CN115062639A - Pedal frequency resolving method based on acceleration power meter - Google Patents
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Abstract
The invention discloses a pedal frequency resolving method based on an accelerometer, which has the technical scheme key points that: acquiring an acceleration time sequence of a double shaft; carrying out filtering processing on the data in the window; performing feature extraction on the biaxial data; judging a normal state; and (4) step frequency output, and step frequency is calculated according to a formula. Whether the riding state is normal or not is judged through the extracted features, the riding state can be correctly judged without torsion, available data types are increased through acquisition of double-axis acceleration data, and robustness of a pedaling frequency settlement system is improved.
Description
Technical Field
The invention relates to the technical field of pedal frequency calculation, in particular to a pedal frequency calculation method based on an acceleration power meter.
Background
Most of the transmission power meters judge abnormal riding through torsion, but the method has the condition that the threshold value is not converged, so that the identification error is easily caused, and the identification method is not suitable for pedal frequency products without the torsion meters; the problem of poor robustness exists in a pedal frequency resolving system of a single-sensor system because a sensor is single or input data is not subjected to characteristic enhancement;
in order to solve the problems, the application provides a pedal frequency calculation method based on an accelerometer.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a pedaling frequency calculating method based on an acceleration power meter, which judges whether the riding state is normal or not through the extracted features, can correctly judge the riding state without torsion, increases the available data types through the acquisition of double-axis acceleration data, and improves the robustness of a pedaling frequency settlement system.
In order to achieve the purpose, the invention provides the following technical scheme: a pedal frequency resolving method based on an acceleration power meter comprises the following steps:
acquiring acceleration time sequences of double shafts, acquiring double shaft data according to a determined time interval, storing the data of two sequences in one time interval into a sliding window, and sequentially displaying sequence numerical values of different time intervals by the sliding window;
carrying out filtering processing on the data in the window;
extracting the characteristics of the double-axis data, and extracting the peak point, the valley point, the zero point of the two sequence data and the monotonous increase and monotonous decrease of numerical values in the sequences;
judging a normal state, namely judging whether the characteristics of two sequences in a window accord with a preset characteristic condition or not according to a preset characteristic condition, if so, performing frequency-stepping output, and if not, directly performing filtering processing on data of a next window;
and (3) step frequency output, calculating step frequency according to a formula, wherein the calculation formula is as follows:
wherein cadence represents the cadence frequency, T is the output frequency of the signal, and Δ T is the time interval between two characteristic points, and the unit is second.
By adopting the technical scheme, whether the riding state is normal or not is judged by the extracted features, the riding state can be correctly judged without torsion, the data features are enhanced by acquiring the biaxial acceleration data, and the poor robustness is reduced.
The invention is further configured to: the two sequence values obtained in the window are respectively marked as Acc x [acc 1 ，……，acc n ]And Acc y [acc 1 ，……，acc n ]。
The invention is further configured to: the process of filtering the data in the window is as follows:
wherein X represents the input acceleration sequence, Y represents the output filtered value, and a and b represent the denominator vector and the numerator vector of the corresponding butterworth filter.
By adopting the technical scheme, the accuracy of the finally obtained step frequency data can be improved through filtering processing.
The invention is further configured to: the preset characteristic conditions comprise two conditions, wherein the first condition is that the x-axis data is a peak point, and the gradient characteristic of the y-axis data is monotonously increased;
the second condition is that the x-axis data is a zero point, the gradient characteristic of the x-axis data is monotonously increased and the y-axis data is a peak point, or the x-axis data is a zero point, the x-axis data is monotonously decreased and the y-axis data is a valley point.
The invention is further configured to: when the data characteristics of the two sequences in the judgment window meet the condition one, recording a time point corresponding to the middle position of the acceleration sequence, storing the time point in the peak sequence, and taking the time interval between the time point stored in the peak sequence and the peak point in the acceleration sequence as delta t.
The invention is further configured to: and when the data characteristics of the two sequences in the window are judged to meet the second condition, recording a time point corresponding to the middle position of the acceleration sequence, storing the time point in the zero-point sequence, and taking the time interval between the time point in which the zero-point sequence is stored and the zero-point time in the time sequence as delta t.
The invention is further configured to: and the prior estimation is carried out, the occurrence of the data characteristic of the condition two is earlier than the occurrence of the data characteristic of the condition one in a quarter of a period, and after the pedal frequency output corresponding to the condition two is obtained, the prior estimation is carried out, wherein the formula is as follows:
wherein cad 1|2 Estimated cadence, cad, a priori 2 The condition two corresponds to the frequency step value.
