CN116712064A - Intelligent method for evaluating moment of lower limb joint during sprinting by resistance torsion - Google Patents
Intelligent method for evaluating moment of lower limb joint during sprinting by resistance torsion Download PDFInfo
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- 210000003141 lower extremity Anatomy 0.000 title claims abstract description 81
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- 238000013135 deep learning Methods 0.000 claims description 6
- 238000011156 evaluation Methods 0.000 claims description 6
- 230000005484 gravity Effects 0.000 claims description 6
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- 238000012360 testing method Methods 0.000 claims description 3
- 230000008733 trauma Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 5
- 210000001503 joint Anatomy 0.000 description 10
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- 230000009286 beneficial effect Effects 0.000 description 1
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1118—Determining activity level
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- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1116—Determining posture transitions
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- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/112—Gait analysis
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- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
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- A61B5/1121—Determining geometric values, e.g. centre of rotation or angular range of movement
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- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1126—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb using a particular sensing technique
- A61B5/1128—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb using a particular sensing technique using image analysis
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- A63B24/0062—Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
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- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B71/00—Games or sports accessories not covered in groups A63B1/00 - A63B69/00
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Abstract
The invention provides an intelligent method for evaluating moment of a lower limb joint in sprinting by resistance torsion, which comprises the following steps: s1, personal information of an athlete is collected and input to a data recording module of an upper computer; s2, acquiring lower limb joint moment data of the athlete when the athlete is subjected to resistance torsion during sports of a sports field, and transmitting the lower limb joint moment data to an upper computer; s3, the upper computer processes the acquired lower limb joint moment data to obtain a lower limb joint mechanical state; s4, comparing the difference between the mechanical state of the instantaneous lower limb joint of the athlete and the normal state of the known human body, and if the difference exceeds a threshold value, displaying corresponding error movement information and sending corresponding warning information by the upper computer. According to the invention, the motion information of the athlete is estimated by collecting and processing the moment data of the joints of the lower limbs of the athlete and processing the moment data to obtain the mechanical state information of the joints of the lower limbs and finally comparing the mechanical state information with the known normal state of the human body, so that the motion information of the athlete can be more effectively analyzed.
Description
Technical Field
The invention relates to the technical field, in particular to an intelligent method for evaluating moment of a lower limb joint in sprinting by resistance torsion.
Background
Along with the development of science and technology, modern people pay attention to exercise and fitness gradually. In recent years, in the field of sports at home and abroad, particularly in the sprint sport of athletes, in order to realize the concepts of rapidness and strength, the sports posture of the athletes is adjusted. In the prior art, the motion state of the athlete is generally captured by an infrared motion capture device, and then whether the athlete has wrong posture information in the motion is analyzed.
However, the infrared motion capture device can only capture gesture information and cannot analyze moment information of the joints of the lower limbs, so that force data of the joints of the lower limbs of the athlete in actual movement cannot be well analyzed, and the movement efficiency of the athlete cannot be comprehensively improved only by gesture analysis.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: the intelligent method for evaluating moment of the lower limb joints of the sprint by resistance torsion is provided, and the problem that moment information of the lower limb joints of the athlete cannot be analyzed in the prior art is solved, so that the movement efficiency of the athlete is improved.
The invention solves the problems by adopting the following technical scheme: an intelligent method for evaluating moment of a lower limb joint in sprinting by resistance torsion comprises the following steps:
s1, personal information of an athlete is collected and input to a data recording module of an upper computer;
s2, acquiring lower limb joint moment data of the athlete when the athlete is subjected to resistance torsion during sports of a sports field, and transmitting the lower limb joint moment data to an upper computer;
s3, the upper computer processes the acquired lower limb joint moment data to obtain a lower limb joint mechanical state;
s4, comparing the difference between the mechanical state of the instantaneous lower limb joint of the athlete and the normal state of the known human body, and if the difference exceeds a threshold value, displaying corresponding error movement information and sending corresponding warning information by the upper computer;
wherein the mechanical state of the lower limb joint comprises acting moment of muscle groups around the joint, or pressure born by hip joint, or pressure born by knee joint, or tension born by ankle joint connective tissue, or tension born by knee joint connective tissue, or tension born by hip joint connective tissue; the normal state of the human body is known to be the state sensed without trauma or illness.
Compared with the prior art, the invention has the advantages that: the motion information of the athlete can be more effectively analyzed by collecting and processing the moment data of the joints of the lower limbs of the athlete and obtaining the mechanical state information of the joints of the lower limbs and finally comparing the mechanical state information with the known normal state of the human body.
