Method for determining motion parameters based on reliable motion data

Abstract

Embodiments of the present disclosure provide a method for determining a motion parameter if motion data is reliable. The motion data is reliable if the motion data satisfies a set of criteria. The method includes obtaining the motion data, confirming whether a set of judgment parameters determined based on the motion data satisfies the set of criteria, and determining an estimate of the motion parameter using the motion data if the set of judgment parameters satisfies the set of criteria.

Description

Technical Field

[0001] This invention relates to a method for determining exercise parameters, and more particularly to a method for determining exercise parameters based on reliable motion data. Background Technology

[0002] Before providing optimized exercise guidance to users to improve their fitness or health, it is essential to accurately estimate the user's motion parameters (e.g., VO). 2max Or FTP (Functional Threshold Power). Typically, during exercise, sensing units are used to estimate motion parameters based on motion data (e.g., heart rate or speed / power). However, in certain situations, such as when the measuring device (e.g., a wearable device) is not properly secured to the skin or is malfunctioning, inaccurate / unreliable motion data is often obtained. Inaccurate / unreliable motion data can lead to inaccurate estimates of the user's motion parameters.

[0003] Therefore, it is beneficial to improve the determination of motion parameters to overcome the above-mentioned shortcomings. Summary of the Invention

[0004] This invention discloses a method for determining whether acquired motion data is reliable, and then determining motion parameters if the motion data is reliable. The motion data is considered reliable if it satisfies a set of criteria. The method includes: acquiring motion data; confirming whether a set of judgment parameters determined based on the motion data satisfies the set of criteria; and if the set of judgment parameters satisfies the set of criteria, using the motion data to determine an estimate of the motion parameters.

[0005] By means of the algorithm implemented in the computer of the present invention, the computer of the present invention performs the operations described in the claims or the following description to determine motion parameters.

[0006] To enable those skilled in the art to better understand the features of the present invention, detailed techniques for implementing the present invention and the above preferred embodiments will be described in the following paragraphs in conjunction with the accompanying drawings. Attached Figure Description

[0007] The above-described aspects and many accompanying advantages of the present invention will become better and more readily understood by referring to the following detailed description taken in conjunction with the accompanying drawings, wherein:

[0008] Figure 1 A schematic block diagram of an exemplary device in this invention is shown;

[0009] Figure 2 This demonstrates a method for determining motion parameters when motion data is reliable;

[0010] Figure 3 It shows Figure 2 An example of the content of the standard set;

[0011] Figures 4A to 4D An example condition is shown to demonstrate the consistency between a first trend of a first subset of internal workload data and a second trend of a first subset of external workload data during a first duration when the intensity of exercise is changed.

[0012] Figures 5A to 5D Example conditions are shown regarding the degree to which a first internal workload data subset follows a first external workload data subset during a first duration when the intensity of the exercise is changed; and

[0013] Figure 6 The motion parameter VO is shown. 2max The accuracy of the estimate under certain conditions. Detailed Implementation

[0014] The present invention is described in detail below. The described embodiments are presented for illustrative and descriptive purposes and are not intended to limit the scope of the invention.

[0015] Sports data

[0016] Motion data is acquired using sensing unit 11 while the user is exercising (sports) during an exercise session. Motion data may include at least one of (i) and (ii): (i) internal workload data associated with the internal workload (internal workload dataset), and (ii) external workload data associated with the external workload. Motion data may also include internal workload data associated with the internal workload (internal workload dataset) and external workload data associated with the external workload (external workload dataset).

[0017] External workload

[0018] External workload data can refer to data acquired during training completed by the user and measured from sensors placed outside the body, independent of the user's internal characteristics.

[0019] Internal workload

[0020] Internal workload data refers to the relative physiological and psychological stress exerted by external workloads, generated as a representation of the body's internal processes. Internal workload is correlated with a user's internal characteristics. External workloads have varying effects on internal workloads among users. The acquired training results can be used to correlate the interactions between internal and external workloads.

[0021] Exercise intensity

[0022] Exercise intensity data refers to how much energy a user expends during an activity. Exercise intensity defines the degree to which the body must exert effort to overcome the activity / exercise. Exercise intensity can be measured in the form of internal workload. Parameters of exercise intensity related to internal workload can be related to heart rate, oxygen consumption, pulse, respiratory rate, and RPE (Responsive Physical Examination). Exercise intensity can also be measured in the form of external workload. Parameters of exercise intensity related to external workload can be related to speed, acceleration, power, force, energy expenditure rate, movement intensity, movement rhythm, or other kinetic data resulting from external workloads that lead to energy expenditure. Heart rate is commonly used as a parameter of exercise intensity.

[0023] Criterion set

[0024] Figure 3 An example of criterion set 24 is provided. To obtain reliable motion data for determining motion parameters, this invention establishes criterion set 24 to verify the reliability of the motion data. Criterion set 24 may include a first criterion (i), a second criterion (ii), and so on.

[0025] Determine the parameter set

[0026] Figure 3 An example of a decision parameter set 25 is provided. The decision parameter set 25 is associated with a reliability metric determined during the estimation of motion parameters. The decision parameter set 25 can be defined and used as part of a criterion set 24. The motion data is considered reliable for determining the motion parameters if the decision parameter set 25 (e.g., at least one value of the decision parameter set 25) satisfies criterion set 24 (criterion set 24 is satisfied if all criteria in criterion set 24 are satisfied). The decision parameter set 25 may include a first decision parameter J1, a second decision parameter J2, etc.

