CN111375174B - Intelligent running machine based on knee joint movement information - Google Patents
Intelligent running machine based on knee joint movement information Download PDFInfo
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- CN111375174B CN111375174B CN201811644768.7A CN201811644768A CN111375174B CN 111375174 B CN111375174 B CN 111375174B CN 201811644768 A CN201811644768 A CN 201811644768A CN 111375174 B CN111375174 B CN 111375174B
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- 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
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- 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
- A63B71/06—Indicating or scoring devices for games or players, or for other sports activities
- A63B71/0619—Displays, user interfaces and indicating devices, specially adapted for sport equipment, e.g. display mounted on treadmills
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- 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
- A63B71/06—Indicating or scoring devices for games or players, or for other sports activities
- A63B71/0619—Displays, user interfaces and indicating devices, specially adapted for sport equipment, e.g. display mounted on treadmills
- A63B71/0622—Visual, audio or audio-visual systems for entertaining, instructing or motivating the user
- A63B2071/0625—Emitting sound, noise or music
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B2220/00—Measuring of physical parameters relating to sporting activity
- A63B2220/80—Special sensors, transducers or devices therefor
- A63B2220/806—Video cameras
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- 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
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Abstract
The invention relates to an intelligent treadmill based on knee joint movement information. The intelligent treadmill comprises a data acquisition module (101), a processor (102), an alarm module (103) and a control module (104) which are arranged on an intelligent treadmill body; the data acquisition module (101) is used for acquiring measurement signals of knee joints and legs; the processor (102) is used for receiving the measurement signals and generating knee joint movement information for analysis and evaluation based on the measurement signals; the alarm module (103) is used for receiving the damage degree of the knee joint and the running state analysis and evaluation result and generating alarm information according to the analysis and evaluation result; the control module (104) is used for receiving the analysis and evaluation result of the damage degree of the knee joint and the running state and adjusting the running mode according to the analysis and evaluation result. According to the intelligent running machine provided by the invention, a user can receive alarm information and automatically adjust the running mode to correct the running posture in the running process, so that the effects of protecting knee joints and correcting the wrong running posture are achieved.
Description
Technical Field
The invention belongs to the field of fitness equipment, and particularly relates to an intelligent treadmill based on a knee joint movement signal.
Background
In recent years, with the improvement of living standard of people, the requirement of people on health is higher and higher, and running is one of the favorite sports of people. The running action has the advantages of simple key and small difficulty, and is an all-round aerobic exercise, which can effectively consume heat, exercise heart and lung functions and even improve sleep quality, and the running on the treadmill can also adjust speed and record running time, avoid the interference of external air pollution or weather adverse factors such as rain mist and the like, and is also beneficial for users to record the exercise amount or make and implement scientific fitness plans. Therefore, among the fitness equipments in the gymnasium, the treadmill is popular with people.
After some people take exercise on the running machine, the knee joints can feel painful, and after the people run for a long time, even if some people have symptoms such as repeated swelling and effusion of the knee joints, osteoarthritis can be caused in severe cases. Even if warming is performed as required before running, the amount of exercise is reduced, and such problems have not been solved. It has been studied that these symptoms are mostly caused by incorrect posture during exercise, and the wrong running posture causes much damage to the knee, and some are even irreversible. Therefore, it can be concluded that the health of the knee joint is closely related to running.
The existing common running machine is difficult to notice whether the posture of a common person meets the requirements or not and does not know whether the knee of the common person is injured or not when the common person exercises, so that a user can exercise in wrong posture for a long time, and even the knee is injured in an unknown condition.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides an intelligent treadmill based on knee joint movement information.
One embodiment of the present invention provides an intelligent treadmill based on knee joint movement information, comprising: the data acquisition module 101, the processor 102 and the alarm module 103 are arranged on the intelligent treadmill body; wherein the content of the first and second substances,
the data acquisition module 101 is used for acquiring measurement signals of knee joints and legs;
the processor 102 is connected to the data acquisition module 101, and is configured to receive the measurement signal, generate knee joint motion information based on the measurement signal, and perform analysis and evaluation on the damage degree of the knee joint and the running state according to the knee joint motion information;
the alarm module 103 is connected to the processor 102, and is configured to receive the damage degree of the knee joint and the running state analysis and evaluation result, and determine whether to generate alarm information according to the analysis and evaluation result.