Preferably, in step S2, collecting lower limb joint moment data includes: pressure data of pressure sensors arranged on the feet of the athlete and lower limb joint angle, acceleration and yaw rate data of inertial sensors arranged on the lower leg sections of the athlete are collected.
The technical scheme has the technical effects that: by analyzing the pressure data of the foot and the lower limb joint movement data of the lower leg section, more comprehensive lower limb joint movement information can be obtained.
Preferably, the pressure sensor is arranged on a plantar pressure center path of the athlete in the footwear.
The technical scheme has the technical effects that: the pressure data acquisition device is arranged on a foot sole pressure center travel route, and can acquire pressure data to the greatest extent.
Preferably, the pressure sensor includes a first pressure sensor disposed on a ball portion of the athlete's foot and a second pressure sensor disposed on a heel portion of the athlete's foot.
The technical scheme has the technical effects that: better movement data can be obtained by analyzing the blind pressure information at the sole and heel respectively.
Preferably, in step S2, the pressure sensor and the inertial sensor transmit the moment data of the lower limb joint to the upper computer through a radio frequency transceiver, a bluetooth transceiver or a ZigBee wireless communication unit.
The technical scheme has the technical effects that: and the lower limb joint moment data acquired by the pressure sensor and the inertial sensor are transmitted to the upper computer in a wireless data transmission mode, so that the data transmission is convenient.
Preferably, in step S3, the upper computer determines the start point of the gait cycle according to whether the pressure data appears or whether the pressure data signal is in a relative intensity ratio, and calculates the time sequence of the gait cycle according to the pressure data; the upper computer calculates the foot reaction force according to the pressure data and the preset correction parameters; the upper computer calculates and obtains the mechanical state of the lower limb joint according to the time sequence of gait cycle, foot reaction force, lower limb joint angle, acceleration and yaw rate data.
Preferably, in step S2, collecting the moment data of the lower limb joint further includes: collecting a plurality of pictures captured by a plurality of cameras in the same time sequence space; in step S3, the upper computer analyzes a plurality of images captured in the same time sequence space, combines the mechanical state of the lower limb joint, the spatial calibration data and the identification of the motion environment, synthesizes the three-dimensional data of the motion, and constructs a three-dimensional motion model; in step S4, if the difference exceeds the threshold, the corresponding instantaneous mechanical state of the lower limb joint is marked in the three-dimensional motion model.
The technical scheme has the technical effects that: the three-dimensional motion model of the motion is synthesized through the capture of the multiple cameras, the state of the athlete during the instantaneous motion is conveniently observed through the three-dimensional motion model, and the motion gesture is conveniently analyzed.
Preferably, the spatial calibration data includes the overall movement speed, the overall movement acceleration, the body gravity center movement track and the kinematic index data of the sports apparatus of the athlete.
Preferably, constructing the three-dimensional motion model means: the upper computer synthesizes the three-dimensional data of the motion, realizes automatic recognition of the positions of the gravity center, the feet and the knee joints of the human body by utilizing an image recognition and optimization deep learning algorithm, and combines the mechanical characteristics of special motion to formulate motion recognition so as to construct a three-dimensional motion model; wherein, the optimization deep learning algorithm refers to: and relearning the data sets of different motions through kinematic and dynamic tests, and supplementing places with errors and inaccuracy in recognition of special motions.
Drawings
FIG. 1 is a flow chart of an intelligent method for evaluating moment of a lower limb joint in sprinting by resistance torsion.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
As shown in fig. 1, the present embodiment relates to an intelligent method for obtaining moment evaluation of a lower limb joint during sprinting by resistance torsion, which includes the following steps:
s1, personal information of an athlete is collected and input to a data recording module of an upper computer;
mainly records the height and weight information of the athlete, and is convenient for constructing a three-dimensional movement model in the later period.
S2, acquiring lower limb joint moment data of the athlete when the athlete is subjected to resistance torsion during sports of a sports field, and transmitting the lower limb joint moment data to an upper computer;
s3, the upper computer processes the acquired lower limb joint moment data to obtain a lower limb joint mechanical state;
s4, comparing the difference between the mechanical state of the instantaneous lower limb joint of the athlete and the normal state of the known human body, and if the difference exceeds a threshold value, displaying corresponding error movement information and sending corresponding warning information by the upper computer;
wherein the mechanical state of the lower limb joint comprises acting moment of muscle groups around the joint, or pressure born by hip joint, or pressure born by knee joint, or tension born by ankle joint connective tissue, or tension born by knee joint connective tissue, or tension born by hip joint connective tissue; the normal state of the human body is known to be the state sensed without trauma or illness.
