CN107303181B - Step motion identification method based on six-axis sensor - Google Patents
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Abstract
The invention discloses a step motion identification method based on a six-axis sensor. It comprises the following steps: the method comprises the steps that a microprocessor reads detection data output by a six-axis sensor, draws a combined acceleration change curve graph of XYZ three-axis combined acceleration, detects wave crests in the combined acceleration change curve graph, removes the wave crest when the XYZ three-axis combined acceleration corresponding to a certain wave crest is smaller than or equal to 1.5g, removes the wave crest with the minimum XYZ three-axis combined acceleration in two adjacent wave crests when a sampling point between the two adjacent wave crests is smaller than a set value K, and the time point corresponding to the wave crest is the moment when a foot falls on the ground; and judging the landing posture of each foot at the landing moment according to the Y-axis angular velocity values corresponding to the N continuous sampling time points after the landing moment of each foot. The invention can identify the landing posture of the feet of a person during exercise, and is convenient for the user to know the walking posture or running posture.
Description
Technical Field
The invention relates to the technical field of foot step motion recognition, in particular to a foot step motion recognition method based on a six-axis sensor.
Background
Modern people pay more attention to daily exercise of themselves, and step counting is widely applied to intelligent running shoes as a monitoring means for effectively recording and monitoring exercise. The existing intelligent running shoes are internally provided with a three-axis acceleration sensor, but the existing intelligent running shoes can only simply count steps, cannot identify the landing posture of feet when people move, and are not beneficial to users to know the walking posture or the running posture.
Disclosure of Invention
The invention aims to overcome the technical problems that a three-axis acceleration sensor is installed in the existing intelligent running shoe, only can simply count steps and cannot identify the landing posture of a foot when a person moves, and provides a step movement identification method based on a six-axis sensor.
The invention discloses a step movement identification method based on a six-axis sensor, which comprises the following steps of:
the method comprises the steps that a microprocessor reads detection data output by a six-axis sensor, draws a combined acceleration change curve graph of XYZ three-axis combined acceleration, detects wave peaks in the combined acceleration change curve graph, removes the wave peak when the XYZ three-axis combined acceleration corresponding to a certain wave peak is smaller than or equal to a set value F, removes the wave peak with the minimum XYZ three-axis combined acceleration in two adjacent wave peaks when a sampling point between the two wave peaks is smaller than a set value K, and the time point corresponding to the wave peak is the moment when a foot falls to the ground;
the method for judging the foot landing posture of a certain foot landing moment by the microprocessor comprises the following steps: finding Y-axis angular velocity values corresponding to N continuous sampling time points after the landing moment of the foot, finding a minimum value MIN (Gy) in the N Y-axis angular velocity values, selecting a Y-axis angular velocity value corresponding to the sampling time point after the minimum value MIN (Gy) from the N Y-axis angular velocity values, finding a maximum value MAX (Gy) from the selected Y-axis angular velocity values, calculating an average value AVG (Gy) of the minimum value MIN (Gy) and the maximum value MAX (Gy), judging that the landing posture of the foot Gy at the landing moment is the landing posture of the front foot if the AVG (Gy) is greater than M1 or MAX (Gy) is greater than M2, and otherwise judging that the landing posture of the foot at the landing moment is the landing posture of the rear foot.
In the technical scheme, the six-axis sensor is arranged in a shoe body, the positive direction of an X axis of the six-axis sensor faces the front of the shoe body, the positive direction of a Y axis faces the left side of the shoe body, the positive direction of a Z axis is vertically upward, and the landing posture of a foot is calculated by detecting the motion condition of a single foot of a human body.
According to the method, the wave crests are judged according to the maximum value of a change curve in a combined acceleration change curve graph, when the combined acceleration of the three X, Y and Z axes exceeds a set value F, the wave crests can enter judgment conditions, meanwhile, pseudo wave crests are introduced, the limit frequency of walking or running of a person can be estimated, the distance between the wave crests is not smaller than K sampling points, when the distance between the two wave crests is smaller than K, a larger value can be selected as the wave crest of a step, namely the wave crest with the larger value is a true wave crest, and the wave crest with the smaller value is the pseudo wave crest.
