CN115531806A - Posture recognition method and device - Google Patents

Posture recognition method and device Download PDF

Info

Publication number
CN115531806A
CN115531806A CN202211060968.4A CN202211060968A CN115531806A CN 115531806 A CN115531806 A CN 115531806A CN 202211060968 A CN202211060968 A CN 202211060968A CN 115531806 A CN115531806 A CN 115531806A
Authority
CN
China
Prior art keywords
current value
axis current
treadmill
time
moment
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202211060968.4A
Other languages
Chinese (zh)
Inventor
刘杰
吴烨锴
胡锋
程红辉
丁德亮
李树普
胡烨琪
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wolong Electric Drive Group Co Ltd
Original Assignee
Wolong Electric Group Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wolong Electric Group Co Ltd filed Critical Wolong Electric Group Co Ltd
Priority to CN202211060968.4A priority Critical patent/CN115531806A/en
Publication of CN115531806A publication Critical patent/CN115531806A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B22/00—Exercising apparatus specially adapted for conditioning the cardio-vascular system, for training agility or co-ordination of movements
    • A63B22/02—Exercising apparatus specially adapted for conditioning the cardio-vascular system, for training agility or co-ordination of movements with movable endless bands, e.g. treadmills
    • A63B22/0235—Exercising apparatus specially adapted for conditioning the cardio-vascular system, for training agility or co-ordination of movements with movable endless bands, e.g. treadmills driven by a motor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2230/00—Measuring physiological parameters of the user
    • A63B2230/62—Measuring physiological parameters of the user posture

Landscapes

  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Cardiology (AREA)
  • Vascular Medicine (AREA)
  • General Health & Medical Sciences (AREA)
  • Physical Education & Sports Medicine (AREA)
  • Rehabilitation Tools (AREA)

Abstract

The embodiment of the invention discloses a posture identification method and a posture identification device. The method comprises the following steps: acquiring a q-axis current value generated when the motor drives the treadmill; determining characteristic data of the treadmill in each current cycle according to the q-axis current value, wherein the characteristic data of the treadmill comprise a plurality of characteristic moments and q-axis current values corresponding to the characteristic moments, and the characteristic moments form a current cycle of the motor for completing a load sudden change process; and identifying the body state of the sporter on the treadmill according to the heavy load stabilization starting time and the heavy load stabilization finishing time in the plurality of characteristic times. The invention solves the technical problem of higher detection cost caused by detecting the body state of the sporter on the treadmill by using the sensor of the treadmill in the related technology, and achieves the technical effects of no need of the sensor, simpler implementation process, reduction of failure rate and detection cost and improvement of detection precision.