[0027] Feature parameter set

[0028] Figure 3 An example of feature parameter set 26 is provided. Feature parameter set 26 can be derived from motion data. Parameters in decision parameter set 25 can be determined based on parameters in feature parameter set 26. Parameters in feature parameter set 26 can be correlated with the reliability of motion parameter estimation and can be used as parameters in decision parameter set 25. Feature parameter set 26 may include a first feature parameter F1, a second feature parameter F2, a third feature parameter F3, etc.

[0029] The method of the present invention can be applied to a variety of devices, such as measurement systems worn on an individual (e.g., devices attached to a wristband or chest strap), wrist devices, mobile devices, portable devices, personal computers, servers, or combinations thereof.

[0030] Figure 1 A schematic block diagram of an exemplary device 10 according to the present invention is shown. Device 10 may include a sensing unit 11, a processing unit 12, a memory unit 13, and a display unit 14. Each unit of the device 10 may communicate with another unit via wired or wireless means. The sensing unit 11 may be in one device (e.g., a device worn on an individual or a watch), and the processing unit 12 may be another device (e.g., a mobile device or a mobile phone). Alternatively, the sensing unit 11 and the processing unit 12 may be in a single device (e.g., a device worn on an individual or a watch). The sensing unit 11 may be attached to or built into a strap worn on an individual. The sensing unit 11 may be a sensor (e.g., a heart activity sensor) that can measure signals associated with physiological data, cardiovascular data, or internal workload of the human body. Signals may be measured when the sensor unit 11 is in contact with the skin of the chest, wrist, or any other part of the human body. The processing unit 12 may be any suitable processing device for executing software instructions, such as a central processing unit (CPU). The processing unit 12 may be a computing unit.

[0031] Device 10 may include at least one device; a first portion of the computing unit may be in one device (e.g., a device worn on an individual or a watch), and a second portion of the computing unit may be in another device (e.g., a mobile device or a mobile phone); and the first portion of the computing unit may communicate with the second portion of the computing unit via wired or wireless means; the first portion and the second portion of the computing unit may be in a single device (e.g., a device worn on an individual or a watch). Memory unit 13 may include random access memory (RAM) and read-only memory (ROM), but the invention is not limited to this. Memory unit 13 may include any suitable non-transitory computer-readable medium, such as ROM, CD-ROM, DVD-ROM, etc. Moreover, a non-transitory computer-readable medium is a tangible medium. A non-transitory computer-readable medium includes computer program code that, when executed by processing unit 12, causes device 10 to perform a desired operation (e.g., the operation as described in claim). Display unit 14 may be a display for displaying estimates of motion parameters. Optionally, a first reference value for a first physiological parameter and a second reference value for a second physiological parameter may also be displayed. The display mode may be in the form of words, speech, or images. The sensing unit 11, processing unit 12, memory unit 13 and display unit 14 in device 10 can have any suitable configuration, which is not described in detail here.

[0032] Figure 2 A method 20 for determining motion parameters is shown, assuming the motion data is considered reliable. The motion data is considered reliable if the analysis of the motion data satisfies criterion set 24; that is, criterion set 24 is satisfied if all criteria in criterion set 24 are met. The method includes:

[0033] Step 21: Acquire motion data;

[0034] Step 22: Confirm whether the set of judgment parameters determined based on motion data satisfies the criterion set;

[0035] Step 23: If the set of judgment parameters satisfies the criterion set, then use the motion data to determine the estimation of the motion parameters.

[0036] Implementation Method (A)

[0037] When a user exercises, they can choose to: (Type 1) significantly change the exercise intensity, and (Type 2) maintain a constant exercise intensity or keep the exercise intensity within a certain range. In Type 1, the variance of the exercise intensity can be higher than the variance threshold TA1, which can be evaluated in the algorithm of this invention. In Type 2, the variance of the exercise intensity can be lower than the variance threshold TA2, which can also be evaluated in the algorithm of this invention. Because the exercise data in Type 1 is more complex than that in Type 2, and the deviation between internal and external workload data in Type 1 can be higher than that in Type 2, embodiment (A) of this invention focuses on performing the algorithm primarily on the exercise data in Type 1 to obtain reliable exercise data for determining exercise parameters.

[0038] The exercise data acquired during the exercise process may include an internal workload dataset and an external workload dataset (in step 21). The internal workload dataset corresponds temporally to the external workload dataset acquired simultaneously or at the same time. The internal workload dataset may include a first parameter associated with exercise intensity. The first parameter of exercise intensity may include heart rate, oxygen consumption, pulse, respiratory rate, and RPE (subjective physical assessment). Preferably, the first parameter of exercise intensity is heart rate. The external workload dataset may include a second parameter associated with exercise intensity. The second parameter of exercise intensity may include speed, acceleration, power, force, energy expenditure rate, motion intensity, motion cadence, or other kinetic data generated by external workloads that lead to energy expenditure. Preferably, the second parameter is a measured speed of the user acquired during running or a measured power level acquired during cycling.

[0039] The sensing unit 11 can be used to acquire internal workload datasets and external workload datasets. In one embodiment, the internal workload dataset can be measured by a first sensor of the sensing unit 11, and the external workload data can be measured by a second sensor of the sensing unit 11. The first sensor may be different from the second sensor. For example, the internal workload dataset is cardiac activity data, and the first sensor is a cardiac activity sensor; the external workload data is motion data, and the second sensor is a motion sensor. Each / one of the internal workload dataset and the external workload data can be derived from the raw data measured by the corresponding sensor.