The invention is further configured to: the prior estimation is followed by the update of the covariance matrix, which is expressed as follows:
wherein P is a co-factor array, and R and Q are system errors in the step frequency corresponding to the resolving condition II.
The invention is further configured to: and after the step frequency output condition is a corresponding step frequency value, carrying out posterior estimation, wherein the formula is as follows:
wherein
For the final output of the optimum step frequency value, cad 1 Condition a corresponding step frequency value, cad 1|2 The predicted cadence value is estimated a priori.
By adopting the technical scheme, a more accurate tread frequency value can be obtained through prior estimation and posterior estimation.
The invention is further configured to: after the optimal pedal frequency output is carried out, the covariance matrix is updated firstly, then the prior estimation is carried out, and the covariance matrix updating formula is as follows:
P＝(1-K)*P。
in summary, compared with the prior art, the invention has the following beneficial effects:
1. whether the riding state is normal or not is judged through the extracted features, the riding state can be correctly judged without torque, available data types are increased through acquisition of double-axis acceleration data, and the robustness of a pedaling frequency settlement system is improved;
2. according to the invention, through filtering processing, the accuracy of the finally obtained frequency stepping data can be improved;
3. according to the invention, through prior estimation and posterior estimation, a more accurate pedal frequency value can be obtained.
Drawings
FIG. 1 is a schematic illustration of method steps of an embodiment;
FIG. 2 is a sequence of x-axis and y-axis when riding;
FIG. 3 is a time sequence of the x-axis and the y-axis in the inverted chain riding state.
Detailed Description
In order to make the technical solutions of the present invention better understood, the following description of the technical solutions of the present invention with reference to the accompanying drawings of the present invention is made clearly and completely, and other similar embodiments obtained by a person of ordinary skill in the art without any creative effort based on the embodiments in the present application shall fall within the protection scope of the present application. In addition, directional terms such as "upper", "lower", "left", "right", etc. in the following embodiments are directions with reference to the drawings only, and thus, the directional terms are used for illustrating the present invention and not for limiting the present invention.
The invention is further described with reference to the drawings and the preferred embodiments.
Example (b): a pedal frequency calculation method based on an accelerometer, referring to fig. 1, comprising:
acquiring acceleration time sequences of two shafts, acquiring data of the two-shaft accelerometer according to a determined time interval, storing the data of two sequences in one time interval into a sliding window, and sequentially displaying sequence numerical values of different time intervals by the sliding window; the data acquired by the dual-axis accelerometer are x-axis acceleration data and y-axis acceleration data respectively, and the two sequences respectively refer to a time sequence of the x-axis acceleration data and a time sequence of the y-axis acceleration data.
Carrying out filtering processing on the data in the window;
extracting the characteristics of the biaxial data, and extracting the peak point, the valley point, the zero point and the monotonous increase and monotonous decrease of sequence numerical values of the two sequence data; monotonic increase and monotonic decrease refer to the gradient characteristic of the sequence data.
And judging the normal state, namely judging whether the characteristics of the two sequences in the window accord with the preset characteristic conditions or not according to the preset characteristic conditions, if so, performing frequency-stepping output, and if not, directly performing filtering processing on the data of the next window.
And (3) step frequency output, calculating step frequency according to a formula, wherein the calculation formula is as follows:
wherein cadence represents the cadence, T is the output frequency of the signal, and Δ T is the time interval between two characteristic points, and the unit is second.
Specifically, when acquiring a biaxial acceleration time series, the two acquired series values are respectively recorded as Acc x [acc 1 ，……，acc n ]And Acc y [acc 1 ，……，acc n ]。
Optimally, in the present embodiment, the data acquisition frequency is set to 100hz, and the time interval is set to 10 ms.
Specifically, when filtering the data in the window, the process is as follows:
wherein X represents the input acceleration sequence, Y represents the output filtered value, and a and b represent the denominator vector and the numerator vector of the corresponding butterworth filter.
The influence of noise on the data accuracy can be reduced by filtering.
Specifically, when the characteristics of the biaxial data are extracted, peak detection, valley detection, zero detection and monotonic detection are mainly performed; when the peak value is detected, the value of the middle position of the acceleration sequence is a maximum value and is greater than the peak value judgment threshold value, and the value is marked as a peak value point;
when the valley value is detected, the value of the middle position of the acceleration sequence is a minimum value, and is smaller than a valley value judgment threshold value, and the value is marked as a valley value point;
during zero point detection, the absolute value of the middle position value of the x-axis acceleration sequence is smaller than a zero point judgment threshold, and then the gradient characteristic of the x-axis acceleration data is single-point increase and corresponds to a y-axis peak value or the gradient characteristic of the x-axis acceleration data is monotonously decrease and corresponds to a y-axis valley value;
when monotone detection is performed, monotone increase or monotone decrease is performed when the values in the acceleration sequence are acquired.