Description

Posture recognition method and device
Technical Field
The invention relates to the technical field of data processing of treadmills, in particular to a posture identification method and device.
Background
When the user uses the treadmill, the current data such as the number of steps, the speed, the kilometer number, the user posture and the like of the user need to be acquired in real time, and are displayed through a display panel, an APP and the like and used as a data base for big data analysis. However, for the detection of the human body posture on the treadmill, in the prior art, a posture test is usually performed by installing a grating or a pressure sensitive sensor on a running machine pedal and the like to detect the human body action, and in order to improve the detection precision, a large number of sensor devices are installed, so that not only the detection cost is high, but also the detection system of the treadmill is too complex, and in a complex application scenario, once a certain sensor device fails, the detection result of the treadmill is inevitably inaccurate.
An effective solution to the above problems has not been proposed.
Disclosure of Invention
The embodiment of the invention provides a posture identification method and a posture identification device, which are used for at least solving the technical problem of higher detection cost caused by the fact that a sensor device of a running machine is used for detecting the posture of an exerciser on the running machine in the related technology.
According to an aspect of an embodiment of the present invention, there is provided a posture identifying method, including: acquiring a q-axis current value generated when the motor drives the treadmill; determining characteristic data of the treadmill in each current cycle according to the q-axis current value, wherein the characteristic data of the treadmill comprise a plurality of characteristic moments and q-axis current values corresponding to the characteristic moments, and the characteristic moments form one current cycle of the motor for completing a load sudden change process; and identifying the body state of the sporter on the treadmill according to the heavy load stabilization starting time and the heavy load stabilization ending time in the characteristic times.
Optionally, when the characteristic time is a load sudden change starting time, determining characteristic data of the treadmill in each current period according to the q-axis current value, including: judging whether the q-axis current value is greater than or equal to a first current threshold of the motor, wherein the first current threshold is the product of the q-axis current value of the motor in an unloaded state and a first preset multiple; under the condition that the q-axis current value is smaller than the first current threshold, continuously judging whether the q-axis current value is larger than or equal to the first current threshold or not until the q-axis current value is larger than or equal to the first current threshold; and determining that the motor is at the load sudden change starting moment when the q-axis current value is larger than or equal to the first current threshold, and taking the q-axis current value as a q-axis current value corresponding to the load sudden change starting moment.
Optionally, when the characteristic time is a first load peak time or a second load peak time, determining the characteristic data of the treadmill in each current cycle according to the q-axis current value includes: judging whether the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment; when the q-axis current value at the next moment is larger than or equal to the q-axis current value at the previous moment, continuously judging whether the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment or not until the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment; determining that the motor is at the first load peak time or the second load peak time when the q-axis current value at the next time is smaller than the q-axis current value at the previous time, wherein the q-axis current value at the previous time is taken as the q-axis current value corresponding to the first load peak time when the motor is at the first load peak time; or, when the motor is at the second load peak time, the q-axis current value at the previous time is taken as the q-axis current value corresponding to the second load peak time.
Optionally, when the characteristic time is a heavy load stabilization starting time, determining characteristic data of the treadmill in each current period according to the q-axis current value includes: judging whether the q-axis current value is continuously and repeatedly in a first preset current range of the motor; determining that the motor is in the heavy load stabilization starting moment when the q-axis current value is continuously in the first preset current range for multiple times, and taking the q-axis current value as a q-axis current value corresponding to the heavy load stabilization starting moment; the first preset current range is determined according to a first average current value and an error current value, and the first average current value is calculated according to q-axis current values of the motor at multiple moments in a heavy-load stable state.
Optionally, when the characteristic time is a heavy load stabilization end time, determining characteristic data of the treadmill in each current period according to the q-axis current value includes: judging whether the q-axis current value is larger than or equal to a second current threshold value of the motor, wherein the second current threshold value is the product of an average q-axis current value corresponding to the motor in a heavy-load stable state and a second preset multiple; under the condition that the q-axis current value is smaller than the second current threshold, continuously judging whether the q-axis current value is larger than or equal to the second current threshold or not until the q-axis current value is larger than or equal to the second current threshold; and determining that the motor is at the heavy load stabilization ending time under the condition that the q-axis current value is greater than or equal to the second current threshold, and taking the q-axis current value as the q-axis current value corresponding to the heavy load stabilization ending time.
Optionally, when the characteristic time is an idle time, determining characteristic data of the treadmill in each current period according to the q-axis current value includes: judging whether the q-axis current value is continuously and repeatedly within a second preset current range of the motor; determining that the motor is in the no-load moment when the q-axis current value is continuously in the second preset current range for multiple times, and taking the q-axis current value as a q-axis current value corresponding to the no-load moment; wherein the second predetermined current range is determined according to a second average current value and an error current value, and the second average current value is calculated according to q-axis current values of the motor in an unloaded state in a plurality of current cycles.
Optionally, identifying the posture of the athlete on the treadmill according to a heavy load stabilization starting time and a heavy load stabilization ending time of the plurality of characteristic times, comprising: calculating the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment; determining that the body posture of the sporter on the treadmill is fallen when the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is greater than a first time threshold; wherein the first time threshold is determined according to a reference library when the motor drives the treadmill.
Optionally, identifying the posture of the athlete on the treadmill according to a heavy load stabilization starting time and a heavy load stabilization ending time of the plurality of characteristic times, comprising: determining that the body posture of the sporter on the treadmill is slow walking when the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is less than or equal to a first time threshold and the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is greater than a second time threshold; wherein the first time threshold and the second time threshold are both determined according to a reference library when the motor drives the treadmill, and the first time threshold is greater than the second time threshold.
Optionally, identifying the posture of the athlete on the treadmill according to a heavy load stabilization starting time and a heavy load stabilization ending time of the plurality of characteristic times, comprising: under the condition that the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is less than a second time threshold, determining that the body posture of the sporter on the treadmill is fast running; wherein the second time threshold is determined according to a reference library when the motor drives the treadmill.
Optionally, identifying the posture of the athlete on the treadmill according to a heavy load stabilization starting time and a heavy load stabilization ending time of the plurality of characteristic times, comprising: acquiring a first ratio and a second ratio, wherein the first ratio is the ratio of a q-axis current value corresponding to a first load peak moment to a q-axis current value corresponding to a heavy load stabilization moment, and the second ratio is the ratio of a q-axis current value corresponding to a second load peak moment to a q-axis current value corresponding to the heavy load stabilization moment; under the conditions that the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is less than a second time threshold, the first ratio is greater than a first preset threshold and the second ratio is greater than a second preset threshold, determining that the posture of the sporter on the treadmill is a high jump; wherein the second time threshold, the first predetermined threshold, and the second predetermined threshold are determined according to a reference library when the motor drives the treadmill.
Optionally, the reference library when the motor drives the treadmill is obtained by comparing the feature data of the treadmill with the typical feature data of the treadmill in a typical reference library, wherein the reference library when the motor drives the treadmill is obtained by comparing the feature data of the treadmill with the typical feature data of the treadmill in the typical reference library, and includes: calculating the average value of the characteristic data of the treadmill in a plurality of current periods to obtain average characteristic data, wherein the average characteristic data comprises a plurality of characteristic moments and average q-axis current values corresponding to the characteristic moments; performing least square calculation on the average characteristic data and a plurality of groups of the typical characteristic data to obtain a minimum calculation result; and determining the reference library when the motor drives the running machine according to the minimum calculation result.
According to another aspect of the embodiments of the present invention, there is also provided a posture identifying apparatus, including: the acquisition module is used for acquiring a q-axis current value generated when the motor drives the treadmill; the determining module is used for determining characteristic data of the treadmill in each current cycle according to the q-axis current value, wherein the characteristic data of the treadmill comprises a plurality of characteristic moments and q-axis current values corresponding to the characteristic moments, and the characteristic moments form one current cycle of the motor in which a load sudden change process is completed; and the identification module is used for identifying the body state of the sporter on the treadmill according to the heavy load stabilization starting moment and the heavy load stabilization ending moment in the characteristic moments.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the method steps of any of the above.
