object during the preset period of time can be determined based on the motion data. Thus, the motion data of the target object during the motion process can be determined based on the electromyographic signal of the target device during the motion process, and then the energy consumed by the motion can be accurately determined based on the motion data.

[0047] Figure 2 A flow chart of a method for determining exercise energy consumption provided by an embodiment of the present disclosure is shown as follows: Figure 2 As shown, the method for determining the exercise energy consumption may include the following steps:

[0048] Step 201: Acquire the electromyographic signal corresponding to the target object collected by the wearable device.

[0049] It should be noted that the method disclosed in the present invention for determining the energy consumed by a target object during exercise based on electromyographic signals is more accurate in certain specific types of exercise (such as anaerobic exercises such as lifting dumbbells and squatting, in which the displacement of the limbs and the direction of the force they bear are consistent) than in other types of exercise (such as running, cycling, etc.).

[0050] Therefore, in some possible implementations, when a preset control in the wearable device is touched, the motion type corresponding to the target object can be obtained, and when the motion type is a preset type, the electromyographic signal corresponding to the target object collected by the wearable device within a preset time period can be obtained.

[0051] Among them, the preset type can be anaerobic exercise in which the displacement and muscle strength values ​​generated by exercises such as lifting dumbbells and squatting are in the same direction.

[0052] The preset control may be a control corresponding to a motion type, and when the preset control is touched, the motion type corresponding to the preset control is determined to be the motion type corresponding to the target object.

[0053] Step 202: extract features from the electromyographic signal to obtain electromyographic features corresponding to the electromyographic signal.

[0054] In some possible implementations, before extracting features from the electromyographic signal, the electromyographic signal may be filtered to eliminate motion artifacts and some noise interference. Specifically, the electromyographic signal may be notched filtered at 50 Hz and its multiples to eliminate power frequency interference. Then, a Butterworth bandpass filter at 20 to 500 Hz may be performed to eliminate motion artifacts and some high-frequency interference to obtain an accurate electromyographic signal.

[0055] Among them, power frequency interference refers to the electromagnetic field radiated by the power supply and lines in wearable devices, which interferes with the normal operation of wearable devices.

[0056] Motion artifacts refer to interference signals that are not related to muscle movement and appear in the collected EMG signals. When collecting EMG signals, they may be affected by mechanical vibrations, electromagnetic radiation, etc. from the surrounding environment, thus generating motion artifacts. These artifact signals may be mixed with the real muscle activity signals.

[0057] High-frequency interference refers to the interference of high-frequency noise in the surrounding environment on the collected electromyographic signals. That is, the electromyographic signals collected by wearable electronic devices contain high-frequency noise in the surrounding environment. Therefore, a Butterworth bandpass filter can be used to help remove high-frequency interference.

[0058] In some possible implementations, the electromyographic signal can be divided based on a preset time window to obtain multiple sub-electromyographic signals corresponding to the electromyographic signal, and then feature extraction is performed on the sub-electromyographic signals to obtain the root mean square and average absolute value corresponding to each sub-electromyographic signal as the electromyographic features corresponding to the electromyographic signal.

[0059] Among them, the root mean square (RMS) is a common indicator of signal strength. The root mean square can reflect the overall energy or amplitude of the sub-electromyographic signal. The root mean square value of the sub-electromyographic signal can be calculated by the following steps:

[0060] (1) Sampling the sub-electromyographic signal to obtain multiple sampling points; (2) squaring the value of each sampling point of the sub-electromyographic signal; (3) calculating the average value of the squared values; (4) taking the square root of the average value as the root mean square of the sub-electromyographic signal.

[0061] The mean absolute value (MAV) can represent the overall amplitude of the sub-electromyographic signal. The mean absolute value of each sub-electromyographic signal can be calculated by the following steps:

[0062] (1) Sampling the sub-electromyographic signal to obtain a plurality of sampling points; (2) summing the absolute value of each sampling point of the sub-electromyographic signal; and (3) dividing the summation result by the number of sampling points to obtain an average absolute value.

[0063] The preset time window may be 3s, 1s, etc., which are preset.

[0064] Optionally, the preset time window can also be determined according to the type of exercise. The faster the exercise speed corresponding to the exercise type, the shorter the preset time window; the slower the exercise speed corresponding to the exercise type, the longer the preset time window. For example, the length of the time window corresponding to the high leg raise exercise is greater than the length of the time window corresponding to the squat.