In an embodiment of the present invention, the apparatus further comprises a control module 104, wherein the control module 104 is connected to the processor 102, and is configured to receive the analysis and evaluation result of the damaged knee joint and the running state, and adjust the running mode according to the analysis and evaluation result.
In one embodiment of the invention, the measurement signals comprise a knee joint motion signal, a thigh motion signal, a calf motion signal and a leg posture signal.
In one embodiment of the present invention, the data acquisition module 101 includes a knee joint sensor unit 1011 and a leg posture acquisition unit 1012; wherein the content of the first and second substances,
the knee joint sensor unit 1011 is used for collecting the knee joint movement signal, the thigh movement signal and the shank movement signal;
the leg posture collecting unit 1012 is configured to collect the leg posture signal.
In one embodiment of the invention, the knee joint movement signal comprises a knee joint vibration signal and a knee joint sound signal.
In one embodiment of the present invention, the knee joint sensor unit 1011 includes an acceleration sensor 1011a, an acoustic sensor 1011b, the first attitude sensor 1011c, and the second attitude sensor 1011 d; wherein the content of the first and second substances,
the acceleration sensor 1011a is configured to acquire the knee joint vibration signal and send the knee joint vibration signal to the processor 102;
the acoustic sensor 1011b is configured to acquire the knee joint sound signal and send the knee joint sound signal to the processor 102;
the first posture sensor 1011c is configured to acquire the thigh posture signal and send the thigh posture signal to the processor 102;
the second attitude sensor 1011d is configured to acquire the shank attitude signal and send the shank attitude signal to the processor 102.
In one embodiment of the present invention, the leg posture collecting unit 1012 includes a camera 1012a and a DSP1012 b; wherein the content of the first and second substances,
the camera 1012a is arranged at the position of the intelligent running machine corresponding to the leg of the user and is used for collecting the leg running video of the user;
the DSP1012b is configured to receive the leg running video and process the leg running video to form the leg posture signal.
In one embodiment of the present invention, the leg posture acquiring unit 1012 further includes a first memory 1012c, and the first memory 1012c is used for storing the leg running video.
In one embodiment of the present invention, the leg gesture capturing unit 1012 further includes a wireless communication unit 1012d, and the wireless communication unit 1012d is configured to upload the leg running video to a cloud server.
In an embodiment of the present invention, the intelligent treadmill further includes a second memory 105, and the second memory 105 is respectively connected between the processor 102 and the alarm module 103 and the control module 104 for storing the damage degree of the knee joint and the running state analysis and evaluation result.
Compared with the prior art, the intelligent treadmill provided by the invention at least has the following beneficial effects:
the user can receive the alarm information in the scene that the knee joint is damaged or the running posture is wrong, and the running posture is corrected according to the running mode automatically adjusted by the intelligent running machine, so that the effects of protecting the knee joint and correcting the wrong running posture are achieved.
Drawings
Fig. 1 is a schematic view of an intelligent treadmill based on knee joint movement information according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a data acquisition module according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a knee joint sensor unit according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a leg posture collecting unit according to an embodiment of the present invention;
fig. 5 is a schematic diagram of another leg posture acquiring unit provided by the embodiment of the invention;
fig. 6 is a schematic diagram of another leg posture collecting unit provided in the embodiment of the present invention;
fig. 7 is a schematic view of another intelligent treadmill based on knee joint movement information according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
Example one
Referring to fig. 1, fig. 1 is a schematic view of an intelligent treadmill based on knee joint movement information according to an embodiment of the present invention. Specifically, the intelligent treadmill may include: the intelligent treadmill comprises a data acquisition module 101, a processor 102, an alarm module 103 and a control module 104 which are arranged on an intelligent treadmill body. The processor 102 is connected to the data acquisition module 101, and the alarm module 103 and the control module 104 are connected to the processor 102.
The data acquisition module 101 is used for acquiring measurement signals of knee joints and leg parts of a human body and sending the measurement signals to the processor 102.
Specifically, the data acquisition module 101 may acquire measurement signals of a knee joint and a leg of a human body, where the measurement signals include knee joint motion signals, thigh motion signals, calf motion signals, and human leg posture signals, and transmit the measurement signals to the processor 102, and the processor 102 generates knee joint motion information based on the measurement signals, and evaluates the degree of damage to the knee joint and the running state of the human body according to the knee joint motion information.