Specifically, when a characteristic condition is preset, two preset characteristics are set, wherein the first condition is that x-axis data is a peak point, and the gradient characteristic of y-axis data is monotonically increased;
and the second condition is that the x-axis data is a zero point, the gradient characteristic of the x-axis data is monotonously increased and the y-axis data is a peak point, or the x-axis data is a zero point, the x-axis data is monotonously decreased and the y-axis data is a valley point.
And when the normal state is judged, when the data characteristics of the two sequences in the window accord with different preset characteristics, different delta t values are selected to calculate the step frequency so as to ensure the accuracy of the finally obtained step frequency value.
As shown in fig. two, in the normal riding process, the peak point of the x-axis data corresponds to the zero point of the y-axis data after baseline removal, and the y-axis data in the process shows a monotone increasing trend and meets the condition one;
as shown in fig. three, in the time series of the x-axis and the y-axis of the inverted chain riding state (abnormal riding), the peak point of the x-axis corresponds to the zero point of the y-axis after baseline removal, but the y-axis data corresponding to the peak point shows a monotonically decreasing trend. According to the invention, by identifying the characteristics, the inverted chain riding state in the riding state is rejected, and the step frequency calculation is not carried out.
When the data characteristics of the two sequences in the judgment window meet the condition one, recording a time point corresponding to the middle position of the acceleration sequence, storing the time point in the peak sequence, and taking the time interval between the time point stored in the peak sequence and the peak point in the acceleration sequence as delta t;
and when the data characteristics of the two sequences in the window are judged to meet the second condition, recording a time point corresponding to the middle position of the acceleration sequence, storing the time point in the zero-point sequence, and taking the time interval between the time point in which the zero-point sequence is stored and the zero-point time in the time sequence as delta t.
In order to improve the accuracy of the finally output step frequency, after the step frequency obtained by calculation after the condition I is met and the step frequency obtained by calculation after the condition II is met are obtained, fusion output is carried out based on the characteristics of the step frequency and the step frequency, and the optimal step frequency value is obtained. For convenience of understanding, the step frequency obtained by calculation after the condition one is met is represented by the step frequency corresponding to the condition one, and the step frequency obtained by calculation after the condition two is met is represented by the step frequency corresponding to the condition two; the calculation frequency of the pedal frequency corresponding to the condition one is half of the settlement frequency of the pedal frequency corresponding to the condition two, so that the following fusion design is carried out according to the characteristics of the two components: because the step frequency resolved by the zero point is on the step frequency corresponding to the condition two, the step frequency corresponding to the condition two can be resolved by a quarter period of the step frequency corresponding to the condition one, prior estimation is carried out by the step frequency corresponding to the step frequency two, and when the prior estimation is carried out, the formula is as follows:
wherein cad 1|2 Estimated cadence, cad, a priori 2 The condition two corresponds to the frequency step value.
After the prior estimation, the covariance matrix is updated, and the updating formula is as follows:
wherein P is a co-factor array, and R and Q are system errors in the step frequency corresponding to the resolving condition II.
After the step frequency output condition is a corresponding step frequency value, posterior estimation is carried out, and the calculation formula is as follows:
wherein
For the final output of the optimum step frequency value, cad 1 Condition a corresponding step frequency value, cad 1|2 The predicted cadence value is estimated a priori.
After the optimal pedal frequency output is carried out, the covariance matrix is updated firstly, then the prior estimation is carried out, and the covariance matrix updating formula is as follows:
P＝(1-K)*P。
the above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.
Claims (10)
1. A pedal frequency resolving method based on an accelerometer is characterized in that: the method comprises the following steps:
acquiring acceleration time sequences of double shafts, acquiring double shaft data according to a determined time interval, storing the data of two sequences in one time interval into a sliding window, and sequentially displaying sequence numerical values of different time intervals by the sliding window;
carrying out filtering processing on the data in the window;
extracting the characteristics of the biaxial data, and extracting the peak point, the valley point, the zero point of the two sequence data and the monotonous increase and monotonous decrease of numerical values in the sequence;
judging a normal state, namely judging whether the characteristics of two sequences in a window accord with a preset characteristic condition or not according to a preset characteristic condition, if so, performing frequency-stepping output, and if not, directly performing filtering processing on data of a next window;
and (3) step frequency output, calculating step frequency according to a formula, wherein the calculation formula is as follows:
wherein cadence represents the cadence frequency, T is the output frequency of the signal, and Δ T is the time interval between two characteristic points, and the unit is second.