[0040] When using Type 1, the motion segment may include a first duration. The first duration may be a continuous duration or a total duration comprising a number of smaller durations. There are intervals between adjacent smaller durations. The internal workload dataset includes a first subset of internal workload data within the first duration, and the external workload dataset includes a first subset of external workload data within the first duration (i.e., the first internal workload data subset corresponds temporally to the first external workload data subset). Within the first duration using Type 1, at least one variance of the first internal workload data subset and the first external workload data subset may be greater than a variance threshold TB. In a first example, the variance of the first internal workload data may be greater than a variance threshold TB1; in a second example, the variance of the first external workload data may be greater than a variance threshold TB2; in a third example, the variance of the first internal workload data may be greater than a variance threshold TB3, and the variance of the first external workload data may be greater than a variance threshold TB4.

[0041] Because the internal workload (e.g., heart rate) generated when an external workload (e.g., speed) is generated has a time-delay effect, the first external workload data subset (e.g., speed) can be determined by modifying the first initial internal workload data subset (e.g., initial speed) so that the first external workload data subset (e.g., speed) is more synchronized with the first internal workload data subset (e.g., heart rate) compared to the first initial internal workload data subset (e.g., initial speed). The first initial internal workload data subset (e.g., initial speed) can be modified by any suitable method, such as a moving average method.

[0042] To obtain reliable motion data for determining motion parameters, the present invention establishes a set of criteria 24 for verifying the reliability of the motion data (step 22). The set of criteria may include at least one subset of criteria or at least one criterion. Figure 3 It shows Figure 2 An embodiment of the contents of criterion set 24 in step 22. A set of judgment parameters 25 (e.g., related to the reliability of the estimation of motion parameters) can be defined and used in criterion set 24. Figure 3 The parameters J1, J2, ... are used. If at least one value of the judgment parameter set 25 satisfies the criterion set 24 (i.e., if all criteria in the criterion set 24 are satisfied, then the criterion set 24 is satisfied), then the motion data is considered reliable in determining the motion parameters. Furthermore, high precision in estimating the judgment parameter set 25 can accurately determine whether the motion data is reliable for further determining the motion parameters. Therefore, to improve the accuracy of estimating the judgment parameter set 25, the present invention is based on a first characteristic parameter (see...). Figure 3 The first feature parameter is the consistency between the first trend of the first internal workload data subset and the second trend of the first external workload data subset when using type 1.

[0043] Figures 4A to 4D The diagram illustrates some conditions for consistency between the first trend of the first internal workload data subset and the second trend of the first external workload data subset during the first duration when using Type 1. Each of the first trend of the first internal workload data subset and the second trend of the first external workload data subset can be an increasing trend of the corresponding motion intensity over time, or a decreasing trend of the corresponding motion intensity over time. For ease of description, Figures 4A to 4D The upper part of each shows only a portion of the first internal workload data subset. Figures 4A to 4D The lower part of each shows only the corresponding portion of the first external workload data subset. Figures 4A to 4DEach of the left and right ends of each curve represents a relative high or low point. Figure 4A In the first internal workload data subset, the first trend and the second trend of the first external workload data subset each represent an increasing trend in the corresponding motion intensity over time, thus exhibiting high trend consistency. Figure 4C In the first internal workload data subset, the first trend and the second trend of the first external workload data subset each represent a decreasing trend in the corresponding motion intensity over time, thus exhibiting high trend consistency. Figure 4B and Figure 4D In the first internal workload data subset, the first trend is different from the second trend of the first external workload data subset, resulting in low trend consistency.

[0044] During the first duration when using Type 1, the higher the consistency between the first trend of the first internal workload data subset and the second trend of the first external workload data subset, the smaller the deviation between the internal workload data and the external workload data (in Figures 4A to 4D (This is more evident in the middle). Because consistency is associated with bias, the accuracy of the estimation of the judgment parameter set 25 is improved by using the first characteristic parameter (which is associated with the reliability of the estimation of motion parameters).

[0045] In one embodiment, the first characteristic parameter is the degree of correlation (e.g., correlation coefficient) between a first internal workload data subset and a first external workload data subset during a first duration of type 1.

[0046] To further improve the accuracy of the estimated parameter set or to accurately determine whether the estimated first feature parameter is reliable, this invention is based on a second feature parameter (referring to...). Figure 3 The judgment parameter set is determined by one of parameters F1, F2, and F3 in the feature parameter set 26. The second feature parameter is the degree to which the first internal workload data subset follows (closes to) the first external workload data subset during the first duration of type 1. Generally, it is meaningful to determine that the first internal workload data subset follows the first external workload data subset if the consistency between the first trend of the first internal workload data subset and the second trend of the first external workload data subset is sufficiently high. Therefore, due to high trend consistency, Figure 4A and Figure 4C The first internal workload data subset and the first external workload data subset shown are prioritized in determining the degree to which the first internal workload data subset follows the first external workload data subset, which will... Figures 5A to 5DThe following is a detailed description. Preferably, if the second feature parameter is considered when determining the set of judgment parameters 25, then the present invention determines the set of judgment parameters 25 based on the combination of the first feature parameter and the second feature parameter.

[0047] Figures 5A to 5D This illustrates some conditions regarding the degree to which the first internal workload data subset follows the first external workload data subset during the first duration of type 1. For ease of description, Figures 5A to 5D Each of the upper sections only shows a portion of the first internal workload data subset; Figures 5A to 5D Each lower section shows only the (temporal) corresponding portion of the first external workload data subset. Figures 5A to 5D Each curve has a relative high or low point at its left and right ends. The numbers on the vertical axis represent the normalized motion intensity. Figure 5A In the first trend of the first internal workload data subset and the second trend of the first external workload data subset, each is a trend of increasing corresponding motion intensity over time, and both have the same increment of normalized motion expansion, thus exhibiting a high degree of following. Figure 5C In the first trend of the first internal workload data subset and the second trend of the first external workload data subset, each is a decreasing trend of the corresponding motion intensity over time, and both have the same reduction in normalized motion extent, thus exhibiting a high degree of following. Figure 5B In the first trend of the first internal workload data subset and the second trend of the first external workload data subset, each is an increasing trend of the corresponding motion intensity over time, and both have increments with different normalized motion expansion, thus exhibiting low following degree. Figure 5D In the first trend of the first internal workload data subset and the second trend of the first external workload data subset, each is a decreasing trend of the corresponding motion intensity over time, and has a different reduction in normalized motion extension, thus exhibiting a low degree of following.