In addition, since the difference between the knee joint motion signal generated by the damaged knee joint in the moving state and the knee joint motion signal generated by the undamaged knee joint in the moving state is large, the knee joint motion signal generated by the knee joint in the moving state represents the damage degree of the knee joint.
In addition, when the human body is in motion, the knee joint is also in a motion state, and the joint form of each bone and the degree of compression of each bone in the knee joint are different depending on the posture and the motion speed of the human body. It can be understood that the motion states of the human body are different, the states of the knee joints are also different, and the states of the knee joints are closely related to the motion states of the lower limbs of the human body. The leg position signal may include a signal indicating the position of the leg, such as the stride of the leg, the stride frequency of the leg, the position of the thigh, the position of the calf, the knee bending angle, and the height of the knee from the ground. Further, the knee flexion angle can be calculated from the posture of the upper leg and the posture of the lower leg.
The processor 102 is configured to receive the measurement signals of the knee joint and the leg of the human body sent by the data acquisition module 101, generate knee joint motion information based on the measurement signals, analyze and evaluate the damage degree of the knee joint and the running state of the human body according to the knee joint motion information, and send an analysis and evaluation result to the alarm module 103 and the control module 104.
Specifically, the processor 102 receives measurement signals of knee joints and legs of the human body sent by the data acquisition module 101, that is, the processor 102 receives knee joint motion signals, thigh motion signals, shank motion signals and leg posture signals; the knee joint motion signals, the thigh motion signals, the shank motion signals and the leg posture signals are used for generating knee joint motion information, further, the damage degree of the knee joint and the running state of the human body are analyzed and evaluated according to the knee joint motion information, and the analysis and evaluation results are sent to the alarm module 103 and the control module 104.
Specifically, the knee joint movement information may include a feature value of the knee joint movement signal, a feature value of the thigh movement signal, a feature value of the shank movement signal, and a feature value of the leg posture signal. The characteristic value of the knee joint motion signal, the characteristic value of the thigh motion signal, and the characteristic value of the shank motion signal may be characteristic values of the knee joint motion signal, the thigh motion signal, and the shank motion signal in a time domain and/or a frequency domain, for example, the characteristic value of the knee joint motion signal in the time domain may be a root mean square, a kurtosis, a skewness, and the like, the characteristic value of the knee joint motion signal in the frequency domain may be a frequency spectrum, an energy spectrum, a mean frequency, a power spectrum average value, and the like, and the characteristic value of the knee joint motion signal in the time-frequency domain may be a wavelet packet transform coefficient, and the like. Likewise, the feature value of the leg posture signal may be a feature value of the leg posture signal in a time domain and/or a frequency domain, for example, the feature value of the leg posture signal in the time domain may be a root mean square, a kurtosis, a skewness, and the like, the feature value in the frequency domain may be a frequency spectrum, an energy spectrum, a mean frequency, a power spectrum average value, and the like, and the feature value in the time-frequency domain may be a wavelet packet transform coefficient, and the like. Thus, the generated knee joint movement information can reflect the characteristics and the change conditions of the knee joint movement signal and the leg posture signal.
Wherein, the value of the knee joint movement signal can comprise a characteristic value of the knee joint vibration signal and a characteristic value of the knee joint sound signal.
The characteristic value of the knee joint vibration signal may be a characteristic value of the knee joint vibration signal in a time domain and/or a frequency domain, the characteristic value of the knee joint sound signal may be a characteristic value of the knee joint sound signal in the time domain and/or the frequency domain, for example, the characteristic values of the knee joint vibration signal and the sound signal in the time domain may be a root mean square, a kurtosis, a skewness, and the like, the characteristic values of the knee joint vibration signal and the sound signal in the frequency domain may be a frequency spectrum, an energy spectrum, a mean frequency, a power spectrum average value, and the like, and the characteristic values of the knee joint vibration signal and the sound signal in the time domain may be wavelet packet transform coefficients, and the like. Therefore, the characteristic values of the knee joint vibration signal and the knee joint sound signal can visually represent the characteristics of the knee joint vibration signal and the knee joint sound signal from the time domain and/or the frequency domain.