[0065] Optionally, the electromyographic signal may be divided using a sliding window method. For example, the sliding window size is set to 1 second, and each window slides back 250 ms, and adjacent windows have 75% overlapping signals.

[0066] Optionally, the root mean square and average absolute value corresponding to each sub-electromyographic signal can also be normalized. Specifically, the maximum and minimum values ​​of the root mean square corresponding to the electromyographic signal and the maximum and minimum values ​​of the corresponding average absolute value can be obtained first, and then the root mean square of each sub-electromyographic signal is normalized based on the maximum and minimum values ​​of the root mean square, and the average absolute value of each sub-electromyographic signal is normalized based on the maximum and minimum values ​​of the average absolute value.

[0067] In some possible implementations, if there are multiple electromyographic signals, it is necessary to obtain the maximum and minimum root mean square values ​​corresponding to each electromyographic signal, and the maximum and minimum corresponding average absolute values; then, based on the maximum and minimum root mean square values ​​corresponding to each electromyographic signal, the root mean square of the sub-electromyographic signal corresponding to each electromyographic signal is normalized; based on the maximum and minimum average absolute values ​​corresponding to each electromyographic signal, the average absolute value of the sub-electromyographic signal corresponding to each electromyographic signal is normalized.

[0068] It should be noted that, since each electromyographic signal corresponds to a different part, the changes in electromyographic signals corresponding to different parts under the same type of movement are also different. Therefore, the root mean square of the sub-electromyographic signal corresponding to each electromyographic signal can be normalized based on the maximum and minimum root mean square values ​​corresponding to each electromyographic signal; the average absolute value of the sub-electromyographic signal corresponding to each electromyographic signal can be normalized based on the maximum and minimum average absolute values ​​corresponding to each electromyographic signal, thereby improving the accuracy of the normalized root mean square and average absolute value.

[0069] It should be noted that the movement of the target object within the preset time period is often repetitive, such as repeated dumbbell lifting, repeated squatting, etc. In order to avoid repeated movement for multiple times, but only obtaining one displacement and force value, resulting in inaccurate determination of the energy consumed by the movement, in the embodiment of the present disclosure, the electromyographic signal can be divided based on a preset time window to obtain multiple sub-electromyographic signals, and then based on the electromyographic features corresponding to the multiple sub-electromyographic signals, the energy consumed by the target object each time it moves can be determined, so that the energy consumed by the target object during the preset time period can be accurately determined.

[0070] Step 203, determining the motion data of the target object according to the electromyographic characteristics, wherein the motion data includes the displacement corresponding to the limb and the force value borne by the limb.

[0071] Among them, the displacement corresponding to the limb is the displacement generated by the limb during the movement. The force value borne by the limb can be the force generated by the object on the limb during the movement of the limb. For example, when the target object lifts a dumbbell, the dumbbell generates a force on the target object's hand.

[0072] It should be noted that when the target object moves, multiple limbs in the body often move together, and the corresponding movements of each limb may be different. Therefore, it is necessary to calculate the displacement and force value corresponding to each limb separately, that is, it is necessary to divide the multiple sub-electromyographic signals in each time window to determine the target sub-electromyographic signals corresponding to each limb in each time window.

[0073] Specifically, the limb identifier (for example, arm, leg, etc.) corresponding to each electromyographic signal can be determined according to the limb deployed by the electromyographic signal acquisition module that collects each electromyographic signal. Then, when the electromyographic signal is divided into multiple sub-electromyographic signals, each sub-electromyographic signal also has the same limb identifier. Therefore, based on the limb identifier, multiple sub-electromyographic signals can be divided to determine the target sub-electromyographic signal corresponding to each limb in each time window.

[0074] Alternatively, the electromyographic signals corresponding to each limb can be determined based on the electromyographic signals collected by each electromyographic signal acquisition module and the deployed limbs, and then the electromyographic signals corresponding to each limb can be divided based on a preset time window to obtain the target sub-electromyographic signals corresponding to each limb in each time window.

[0075] In some possible implementations, when there are multiple electromyographic signals, the multiple sub-electromyographic signals in each time window are divided to determine the target sub-electromyographic signals corresponding to each limb in each time window. Then, based on the first mapping relationship between the electromyographic characteristics and the displacement corresponding to each limb, the root mean square and the average absolute value of the target sub-electromyographic signals corresponding to each limb in each time window are processed to obtain the displacement corresponding to each limb in each time window.

[0076] The first mapping relationship may be generated by analyzing the electromyographic features corresponding to the sample electromyographic signals collected in advance for each limb and the sample displacements corresponding to the sample electromyographic signals.