[0048] During the first duration when using Type 1, the greater the degree to which the first internal workload data subset follows the first external workload data subset, the smaller the deviation between the internal workload data and the external workload data. Because the degree is related to the deviation, a second characteristic parameter can be used to improve the accuracy of the estimation of the judgment parameter set 25, which is associated with the reliability of the estimated motion parameters. Alternatively, the second characteristic parameter can be used to accurately determine whether the estimated first characteristic parameter is reliable.

[0049] In one embodiment, the second characteristic parameter is the slope in a regression analysis (e.g., linear regression) of a first internal workload data subset and a first external workload data subset over a first duration of type 1.

[0050] To further improve the accuracy of estimating the judgment parameter set 25, this invention is based on a third feature parameter (see...). Figure 3 The judgment parameter set 25 is determined by one of parameters F1, F2, and F3 in the feature parameter set 26, and the third feature parameter is the duration of the first duration for acquiring the first internal workload data subset and the first external workload data subset during the first duration of using type 1. Preferably, the present invention determines the judgment parameter set 25 based on a combination of the first feature parameter, the second feature parameter, and the third feature parameter. Preferably, if the third feature parameter is considered when determining the judgment parameter set 25, the present invention determines the judgment parameter set 25 based on a combination of the first feature parameter, the second feature parameter, and the third feature parameter.

[0051] The motion segment may include a second duration employing Type 2. The second duration may be a continuous duration or a total duration comprising many smaller durations. There are intervals between adjacent smaller durations. The internal workload dataset includes a second subset of internal workload data within the second duration, and the external workload dataset includes a second subset of external workload data within the second duration (i.e., the second internal workload data subset corresponds temporally to the second external workload data subset). In the second duration employing Type 2, the variance of at least one of the second internal workload data subset and the second external workload data subset may be lower than a second variance threshold TC. In a first example, the variance of the second internal workload data may be higher than TC1; in a second example, the variance of the second external workload data may be higher than TC2; in a third example, the variance of the first internal workload data may be higher than TC3, and the variance of the first external workload data may be higher than TC4.

[0052] It can be based on any suitable feature parameter (see Figure 3The set of decision parameters 25 is determined by parameters F1, F2, and F3 from the feature parameter set. In one embodiment, type 2 motion data can be used in the algorithm to obtain reliable motion data for determining motion parameters. Feature parameters can be associated with a second internal workload data subset and a second external workload data subset. For example, a feature parameter is the error (e.g., average error) between the data (including the second internal workload data subset and the second external workload data subset during the second duration of type 2) and the regression line in the regression analysis (e.g., linear regression) of the data. A feature parameter can be the duration of the second duration during the second duration of type 2 when acquiring the second internal workload data subset and the second external workload data subset.

[0053] If the result of "the judgment parameter set satisfies the criterion set" is yes, then the motion data is used to determine the estimate of the motion parameters (step 23). Motion parameters can be calculated based on the motion data. Specifically, the motion data may include a first portion of motion data that satisfies criterion set 24 (i.e., the judgment parameter set 25 determined based on the first portion of motion data satisfies criterion set 24) and a second portion of motion data that does not satisfy criterion set 24 (i.e., the judgment parameter set 25 determined based on the second portion of motion data does not satisfy criterion set 24); motion parameters can be calculated based on the first portion of motion data that satisfies criterion set 24 (not based on the second portion of motion data that does not satisfy criterion set 24). Motion parameters can be calculated based on at least one of a first internal workload data subset and a first external workload data subset. In a first example, motion parameters can be calculated based on the first internal workload data subset; in a second example, motion parameters can be calculated based on the first external workload data subset; in a third example, motion parameters can be calculated based on a combination of the first internal workload data subset and the first external workload data subset. Motion parameters can be calculated based on at least one of the internal workload dataset and the external workload dataset. In the first example, motion parameters can be calculated based on an internal workload dataset; in the second example, motion parameters can be calculated based on an external workload dataset; and in the third example, motion parameters can be calculated based on a combination of a first internal workload dataset and a first external workload dataset. Conversely, if the result of "the set of judgment parameters satisfies the criterion set" is negative, then motion data is not used to determine the estimate of motion parameters.

[0054] Determining the motion parameter estimate may include (1) calculating the motion parameters based on at least one of the first internal workload data subset and the first external workload data subset after confirming that the judgment parameter set 25 satisfies the criterion set 24 (i.e., the result in step 23 is yes); (2) calculating the motion parameters based on at least one of the first internal workload data subset and the first external workload data subset before confirming whether the judgment parameter set 25 satisfies the criterion set 24, and then retaining the motion parameters calculated based on at least one of the first internal workload data subset and the first external workload data subset after confirming that the judgment parameter set 25 satisfies the criterion set 24 (i.e., the result in step 23 is yes). After determining the motion parameter estimate, the motion parameter estimate may be displayed by the display unit 14 and / or the motion parameter estimate may be processed to generate the next motion parameter / higher-order motion parameter.