2. The step frequency calculation method based on the accelerometer power meter according to claim 1, wherein: the two sequence values obtained in the window are respectively marked as Acc x [acc 1 ，……，acc n ]And Acc y [acc 1 ，……，acc n ]。
3. The step frequency calculation method based on the accelerometer power meter according to claim 2, wherein: the process of filtering the data in the window is as follows:
wherein X represents the input acceleration sequence, Y represents the output filtered value, and a and b represent the denominator vector and the numerator vector of the corresponding butterworth filter.
4. The step frequency calculation method based on the accelerometer power meter according to claim 1, wherein: the preset characteristic conditions comprise two conditions, wherein the first condition is that the x-axis data is a peak point, and the gradient characteristic of the y-axis data is monotonously increased;
and the second condition is that the x-axis data is a zero point, the gradient characteristic of the x-axis data is monotonously increased and the y-axis data is a peak point, or the x-axis data is a zero point, the x-axis data is monotonously decreased and the y-axis data is a valley point.
5. The step frequency calculation method based on the accelerometer power meter as claimed in claim 4, wherein: when the data characteristics of the two sequences in the judgment window meet the condition one, recording a time point corresponding to the middle position of the acceleration sequence, storing the time point in the peak sequence, and taking the time interval between the time point stored in the peak sequence and the peak point in the acceleration sequence as delta t.
6. The step frequency calculation method based on the accelerometer power meter as claimed in claim 5, wherein: and when the data characteristics of the two sequences in the window are judged to meet the second condition, recording a time point corresponding to the middle position of the acceleration sequence, storing the time point in the zero-point sequence, and taking the time interval between the time point in which the zero-point sequence is stored and the zero-point time in the time sequence as delta t.
7. The step frequency calculation method based on the accelerometer power meter as claimed in claim 6, wherein: and the prior estimation is carried out, the occurrence of the data characteristic of the condition two is earlier than the occurrence of the data characteristic of the condition one in a quarter of a period, and after the pedal frequency output corresponding to the condition two is obtained, the prior estimation is carried out, wherein the formula is as follows:
wherein cad 1|2 Estimated cadence, cad, a priori 2 The condition two corresponds to the frequency step value.
8. The step frequency calculation method based on the accelerometer power meter as claimed in claim 7, wherein: the prior estimation is followed by the update of the covariance matrix, which is expressed as follows:
wherein P is a co-factor array, and R and Q are system errors in the step frequency corresponding to the resolving condition II.
9. The step frequency calculation method based on the accelerometer power meter according to claim 8, wherein: and after the step frequency output condition is a corresponding step frequency value, carrying out posterior estimation, wherein the formula is as follows:
wherein
For the final output of the optimum step frequency value, cad 1 Condition a corresponding step frequency value, cad 1|2 The predicted cadence value is estimated a priori.
10. The step frequency calculation method based on the accelerometer power meter according to claim 9, wherein: after the optimal pedal frequency output is carried out, the covariance matrix is updated firstly, then the prior estimation is carried out, and the covariance matrix updating formula is as follows:
P＝(1-K)*P。
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| CN109364454A (en) * | 2018-10-23 | 2019-02-22 | 安徽华米信息科技有限公司 | Step on frequency analysis method, apparatus, wearable device and system |
| CN110114264A (en) * | 2016-12-28 | 2019-08-09 | 雅马哈发动机株式会社 | Electronic auxiliary system and electric auxiliary vehicle |
| CN111512346A (en) * | 2018-04-13 | 2020-08-07 | 华为技术有限公司 | Method for calculating bicycle pedaling frequency, wearable device and storage medium |
| US10973440B1 (en) * | 2014-10-26 | 2021-04-13 | David Martin | Mobile control using gait velocity |
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- 2022-04-06 CN CN202210357539.7A patent/CN115062639A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10973440B1 (en) * | 2014-10-26 | 2021-04-13 | David Martin | Mobile control using gait velocity |
| US20160271451A1 (en) * | 2015-03-20 | 2016-09-22 | Strength Master Fitness Tech. Co., Ltd. | Wearable Device used in Various Exercise Devices |
| US20180067565A1 (en) * | 2015-08-07 | 2018-03-08 | Fitbit, Inc. | User identification via motion and heartbeat waveform data |
| CN110114264A (en) * | 2016-12-28 | 2019-08-09 | 雅马哈发动机株式会社 | Electronic auxiliary system and electric auxiliary vehicle |
| CN111512346A (en) * | 2018-04-13 | 2020-08-07 | 华为技术有限公司 | Method for calculating bicycle pedaling frequency, wearable device and storage medium |
| CN109364454A (en) * | 2018-10-23 | 2019-02-22 | 安徽华米信息科技有限公司 | Step on frequency analysis method, apparatus, wearable device and system |
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