[0077] In some possible implementations, based on the second mapping relationship between the electromyographic characteristics corresponding to each limb and the force value, the root mean square and average absolute value of the target sub-electromyographic signals corresponding to each limb in each time window are processed to obtain the force value borne by each limb in each time window.

[0078] The second mapping relationship may be generated by analyzing the electromyographic features corresponding to the sample electromyographic signals collected in advance for each limb and the sample force values ​​corresponding to the sample electromyographic signals. The sample force values ​​may be collected by mechanical sensors.

[0079] In some possible implementations, the root mean square and average absolute value of the target sub-electromyographic signals corresponding to each limb in each time window may be input into a preset first model to obtain the displacement corresponding to each limb in each time window.

[0080] The first model can be generated based on the pre-collected sample electromyographic signals of each limb and the collected sample displacement training corresponding to the sample electromyographic signals. Specifically, the electromyographic features and sample displacement corresponding to the processed sample electromyographic signals are input into a machine learning model or a deep learning model, and the back propagation algorithm is used to optimize the weight matrix and bias in the model to obtain the first model.

[0081] Among them, the backpropagation algorithm is an algorithm for training an artificial neural network model. In the embodiment of the present disclosure, the prediction error of the first model can be backpropagated from the output layer to the input layer of the first model to update the weight and bias of the first model according to the prediction error, thereby optimizing the performance of the first model.

[0082] In some possible implementations, the root mean square and average absolute value of the target sub-electromyographic signals corresponding to each limb in each time window can be input into a preset second model to obtain the force value borne by each limb in each time window.

[0083] The second model can be generated based on the pre-collected sample electromyographic signals of each limb and the collected sample force values ​​corresponding to the sample electromyographic signals. Specifically, the electromyographic features and sample force values ​​corresponding to the processed sample electromyographic signals are input into a machine learning model or a deep learning model, and the back propagation algorithm is used to optimize the weight matrix and deviation in the model to obtain the second model.

[0084] There are many choices for machine learning models and deep learning models, such as xgboost model, decision tree model, Gaussian process regression model, Bayesian model, etc. The following takes xgboost as an example. xgboost has linear regression and decision tree boosters. Since the decision tree booster is more suitable for time series signals and has better performance, the decision tree booster is selected. Among them, the learning rate needs to be tuned according to the data. In this case, it can be set to 0.5. The maximum depth of the decision tree represents the complexity of the decision tree. Since the electromyographic signal estimates motion and force at the same time, a simple model cannot fit an accurate nonlinear relationship. Therefore, a larger value can be selected for the maximum depth, for example, the maximum depth can be 15.

[0085] The reason why the present disclosure adopts two independent models to estimate displacement and force respectively is that there is a coupling relationship between displacement and force. A single model estimates displacement and force simultaneously, which places high demands on muscles and the decoupling ability of the model.

[0086] Step 204: determining the energy consumed by the target object's movement according to the movement data.

[0087] In some possible implementations, the product of the displacement and force value corresponding to each limb in each time window can be first determined as the first sub-energy consumed by each limb in each time window, and then the sum of the first sub-energy consumed by each limb in each time window can be determined as the second sub-energy consumed by the target object in each time window. Finally, the sum of each second sub-energy is determined as the energy consumed by the target object during the target time period.

[0088] In the disclosed embodiment, the electromyographic signal corresponding to the target object collected by the wearable device can be first obtained, and then the electromyographic signal can be feature extracted to obtain the electromyographic feature corresponding to the electromyographic signal, and then the displacement corresponding to the limbs and the force value borne by the limbs of the target object in a preset time period can be determined according to the electromyographic feature, and finally the energy consumed by the movement of the target object can be determined according to the displacement corresponding to the limbs and the force value borne by the limbs. Thus, based on the electromyographic feature of the electromyographic signal, the displacement corresponding to the limbs of the target object in a preset time period and the force value borne by the limbs can be accurately determined, and then based on the displacement and force value, the work done by the target object in the preset time period, that is, the energy consumed by the movement, can be determined, so that the energy consumed by the movement of the target object can be more accurately determined.

[0089] Figure 3 A flow chart of a method for determining exercise energy consumption provided by an embodiment of the present disclosure is shown as follows: Figure 3 As shown, the method for determining the exercise energy consumption may include the following steps:

[0090] Step 301: Acquire the electromyographic signal corresponding to the target object collected by the wearable device within a preset time period.