[0055] In embodiment (A), the exercise parameters may be energy expenditure, fitness performance level (fitness performance level may include health-related fitness and exercise / skill-related fitness, which can also be improved by engaging in physical activity or training, such as VO2max or FTP (functional threshold power)), first lactate threshold (LT1), second lactate threshold (LT2), maximum heart rate (HRmax) or minimum heart rate (HRmin), training load, fatigue, training effect, recovery, and endurance. Exercise parameters can be calculated by any suitable method. For example, endurance and energy expenditure can be determined by reference to U.S. Applications 14 / 718,104, 17 / 070,040, and 17 / 070,947; maximum heart rate can be determined by reference to U.S. Application 17 / 376,146; and fitness performance level (e.g., VO2max or FTP (functional threshold power)) can be determined by any suitable method based on maximum cardiac activity parameters (e.g., maximum heart rate (HRMAX)) (e.g., a combination of maximum cardiac activity parameters with statistics from internal and external workload data).

[0056] In order to obtain reliable motion data for determining motion parameters, criterion set 24 may have any suitable content for verifying whether the motion data is reliable (step 22).

[0057] Example (A-1)

[0058] In one embodiment of the criterion set, criterion set 24 includes a first criterion that describes a first judgment parameter of judgment parameter set 25 as being higher than a reliability threshold and that the first judgment parameter of judgment parameter set 25 is the reliability of the estimated motion parameters. The reliability of the estimated motion parameters can be determined based on a first characteristic parameter (i.e., the consistency between a first trend of a first internal workload data subset and a second trend of a first external workload data subset).

[0059] The reliability of the motion parameter estimation can be further determined based on the second characteristic parameter (i.e., the degree to which the first internal workload data subset follows the first external workload data subset). Preferably, the reliability of the motion parameter estimation is determined based on a combination of the first and second characteristic parameters.

[0060] The following algorithm is a first example of determining the reliability of the estimation of motion parameters; however, the invention is not limited to this.

[0061] R(F1,F2)=c1*F1+c2*F2+any other suitable term(1)

[0062] In a preferred embodiment, R(F1, F2) = c1*F1 + c2*F2

[0063] R is the reliability of the estimation of the motion parameters (i.e., the first judgment parameter of the judgment parameter set 25), F1 is the consistency between the first trend of the first internal workload data subset and the second trend of the first external workload data subset (i.e., the first characteristic parameter), F2 is the degree to which the first internal workload data subset follows the first external workload data subset (i.e., the second characteristic parameter), and each of c1 and c2 is a coefficient adjusted based on the observation of physiological phenomena.

[0064] The following algorithm is a second example of determining the reliability of the estimation of motion parameters; however, the invention is not limited to this.

[0065] If F2 > THQ, R(F1, F2) = c1 * F1 + any other suitable term (2)

[0066] In a preferred embodiment, if F2>THQ, then R(F1,F2)=c1*F1.

[0067] R is the reliability of the estimation of motion parameters (i.e., the first judgment parameter of the parameter set), F1 is the consistency between the first trend of the first internal workload data subset and the second trend of the first external workload data subset (i.e., the first characteristic parameter), F2 is the degree to which the first internal workload data subset follows the first external workload data subset (i.e., the second characteristic parameter), THQ is the threshold of F2, which is used to determine whether the estimated first characteristic parameter is reliable, and c1 is a coefficient adjusted based on the observation of physiological phenomena.

[0068] Example (A-2)

[0069] In one embodiment of the criterion set 24, the criterion set 24 includes a first criterion that describes a first judgment parameter of the judgment parameter set 25 as being higher than a consistency threshold, and the first judgment parameter of the judgment parameter set 25 is a first characteristic parameter (i.e., the consistency between a first trend of a first internal workload data subset and a second trend of a first external workload data subset).

[0070] The criterion set 24 may also include a second criterion that describes the second judgment parameter of the judgment parameter set 25 as being higher than a degree threshold, and the second characteristic parameter of the judgment parameter set 25 is the second characteristic parameter (i.e., the degree to which the first internal workload data subset follows the first external workload data subset).

[0071] Experimental results of Example (A)

[0072] Figure 6 The accuracy of the motion parameter (VO2max) estimation is shown. The left side shows the VO2max distribution for users who did not use the method of this invention. The right side shows the VO2max distribution for users obtained using the method of this invention. As shown, the VO2max distribution is narrowed to improve the accuracy of the VO2max estimation.

[0073] Example (B)

[0074] Embodiment (B) of the present invention focuses on performing algorithms primarily on motion data with significantly increasing / gradually increasing motion intensity to obtain reliable motion data for determining motion parameters. Motion data with significantly increasing / gradually increasing motion intensity may mean that the motion intensity of most of the motion data gradually increases during the duration, but the motion intensity of a small subset of the motion data decreases during the duration. Motion data with significantly increasing / gradually increasing motion intensity can be used to increase the accuracy of estimations of motion parameters associated with strenuous exercise.

[0075] Example (B-1)

[0076] During the exercise, motion data is acquired using sensing unit 11 (in step 21). The motion data may use a first parameter of exercise intensity. The first parameter of exercise intensity associated with the internal workload dataset may include heart rate, oxygen consumption, pulse, respiratory rate, and RPE (subjective physical assessment). Preferably, the first parameter of exercise intensity associated with the internal workload is heart rate. The first parameter of exercise intensity associated with the external workload may include speed, acceleration, power, force, energy expenditure rate, motion intensity, motion rhythm, or other kinetic data generated by the external workload that leads to energy expenditure. Preferably, the first parameter of exercise intensity associated with the external workload is speed. Preferably, the first parameter of exercise intensity associated with the external workload is power. More preferably, the first parameter of exercise intensity associated with the external workload is speed measured during running, or power measured during cycling. The sensor used to acquire the motion data depends on the first parameter of exercise intensity used in the motion data; for example, if the motion dataset is cardiac activity data, the first sensor is a cardiac activity sensor. If the external workload data is motion data, the second sensor is a motion sensor. Motion data can be derived from the raw data measured by the corresponding sensors.