In the embodiment of the invention, the q-axis current value generated when the motor drives the running machine is obtained; determining characteristic data of the running machine in each current cycle according to the q-axis current value, wherein the characteristic data of the running machine comprise a plurality of characteristic moments and q-axis current values corresponding to the characteristic moments, and the characteristic moments form a current cycle of the motor for completing a load mutation process; and identifying the body state of the sporter on the treadmill according to the heavy load stabilization starting time and the heavy load stabilization finishing time in the plurality of characteristic times. That is to say, the embodiment of the present invention determines the characteristic data of the treadmill in each current cycle by using the obtained q-axis current value generated when the treadmill is driven by the motor, and then identifies the body state of the athlete on the treadmill according to the start time and the end time of the heavy load stabilization in the characteristic data of the treadmill, thereby solving the technical problem of higher detection cost caused by detecting the body state of the athlete on the treadmill by using the sensor of the treadmill in the related art, achieving the technical effects of no need of the sensor, simpler implementation process, reduced failure rate and detection cost, and improved detection precision.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of a posture identifying method according to an embodiment of the present invention;
FIG. 2 is a schematic representation of characteristic data of the treadmill during a current cycle provided by an alternative embodiment of the present invention;
FIG. 3 is a flow chart of a method for determining a reference library for a motor driven treadmill according to an alternative embodiment of the present invention;
FIG. 4 is a flow chart of a posture identification method provided in an alternative embodiment of the present invention;
fig. 5 is a schematic diagram of a posture identifying device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
It should be noted that the terms "first", "second", and the like in the description and claims of the present invention and the accompanying drawings are used for distinguishing different objects, and are not used for limiting a specific order.
According to an aspect of embodiments of the present invention, there is provided a posture identifying method, it should be noted that the steps shown in the flowchart of the drawings may be executed in a computer system such as a set of computer executable instructions, and that although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that here.
Fig. 1 is a flowchart of a posture identifying method according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:
step S102, obtaining a q-axis current value generated when a motor drives a running machine;
when the motor drives the treadmill, the q-axis current value can be acquired in real time, and can also be acquired according to a preset time period. The predetermined time period can be flexibly set according to a specific application scenario, and represents a short time interval, for example, 0.05ms, 0.15ms, 5ms, and the like.
In addition, when the exerciser exercises on the treadmill, exercise motions at different times act on the treadmill, so that the load of the treadmill constantly changes, which causes a q-axis current value generated when the motor drives the treadmill to constantly change. It should be noted that, when the exerciser exercises on the treadmill, the q-axis current value has a periodic variation law.
Step S104, determining characteristic data of the treadmill in each current period according to the q-axis current value, wherein the characteristic data of the treadmill comprises a plurality of characteristic moments and q-axis current values corresponding to the characteristic moments, and the characteristic moments form a current period of the motor for completing a load sudden change process;
optionally, the q-axis current value generated when the motor drives the treadmill is processed by using an adaptive learning algorithm, and the characteristic data of the treadmill in each current period is automatically identified.
The plurality of characteristic moments include, but are not limited to, a load jump start moment, a first load peak moment, a reload stability start moment, a reload stability end moment, a second load peak moment, and an empty moment. For example, the load sudden change starting time, the first load peak time, the heavy load stabilization starting time, the heavy load stabilization ending time, the second load peak time and the no-load time may constitute a current cycle of the motor for completing the load sudden change process; for another example, the load sudden change start time, the first load peak time, the heavy load stabilization start time, the heavy load stabilization end time, the second load peak time, and the no-load time may also constitute one current cycle of the motor for completing the load sudden change process. It should be noted that the load sudden change starting time is a starting time of a current cycle when the motor completes the load sudden change process, and the no-load time is an ending time of a current cycle when the motor completes the load sudden change process. In addition, the characteristic data of the treadmill in each current period corresponds to the movement of the sporter on the treadmill one by one, namely, the movement of the sporter on the treadmill at different moments has a q-axis current value corresponding to one characteristic moment and corresponds to the q-axis current value. It should be noted that a plurality of time instants may be spaced between every two characteristic time instants.
And step S106, identifying the body state of the sporter on the treadmill according to the heavy load stabilization starting time and the heavy load stabilization finishing time in the plurality of characteristic times.
In the embodiment of the invention, the q-axis current value generated when the motor drives the running machine is obtained; determining characteristic data of the treadmill in each current cycle according to the q-axis current value, wherein the characteristic data of the treadmill comprise a plurality of characteristic moments and q-axis current values corresponding to the characteristic moments, and the characteristic moments form a current cycle of the motor for completing a load sudden change process; and identifying the body state of the sporter on the running machine according to the heavy load stabilization starting time and the heavy load stabilization ending time in the plurality of characteristic times. That is to say, the embodiment of the present invention determines the characteristic data of the treadmill in each current cycle by using the obtained q-axis current value generated when the treadmill is driven by the motor, and then identifies the body state of the athlete on the treadmill according to the start time and the end time of the heavy load stabilization in the characteristic data of the treadmill, thereby solving the technical problem of higher detection cost caused by detecting the body state of the athlete on the treadmill by using the sensor of the treadmill in the related art, achieving the technical effects of no need of the sensor, simpler implementation process, reduced failure rate and detection cost, and improved detection precision.
In an alternative embodiment, the characteristic times may be a load sudden change starting time, a first load peak time, a heavy load stabilization starting time, a heavy load stabilization ending time, a second load peak time, and an empty time in sequence.
Fig. 2 is a schematic diagram of characteristic data of a treadmill in a current cycle according to an alternative embodiment of the present invention, as shown in fig. 2, t1, t2, t3, t4, t5, t6 and t7 form a current cycle of a completed load sudden change process, where t1 is a load sudden change start time of the current cycle, t2 is a first load peak (i.e., a peak of q-axis current when a user falls on the treadmill) of the current cycle, t3 is a start of heavy load stabilization (i.e., a peak of q-axis current when the user is stationary on the treadmill) of the current cycle, t4 is a stable time of heavy load of the current cycle, t5 is an end time of heavy load stabilization of the current cycle, t6 is a second load peak (i.e., a peak of q-axis current when the user jumps on the treadmill) of the current cycle, and t7 is an idle time (i.e., the user completely leaves the treadmill on the treadmill) of the current cycle. Through the seven characteristic moments and the corresponding q-axis current values, the running step number, the running speed, the running posture and the running mileage can be calculated. In addition, t0 is the idle (the exerciser completely exits the treadmill) time of the previous current cycle.
In an alternative embodiment, when the characteristic time is a load sudden change starting time, determining characteristic data of the treadmill in each current period according to the q-axis current value comprises the following steps: judging whether the q-axis current value is greater than or equal to a first current threshold of the motor, wherein the first current threshold is the product of the q-axis current value of the motor in an unloaded state and a first preset multiple; under the condition that the q-axis current value is smaller than the first current threshold value, continuously judging whether the q-axis current value is larger than or equal to the first current threshold value or not until the q-axis current value is larger than or equal to the first current threshold value; and under the condition that the q-axis current value is greater than or equal to the first current threshold value, determining that the motor is at the load sudden change starting moment, and taking the q-axis current value as the q-axis current value corresponding to the load sudden change starting moment.
In order to avoid detection of false triggering, whether the q-axis current value is greater than or equal to a first current threshold of the motor needs to be judged, and if the q-axis current value is smaller than the first current threshold, whether the q-axis current value is greater than or equal to the first current threshold is continuously judged until the q-axis current value is greater than or equal to the first current threshold; if the q-axis current value is larger than or equal to the first current threshold, it can be determined that the motor is at the load sudden change starting moment, and the q-axis current value is taken as a q-axis current value corresponding to the load sudden change starting moment. It should be noted that the first current threshold is a product of a q-axis current value of the motor in the no-load state and a first predetermined multiple, and the first predetermined multiple may be set according to actual needs, for example, the first predetermined multiple may be 1.1, 1.2, 1.5, and the like.
In the above embodiment of the present application, the q-axis current value is compared with the first current threshold, and whether the motor is at the load sudden change starting time and the q-axis current value corresponding to the characteristic time are determined according to the comparison result, so that the misjudgment of the characteristic time caused by the fact that the q-axis current value is too small is effectively avoided.
In an alternative embodiment, determining the characteristic data of the treadmill in each current cycle according to the q-axis current value when the characteristic time is the first load peak time or the second load peak time comprises: judging whether the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment; when the q-axis current value at the next moment is larger than or equal to the q-axis current value at the previous moment, continuously judging whether the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment or not until the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment; determining that the motor is at a first load peak moment or a second load peak moment under the condition that the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment, wherein when the motor is at the first load peak moment, the q-axis current value at the previous moment is taken as the q-axis current value corresponding to the first load peak moment; or, when the motor is at the second load peak time, the q-axis current value at the previous time is taken as the q-axis current value corresponding to the second load peak time.