[0091] Step 302: determining the motion data of the target object according to the electromyographic signal.

[0092] Step 303: Determine the energy consumed by the target object's movement according to the movement data.

[0093] The specific implementation forms of steps 301 to 303 can refer to the detailed descriptions in other embodiments of the present disclosure, and will not be described in detail here.

[0094] Step 304: determining the energy consumed by the basal metabolism of the target object within a preset period of time.

[0095] It should be noted that the target object consumes energy not only due to exercise but also due to its own basal metabolism during the target time period. Therefore, the energy consumed by the target object's basal metabolism during the preset time period can be further determined.

[0096] In some possible implementations, the height, weight, age, and gender of the target object can be first obtained; then, based on the target function corresponding to the gender, and the height, weight, and age, the energy consumed by the target object's basal metabolism per day can be determined; finally, based on the energy consumed by the basal metabolism per day, the energy consumed by the target object's basal metabolism within a preset time period can be determined.

[0097] Among them, the objective function corresponding to boys can be:

[0098] BMR_MALE=(278+58*WEIGHT+21*HEIGHT-28*AGE)*4.19*1000

[0099] The objective function for girls can be:

[0100] BMR_FEMALE=(2741+40*WEIGHT+8*HEIGHT-20*AGE)*4.19*1000

[0101] Among them, BMR_MALE is the energy consumed by boys' basal metabolism every day, BMR_FEMALE is the energy consumed by girls' basal metabolism every day, the unit is joule, WEIGHT is weight in kilograms, HEIGHT is height in centimeters, and AGE is age in years.

[0102] Step 305: The sum of the energy consumed by exercise and the energy consumed by basal metabolism is determined as the total energy consumed by the target object in a preset period of time.

[0103] Therefore, after determining the energy consumed by exercise and the energy consumed by basal metabolism during the preset period, the sum of the energy consumed by exercise and the energy consumed by basal metabolism can be determined as the total energy consumed by the target object during the preset period.

[0104] In the disclosed embodiment, the electromyographic signal corresponding to the target object collected by the wearable device within a preset period of time can be first obtained, and then the motion data of the target object can be determined based on the electromyographic signal, and the energy consumed by the target object during the motion can be determined based on the motion data. Then, the energy consumed by the basal metabolism of the target object during the preset period of time can be determined, and finally the sum of the energy consumed by the motion and the energy consumed by the basal metabolism can be determined as the total energy consumed by the target object during the preset period of time. Therefore, after determining the energy consumed by the target object during the preset period of time, the energy consumed by the basal metabolism can be further obtained, so that the total energy consumed by the target object during the preset period of time can be accurately determined.

[0105] In order to implement the above embodiment, the present disclosure also proposes a device for determining exercise energy consumption.

[0106] Figure 4 A schematic diagram of the structure of a device for determining exercise energy consumption provided in an embodiment of the present disclosure.

[0107] like Figure 4 As shown, the motion energy consumption determination device 400 may include:

[0108] An acquisition module 410 is used to acquire an electromyographic signal corresponding to a target object collected by a wearable device;

[0109] A first determination module 420 is used to determine the motion data of the target object according to the electromyographic signal;

[0110] The second determination module 430 is used to determine the energy consumed by the movement of the target object according to the movement data.

[0111] In some possible implementations, the first determining module 420 is configured to:

[0112] Perform feature extraction on the electromyographic signal to obtain the electromyographic features corresponding to the electromyographic signal;

[0113] The motion data of the target object is determined according to the electromyographic characteristics, wherein the motion data includes the displacement of the limbs and the force value borne by the limbs.

[0114] In some possible implementations, the first determining module 420 is configured to:

[0115] Based on a preset time window, the electromyographic signal is divided to obtain a plurality of sub-electromyographic signals corresponding to the electromyographic signal;

[0116] The sub-electromyographic signals are subjected to feature extraction to obtain the root mean square and average absolute value corresponding to each sub-electromyographic signal.

[0117] In some possible implementations, the first determining module 420 is configured to:

[0118] When there are multiple sub-electromyographic signals in each time window, the multiple sub-electromyographic signals are divided to determine a target sub-electromyographic signal corresponding to each limb in each time window;

[0119] Based on the first mapping relationship between the electromyographic characteristics and displacement corresponding to each limb, the root mean square and the average absolute value of the target sub-electromyographic signal corresponding to each limb in each time window are processed to obtain the displacement corresponding to each limb in each time window;

[0120] Based on the second mapping relationship between the electromyographic characteristics corresponding to each limb and the force value, the root mean square and average absolute value of the target sub-electromyographic signals corresponding to each limb in each time window are processed to obtain the force value borne by each limb in each time window.