Specifically, based on the characteristic value of the knee joint motion signal, the characteristic value of the thigh motion signal and the characteristic value of the shank motion signal, a classification result is obtained by using a pre-trained model, and the damage degree of the knee joint is determined according to the classification result. It should be noted that the pre-trained model for performing the classification processing may be a Support Vector Machine (SVM), a deep learning algorithm, a K-nearest neighbor algorithm, a bayesian algorithm, or other Machine learning algorithm models.
Specifically, the SVM model may be a Radial Basis Function (RBF) kernel-based SVM model. Of course, other kernel functions, such as polynomial kernel function, laplacian kernel function, Sigmoid kernel function, etc., may be selected according to the actual situation.
In addition, the SVM model trained in advance can be a two-classification SVM model, the corresponding classification results are two types, and the damage degrees of the knee joint corresponding to the two types of classification results are respectively undamaged and damaged; the pre-trained SVM model can also be a multi-classification SVM model, the corresponding classification results can be at least five types, the damage degrees of the knee joint corresponding to the classification results can be respectively undamaged and damaged, wherein the damage can be classified according to the damage degrees and is divided into at least one-stage damage, two-stage damage, three-stage damage and four-stage damage.
Of course, the classification result corresponding to the multi-classification SVM model may also be six or more, and in general, the damage may be subdivided, so that the finally determined damage degree of the knee joint is more accurate.
The processor 102 may determine the degree of damage of the knee joint based on the classification result obtained by using a pre-trained model based on the feature value of the knee joint motion signal, the feature value of the thigh motion signal, and the feature value of the shank motion signal, and a preset expected value corresponding to the degree of damage set in training the model.
For example, when the pre-trained model is a two-class SVM model, the expected values of the classification results corresponding to the knee joint motion signal, the thigh motion signal, and the calf motion signal generated by the intact knee joint are set to 1, and the expected values of the classification results corresponding to the knee joint motion signal, the thigh motion signal, and the calf motion signal generated by the damaged knee joint are set to-1 when the original two-class SVM model is trained, then when the classification result is 1, it can be determined that the knee joint is not damaged, and when the classification result is-1, it can be determined that the knee joint is damaged.
Specifically, based on the characteristic value of the leg posture signal, the running state of the human body is obtained by comparison using a preset standard numerical value. The running state comprises a stride state, a step frequency state, a knee landing state and a leg lifting height state. When the legs are lifted too high and the stride is too large during exercise, energy is wasted, energy of impact when the human body falls to the ground is increased, and safety of knees is not facilitated; when the bending angle of the knee falling to the ground with the straight knee of the front leg is small, the impact energy directly impacts the knee joint due to the reduction of the buffer capacity of the maximum muscle of the thigh, which is not beneficial to the safety of the knee; in addition, the pressure on the knee per landing can be reduced when the number of landings is increased, so that the lower the frequency, the greater the damage to the knee at the same speed. Further, the stride characteristic value of the leg is compared with the standard value of the stride of the leg, when the stride characteristic value of the leg is larger than the standard value of the stride of the leg, the running state is a large stride state, when the step frequency characteristic value of the leg is smaller than the standard value of the step frequency of the leg, the running state is a small step frequency state, when the knee bending angle characteristic value is smaller than the standard value of the knee bending angle, the running state is a straight knee landing state, and when the height characteristic value of the knee from the ground is larger than the standard value of the knee from the ground, the running state is a leg lifting state.
The alarm module 103 is configured to receive the analysis and evaluation result sent by the processor 102, and generate alarm information according to the analysis and evaluation result.
Specifically, the alarm module 103 receives the analysis and evaluation result sent by the processor 102, wherein the analysis and evaluation result includes the degree of knee injury and the running state of the user. When the degree of knee injury is higher than the preset injury degree critical value, the alarm module 103 generates a knee injury alarm. When the size obtained by subtracting the standard value of the stride of the leg from the stride characteristic value of the leg in the large stride state is higher than a preset first threshold value, the alarm module generates large-stride alarm; when the standard value of the leg step frequency in the small step frequency state minus the step frequency characteristic value of the leg is larger than a preset second threshold value, the alarm module generates a small step frequency alarm; when the standard value of the knee bending angle in the straight knee landing state minus the characteristic value of the knee bending angle is larger than a preset third threshold value, the alarm module generates straight knee landing alarm; and when the height characteristic value of the knee at the high leg lifting state minus the standard value of the height of the knee at the ground is larger than a preset fourth threshold value, the alarm module generates a high leg lifting alarm. Further, the prompt signal of the alarm information may be an alarm buzzer, an alarm lamp, or a voice prompt. The user can adjust the running state according to the alarm information after receiving the alarm prompt, thereby achieving the effects of correcting wrong running postures and further protecting knees.