It should be noted that the same determination method is used for the first load peak time and the second load peak time. For example, after determining that the motor is at the load sudden change start time, it may be determined whether the q-axis current value at the next time is smaller than the q-axis current value at the previous time; if the q-axis current value at the next moment is greater than or equal to the q-axis current value at the previous moment, whether the q-axis current value at the next moment is less than the q-axis current value at the previous moment can be continuously judged until the q-axis current value at the next moment is less than the q-axis current value at the previous moment; if the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment, it can be determined that the motor is at the first load peak moment, and the q-axis current value at the previous moment is taken as the q-axis current value corresponding to the first load peak moment. For another example, after it is determined that the motor is at the end time of the heavy load stabilization, it may be determined whether the q-axis current value at the next time is smaller than the q-axis current value at the previous time; if the q-axis current value at the next moment is greater than or equal to the q-axis current value at the previous moment, whether the q-axis current value at the next moment is less than the q-axis current value at the previous moment can be continuously judged until the q-axis current value at the next moment is less than the q-axis current value at the previous moment; if the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment, it can be determined that the motor is at the second load peak moment, and the q-axis current value at the previous moment is taken as the q-axis current value corresponding to the second load peak moment.
In the above embodiment of the present application, the first load peak time or the second load peak can be accurately determined by comparing the q-axis current value at the next time with the q-axis current value at the previous time.
In an alternative embodiment, determining the characteristic data of the treadmill in each current cycle according to the q-axis current value when the characteristic time is the heavy load stabilization starting time comprises: judging whether the q-axis current value is continuously and repeatedly in a first preset current range of the motor; determining the motor to be in the heavy load stabilization starting moment when the q-axis current value is continuously and repeatedly in the first preset current range, and taking the q-axis current value as the q-axis current value corresponding to the heavy load stabilization starting moment; the first preset current range is determined according to a first average current value and an error current value, and the first average current value is calculated according to q-axis current values of the motor at multiple moments in a heavy-load stable state.
Optionally, after determining that the motor is at the second load peak time, it needs to be determined whether the q-axis current value is continuously within the first predetermined current range of the motor for multiple times; if the q-axis current value is continuously within the first preset current range for multiple times, determining that the motor is at the heavy load stabilization starting moment, and taking the q-axis current value as the q-axis current value corresponding to the heavy load stabilization starting moment; it should be noted that the first predetermined current range is determined according to a first average current value and an error current value, the first average current value is an average value calculated according to q-axis current values of the motor at multiple times in a heavy-load stable state, and the error current value may be set according to the needs of an application scenario, which is not described herein any more.
In the above embodiment of the present application, it is determined whether the motor is at the heavy load stabilization starting time by determining whether the q-axis current value is continuously in the first predetermined current range for a plurality of times, so that the heavy load stabilization starting time can be accurately calculated.
In an alternative embodiment, when the characteristic time is the end time of heavy load stabilization, determining the characteristic data of the treadmill in each current period according to the q-axis current value comprises: judging whether the q-axis current value is greater than or equal to a second current threshold value of the motor, wherein the second current threshold value is the product of an average q-axis current value corresponding to the motor in a heavy-load stable state and a second preset multiple; under the condition that the q-axis current value is smaller than the second current threshold, continuously judging whether the q-axis current value is larger than or equal to the second current threshold or not until the q-axis current value is larger than or equal to the second current threshold; and under the condition that the q-axis current value is greater than or equal to the second current threshold value, determining that the motor is at the heavy load stabilization ending time, and taking the q-axis current value as the q-axis current value corresponding to the heavy load stabilization ending time.
In order to more accurately determine the end time of the motor in the heavy-load stable state, whether the q-axis current value is greater than or equal to a second current threshold value of the motor needs to be judged; if the q-axis current value is smaller than the second current threshold, whether the q-axis current value is larger than or equal to the second current threshold or not can be continuously judged until the q-axis current value is larger than or equal to the second current threshold; if the q-axis current value is larger than or equal to the second current threshold value, the motor can be determined to be at the heavy load stabilization ending time, and the q-axis current value is used as the q-axis current value corresponding to the heavy load stabilization ending time. It should be noted that the second current threshold is a product of an average q-axis current value corresponding to the motor in a heavy-load stable state and a second predetermined multiple; the average q-axis current value corresponding to the motor in the heavy-load stable state is obtained by averaging the q-axis current values at multiple times when the motor is in the heavy-load stable state, and the second predetermined multiple may be set according to actual needs, for example, the second predetermined multiple may be 1.1, 1.2, 1.5, and the like.
In the above embodiment of the present application, the q-axis current value is compared with the second current threshold, and whether the motor is at the load sudden change end time and the q-axis current value corresponding to the characteristic time are determined according to the comparison result, so that the heavy load stabilization end time is obtained more accurately.
In an alternative embodiment, determining the characteristic data of the treadmill in each current cycle according to the q-axis current value when the characteristic time is the idle time comprises: judging whether the q-axis current value is continuously and repeatedly in a second preset current range of the motor or not; determining the motor at the no-load moment when the q-axis current value is continuously and repeatedly in a second preset current range, and taking the q-axis current value as the q-axis current value corresponding to the no-load moment; the second preset current range is determined according to a second average current value and an error current value, and the second average current value is calculated according to q-axis current values of the motor in the no-load state in a plurality of current periods.
In order to accurately calculate the end time of a current cycle when the motor finishes a load sudden change process, whether the q-axis current value is continuously in a second preset current range of the motor for multiple times needs to be judged; if the q-axis current value is continuously within a second preset current range for multiple times, determining that the motor is in the no-load moment, and taking the q-axis current value as the q-axis current value corresponding to the no-load moment; it should be noted that the second predetermined current range is determined according to a second average current value and an error current value, the second average current value is calculated according to a q-axis current value of the motor in the no-load state in multiple current cycles, and the error current value may be set according to the needs of an application scenario, which is not described in detail herein; in addition, the value of the second average current value in the first current period is the q-axis current value of the motor which is always in the no-load state.
In the above embodiment of the present application, it is determined whether the motor is at the no-load time by determining whether the q-axis current value is continuously in the second predetermined current range a plurality of times, and the no-load time can be accurately calculated.
In an optional embodiment, when the characteristic time is a heavy load stabilization end time, the method further includes: calculating to obtain a third average current value according to the q-axis current value between the heavy load stabilization starting moment and the heavy load stabilization finishing moment and the duration of the motor in the heavy load stable state; and replacing the first average current value with a third average current value, and updating the first preset current range of the motor.
Optionally, all q-axis current values from the heavy load stabilization starting time to the heavy load stabilization ending time and the sampling times from the heavy load stabilization starting time to the heavy load stabilization ending time may be obtained, and then all q-axis current values are divided by the sampling times to obtain a third average current value, where the third average current value is used to replace the first average current value, so as to update the first predetermined current range of the motor. In the next current cycle, the updated first predetermined current range may be used to determine whether the motor is at a heavy load stabilization start time.
In the above embodiments of the present application, since the current cycle has repeatability, by updating the first predetermined current range of the motor, the characteristic time determination of the next current cycle can be made more accurate.
In an alternative embodiment, the comparing the characteristic data of the treadmill with the characteristic data of the treadmill in the typical reference library to obtain the reference library when the treadmill is driven by the motor comprises: calculating the average value of the characteristic data of the treadmill in a plurality of current periods to obtain average characteristic data, wherein the average characteristic data comprises a plurality of characteristic moments and average q-axis current values corresponding to the plurality of characteristic moments; performing least square calculation on the average characteristic data and the multiple groups of typical characteristic data respectively to obtain a minimum calculation result; and determining a reference library when the motor drives the treadmill according to the minimum calculation result.
Alternatively, the q-axis current values corresponding to a plurality of characteristic times in a plurality of current periods may be averaged to obtain average characteristic data, for example, the q-axis current values corresponding to load sudden change start times in a plurality of current periods may be averaged to obtain average q-axis current values corresponding to load sudden change start times, and the average q-axis current values at other characteristic times may be calculated in a sequential manner, so as to obtain average characteristic data finally. Further, performing least square calculation on the average characteristic data and the multiple groups of typical characteristic data respectively to obtain a minimum calculation result; and finally, determining a reference library when the motor drives the treadmill according to the minimum calculation result.
In the above-described embodiment of the present application, as the q-axis current value generated when the motor drives the treadmill is acquired, the reference library when the motor drives the treadmill can be determined in real time and updated.
Optionally, N groups of typical reference library storage spaces may be allocated to store typical characteristic data, and after the treadmill is started, by sampling the characteristic data of the treadmill in the previous M current periods and performing least square calculation with the N groups of stored typical characteristic data, a value with the minimum result is selected as the reference library for the current operation and the reference library is updated in real time along with the current operation result, so that the treadmill can quickly enter the optimal operation state.