[0121] In some possible implementations, the second determining module 430 is configured to:

[0122] The product of the displacement and the force value corresponding to each limb in each time window is determined as the first sub-energy consumed by each limb in each time window;

[0123] The sum of the first sub-energy consumed by each limb in each time window is determined as the second sub-energy consumed by the target object in each time window;

[0124] The sum of each second sub-energy is determined as the energy consumed by the target object movement.

[0125] In some possible implementations, the first determining module 420 is configured to:

[0126] Obtain the maximum and minimum values ​​of the root mean square corresponding to the electromyographic signal, and the maximum and minimum values ​​of the corresponding average absolute value;

[0127] The RMS of each sub-EMG signal was normalized based on the maximum and minimum RMS values;

[0128] The mean absolute value of each sub-EMG signal was normalized based on the maximum and minimum values ​​of the mean absolute value.

[0129] In some possible implementations, the acquisition module is used to:

[0130] The electromyographic signal corresponding to the target object collected by the wearable device within a preset time period is obtained, and the preset time period is a time period from the moment when the target object starts to move to the current moment.

[0131] In some possible implementations, a third determining module is further included, configured to:

[0132] Determine the energy consumed by the target subject's basal metabolism during a preset period of time;

[0133] The sum of the energy consumed by exercise and the energy consumed by basal metabolism is determined as the total energy consumed by the target object within a preset period of time.

[0134] In some possible implementations, a third determining module is further included, which is specifically configured to:

[0135] Get the height, weight, age, and gender of the target object;

[0136] Based on the objective function corresponding to gender, as well as height, weight, and age, determine the target subject's daily basal metabolic energy consumption;

[0137] Based on the energy consumed by the daily basal metabolism, the energy consumed by the basal metabolism of the target object in a preset period of time is determined.

[0138] In some possible implementations, the acquisition module 410 is configured to:

[0139] When a preset control in the wearable device is touched, obtaining a motion type corresponding to the target object;

[0140] When the motion type is a preset type, an electromyographic signal corresponding to the target object collected by the wearable device within a preset time period is obtained.

[0141] The functions and specific implementation principles of the above modules in the embodiments of the present disclosure can be referred to the above method embodiments, and will not be repeated here.

[0142] The apparatus for determining the energy consumed by exercise in the embodiment of the present disclosure can first obtain the electromyographic signal corresponding to the target object collected by the wearable device, then determine the motion data of the target object based on the electromyographic signal, and finally determine the energy consumed by the target object based on the motion data. Thus, the motion data of the target object during the motion process can be determined based on the electromyographic signal of the target device during the motion process, and then the energy consumed by the exercise can be accurately determined based on the motion data.

[0143] In order to implement the above embodiments, the present disclosure also proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for determining exercise energy consumption proposed in the above embodiments of the present disclosure.

[0144] In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for determining the exercise energy consumption proposed in the above embodiments of the present disclosure is implemented.

[0145] Figure 5 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 5 The electronic device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0146] like Figure 5 As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0147] The bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnection (PCI) bus.

[0148] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0149] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 5 not shown, usually called a "hard drive"). Although Figure 5 Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a compact disc read only memory (CD-ROM), a digital versatile disc read only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the various embodiments of the present disclosure.

[0150] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described in the present disclosure.

[0151] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 12, and / or may communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0152] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the methods mentioned in the above embodiments.

[0153] The technical solution disclosed in the present invention can first obtain the electromyographic signal corresponding to the target object collected by the wearable device within a preset period of time, then determine the motion data of the target object within the preset period of time based on the electromyographic signal, and finally determine the energy consumed by the target object during the preset period of time based on the motion data. Thus, the motion data of the target object during the motion process can be determined based on the electromyographic signal of the target device during the motion process, and then the energy consumed by the motion can be accurately determined based on the motion data.

[0154] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0155] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0156] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present disclosure belong.

[0157] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0158] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0159] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0160] In addition, each functional unit in each embodiment of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0161] The storage medium mentioned above may be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present disclosure. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present disclosure.

Claims

1. A method for determining energy consumption during exercise, characterized in that: include: Obtaining electromyographic signals corresponding to the target object collected by the wearable device; Determining motion data of the target object according to the electromyographic signal; The energy consumed by the movement of the target object is determined according to the movement data.