The control module 104 is used for receiving the analysis and evaluation result sent by the processor 102 and adjusting the running mode of the treadmill according to the analysis and evaluation result.
Specifically, the control module 104 receives the analysis evaluation result sent by the processor 102, wherein the analysis evaluation result includes the degree of knee injury and the running state of the user. When the knee injury degree is higher than the preset injury degree critical value, the control module 104 may control the treadmill to be in a stop mode to allow the user to have a forced rest, or may control the treadmill to be in a slow mode or a deceleration mode to allow the user to have a slow-walking rest, thereby achieving the effect of protecting the knees of the user. When the value obtained by subtracting the standard value of the stride of the leg from the stride characteristic value of the leg in the large stride state is higher than the preset first threshold value, and the duration of the state is too long, the control module 104 may control the treadmill to be in the slow mode or the deceleration mode so as to reduce the stride of the user; when the value obtained by subtracting the step frequency characteristic value of the leg from the standard value of the leg step frequency in the small step frequency state is greater than the preset second threshold value and the duration of the state is too long, the control module 104 may control the treadmill to be in the fast mode or the acceleration mode so as to increase the step frequency of the user; when the value obtained by subtracting the characteristic value of the knee bending angle from the standard value of the knee bending angle in the straight knee landing state is greater than the preset third threshold value, and the duration of the state is too long, the control module 104 may control the treadmill to be in the stop mode so as to stop the user from the wrong running mode in which the straight knee landing state; when the height characteristic value of the knee from the ground minus the standard value of the height of the knee from the ground in the leg-lifting state is greater than the preset fourth threshold value, and the state lasts too long, the control module 104 may control the treadmill to be in the slope mode so as to increase the leg-lifting resistance of the user. Wherein the duration can be set manually by a user.
Therefore, the intelligent treadmill based on the knee joint movement information provided by the embodiment of the invention can analyze and evaluate the knee damage degree and the running state through the knee joint movement information, the alarm module can generate an alarm based on the knee damage degree and the running state after the analysis and evaluation, and the control module can intelligently control the running mode based on the knee damage degree and the running state after the analysis and evaluation, so that a user can receive alarm information in a scene with damaged knee joints or wrong running postures and correct the running postures according to the running mode automatically adjusted by the intelligent treadmill, thereby achieving the effects of protecting the knee joints and correcting the wrong running postures.
Example two
Referring to fig. 2 to 6, fig. 2 is a schematic diagram of a data acquisition module according to an embodiment of the present invention; FIG. 3 is a schematic diagram of a knee joint sensor unit according to an embodiment of the present invention; fig. 4 is a schematic diagram of a leg posture collecting unit according to an embodiment of the present invention; fig. 5 is a schematic diagram of another leg posture acquiring unit provided by the embodiment of the invention; fig. 6 is a schematic diagram of another leg posture collecting unit according to an embodiment of the present invention. On the basis of the above embodiments, the data acquisition module 101 is explained in detail. As shown in fig. 2, the data acquisition module 101 includes: a knee joint sensor unit 1011 and a leg posture collecting unit 1012; a knee joint sensor unit 1011 for the knee joint movement signal; the leg posture collecting unit 1012 is used to collect leg posture signals. As shown in fig. 3, the knee joint sensor unit 1011 includes an acceleration sensor 1011a, an acoustic sensor 1011b, a first posture sensor 1011c, and a second posture sensor 1011d, and as shown in fig. 4, the leg posture acquiring unit 1012 includes a camera 1012a and a Digital Signal Processing (DSP 1012b for short).
The acceleration sensor 1011a is configured to acquire a knee joint vibration signal and send the knee joint vibration signal to the processor 102.