The motion information of the athlete can be more effectively analyzed by collecting and processing the moment data of the joints of the lower limbs of the athlete and obtaining the mechanical state information of the joints of the lower limbs and finally comparing the mechanical state information with the known normal state of the human body.
In step S2, collecting lower limb joint moment data includes: pressure data of pressure sensors arranged on the feet of the athlete and lower limb joint angle, acceleration and yaw rate data of inertial sensors arranged on the lower leg sections of the athlete are collected.
By analyzing the pressure data of the foot and the lower limb joint movement data of the lower leg section, more comprehensive lower limb joint movement information can be obtained.
The pressure sensor is arranged on a plantar pressure center path of the athlete's foot. The pressure data acquisition device is arranged on a foot sole pressure center travel route, and can acquire pressure data to the greatest extent.
The pressure sensor includes a first pressure sensor disposed on a ball portion of the athlete's foot and a second pressure sensor disposed on a heel portion of the athlete's foot. Better movement data can be obtained by analyzing the blind pressure information at the sole and heel respectively.
In step S2, the pressure sensor and the inertial sensor transmit the moment data of the lower limb joint to the upper computer through the radio frequency transceiver, the bluetooth transceiver or the ZigBee wireless communication unit. And the lower limb joint moment data acquired by the pressure sensor and the inertial sensor are transmitted to the upper computer in a wireless data transmission mode, so that the data transmission is convenient.
In step S3, the upper computer determines the start point of the gait cycle according to whether the pressure data appears or whether the pressure data signal is in a relative intensity ratio, and calculates the time sequence of the gait cycle according to the pressure data; the upper computer calculates the foot reaction force according to the pressure data and the preset correction parameters; the upper computer calculates and obtains the mechanical state of the lower limb joint according to the time sequence of gait cycle, foot reaction force, lower limb joint angle, acceleration and yaw rate data.
In step S2, collecting the moment data of the lower limb joint further includes: collecting a plurality of pictures captured by a plurality of cameras in the same time sequence space; in step S3, the upper computer analyzes a plurality of images captured in the same time sequence space, combines the mechanical state of the lower limb joint, the spatial calibration data and the identification of the motion environment, synthesizes the three-dimensional data of the motion, and constructs a three-dimensional motion model; in step S4, if the difference exceeds the threshold, the corresponding instantaneous mechanical state of the lower limb joint is marked in the three-dimensional motion model.
The three-dimensional motion model of the motion is synthesized through the capture of the multiple cameras, the state of the athlete during the instantaneous motion is conveniently observed through the three-dimensional motion model, and the motion gesture is conveniently analyzed.
In this embodiment, the spatial calibration data includes the overall movement speed, the overall movement acceleration, the body center of gravity movement locus, and the kinematic index data of the sports apparatus of the athlete's movement.
Wherein, construct three-dimensional motion model means: the upper computer synthesizes the three-dimensional data of the motion, realizes automatic recognition of the positions of the gravity center, the feet and the knee joints of the human body by utilizing an image recognition and optimization deep learning algorithm, and combines the mechanical characteristics of special motion to formulate motion recognition so as to construct a three-dimensional motion model; wherein, the optimization deep learning algorithm refers to: and relearning the data sets of different motions through kinematic and dynamic tests, and supplementing places with errors and inaccuracy in recognition of special motions.
The beneficial effects of the invention are as follows: the motion information of the athlete can be more effectively analyzed by collecting and processing the moment data of the joints of the lower limbs of the athlete and obtaining the mechanical state information of the joints of the lower limbs and finally comparing the mechanical state information with the known normal state of the human body.
While the foregoing description illustrates and describes the preferred embodiments of the present invention, it is to be understood that the invention is not limited to the forms disclosed herein, but is not to be construed as limited to other embodiments, and is capable of numerous other combinations, modifications and environments and is capable of changes or modifications within the scope of the inventive concept as described herein, either as a result of the foregoing teachings or as a result of the knowledge or technology in the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.
Although the present disclosure is described above, the scope of protection of the present disclosure is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the disclosure, and these changes and modifications will fall within the scope of the invention.