[0077] To obtain reliable motion data for determining motion parameters, this invention sets a criterion set 24 to verify the reliability of the motion data (step 22). The criterion set may include at least one subset of criteria or at least one criterion. Figure 3 It shows Figure 2 An embodiment of the contents of criterion set 24 in step 22. A set of judgment parameters 25 (e.g., related to the reliability of the estimation of motion parameters) can be defined and used in criterion set 24. Figure 3 The parameters J1, J2, ... are used in the determination of motion data. If the set of judgment parameters 25 satisfies the criterion set 24 (i.e., if all criteria in the criterion set 24 are satisfied, then the criterion set 24 is satisfied), the motion data is reliable for determining the motion parameters. The criterion set 24 includes a comparison between the set of judgment parameters 25 and a corresponding threshold to determine whether the motion data is reliable. Therefore, high precision of the corresponding threshold of the set of judgment parameters 25 can accurately determine whether the motion data is reliable for further determining the motion parameters. Therefore, in order to improve the precision of the corresponding threshold of the set of judgment parameters 25, the corresponding threshold of the set of judgment parameters 25 is associated with a first historical record of the set of judgment parameters 25 in this invention.

[0078] In one embodiment of criterion set 24, criterion set 24 includes a first criterion describing a first judgment parameter of judgment parameter set 25 as being higher than a first intensity threshold and the first judgment parameter of judgment parameter set 25 as a first parameter of exercise intensity. The first intensity threshold may be associated with a first historical record of the first parameter of exercise intensity. In one embodiment, the first intensity threshold is determined based on a first statistic of the first parameter of exercise intensity; for example, the first intensity threshold is a first statistical value (e.g., average or median) of the first parameter of exercise intensity. If the first parameter of exercise intensity is higher than the first intensity threshold associated with the first historical record of the first parameter of exercise intensity, the exercise data may have a significantly increasing / gradually increasing exercise intensity, and therefore, embodiment (B) of the present invention may focus on performing algorithms primarily on exercise data with significantly increasing / gradually increasing exercise intensity to obtain reliable exercise data for determining exercise parameters. Optionally, criterion set 24 may include any other criteria different from the first criterion; for example, the first parameter of exercise intensity is higher than a first constant intensity threshold; this criterion may confirm that the user is exercising (e.g., vigorous exercise) to further refine the determination of whether the exercise data is reliable for further determination of exercise parameters.

[0079] If the result of "the judgment parameter set satisfies the criterion set" is "yes" (step 23), then motion data is used to determine the estimate of the motion parameters. Motion parameters can be calculated based on the motion data. Specifically, the motion data may include a first portion of motion data that satisfies criterion set 24 (i.e., the judgment parameter set 25 determined based on the first portion of motion data satisfies criterion set 24) and a second portion of motion data that does not satisfy criterion set 24 (i.e., the judgment parameter set 25 determined based on the second portion of motion data does not satisfy criterion set 24); motion parameters can be calculated based on the first portion of motion data that satisfies criterion set 24 (but not based on the second portion of motion data that does not satisfy criterion set 24). Conversely, if the result of "the judgment parameter set satisfies the criterion set" is "no", then motion data is not used to determine the estimate of the motion parameters.

[0080] Determining the estimation of motion parameters may include (1) calculating motion parameters based on motion data after confirming that the judgment parameter set 25 satisfies the criterion set 24 (i.e., the result in step 23 is yes); and (2) calculating motion parameters based on motion data before confirming whether the judgment parameter set 25 satisfies the criterion set 24, and then retaining the motion parameters calculated based on motion data after confirming that the judgment parameter set 25 satisfies the criterion set 24 (i.e., the result in step 23 is yes). After determining the estimation of motion parameters, the estimation of motion parameters can be displayed through the display unit 14 or the estimated value of motion parameters can be processed to generate the next motion parameter / higher-order motion parameter.

[0081] Example (B-2)

[0082] The exercise data acquired during the exercise process may include internal workload data and external workload data (in step 21). The internal workload data corresponds temporally to the external workload data. The internal workload dataset may include a first parameter of exercise intensity. The first parameter of exercise intensity may include heart rate, oxygen consumption, pulse, respiratory rate, and RPE (Responsive Physical Examination). Preferably, the first parameter of exercise intensity is heart rate. The external workload data may include a second parameter of exercise intensity. The second parameter of exercise intensity may include speed, acceleration, power, force, energy expenditure rate, movement intensity, movement rhythm, or other kinetic data generated by external workloads that lead to energy expenditure. Preferably, the second parameter of exercise intensity is speed. Preferably, the second parameter of exercise intensity is power. More preferably, the second parameter of exercise intensity is speed measured during running, and the second parameter of exercise intensity is power measured during cycling.

[0083] The internal workload dataset and the external workload dataset can be acquired using the sensing unit 11. In one embodiment, the internal workload dataset can be measured by a first sensor of the sensing unit 11, and the external workload dataset can be measured by a second sensor of the sensing unit 11. The first sensor may be different from the second sensor. For example, the internal workload dataset is cardiac activity data, and the first sensor is a cardiac activity sensor; the external workload data is motion data, and the second sensor is a motion sensor. Each / one of the internal workload dataset and the external workload data can be derived from the raw data measured by the corresponding sensor.