Specifically, the acceleration sensor 1011a may acquire a knee joint vibration signal of the human body by contacting a knee joint surface of the human body.
Since vibration signals are generated between bones, soft tissues and other structures inside the knee joint due to the movement of the knee joint, the vibration signals generated by the damaged knee joint can be distinguished from the vibration signals generated by the undamaged knee joint, and therefore, the vibration signals of the knee joint of the human body can be acquired by using the acceleration sensor 1011 a.
In practical applications, in order to improve the accuracy of the measured knee joint vibration signal, the number of the acceleration sensors 1011a may be multiple; the acceleration sensor 1011a may be a micro accelerometer, but may be any other sensor having a function of measuring vibration.
And the acoustic sensor 1011b is used for acquiring a knee joint sound signal generated by the knee joint of the human body and sending the knee joint sound signal to the processor 102.
Specifically, the acoustic sensor 1011b may acquire a knee joint sound signal generated by a knee joint of the human body, and the knee joint vibration signal and the knee joint sound signal together constitute a knee joint movement signal. The processor 102 generates knee joint motion information based on the knee joint motion signal.
In addition, sound is generated between structures such as bones and soft tissues inside the knee joint due to knee joint movement, that is, a sound signal of the knee joint, so that the sound signal of the knee joint generated by the knee joint can be acquired by using the acoustic sensor 1011b, and the damage degree of the knee joint can be determined based on the sound signal, so that the accuracy of the finally determined damage degree of the knee joint can be improved.
In practical applications, the acoustic sensor 1011b may be a contact microphone, such as a stethoscope, or the like, or a piezoelectric film.
The characteristic value of the knee joint vibration signal may be a characteristic value of the knee joint vibration signal in a time domain and/or a frequency domain, the characteristic value of the knee joint sound signal may be a characteristic value of the knee joint sound signal in the time domain and/or the frequency domain, for example, the characteristic values of the knee joint vibration signal and the sound signal in the time domain may be a root mean square, a kurtosis, a skewness, and the like, the characteristic values of the knee joint vibration signal and the sound signal in the frequency domain may be a frequency spectrum, an energy spectrum, a mean frequency, a power spectrum average value, and the like, and the characteristic values of the knee joint vibration signal and the sound signal in the time domain may be wavelet packet transform coefficients, and the like. Therefore, the characteristic values of the knee joint vibration signal and the knee joint sound signal can visually represent the characteristics of the knee joint vibration signal and the knee joint sound signal from the time domain and/or the frequency domain.
Since the knee joint movement signal may include the knee joint movement signal within a preset measurement period in a general case, the characteristic value of the movement signal of the knee joint may be generated based on the knee joint movement signal within the above-mentioned one preset measurement period.
In a particular application, the thigh motion signal comprises a thigh posture signal; the lower leg motion signal comprises a lower leg posture signal. The first posture sensor 1011c is configured to acquire the thigh posture signal in the user motion state, and send the thigh posture signal to the processor 102.
The second attitude sensor 1011d is configured to acquire the shank attitude signal in a human motion state, and send the shank attitude signal to the processor 102.
Specifically, the thigh posture signal may be posture information of a thigh, and the shank posture signal may be posture information of a shank, so that the processor 102 obtains information that represents a knee joint posture, such as an angle and an acceleration of the knee joint, based on the posture information of the thigh and the shank.
In practical applications, the first attitude sensor 1011c and the second attitude sensor 1011d may be gyroscopes, and of course, the first attitude sensor 1011c and the second attitude sensor 1011d may be other sensors having a function of measuring attitude information.
The characteristic value of the thigh motion signal and the shank motion signal may be a characteristic value of the thigh motion signal and the shank motion signal in a time domain and/or a frequency domain, for example, the characteristic value of the thigh motion signal and the shank motion signal in the time domain may be an average value, a root mean square, and the like, and the characteristic value of the thigh motion signal and the shank motion signal in the frequency domain may be a frequency spectrum, an energy spectrum, a power spectrum, and the like. Therefore, the characteristic values of the thigh motion signal and the shank motion signal can respectively and visually represent the characteristics of the thigh motion signal and the shank motion signal from a time domain and a frequency domain.