Fig. 3 is a flowchart of determining a reference library when a motor drives a treadmill according to an alternative embodiment of the present invention, as shown in fig. 3, typical feature data of N groups of treadmills are stored in a typical reference library, and typical feature data of an I-th group of treadmills in the typical reference library is read; performing least square calculation on the characteristic data of the running machine and the typical characteristic data of the I group of running machines to obtain a calculation result RES [ I ]; judging whether I = = N is true; if the judgment is not true, I + +, continuously reading the typical characteristic data of the next group of treadmills in the typical reference library; if yes, selecting the minimum calculation result MIN (RES [ I ]); and finally, determining a reference library when the motor drives the treadmill according to the minimum calculation result.
Typical characteristic data of the treadmill in the above-described typical reference library are established based on the weight, the height of the running jump, the running speed, and the like of the exerciser. The typical characteristic data of the treadmill includes a plurality of characteristic times and typical q-axis current values corresponding to the plurality of characteristic times. The reference library when the motor drives the running machine is obtained in real time according to the comparison between the characteristic data of the running machine and the typical characteristic data of the running machine in the typical reference library. The reference library comprises a plurality of characteristic moments and q-axis current values corresponding to the characteristic moments, and the data of the reference library is used as a reference standard to realize the detection of the action posture of the human body. For example, a reference threshold is set through a plurality of characteristic moments in a reference library and q-axis current values corresponding to the characteristic moments, subsequently acquired characteristic data of the treadmill are compared with the reference threshold, and a human body motion posture is determined according to a comparison result, wherein the human body motion posture is also called a posture, and the human body motion posture includes but is not limited to falling, walking slowly, running quickly, jumping and the like.
In an alternative embodiment, identifying the body posture of the athlete on the treadmill according to the heavy load stabilization starting time and the heavy load stabilization ending time of the plurality of characteristic times comprises: calculating the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment; under the condition that the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is greater than a first time threshold, determining that the posture of the sporter on the treadmill is fallen; wherein the first time threshold is determined according to a reference library when the motor drives the treadmill.
Optionally, first, the duration from the heavy load stabilization starting time to the heavy load stabilization ending time needs to be calculated; then judging whether the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is greater than a first time threshold value or not; if the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is greater than a first time threshold, determining that the posture of the sporter on the treadmill is fallen; and if the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is less than or equal to a first time threshold, judging whether the body state of the exerciser on the treadmill is slow walking or fast running. Wherein the first time threshold is determined according to a reference library when the motor drives the treadmill.
To further determine that the athlete is falling on the treadmill, the following test criteria can be used:
Figure BDA0003825314260000111
wherein, N represents the number of turns of the motor in the duration from the heavy-load stabilization starting moment to the heavy-load stabilization ending moment, K represents the rotation speed ratio of the motor and the belt, L represents the single-side length of the belt, D represents the diameter of a driving wheel of the belt, and pi represents the circumferential rate.
Whether the sporter falls down on the treadmill can be more accurately determined through the test standard, the sporter falls down on the treadmill when the test standard is met, and the sporter does not fall down on the treadmill when the test standard is not met.
In the above embodiment of the application, by comparing the duration from the heavy load stabilization starting time to the heavy load stabilization ending time with the first time threshold, if the duration from the heavy load stabilization starting time to the heavy load stabilization ending time is greater than the first time threshold, it indicates that the duration from the heavy load stabilization starting time to the heavy load stabilization ending time is too long or continues all the time, and further, it is determined that the posture of the athlete on the treadmill falls more accurately.
After the body state of the sporter on the running machine is determined to be a falling state, the motor is controlled to stop driving the running machine, and therefore personal safety of the sporter when the sporter moves on the running machine is effectively guaranteed.
In an alternative embodiment, identifying the posture of the athlete on the treadmill according to a start time of the stabilization of the heavy load and an end time of the stabilization of the heavy load among the plurality of characteristic times includes: under the condition that the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is less than or equal to a first time threshold and the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is greater than a second time threshold, determining that the body posture of the sporter on the treadmill is slow walking; the first time threshold and the second time threshold are determined according to a reference library when the motor drives the running machine, and the first time threshold is larger than the second time threshold.
In an alternative embodiment, identifying the posture of the athlete on the treadmill according to a start time of the stabilization of the heavy load and an end time of the stabilization of the heavy load among the plurality of characteristic times includes: under the condition that the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is less than a second time threshold, determining that the body posture of the sporter on the treadmill is fast running; wherein the second time threshold is determined according to a reference library when the motor drives the treadmill.
Optionally, first, the duration from the heavy load stabilization starting time to the heavy load stabilization ending time needs to be calculated; then judging whether the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is greater than a first time threshold value or not; if the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is less than or equal to a first time threshold, judging whether the body state of the sporter on the treadmill is slow walking or not, and further judging whether the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is greater than a second time threshold or not; if the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is greater than a second time threshold, determining that the posture of the sporter on the treadmill is slow walking; if the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is less than a second time threshold, determining that the posture of the sporter on the treadmill is fast running; if the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is equal to the second time threshold, it indicates that the body state of the sporter on the running machine remains unchanged.
Alternatively, the second time threshold may be set as a duration from the reload stabilization start time to the reload stabilization end time in the reference library.
In the above embodiment of the present application, the duration from the start time of the stabilization of the heavy load to the end time of the stabilization of the heavy load is compared with the first time threshold and the second time threshold in sequence, and it is determined accurately that the posture of the athlete on the treadmill is slow walking or fast running according to the comparison result.
The determination as to whether the posture of the athlete on the treadmill is slow walking or fast running is performed under the condition that the posture of the athlete on the treadmill is not fallen.
In an alternative embodiment, identifying the posture of the athlete on the treadmill according to a start time of the stabilization of the heavy load and an end time of the stabilization of the heavy load among the plurality of characteristic times includes: acquiring a first ratio and a second ratio, wherein the first ratio is the ratio of a q-axis current value corresponding to a first load peak moment to a q-axis current value corresponding to a heavy load stabilization moment, and the second ratio is the ratio of a q-axis current value corresponding to a second load peak moment to a q-axis current value corresponding to the heavy load stabilization moment; under the conditions that the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is less than a second time threshold, the first ratio is greater than a first preset threshold and the second ratio is greater than a second preset threshold, determining that the posture of the sporter on the treadmill is a high jump; the second time threshold, the first preset threshold and the second preset threshold are determined according to a reference library when the motor drives the running machine.
Optionally, after determining that the posture of the athlete on the treadmill is fast running, respectively obtaining a first ratio and a second ratio, and if the first ratio is greater than a first predetermined threshold and the second ratio is greater than a second predetermined threshold, determining that the posture of the athlete on the treadmill is high jump; the first predetermined threshold may be set as a ratio of a q-axis current value corresponding to a first load peak time in the reference library to a q-axis current value corresponding to a heavy load stabilization time; the second predetermined threshold may be set as a ratio of a q-axis current value corresponding to a second load peak time in the reference library to a q-axis current value corresponding to a heavy load stabilization time.
In the above-described embodiment of the present application, it is possible to accurately determine that the posture of the exerciser on the treadmill is a high jump, on the basis of determining that the posture of the exerciser on the treadmill is a fast run.
In addition, if the first ratio is equal to or smaller than a first predetermined threshold value and the second ratio is equal to or smaller than a second predetermined threshold value, the posture position of the sporter on the treadmill is determined to be unchanged or not high jump.
Fig. 4 is a flowchart of a posture identifying method according to an alternative embodiment of the present invention, as shown in fig. 4, the method includes the following steps: reading a plurality of characteristic moments, wherein the characteristic moments are a load sudden change starting moment t1, a first load peak moment t2, a heavy load stabilization starting moment t3, a heavy load stabilization moment t4, a heavy load stabilization ending moment t5, a second load peak moment t6 and a no-load moment t7 in sequence; judging whether t5-t3 is larger than a first time threshold t _ limit or not; if t5-t3 is larger than t _ limit, the body state of the sporter on the treadmill is judged to be falling, the speed of the treadmill is set to be 0, and the fault shutdown is displayed; if t5-t3 is less than or equal to t _ limit, continuing to judge whether t5-t3 is greater than a second time threshold t _ low; if t5-t3 is larger than t _ low, the body state of the sporter on the treadmill is judged to be slow walking; if t5-t3 is less than t _ low, the body state of the sporter on the running machine is judged to be fast running; t5-t3 are equal to t _ low, indicating that the state is unchanged; after the body state of the sporter on the running machine is judged to be fast running, whether Q (t 2)/Q (t 4) is larger than a first preset threshold value M and whether Q (t 6)/Q (t 4) is larger than a second preset threshold value N is judged, and then the body state of the sporter on the running machine is judged to be high jump.