2. The method according to claim 1, characterized in that Determining the motion data of the target object according to the electromyographic signal includes: Performing feature extraction on the electromyographic signal to obtain the electromyographic feature corresponding to the electromyographic signal; The motion data of the target object is determined according to the electromyographic characteristics, wherein the motion data includes the displacement corresponding to the limb and the force value borne by the limb.

3. The method according to claim 2, characterized in that The step of extracting features from the electromyographic signal to obtain electromyographic features corresponding to the electromyographic signal includes: Based on a preset time window, the electromyographic signal is divided to obtain a plurality of sub-electromyographic signals corresponding to the electromyographic signal; Feature extraction is performed on the sub-electromyographic signals to obtain a root mean square and an average absolute value corresponding to each of the sub-electromyographic signals as the myoelectric features corresponding to the myoelectric signals.

4. The method according to claim 3, characterized in that Determining the motion data of the target object according to the electromyographic characteristics includes: In the case where there are multiple electromyographic signals, multiple sub-electromyographic signals in each time window are divided to determine a target sub-electromyographic signal corresponding to each limb in each time window; Based on the first mapping relationship between the myoelectric characteristics and displacement corresponding to each limb, the root mean square and the average absolute value of the target sub-myoelectric signals corresponding to each limb in each time window are processed to obtain the displacement corresponding to each limb in each time window; Based on the second mapping relationship between the electromyographic characteristics corresponding to each limb and the force value, the root mean square and average absolute value of the target sub-electromyographic signals corresponding to each limb in each time window are processed to obtain the force value borne by each limb in each time window.

5. The method according to claim 4, characterized in that The step of determining the energy consumed by the target object's motion according to the motion data includes: The product of the displacement corresponding to each limb in each time window and the force value is determined as the first sub-energy consumed by each limb in each time window; Determine the sum of the first sub-energy consumed by each limb in each time window as the second sub-energy consumed by the target object in each time window; The sum of each of the second sub-energies is determined as the energy consumed by the movement of the target object.

6. The method according to claim 3, characterized in that After extracting features from the sub-electromyographic signals to obtain the root mean square and average absolute value corresponding to each of the sub-electromyographic signals as the myoelectric features corresponding to the myoelectric signals, the method further includes: Obtaining the maximum and minimum values ​​of the root mean square corresponding to the electromyographic signal, and the maximum and minimum values ​​of the corresponding average absolute value; Based on the maximum and minimum values ​​of the root mean square, normalizing the root mean square of each of the sub-electromyographic signals; The average absolute value of each of the sub-electromyographic signals is normalized based on the maximum and minimum values ​​of the average absolute value.

7. The method according to claim 1, characterized in that The step of obtaining the electromyographic signal corresponding to the target object collected by the wearable device includes: The myoelectric signal corresponding to the target object collected by the wearable device within a preset time period is obtained, and the preset time period is a time period from the moment when the target object starts to move to the current moment.

8. The method according to claim 7, characterized in that After determining the energy consumed by the target object's motion according to the motion data, the method further includes: Determine the energy consumed by the basal metabolism of the target object during the preset period; The sum of the energy consumed by the exercise and the energy consumed by the basal metabolism is determined as the total energy consumed by the target object within the preset time period.

9. The method according to claim 8, characterized in that The determining of the energy consumed by the basal metabolism of the target object within the preset period includes: Obtain the height, weight, age, and gender of the target object; Determine the energy consumed by the target subject's daily basal metabolism based on the target function corresponding to the gender, the height, the weight, and the age; Based on the daily basal metabolic energy consumption, the basal metabolic energy consumption of the target object within the preset time period is determined.

10. The method according to any one of claims 1 to 9, characterized in that: The step of obtaining the electromyographic signal corresponding to the target object collected by the wearable device includes: When a preset control in the wearable device is touched, obtaining a motion type corresponding to the target object; When the motion type is a preset type, an electromyographic signal corresponding to the target object collected by the wearable device is obtained.

11. A device for determining energy consumption during exercise, characterized in that: The device comprises: An acquisition module is used to acquire the electromyographic signal corresponding to the target object collected by the wearable device; A first determination module, used to determine the motion data of the target object according to the electromyographic signal; The second determination module is used to determine the energy consumed by the movement of the target object according to the movement data.

12. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for determining the energy consumed by exercise as described in any one of claims 1 to 10 is implemented.

13. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for determining the exercise energy consumption as described in any one of claims 1-10 is implemented.

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