[0084] The criterion set 24 of embodiment (B-1) may further include a second criterion, which describes that the second judgment parameter of the judgment parameter set 25 is higher than the second intensity threshold and that the second characteristic parameter of the judgment parameter set 25 is a second parameter of motion intensity. In other words, the internal workload dataset of motion data uses the first parameter of motion intensity (corresponding to the first parameter of motion intensity associated with the internal workload in embodiment (B-1), and the external workload data uses the second parameter of motion intensity. The second intensity threshold may be associated with a second historical record of the second parameter of motion intensity. In one embodiment, the second intensity threshold is determined based on a second statistic of the second parameter of motion intensity; for example, the second intensity threshold is a second statistical value (e.g., average or median) of the second parameter of motion intensity. If the second parameter of motion intensity is higher than the second intensity threshold associated with the second historical record of the second parameter of motion intensity, the motion data may have a significantly increasing / gradually increasing motion intensity, and therefore embodiment (B) of the present invention may focus on performing algorithms primarily on motion data with significantly increased motion intensity to obtain reliable motion data for determining motion parameters. Optionally, the criterion set 24 may include any other criteria different from the second criterion; for example, the second parameter of exercise intensity is higher than the second constant intensity threshold; this criterion can confirm that the user is exercising (e.g., vigorous exercise) to further determine whether the exercise data is reliable for further determination of exercise parameters.

[0085] In one embodiment, the set of judgment parameters includes a third judgment parameter determined based on a first characteristic parameter, which is the deviation between internal workload data and external workload data. The third judgment parameter is the degree of deviation between the internal and external workload data, and the criterion set 25 includes a comparison between the third judgment parameter and a deviation threshold for the third judgment parameter. For example, the degree of deviation is expressed in the form of correlation (e.g., correlation coefficient), and if the correlation is higher than a correlation threshold, the motion data is reliable for determining motion parameters. Alternatively, the degree of deviation is expressed in the form of the error (e.g., mean error) between the data (including internal and external workload data) and the regression line in a regression analysis (e.g., linear regression) of the data, and if the error is higher than an error threshold, the motion data is reliable for determining motion parameters.

[0086] If the result of "the judgment parameter set satisfies the criterion set" is "yes" (step 23), then motion data is used to determine the estimate of the motion parameters. Motion parameters can be calculated based on the motion data. Specifically, the motion data may include a first portion of motion data that satisfies criterion set 24 (i.e., the judgment parameter set 25 determined based on the first portion of motion data satisfies criterion set 24) and a second portion of motion data that does not satisfy criterion set 24 (i.e., the judgment parameter set 25 determined based on the second portion of motion data does not satisfy criterion set 24); motion parameters can be calculated based on the first portion of motion data that satisfies criterion set 24 (instead of the second portion of motion data that does not satisfy criterion set 24). Motion parameters can be calculated based on at least one of internal workload data and external workload data. In the first example, motion parameters can be calculated based on internal workload data; in the second example, motion parameters can be calculated based on external workload data; in the third example, motion parameters can be calculated based on a combination of internal workload data and the first external workload data. Conversely, if the result of "the judgment parameter set satisfies the criterion set" is "no", then motion data is not used to determine the estimate of the motion parameters.

[0087] Determining the motion parameter estimate may include (1) calculating the motion parameter based on at least one of the internal workload data and the external workload data after confirming that the judgment parameter set 25 satisfies the criterion set 24 (i.e., the result in step 23 is yes); and (2) calculating the motion parameter based on at least one of the internal workload data and the external workload data before confirming whether the judgment parameter set 25 satisfies the criterion set 24, and then retaining the motion parameter calculated based on at least one of the internal workload data and the external workload data after confirming that the judgment parameter set 25 satisfies the criterion set 24 (i.e., the result in step 23 is yes). After determining the motion parameter estimate, the motion parameter estimate may be displayed through the display unit 14 or the motion parameter estimate may be processed to generate the next motion parameter / higher-order motion parameter.

[0088] In embodiment (B), the exercise parameters may be energy expenditure, fitness performance level (fitness performance level may include health-related fitness and exercise / skill-related fitness (which can also be improved by engaging in physical activity or training), such as VO2max or FTP (functional threshold power)), first lactate threshold (LT1), second lactate threshold (LT2), maximum heart rate (HRmax) or minimum heart rate (HRmin), training load, fatigue, training effect, recovery, and endurance. Exercise parameters can be calculated by any suitable method. For example, endurance and energy expenditure can be determined by reference to U.S. Applications 14 / 718,104, 17 / 070,040, and 17 / 070,947; maximum heart rate can be determined by reference to U.S. Application 17 / 376,146; and fitness performance level (e.g., VO2max or FTP (functional threshold power)) can be determined by any suitable method based on maximum cardiac activity parameters, such as maximum heart rate (HRMAX) (e.g., a combination of maximum cardiac activity parameters with statistics from internal and external workload data).

[0089] This disclosure also provides a computer-readable storage medium for executing a method for determining motion parameters when motion data is reliable. The computer-readable storage medium comprises a plurality of program instructions (e.g., setting program instructions and deploying program instructions) contained therein. If the motion data is reliable, as described above, these program instructions can be loaded and executed therein to perform the method for determining motion parameters described above.

[0090] The above disclosure pertains to its detailed technical content and its inventive features. Those skilled in the art can make various modifications and substitutions based on the described disclosure and suggestions without departing from its characteristics. However, although these modifications and substitutions are not fully disclosed in the above description, they are substantially covered in the appended claims.