According to another aspect of the embodiment of the present invention, there is also provided a posture identifying apparatus, and fig. 5 is a schematic diagram of the posture identifying apparatus provided in the embodiment of the present invention, as shown in fig. 5, the posture identifying apparatus includes: an acquisition module 52, a determination module 54, and an identification module 56. The posture identifying device will be described in detail below.
An obtaining module 52, configured to obtain a q-axis current value generated when the motor drives the treadmill;
a determining module 54, connected to the obtaining module 52, for determining the characteristic data of the treadmill in each current cycle according to the q-axis current value, wherein the characteristic data of the treadmill includes a plurality of characteristic moments and q-axis current values corresponding to the plurality of characteristic moments, and the plurality of characteristic moments form a current cycle in which the motor completes a load sudden change process;
and the identification module 56 is connected to the determination module 54 and is used for identifying the body state of the sporter on the treadmill according to the heavy load stabilization starting moment and the heavy load stabilization ending moment in the plurality of characteristic moments.
It should be noted here that the above-mentioned obtaining module 52, determining module 54 and identifying module 56 correspond to steps S102 to S106 in the method embodiment, and the above-mentioned modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above-mentioned method embodiment.
In the embodiment of the invention, the posture identifying device acquires a q-axis current value generated when a motor drives a running machine; determining characteristic data of the treadmill in each current cycle according to the q-axis current value, wherein the characteristic data of the treadmill comprise a plurality of characteristic moments and q-axis current values corresponding to the characteristic moments, and the characteristic moments form a current cycle of the motor for completing a load sudden change process; and identifying the body state of the sporter on the running machine according to the heavy load stabilization starting time and the heavy load stabilization ending time in the plurality of characteristic times. That is to say, the embodiment of the present invention determines the characteristic data of the treadmill in each current cycle by using the obtained q-axis current value generated when the treadmill is driven by the motor, and then identifies the body state of the athlete on the treadmill according to the start time and the end time of the heavy load stabilization in the characteristic data of the treadmill, thereby solving the technical problem of higher detection cost caused by detecting the body state of the athlete on the treadmill by using the sensor of the treadmill in the related art, achieving the technical effects of no need of the sensor, simpler implementation process, reduced failure rate and detection cost, and improved detection precision.
In an alternative embodiment, the plurality of characteristic moments includes at least: the load sudden change starting moment, the first load peak moment, the heavy load stabilization starting moment, the heavy load stabilization ending moment, the second load peak moment and the no-load moment.
In an alternative embodiment, the determining module 54 includes: the first judgment unit is used for judging whether the q-axis current value is greater than or equal to a first current threshold value of the motor when the characteristic moment is a load sudden change starting moment, wherein the first current threshold value is the product of the q-axis current value of the motor in a no-load state and a first preset multiple; the first processing unit is used for continuously judging whether the q-axis current value is greater than or equal to the first current threshold value or not under the condition that the q-axis current value is smaller than the first current threshold value until the q-axis current value is greater than or equal to the first current threshold value; and the second processing unit is used for determining that the motor is at the load sudden change starting time under the condition that the q-axis current value is greater than or equal to the first current threshold value, and taking the q-axis current value as the q-axis current value corresponding to the load sudden change starting time.
In an alternative embodiment, the determining module 54 includes: the second judging unit is used for judging whether the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment or not when the characteristic moment is the first load peak moment or the second load peak moment; the third processing unit is used for continuously judging whether the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment or not under the condition that the q-axis current value at the next moment is larger than or equal to the q-axis current value at the previous moment until the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment; the fourth processing unit is used for determining that the motor is at a first load peak moment or a second load peak moment when the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment, wherein when the motor is at the first load peak moment, the q-axis current value at the previous moment is taken as the q-axis current value corresponding to the first load peak moment; alternatively, when the motor is at the second load peak time, the q-axis current value at the previous time is set as the q-axis current value corresponding to the second load peak time.
In an alternative embodiment, the determining module 54 includes: the third judging unit is used for judging whether the q-axis current value is continuously and repeatedly in a first preset current range of the motor when the characteristic moment is the heavy load stabilization starting moment; the fifth processing unit is used for determining the motor is at the heavy load stabilization starting moment when the q-axis current value is continuously in the first preset current range for multiple times, and taking the q-axis current value as the q-axis current value corresponding to the heavy load stabilization starting moment; the first preset current range is determined according to a first average current value and an error current value, and the first average current value is calculated according to q-axis current values at a plurality of moments when the motor is in a heavy-load stable state.
In an alternative embodiment, the determining module 54 includes: the fourth judging unit is used for judging whether the q-axis current value is greater than or equal to a second current threshold value of the motor when the characteristic moment is a heavy-load stabilization ending moment, wherein the second current threshold value is the product of an average q-axis current value corresponding to the motor in a heavy-load stable state and a second preset multiple; the sixth processing unit is used for continuously judging whether the q-axis current value is greater than or equal to the second current threshold value or not under the condition that the q-axis current value is smaller than the second current threshold value until the q-axis current value is greater than or equal to the second current threshold value; and the seventh processing unit is used for determining that the motor is at the overload stabilization ending time under the condition that the q-axis current value is greater than or equal to the second current threshold value, and taking the q-axis current value as the q-axis current value corresponding to the overload stabilization ending time.
In an alternative embodiment, the determining module 54 includes: the fifth judging unit is used for judging whether the q-axis current value is continuously and repeatedly in a second preset current range of the motor when the characteristic moment is the no-load moment; the eighth processing unit is used for determining the idle time of the motor when the q-axis current value is continuously in a second preset current range for multiple times, and taking the q-axis current value as a q-axis current value corresponding to the idle time; the second preset current range is determined according to a second average current value and an error current value, and the second average current value is calculated according to q-axis current values of the motor in the no-load state in a plurality of current periods.
In an alternative embodiment, the identification module 56 includes: a first calculation unit for calculating a duration from a reload stabilization start time to a reload stabilization end time; the first determining unit is used for determining that the body posture of the sporter on the treadmill is fallen under the condition that the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is greater than a first time threshold; wherein the first time threshold is determined according to a reference library when the motor drives the treadmill.
In an alternative embodiment, the identification module 56 includes: the second determining unit is used for determining that the body posture of the sporter on the treadmill is slow walking under the condition that the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is less than or equal to the first time threshold and the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is greater than the second time threshold; the first time threshold and the second time threshold are determined according to a reference library when the motor drives the running machine, and the first time threshold is larger than the second time threshold.
In an alternative embodiment, the identification module 56 includes: the third determining unit is used for determining that the body posture of the sporter on the treadmill is fast running under the condition that the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is less than a second time threshold; wherein the second time threshold is determined according to a reference library when the motor drives the treadmill.
In an alternative embodiment, the identification module 56 includes: the device comprises an acquisition unit and a control unit, wherein the acquisition unit is used for acquiring a first ratio and a second ratio, the first ratio is the ratio of a q-axis current value corresponding to a first load peak moment to a q-axis current value corresponding to a heavy load stabilization moment, and the second ratio is the ratio of a q-axis current value corresponding to a second load peak moment to a q-axis current value corresponding to the heavy load stabilization moment; the fourth determining unit is used for determining that the posture of the sporter on the treadmill is high jump under the conditions that the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is less than a second time threshold, the first ratio is greater than a first preset threshold and the second ratio is greater than a second preset threshold; the second time threshold, the first preset threshold and the second preset threshold are determined according to a reference library when the motor drives the running machine.
In an alternative embodiment, the reference library for the motor-driven treadmill is obtained by comparing the characteristic data of the treadmill with the characteristic data of the treadmill in the typical reference library, wherein the apparatus comprises: the first calculation module is used for carrying out average value calculation on the characteristic data of the treadmill in a plurality of current periods to obtain average characteristic data, wherein the average characteristic data comprises a plurality of characteristic moments and average q-axis current values corresponding to the plurality of characteristic moments; the second calculation module is used for performing least square calculation on the average characteristic data and the multiple groups of typical characteristic data respectively to obtain a minimum calculation result; and the processing module is used for determining a reference library when the motor drives the treadmill according to the minimum calculation result.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the method steps of any of the above.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.