Claims (9)
1. An intelligent method for evaluating moment of a lower limb joint in sprinting by resistance torsion is characterized by comprising the following steps: the method comprises the following steps:
s1, personal information of an athlete is collected and input to a data recording module of an upper computer;
s2, acquiring lower limb joint moment data of the athlete when the athlete is subjected to resistance torsion during sports of a sports field, and transmitting the lower limb joint moment data to an upper computer;
s3, the upper computer processes the acquired lower limb joint moment data to obtain a lower limb joint mechanical state;
s4, comparing the difference between the mechanical state of the instantaneous lower limb joint of the athlete and the normal state of the known human body, and if the difference exceeds a threshold value, displaying corresponding error movement information and sending corresponding warning information by the upper computer;
wherein the mechanical state of the lower limb joint comprises acting moment of muscle groups around the joint, or pressure born by hip joint, or pressure born by knee joint, or tension born by ankle joint connective tissue, or tension born by knee joint connective tissue, or tension born by hip joint connective tissue; the normal state of the human body is known to be the state sensed without trauma or illness.
2. The intelligent method for evaluating moment of a lower limb joint in sprinting by resistance torsion according to claim 1, wherein the intelligent method comprises the following steps: in step S2, collecting lower limb joint moment data includes: pressure data of pressure sensors arranged on the feet of the athlete and lower limb joint angle, acceleration and yaw rate data of inertial sensors arranged on the lower leg sections of the athlete are collected.
3. An intelligent method for obtaining moment evaluation of a short run lower limb joint by resistance torsion according to claim 2, wherein: the pressure sensor is arranged on a plantar pressure center path of the athlete's foot.
4. An intelligent method for obtaining moment evaluation of a short run lower limb joint by resistance torsion according to claim 2, wherein: the pressure sensor includes a first pressure sensor disposed on a ball portion of the athlete's foot and a second pressure sensor disposed on a heel portion of the athlete's foot.
5. An intelligent method for obtaining moment evaluation of a short run lower limb joint by resistance torsion according to claim 2, wherein: in step S2, the pressure sensor and the inertial sensor transmit the moment data of the lower limb joint to the upper computer through the radio frequency transceiver, the bluetooth transceiver or the ZigBee wireless communication unit.
6. An intelligent method for obtaining moment evaluation of a short run lower limb joint by resistance torsion according to claim 2, wherein: in the step S3 of the process,
the upper computer judges the starting point of the gait cycle according to whether the pressure data appear or the relative intensity proportion of the pressure data signals, and calculates the time sequence of the gait cycle through the pressure data; the upper computer calculates the foot reaction force according to the pressure data and the preset correction parameters;
the upper computer calculates and obtains the mechanical state of the lower limb joint according to the time sequence of gait cycle, foot reaction force, lower limb joint angle, acceleration and yaw rate data.
7. An intelligent method for obtaining moment evaluation of a short run lower limb joint by resistance torsion according to claim 2, wherein:
in step S2, collecting the moment data of the lower limb joint further includes: collecting a plurality of pictures captured by a plurality of cameras in the same time sequence space;
in step S3, the upper computer analyzes a plurality of images captured in the same time sequence space, combines the mechanical state of the lower limb joint, the spatial calibration data and the identification of the motion environment, synthesizes the three-dimensional data of the motion, and constructs a three-dimensional motion model;
in step S4, if the difference exceeds the threshold, the corresponding instantaneous mechanical state of the lower limb joint is marked in the three-dimensional motion model.
8. The intelligent method for evaluating moment of a lower limb joint in sprinting by resistance torsion according to claim 7, wherein: the space calibration data comprise the overall movement speed, the overall movement acceleration, the body gravity center movement track and the kinematic index data of the sports equipment of the athlete.
9. The intelligent method for evaluating moment of a lower limb joint in sprinting by resistance torsion according to claim 8, wherein the intelligent method comprises the following steps: constructing a three-dimensional motion model refers to: the upper computer synthesizes the three-dimensional data of the motion, realizes automatic recognition of the positions of the gravity center, the feet and the knee joints of the human body by utilizing an image recognition and optimization deep learning algorithm, and combines the mechanical characteristics of special motion to formulate motion recognition so as to construct a three-dimensional motion model;
wherein, the optimization deep learning algorithm refers to: and relearning the data sets of different motions through kinematic and dynamic tests, and supplementing places with errors and inaccuracy in recognition of special motions.
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