Claims

1. A method for determining motion parameters, the method comprising: Motion data in the motion phase is acquired by a sensing unit, wherein the motion data includes (i) an internal workload dataset including a first parameter associated with the motion intensity and (ii) an external workload dataset including a second parameter associated with the motion intensity, wherein the internal workload dataset includes a first subset of internal workload data in a first duration of the motion phase, and the external workload dataset includes a first subset of external workload data in the first duration of the motion phase, wherein at least one of the first internal workload data subset and the first external workload data subset has a variance greater than a first variance threshold; The processing unit confirms whether the set of judgment parameters associated with the reliability metric when estimating the motion parameters meets the set of criteria, wherein the set of judgment parameters is determined based on a first characteristic parameter that has consistency between a first trend in the first internal workload data subset and a second trend in the first external workload data subset. The parameters of the judgment parameter set are determined based on the parameters of the feature parameter set, which is derived from the motion data; the processing unit is any suitable processing device or computing unit for executing software instructions. as well as If the set of judgment parameters satisfies the set of criteria, then the processing unit determines an estimate of the motion parameters calculated based on at least one of the first internal workload data subset and the first external workload data subset.

2. The method according to claim 1, wherein, The set of judgment parameters is further determined based on a second feature parameter, which is the degree to which the first internal workload data subset follows the first external workload data subset.

3. The method according to claim 2, wherein, The set of judgment parameters is further determined based on a third feature parameter, which is the duration of the first duration of acquiring the first internal workload data subset and the first external workload data subset.

4. The method according to claim 1, wherein, The set of judgment parameters includes a first judgment parameter, which is the reliability of the motion parameter estimation, and the set of criteria includes a first criterion describing that the first judgment parameter is higher than a reliability threshold, wherein the reliability of the motion parameter estimation is determined based on a first feature parameter.

5. The method according to claim 4, wherein, The reliability of the motion parameter estimation is further determined based on a second characteristic parameter, which is the degree to which the first internal workload data subset follows the first external workload data subset.

6. The method according to claim 5, wherein, The internal workload dataset further includes a second internal workload data subset during the second duration of the motion phase, and the external workload dataset includes a second external workload data subset during the second duration of the motion phase, wherein at least one of the first internal workload data subset and the first external workload data subset has a variance higher than a variance threshold, wherein at least one of the second internal workload data subset and the second external workload data subset has a second variance less than a second variance threshold, wherein the reliability of the motion parameter estimation is further determined based on a third feature parameter associated with the second internal workload data subset and the second external workload data subset.

7. The method according to claim 1, wherein, The set of judgment parameters includes a first judgment parameter, which is the first feature parameter, and the set of criteria includes a first criterion describing that the first judgment parameter is higher than a consistency threshold.

8. The method according to claim 7, wherein, The set of judgment parameters includes a second judgment parameter, and the set of criteria further includes a second criterion describing the degree to which the second judgment parameter exceeds a certain threshold. The set of judgment parameters is further determined based on a second feature parameter, which is the degree to which the first internal workload data subset follows the first external workload data subset.

9. The method according to claim 1, wherein, The first parameter of the exercise intensity includes heart rate, oxygen consumption, pulse or respiratory rate, and the second parameter of the exercise intensity includes speed, acceleration, power, energy consumption rate or movement rhythm.

10. The method according to claim 1, wherein, Each of the first trend in the first internal workload data subset and the second trend in the first external workload data subset is a trend of increasing or decreasing motion intensity over time.

11. The method of claim 1, wherein the first external workload data subset is determined by modifying the first initial internal workload data subset such that the first external workload data subset is more synchronized with the first internal workload data subset compared to the first initial internal workload data subset.

12. The method according to claim 1, wherein, The exercise parameters are fitness performance level or energy expenditure, and the fitness performance level includes VO. 2max Or FTP (Functional Threshold Power).

13. The method according to claim 1, further comprising displaying the estimate of motion parameters via a display unit.

14. The method of claim 1, wherein the set of judgment parameters includes a first judgment parameter, the first judgment parameter being a first parameter of the exercise intensity, and the set of criteria includes a first criterion describing that the first judgment parameter is higher than a first intensity threshold, wherein the first intensity threshold is associated with a first historical record of the first parameter of the exercise intensity.

15. The method according to claim 14, wherein, The first intensity threshold is determined based on a first statistic of the first parameter of the motion intensity.

16. The method according to claim 15, wherein, The first statistic of the first parameter of the exercise intensity is the average value of the first parameter of the exercise intensity.

17. The method according to claim 16, wherein, The set of judgment parameters includes a second judgment parameter, which is a second parameter of the exercise intensity, and the set of criteria includes a second criterion describing that the second judgment parameter is higher than a second intensity threshold, wherein the second intensity threshold is associated with a second historical record of the second parameter of the exercise intensity.

18. The method according to claim 17, wherein, The set of judgment parameters includes a third judgment parameter determined based on the first feature parameter, wherein the third judgment parameter is the deviation between the internal workload data and the external workload data.

19. The method according to claim 18, wherein, The third judgment parameter is the deviation between the internal workload data and the external workload data, and the criterion set includes a comparison between the third judgment parameter and the deviation threshold of the third judgment parameter.

20. A non-transitory computer-readable storage medium that records an executable computer program, the executable computer program being loaded by an electronic device to perform the following steps: Motion data in the motion phase is acquired by a sensing unit, wherein the motion data includes an internal workload dataset using a first parameter of motion intensity and an external workload dataset using a second parameter of motion intensity, wherein the internal workload dataset includes a first subset of internal workload data in a first duration of the motion phase, and the external workload dataset includes a first subset of external workload data in the first duration of the motion phase, wherein at least one of the first internal workload data subset and the first external workload data subset has a variance greater than a first variance threshold; The processing unit confirms whether the set of judgment parameters associated with the reliability of motion parameter estimation meets the criterion set, wherein the set of judgment parameters is determined based on a first characteristic parameter, which is the consistency between a first trend of the first internal workload data subset and a second trend of the first external workload data subset. as well as If the set of judgment parameters satisfies the set of criteria, then the processing unit determines an estimate of the motion parameters calculated based on at least one of the first internal workload data subset and the first external workload data subset.

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