Since the thigh motion signal and the lower leg motion signal may include a thigh motion signal and a lower leg motion signal within a preset measurement period in a normal case, the characteristic values of the thigh motion signal and the lower leg motion signal may be generated based on the thigh motion signal and the lower leg motion signal within the preset measurement period.
The camera 1012a is disposed at a position of the treadmill corresponding to the leg of the user, and is used for capturing a running video of the leg of the user.
In practical applications, in order to improve the accuracy of the measured leg posture signal, the number of the cameras 1012a may be plural, and at least two cameras may be provided; camera 1012a may be a wide-angle camera.
In one implementation, as shown in fig. 5, the leg posture acquiring unit 1012 may further include a first memory 1012 c.
In one implementation, as shown in fig. 6, the leg posture acquiring unit 1012 may further include a wireless communication unit 1012 d. Wherein the wireless communication unit may be WiFi, bluetooth, etc.
Further, the leg running video of the user collected by the camera 1012a may be stored in the first memory 1012c, or may be uploaded to a cloud server through a wireless communication unit. When the first memory 1012c or the cloud server receives the user leg running video, the user leg running video is transmitted to the DSP1012 b.
The DSP1012b is configured to receive the user leg running video sent by the first memory 1012c or the cloud server, or may directly receive the user leg running video collected by the camera 1012a, and process the video to form a leg posture signal.
Specifically, the DSP1012b receives a user leg running video sent by the first memory 1012c or the cloud server, and performs frame processing on the video, extracting each frame picture. And forming a leg gesture signal according to the extracted picture identification of each frame.
For example, collected gait pictures can be analyzed from the gait cycle profile of a human body, and different features, such as the stride size, the step frequency, the knee landing angle, the leg lifting height and the like, which are exhibited by each person are extracted.
The present embodiment transmits the knee joint motion signal, the thigh motion signal, the calf motion signal acquired by the knee joint sensor unit 1011, and the leg posture signal acquired by the leg posture acquisition unit 1012 to the processor 102.
Therefore, the data acquisition module of the intelligent treadmill based on the knee joint movement information provided by the embodiment of the invention can acquire the knee joint movement signal including the sound signal and the vibration signal, the thigh movement signal, the shank movement signal, and the leg posture signals including the stride size, the step frequency, the knee landing angle, the leg lifting height and the like, so that the processor 102 can analyze and evaluate the damage degree of the knee joint and the running state of the human body, and finally play a role in protecting and preventing the knee joint.
EXAMPLE III
Referring to fig. 7, fig. 7 is a schematic view of another intelligent treadmill based on knee joint movement information according to an embodiment of the present invention. On the basis of the above embodiment, the intelligent treadmill further comprises: a second memory 105, the second memory 105 being coupled to the processor 102, the alarm module 103 and the control module 104 being coupled to the second memory 105.
The processor 102 is further configured to send results of analyzing and evaluating the damage degree of the knee joint and the running state of the human body according to the knee joint motion information to the second memory 105.
Specifically, the processor 102 will analyze and evaluate the damaged degree of the knee joint according to the knee joint motion information, the damaged degree may be undamaged or damaged, wherein the damaged degree may be classified into at least mild damage and severe damage, and of course, the damaged degree may also be four types, five types or more, and the processor 102 may send the damaged degree to the second memory 105 for storage. The processor 102 analyzes and evaluates the running state of the human body according to the knee joint movement information, wherein the running state includes a stride state, a stride frequency state, a knee landing state and a leg lifting height state, and the processor 102 may send all running state results of the human body to the second memory 105 for storage, and may also send a large stride state, a small stride state, a straight knee landing state and a leg lifting height state to the second memory 105 for storage so as to save space.
In practical applications, the second memory may be a (Trans-Flash, TF) memory card, but may also be other devices with a storage function.
In one implementation, as shown in fig. 7, the intelligent treadmill further comprises: a display module 106; the display module 106 is connected to the second memory 105.
And the display module 106 is used for displaying the results of the processor 102 analyzing and evaluating the damage degree of the knee joint and the running state of the human body according to the knee joint motion information. Namely, the display module 106 displays the current damage degree of the knee joint of the user and the running state. Further, the display module 106 may be an LCD display screen.