Claims (12)

1. A posture identification method is characterized by comprising the following steps:
acquiring a q-axis current value generated when the motor drives the treadmill;
determining characteristic data of the treadmill in each current cycle according to the q-axis current value, wherein the characteristic data of the treadmill comprise a plurality of characteristic moments and q-axis current values corresponding to the characteristic moments, and the characteristic moments form one current cycle of the motor for completing a load sudden change process;
and identifying the body state of the sporter on the treadmill according to the heavy load stabilization starting time and the heavy load stabilization ending time in the characteristic times.
2. The method of claim 1, wherein determining the treadmill characteristic data for each current cycle based on the q-axis current value when the characteristic time is a load jump starting time comprises:
judging whether the q-axis current value is greater than or equal to a first current threshold of the motor, wherein the first current threshold is the product of the q-axis current value of the motor in an unloaded state and a first preset multiple;
under the condition that the q-axis current value is smaller than the first current threshold, continuously judging whether the q-axis current value is larger than or equal to the first current threshold or not until the q-axis current value is larger than or equal to the first current threshold;
and determining that the motor is at the load sudden change starting time when the q-axis current value is larger than or equal to the first current threshold, and taking the q-axis current value as a q-axis current value corresponding to the load sudden change starting time.
3. The method of claim 1, wherein determining the treadmill characteristic data for each current cycle based on the q-axis current value when the characteristic time is a first load peak time or a second load peak time comprises:
judging whether the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment;
when the q-axis current value at the next moment is larger than or equal to the q-axis current value at the previous moment, continuously judging whether the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment or not until the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment;
determining that the motor is at the first load peak moment or the second load peak moment when the q-axis current value at the next moment is smaller than the q-axis current value at the previous moment, wherein the q-axis current value at the previous moment is taken as the q-axis current value corresponding to the first load peak moment when the motor is at the first load peak moment; or, when the motor is at the second load peak time, the q-axis current value at the previous time is taken as the q-axis current value corresponding to the second load peak time.
4. The method of claim 1, wherein determining the treadmill characteristic data for each current cycle based on the q-axis current value when the characteristic time is a heavy load stabilization onset time comprises:
judging whether the q-axis current value is continuously and repeatedly in a first preset current range of the motor or not;
determining that the motor is in the heavy load stabilization starting moment when the q-axis current value is continuously in the first preset current range for multiple times, and taking the q-axis current value as a q-axis current value corresponding to the heavy load stabilization starting moment;
the first preset current range is determined according to a first average current value and an error current value, and the first average current value is calculated according to q-axis current values of the motor at multiple moments in a heavy-load stable state.
5. The method of claim 1, wherein determining the treadmill characteristic data for each current cycle based on the q-axis current value when the characteristic time is a heavy load stabilization end time comprises:
judging whether the q-axis current value is larger than or equal to a second current threshold value of the motor, wherein the second current threshold value is the product of an average q-axis current value corresponding to the motor in a heavy-load stable state and a second preset multiple;
under the condition that the q-axis current value is smaller than the second current threshold, continuously judging whether the q-axis current value is larger than or equal to the second current threshold or not until the q-axis current value is larger than or equal to the second current threshold;
and determining that the motor is at the heavy load stabilization ending time under the condition that the q-axis current value is greater than or equal to the second current threshold, and taking the q-axis current value as the q-axis current value corresponding to the heavy load stabilization ending time.
6. The method of claim 1, wherein determining the treadmill characteristic data for each current cycle based on the q-axis current value when the characteristic time is an idle time comprises:
judging whether the q-axis current value is continuously and repeatedly within a second preset current range of the motor;
determining that the motor is in the no-load moment when the q-axis current value is continuously in the second preset current range for multiple times, and taking the q-axis current value as a q-axis current value corresponding to the no-load moment;
wherein the second predetermined current range is determined according to a second average current value and an error current value, and the second average current value is calculated according to q-axis current values of the motor in an unloaded state in a plurality of current cycles.
7. The method of claim 1, wherein identifying the configuration of the athlete on the treadmill based on a start time of reloading stabilization and an end time of reloading stabilization among the plurality of characteristic times comprises:
calculating the duration from the heavy load stabilization starting time to the heavy load stabilization ending time;
determining that the body posture of the sporter on the treadmill is fallen when the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is greater than a first time threshold;
wherein the first time threshold is determined according to a reference library when the motor drives the treadmill.
8. The method of claim 1, wherein identifying the configuration of the athlete on the treadmill based on a start time of reloading stabilization and an end time of reloading stabilization among the plurality of characteristic times comprises:
determining that the body state of the exerciser on the treadmill is slow walking when the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is less than or equal to a first time threshold and the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is greater than a second time threshold;
wherein the first time threshold and the second time threshold are both determined according to a reference library when the motor drives the treadmill, and the first time threshold is greater than the second time threshold.
9. The method of claim 2, wherein identifying the configuration of the athlete on the treadmill based on a reload stabilization starting time and a reload stabilization ending time of the plurality of characteristic times comprises:
under the condition that the duration from the heavy load stabilization starting moment to the heavy load stabilization ending moment is less than a second time threshold, determining that the body posture of the sporter on the treadmill is fast running;
wherein the second time threshold is determined according to a reference library when the motor drives the treadmill.
10. The method of claim 2, wherein identifying the configuration of the athlete on the treadmill based on a start time of reloading stabilization and an end time of reloading stabilization among the plurality of characteristic times comprises:
acquiring a first ratio and a second ratio, wherein the first ratio is the ratio of a q-axis current value corresponding to a first load peak moment to a q-axis current value corresponding to a heavy load stabilization moment, and the second ratio is the ratio of a q-axis current value corresponding to a second load peak moment to a q-axis current value corresponding to the heavy load stabilization moment;
under the conditions that the duration from the heavy-load stabilization starting moment to the heavy-load stabilization ending moment is less than a second time threshold, the first ratio is greater than a first preset threshold and the second ratio is greater than a second preset threshold, determining that the body state of the sporter on the treadmill is a high jump;
wherein the second time threshold, the first predetermined threshold, and the second predetermined threshold are determined according to a reference library when the motor drives the treadmill.
11. The method of any one of claims 7 to 10, wherein the reference library of the motor driving the treadmill is obtained by comparing the characteristic data of the treadmill with the characteristic data of the treadmill in a typical reference library, and wherein the reference library of the motor driving the treadmill is obtained by comparing the characteristic data of the treadmill with the characteristic data of the treadmill in a typical reference library, comprising:
carrying out average value calculation on the characteristic data of the running machine in a plurality of current periods to obtain average characteristic data, wherein the average characteristic data comprises a plurality of characteristic moments and average q-axis current values corresponding to the characteristic moments;
performing least square calculation on the average characteristic data and a plurality of groups of the typical characteristic data to obtain a minimum calculation result;
and determining the reference library when the motor drives the running machine according to the minimum calculation result.
12. A posture identifying device, comprising:
the acquisition module is used for acquiring a q-axis current value generated when the motor drives the treadmill;
the determining module is used for determining characteristic data of the treadmill in each current cycle according to the q-axis current value, wherein the characteristic data of the treadmill comprises a plurality of characteristic moments and q-axis current values corresponding to the characteristic moments, and the characteristic moments form one current cycle of the motor in which a load sudden change process is completed; and the identification module is used for identifying the body state of the sporter on the treadmill according to the heavy load stabilization starting moment and the heavy load stabilization ending moment in the characteristic moments.
CN202211060968.4A 2022-08-31 2022-08-31 Posture recognition method and device Pending CN115531806A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211060968.4A CN115531806A (en) 2022-08-31 2022-08-31 Posture recognition method and device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211060968.4A CN115531806A (en) 2022-08-31 2022-08-31 Posture recognition method and device