The time point corresponding to the wave peak is the time when the foot falls on the ground, the time when the foot falls on the ground can be obtained through the wave peak calculated in the front, and theoretically, the processes of falling on the ground of the front foot and the rear foot are basically in the process of rotating around the Y axis, rotate around the Y axis in the anticlockwise direction, and have positive values and rotate around the Y axis in the clockwise direction, and have negative values. The method comprises the steps of extracting Y-axis angular velocity values corresponding to N continuous sampling time points after a foot landing moment, firstly finding out the minimum value MIN (Gy) of the N Y-axis angular velocity values, then finding out the maximum value MAX (Gy) of the Y-axis angular velocity values corresponding to the residual sampling time points after the minimum value MIN (Gy) is located, calculating the average value AVG (Gy) of the minimum value MIN (Gy) and the maximum value MAX (Gy), judging the foot landing posture of the foot landing moment as a front foot landing posture if the AVG (Gy) is larger than M1 or MAX (Gy) is larger than M2, and otherwise judging the foot landing posture of the foot landing moment as a rear foot landing posture.
Preferably, the step motion recognition method based on the six-axis sensor further comprises the following steps: and the microprocessor calculates the current step counting number B according to the wave crest number A in the combined acceleration change curve graph, wherein B = (A-1) × 2, when the step counting number B is less than c, the numerical value of the step counting number B is cached, the microprocessor does not output the numerical value of the step counting number B, and when the step counting number B is greater than or equal to c, the microprocessor outputs the numerical value of the step counting number B.
c is more than or equal to 6, because only one shoe is provided with the six-axis sensor, a wave crest appears on a resultant acceleration change curve chart once, and the left foot and the right foot of a person walk one step respectively, the relation between the wave crest and the step number is 1 to 2, namely one wave crest corresponds to 2 steps of walking. Because of the existence of the false wave peak, the last wave peak can not judge whether the wave peak is a true wave peak, and the method calculates whether the previous wave peak is the true wave peak only when a wave peak is newly appeared, so that the method counts the step and has the lag of one wave peak. The cache step number is mainly used for judging when the step number starts to enter a step counting state, in order to avoid disturbance such as random shaking of feet and the like, the cache step number is designed to start to enter a step counting mode when the step number is larger than or equal to the step c, the microprocessor outputs the value of the step number B to modules such as a display screen and the like, the step number generated later is accumulated, and the step number is temporarily cached when the step c is not reached.
Preferably, the step motion recognition method based on the six-axis sensor further comprises the following steps: and when no new wave crest appears at d continuous sampling points after the last wave crest in the combined acceleration change curve graph, the microprocessor finishes the step counting, clears the combined acceleration change curve graph, and calculates the total step number C = B +2 of the step counting if the step number B is greater than or equal to C.
c is more than or equal to 6, when no new wave peak appears at d (for example 80) continuous sampling points after the last wave peak, the human body is judged to stop moving, the step counting is finished, and the total step number is added by 2, namely the lagging last wave peak is converted into the step number and added into the total step number.
Preferably, the six-axis sensor has a detection frequency of 25HZ and K of 8-15.
Preferably, N is 8 to 12.
Preferably, the M1 is 80-120 degrees/second, and the M2 is 30-50 degrees/second.
Preferably, when the microprocessor reads the detection data output by the six-axis sensor, the data output by the six-axis sensor is filtered by using a kalman filtering method. The Kalman filtering method enables the obtained data to be smoother, the calculation complexity is lower, and the efficiency is higher.
Preferably, the data reporting mode of the six-axis sensor adopts a FIFO mode.
The invention has the beneficial effects that: the walking posture correcting device can identify the landing posture of feet when a person moves, is convenient for the user to know the walking posture or the running posture, further corrects the walking posture or the running posture, and avoids movement sprain.
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FIG. 1 is a flow chart of the operation of the present invention.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b): in this embodiment, a step motion recognition method based on a six-axis sensor, as shown in fig. 1, includes the following steps:
the method comprises the steps that a microprocessor reads detection data output by a six-axis sensor, filtering is carried out on the data output by the six-axis sensor by adopting a Kalman filtering method, a resultant acceleration change curve graph of XYZ three-axis resultant acceleration is drawn, wave peaks in the resultant acceleration change curve graph are detected, when the XYZ three-axis resultant acceleration corresponding to a certain wave peak is smaller than or equal to 1.5g, the wave peak is removed, when a sampling point between two adjacent wave peaks is smaller than a set value 10, the wave peak with the minimum XYZ three-axis resultant acceleration in the two wave peaks is removed, the wave peak with the maximum XYZ three-axis resultant acceleration is reserved, and a time point corresponding to the wave peak is;
the method for judging the foot landing posture of a certain foot landing moment by the microprocessor comprises the following steps: finding out Y-axis angular velocity values corresponding to 10 continuous sampling time points after the foot landing moment, finding out the minimum value MIN (Gy) in the 10Y-axis angular velocity values, selecting the Y-axis angular velocity value corresponding to the sampling time point after the minimum value MIN (Gy) from the 10Y-axis angular velocity values, finding out the maximum value MAX (Gy) from the selected Y-axis angular velocity values, calculating the average value AVG (Gy) of the minimum value MIN (Gy) and the maximum value MAX (Gy), judging the foot landing posture of the foot landing moment as the landing posture if the AVG (Gy) is more than 100 degrees/second or the MAX (Gy) is more than 40 degrees/second, and otherwise judging the foot landing posture of the foot landing moment as the rear foot landing posture.
And the microprocessor calculates the current step counting number B according to the wave crest number A in the combined acceleration change curve graph, wherein B = (A-1) × 2, when the step counting number B is less than 6, the numerical value of the step counting number B is cached, the microprocessor does not output the numerical value of the step counting number B, and when the step counting number B is greater than or equal to 6, the microprocessor outputs the numerical value of the step counting number B and stores the numerical value.
And when no new peak appears at 80 continuous sampling points after the last peak in the combined acceleration change curve graph, the microprocessor finishes the step counting, clears the combined acceleration change curve graph, and calculates the total step number C = B +2 of the step counting if the step number B is more than or equal to 6.
The six-axis sensor is exemplified by an MPU6500 sensor, the MPU6500 sensor sets the sampling rate to be 25Hz, the acquired calculation axes are AxAyAz and GxGyGz, the range of acceleration is set to be plus or minus 8g, the angular speed range of the gyroscope is plus or minus 500 degrees/s, the data reporting mode adopts an FIFO mode, and the like. For power saving, the sensor will enter a low power mode when not in operation. The sensor can be awakened in the low power consumption mode scene, and a motion interrupt awakening method is mainly adopted, namely when the acceleration of the three X, Y and Z axes exceeds a certain threshold (such as 250 mg), the sensor enters the working mode again.
According to the method, the six-axis sensor is arranged in a shoe body, the positive direction of an X axis of the six-axis sensor faces the front of the shoe body, the positive direction of a Y axis of the six-axis sensor faces the left side of the shoe body, the positive direction of a Z axis of the six-axis sensor faces upwards vertically, and the landing posture and the moving steps of a foot are calculated by detecting the movement condition of a single foot of a human body. And after the detection data output by the six-axis sensor is filtered by a Kalman filtering method, the detection data is processed and identified by a microprocessor. The Kalman filtering method enables the obtained data to be smoother, the calculation complexity is lower, and the efficiency is higher.
According to the method, the wave crests are judged according to the maximum value of a change curve in a filtered combined acceleration change curve graph, when the combined acceleration of the three X, Y and Z axes exceeds 1.5g, the wave crests can be judged, meanwhile, the pseudo wave crests can be introduced, the spacing between the wave crests can not be smaller than 10 sampling points as the limit frequency of walking or running of a person can be estimated, so that when the spacing between the two wave crests is smaller than 10, a larger value can be selected as a step-counting wave crest according to the size of the wave crest, namely the wave crest with a larger value is a true wave crest, and the wave crest with a smaller value is a pseudo wave crest.
The time point corresponding to the wave peak is the time when the foot falls on the ground, the time when the foot falls on the ground can be obtained through the wave peak calculated in the front, and theoretically, the processes of falling on the ground of the front foot and the rear foot are basically in the process of rotating around the Y axis, rotate around the Y axis in the anticlockwise direction, and have positive values and rotate around the Y axis in the clockwise direction, and have negative values. The method comprises the steps of extracting Y-axis angular velocity values corresponding to 10 continuous sampling time points after a foot landing moment, firstly finding out the minimum value MIN (Gy) in the 10Y-axis angular velocity values, then finding out the maximum value MAX (Gy) in the Y-axis angular velocity values corresponding to the residual sampling time points after the minimum value MIN (Gy), calculating the average value AVG (Gy) of the minimum value MIN (Gy) and the maximum value MAX (Gy), judging the foot landing posture of the foot landing moment as a front foot landing posture if the AVG (Gy) is larger than M1 or MAX (Gy) is larger than M2, and otherwise judging the foot landing posture of the foot landing moment as a rear foot landing posture.
Because only one shoe is provided with the six-axis sensor, the wave crest appears once on the combined acceleration change curve chart, and the left foot and the right foot of the person walk one step respectively, the relation between the wave crest and the step number is 1 to 2, namely one wave crest corresponds to 2 steps of walking. Because of the existence of the false wave peak, the last wave peak can not judge whether the wave peak is a true wave peak, and the method calculates whether the previous wave peak is the true wave peak only when a wave peak is newly appeared, so that the method counts the step and has the lag of one wave peak. The cache step number is mainly used for judging when the step number starts to enter a step counting state, in order to avoid disturbance such as random shaking of feet and the like, the cache step number is designed to start to enter a step counting mode when the step number exceeds 5 steps, the microprocessor outputs the value of the step number B to modules such as a display screen and the like, the display screen displays the step number, the step number generated later is accumulated, the step numbers are temporarily cached when the step number does not reach 5 steps, the microprocessor does not output the value of the step number B to the modules such as the display screen and the like, and the display screen does not display the step number.
And when no new peak appears at 80 continuous sampling points after the last peak, judging that the human body stops moving, finishing step counting, and adding 2 to the total step number, namely converting the lagging last peak into the step number and adding the step number to the total step number.
Claims (7)
1. A step motion identification method based on a six-axis sensor is characterized by comprising the following steps:
the method comprises the steps that a microprocessor reads detection data output by a six-axis sensor, draws a combined acceleration change curve graph of XYZ three-axis combined acceleration, detects wave peaks in the combined acceleration change curve graph, removes the wave peak when the XYZ three-axis combined acceleration corresponding to a certain wave peak is smaller than or equal to a set value F, removes the wave peak with the minimum XYZ three-axis combined acceleration in two adjacent wave peaks when a sampling point between the two wave peaks is smaller than a set value K, and the time point corresponding to the wave peak is the moment when a foot falls to the ground;
the method for judging the foot landing posture of a certain foot landing moment by the microprocessor comprises the following steps: finding Y-axis angular velocity values corresponding to N continuous sampling time points after the landing moment of the foot, finding a minimum value MIN (Gy) in the N Y-axis angular velocity values, selecting a Y-axis angular velocity value corresponding to the sampling time point after the minimum value MIN (Gy) from the N Y-axis angular velocity values, finding a maximum value MAX (Gy) from the selected Y-axis angular velocity values, calculating an average value AVG (Gy) of the minimum value MIN (Gy) and the maximum value MAX (Gy), judging that the landing posture of the foot Gy at the landing moment is the landing posture of the front foot if the AVG (Gy) is greater than M1 or MAX (Gy) is greater than M2, and otherwise judging that the landing posture of the foot at the landing moment is the landing posture of the rear foot.
2. The six-axis sensor-based step motion recognition method according to claim 1, further comprising the steps of: and the microprocessor calculates the current step counting number B according to the wave crest number A in the combined acceleration change curve graph, wherein B = (A-1) × 2, when the step counting number B is less than c, the numerical value of the step counting number B is cached, the microprocessor does not output the numerical value of the step counting number B, and when the step counting number B is greater than or equal to c, the microprocessor outputs the numerical value of the step counting number B.
3. The six-axis sensor-based step motion recognition method according to claim 2, further comprising the steps of: and when no new wave crest appears at d continuous sampling points after the last wave crest in the combined acceleration change curve graph, the microprocessor finishes the step counting, clears the combined acceleration change curve graph, and calculates the total step number C = B +2 of the step counting if the step number B is greater than or equal to C.
4. A six-axis sensor based step motion recognition method according to claim 1, 2 or 3, wherein: the detection frequency of the six-axis sensor is 25HZ, and K is 8-15.
5. The method for recognizing the step motion based on the six-axis sensor as claimed in claim 4, wherein: and N is 8-12.
6. A six-axis sensor based step motion recognition method according to claim 1, 2 or 3, wherein: and when the microprocessor reads the detection data output by the six-axis sensor, filtering the data output by the six-axis sensor by adopting a Kalman filtering method.
7. A six-axis sensor based step motion recognition method according to claim 1, 2 or 3, wherein: the data reporting mode of the six-axis sensor adopts an FIFO mode.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110108278B (en) * | 2019-05-22 | 2021-07-09 | 北京卡路里信息技术有限公司 | Foot landing determination method and device based on six-axis sensor |
| CN110595500B (en) * | 2019-07-30 | 2021-08-10 | 福建省万物智联科技有限公司 | Method for accurately counting steps and intelligent shoes |
| CN110876613B (en) * | 2019-09-27 | 2022-07-22 | 深圳先进技术研究院 | Human motion state identification method and system and electronic equipment |
| CN110694252A (en) * | 2019-10-09 | 2020-01-17 | 成都乐动信息技术有限公司 | Running posture detection method based on six-axis sensor |
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