Therefore, the intelligent treadmill provided by the embodiment of the invention can utilize the second memory to store the knee joint damage degree and the running state of the human body analyzed and evaluated by the processor, is convenient for a user to call and check, can also utilize the display mode to monitor the knee joint damage degree and the running state of the user in real time, is convenient for the user to correct wrong postures in time, and further protects the knee joint.
The above description is a detailed description of the intelligent treadmill based on knee joint movement information according to the present invention with reference to the specific preferred embodiments, and it should not be understood that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (9)
1. An intelligent treadmill based on knee joint movement information, comprising: the intelligent treadmill comprises a data acquisition module (101), a processor (102), an alarm module (103) and a control module (104) which are arranged on an intelligent treadmill body; wherein the content of the first and second substances,
the data acquisition module (101) is used for acquiring measurement signals of knee joints and legs;
the processor (102) is connected to the data acquisition module (101) and is used for receiving the measurement signals, generating knee joint movement information based on the measurement signals, and analyzing and evaluating the damage degree of the knee joint and the running state according to the knee joint movement information;
the alarm module (103) is connected to the processor (102) and is used for receiving the damage degree of the knee joint and the running state analysis and evaluation result and determining whether alarm information is generated according to the analysis and evaluation result;
the control module (104) is connected to the processor (102) and is used for receiving the analysis and evaluation results of the damage degree of the knee joint and the running state and adjusting the running mode according to the analysis and evaluation results.
2. The intelligent treadmill of claim 1, wherein the measurement signals comprise a knee joint motion signal, a thigh motion signal, a calf motion signal, and a leg posture signal.
3. The intelligent treadmill of claim 2, wherein the data acquisition module (101) comprises a knee joint sensor unit (1011) and a leg posture acquisition unit (1012); wherein the content of the first and second substances,
the knee joint sensor unit (1011) is used for collecting the knee joint motion signal, the thigh motion signal and the shank motion signal;
the leg posture acquisition unit (1012) is configured to acquire the leg posture signal.
4. The intelligent treadmill of claim 3, wherein the knee joint motion signals comprise a knee joint vibration signal and a knee joint sound signal.
5. The intelligent treadmill of claim 4, wherein the knee joint sensor unit (1011) comprises an acceleration sensor (1011a), an acoustic sensor (1011b), a first attitude sensor (1011c), and a second attitude sensor (1011 d); wherein the content of the first and second substances,
the acceleration sensor (1011a) is used for acquiring the knee joint vibration signal and sending the knee joint vibration signal to the processor (102);
the acoustic sensor (1011b) is used for acquiring the knee joint sound signal and sending the knee joint sound signal to the processor (102);
the first posture sensor (1011c) is used for acquiring the thigh posture signal and sending the thigh posture signal to the processor (102);
the second attitude sensor (1011d) is used for acquiring the shank attitude signal and sending the shank attitude signal to the processor (102).
6. The intelligent treadmill of claim 4, wherein the leg pose acquisition unit (1012) comprises a camera (1012a) and a DSP (1012 b); wherein the content of the first and second substances,
the camera (1012a) is arranged at the position of the intelligent running machine corresponding to the leg of the user and is used for collecting the leg running video of the user;
the DSP (1012b) is used for receiving the leg running video and processing the leg running video to form the leg gesture signal.
7. The intelligent treadmill of claim 6, wherein the leg gesture capture unit (1012) further comprises a first memory (1012c), the first memory (1012c) being configured to store the leg running video.
8. The intelligent treadmill of claim 7, wherein the leg pose acquisition unit (1012) further comprises a wireless communication unit (1012 d).
9. The intelligent treadmill of claim 1, further comprising a second memory (105), wherein the second memory (105) is connected to the processor (102) and the alarm module (103) and the control module (104), respectively, for storing the damage degree of the knee joint and the running state analysis and evaluation result.
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| CN112826709A (en) * | 2021-02-17 | 2021-05-25 | 厦门大学附属中山医院 | Walking training alarm instrument and system for measuring knee joint angle |
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| US9687404B2 (en) * | 2014-08-26 | 2017-06-27 | Elwha Llc | Garment system including at least one muscle or joint activity sensor and at least one actuator responsive to the sensor and related methods |
| CN106422173A (en) * | 2016-11-29 | 2017-02-22 | 广州体育学院 | Leg posture correcting method and device for treadmill and treadmill |
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