Publications (1)

Publication Number Publication Date
CN115531806A true CN115531806A (en) 2022-12-30

Family

ID=84726402

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211060968.4A Pending CN115531806A (en) 2022-08-31 2022-08-31 Posture recognition method and device

Country Status (1)

Country Link
CN (1) CN115531806A (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20040005133A (en) * 2002-07-08 2004-01-16 현대자동차주식회사 Battery full charge management method of electric vehicle
TW200616690A (en) * 2004-11-26 2006-06-01 Alatech Technology Ltd System for adjusting speed of electric running belt based on difference of current load
CN103945904A (en) * 2011-10-27 2014-07-23 艾拉戈利斯-技术公司 Exercise machine
CN112999568A (en) * 2021-02-24 2021-06-22 杭州士腾科技有限公司 Treadmill speed self-adaptive control method, treadmill and control device
WO2022088432A1 (en) * 2020-10-26 2022-05-05 莱克电气绿能科技(苏州)有限公司 Boundary identification method and apparatus of automatic mobile device, and storage medium

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20040005133A (en) * 2002-07-08 2004-01-16 현대자동차주식회사 Battery full charge management method of electric vehicle
TW200616690A (en) * 2004-11-26 2006-06-01 Alatech Technology Ltd System for adjusting speed of electric running belt based on difference of current load
CN103945904A (en) * 2011-10-27 2014-07-23 艾拉戈利斯-技术公司 Exercise machine
WO2022088432A1 (en) * 2020-10-26 2022-05-05 莱克电气绿能科技(苏州)有限公司 Boundary identification method and apparatus of automatic mobile device, and storage medium
CN112999568A (en) * 2021-02-24 2021-06-22 杭州士腾科技有限公司 Treadmill speed self-adaptive control method, treadmill and control device

Similar Documents

Publication Publication Date Title
US7877226B2 (en) Apparatus and method for counting exercise repetitions
CN101809456A (en) System and method for estimating long term characteristics of battery
CN111288986B (en) Motion recognition method and motion recognition device
CN110275636A (en) A kind of touch-control anti-fluttering method, device, system, terminal device and vehicle
CN104238743A (en) Information processing apparatus, and information processing method
CN104713566B (en) A kind of step-recording method, step count set and pedometer
US8606332B2 (en) Gravity axis determination apparatus and mobile terminal apparatus using the same
CN108114455B (en) Running analysis device, running analysis method, and recording medium
CN115531806A (en) Posture recognition method and device
CN112967801A (en) PAI value processing method, PAI value processing device, PAI value processing equipment and storage medium
US9207738B2 (en) Data processing device with adjustable performance level and method of operating such device
CN109085375B (en) Key phase pulse signal monitoring method and device for rotary machine
CN106686371B (en) A kind of frame per second test method, device, equipment and system
CN111669097B (en) Load rotational inertia identification method and system
EP2281612B1 (en) Apparatus and method for counting exercise repetitions
JP2015011442A (en) Electric apparatus and system
CN115591181A (en) Motion parameter determination method and device
US11083396B2 (en) Portable electronic apparatus
CN107844364A (en) A kind of method, apparatus and terminal for adjusting terminal temperature
CN116182895A (en) Motion state detection method, electronic device and chip
KR100842514B1 (en) Method and apparatus for estimating stride
CN112634489A (en) Vehicle state determination method, device and system based on mobile terminal
CN113244595B (en) Action explosive force analysis method and system
CN116028556B (en) Intelligent processing method for transmission data of AR terminal system
CN109985368B (en) Rehabilitation participation degree evaluation method and rehabilitation machine

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination