CN113303766B - System for detecting muscle health state of object of interest - Google Patents
System for detecting muscle health state of object of interest Download PDFInfo
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- CN113303766B CN113303766B CN202110573971.5A CN202110573971A CN113303766B CN 113303766 B CN113303766 B CN 113303766B CN 202110573971 A CN202110573971 A CN 202110573971A CN 113303766 B CN113303766 B CN 113303766B
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
- A61B5/4519—Muscles
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- 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
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- 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
- A61B5/1121—Determining geometric values, e.g. centre of rotation or angular range of movement
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
Abstract
The present application relates to a system for detecting the health status of a muscle to be measured of a subject of interest. The detection system comprises a motion indication module, a motion sensor, a data processing module and a judgment module. First, second and third motion sensors to be placed on the first, second and third body parts, respectively, obtain first, second and third sensor parameters, respectively, when the object of interest performs a predetermined action. The first and second body parts are connected by a first joint and the first and third body parts are connected by a second joint. The data processing module obtains a first joint motion characteristic parameter corresponding to the first joint according to the first sensor parameter and the second sensor parameter, and obtains a second joint motion characteristic parameter corresponding to the second joint according to the first sensor parameter and the third sensor parameter. The judging module determines the health state of the muscle to be tested according to the first joint motion characteristic parameter and the second joint motion characteristic parameter.
Description
Technical Field
The present invention relates to the technical field of detecting muscular problems of an object to be measured by means of a sensor worn by the object to be measured.
Background
Muscles are composed of muscle tissue, which is distributed throughout the body of a human and has a variety of important functions on the human body. The skeleton muscle wraps the skeleton, and when a human body is hit or collided by an external force, the strong muscle can effectively buffer the impact caused by the external force, so that the skeleton is protected from being damaged. In addition, skeletal muscles support the body and help a person maintain various postures. Skeletal muscle is attached to the skeleton, and all the movements of the human body are performed by skeletal muscle contraction, and no movement occurs without skeletal muscle.
When a problem occurs in the muscles of the human body, the movement of the human body may also be followed by the problem. By enabling a person to wear the motion sensor, the information collected by the sensor can be used for obtaining the motion related information of the person to judge whether the motion of the person is abnormal or not, so that the health state of the muscle of the human body is judged, and the subsequent rehabilitation therapy aiming at the muscle is considered.
Disclosure of Invention
According to an embodiment of the invention, a system for detecting a health state of a muscle under test of a first body part of a subject of interest is provided. The detection system comprises a motion indication module, a first motion sensor, a second motion sensor, a third motion sensor, a data processing module and a judgment module. A first motion sensor intended to be placed on a first body part of the object of interest obtains first sensor parameters when the object of interest performs the predetermined action, a second motion sensor intended to be placed on a second body part of the object of interest obtains second sensor parameters when the object of interest performs the predetermined action, and a third motion sensor intended to be placed on a third body part of the object of interest obtains third sensor parameters when the object of interest performs the predetermined action. The first body part and the second body part are connected by a first joint and the first body part and the third body part are connected by a second joint. The data processing module obtains a first joint motion characteristic parameter of a first joint corresponding to the object of interest according to the first sensor parameter and the second sensor parameter, and obtains a second joint motion characteristic parameter of a second joint corresponding to the object of interest according to the first sensor parameter and the third sensor parameter. The judgment module determines a first muscle health risk degree according to the first joint action characteristic parameter, determines a second muscle health risk degree according to the second joint action characteristic parameter, and determines a third muscle health risk degree of the muscle to be detected according to the first muscle health risk degree and the second muscle health risk degree.
Because the muscle to be measured of the first body part is located between the first joint and the second joint, the motion characteristic of each joint is influenced by the health state of the muscle to be measured. At the same time, each joint may be affected by the health of muscles other than the muscle being measured in the first body part, e.g., the movement of the first joint may be affected by muscles in the second body part, and the movement of the second joint may be affected by a third body part. Therefore, the motion characteristics of each joint partially reflect the health state of the muscle to be detected, and the joint determination of the health state of the muscle to be detected by using the different muscle health risk degrees determined by the motion characteristics of different joints can possibly eliminate the influence of the muscle to be detected on the health detection result of the muscle to be detected, so that the detection accuracy is improved, and the occurrence of false alarms is avoided. In addition, the motion sensor worn on the body part adjacent to the joint is beneficial to fixing the sensor on the interested object and facilitating the motion of the interested object, and meanwhile, the motion state of the joint can be obtained more accurately.
According to another embodiment of the invention, when any one of the first muscle health risk degree and the second muscle health risk degree is zero risk, the determination module determines that the third muscle health risk degree of the muscle to be tested is zero risk. If one joint motion characteristic parameter indicates that there is a muscle problem, it may be because there is a problem with muscles of other parts of the body connected to the joint, and therefore if another joint motion characteristic parameter indicates that there is no muscle problem, it may be determined that the muscle to be measured has no health problem.
According to still another embodiment of the present invention, when both the first muscle health risk degree and the second muscle health risk degree are not zero risk, it is determined that the muscle to be tested has a health risk of a third muscle health risk degree, and the third muscle health risk degree is the maximum value of the first muscle health risk degree and the second muscle health risk degree. When different joint action characteristic parameters all show that muscle health problems exist, the possibility that the muscle to be detected has the muscle problems is high. When the value with the greatest risk in the different muscle health risk degrees determined according to the different joint action characteristic parameters is used as the muscle health risk degree of the muscle to be detected, when the health state of the muscle with the problem is improved in a subsequent rehabilitation exercise mode, the muscle can be trained by the exercise with lower requirements on the muscle capacity, the muscle with the problem is protected to the maximum extent, the muscle with the problem is guided by progressive exercise rehabilitation, and the secondary damage of the rehabilitation exercise to the muscle with the problem is avoided.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic structural diagram of a system for detecting a health status of a muscle to be tested of a first body part of a subject of interest according to an embodiment of the present invention.
Detailed Description
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.
Fig. 1 is a schematic structural diagram of a system for detecting a health status of a muscle to be tested of a first body part of a subject of interest according to an embodiment of the present invention.
As shown in fig. 1, according to an embodiment of the invention, a system for detecting a health state of a muscle to be measured of a subject of interest is provided. The system for detecting the health state of a muscle to be measured of an object of interest comprises a motion indication module, a first motion sensor, a second motion sensor, a third motion sensor, a data processing module and a decision module.
The object of interest is a detected user needing to detect whether the muscle has a problem, and the detected user can be an adult or a child. The muscle to be detected is part or all of the muscle of the first body part of the user to be detected, such as thigh back side muscle or thigh muscle of a thigh, hip muscle of a hip, calf front side muscle of a calf and the like.
The action indication module indicates that the object of interest performs a predetermined action. The predetermined action is an action that the user under test can complete or may attempt to complete, such as a squat on one leg, a step forward or an arm lift, etc. The motion indication module may select a predetermined motion from a pre-stored or established database of predetermined motions based on the muscle to be measured. For example, the muscle to be tested is a thigh muscle, and the action indicating module selects a predetermined action corresponding to the thigh muscle test from the predetermined action database. For another example, the muscle to be tested is a forearm muscle, and the motion indication module selects a predetermined motion corresponding to the forearm muscle test from the predetermined motion database. In addition, the selection of the predetermined action can also take the health state known by the tested user into consideration, and the predetermined action with appropriate difficulty is selected from the predetermined action database.
The action indication module may be implemented in a variety of ways. For example, the action indicating module may be a display screen indicating the object of interest to perform the predetermined action by displaying a textual description, a picture or a video of the predetermined action. For another example, the action indication module may be a voice player, and the voice player indicates the object of interest to complete the predetermined action by means of voice prompt.
A first motion sensor intended to be placed on a first body part of the object of interest obtains first sensor parameters when the object of interest performs the predetermined action, a second motion sensor intended to be placed on a second body part of the object of interest obtains second sensor parameters when the object of interest performs the predetermined action, and a third motion sensor intended to be placed on a third body part of the object of interest obtains third sensor parameters when the object of interest performs the predetermined action. The first body part and the second body part are connected by a first joint and the first body part and the third body part are connected by a second joint.
The plurality of body parts may be a plurality of combinations of body parts of the subject of interest. For example, the first, second and third body parts are the waist, thigh and calf, respectively, and correspondingly the first and second joints are the hip and knee joints, respectively. As another example, the first, second and third body parts are the upper arm, lower arm and hand, respectively, and correspondingly, the first and second joints are the elbow joint and wrist joint, respectively.
A motion sensor is a sensor that can measure motion related sensor parameters, such as an inertial sensor, a gyroscope or an accelerometer, etc. The sensor parameters include any one or any combination of velocity, angle, angular velocity, acceleration, angular acceleration, and the like.
The motion sensor may be placed at a location on the subject of interest in a variety of ways, such as by securing the motion sensor to the lower leg of the subject of interest with a strap, and holding the motion sensor by the subject's hand.
The data processing module obtains a first joint motion characteristic parameter of a first joint corresponding to the object of interest according to the first sensor parameter and the second sensor parameter, and obtains a second joint motion characteristic parameter of a second joint corresponding to the object of interest according to the first sensor parameter and the third sensor parameter.
The data processing module can be realized by a processor or an FPGA board and the like. The joint motion characteristic parameters are parameters that can characterize the motion state or result of the joint of the object of interest in the process of completing the predetermined motion, such as the frequency of the vibration of the joint of the object of interest, the flexion and extension angles of the joint of the object of interest, the motion acceleration of the joint of the object of interest, and the like.
During the process of completing the predetermined motion of the object of interest, the sensor parameters obtained by the motion sensor are a plurality of values within a measuring time period, and the joint motion characteristic parameters obtained according to the sensor parameters are a plurality of values within the same measuring time period. The start time of the measurement time may be determined in a number of ways, such as by having the motion indication module indicate a time at which the object of interest has completed a predetermined motion as the start time, and such as by inputting the start time by an operator of the detection system. The end time of the measurement time may be determined in a number of ways, such as detecting the time to complete the predetermined action as the end time, such as entering the end time by an operator of the detection system, or such as indicating the time to complete the next predetermined action by the action indicating module as the end time.
Before the data processing module obtains the joint action characteristic parameters corresponding to the joints according to the sensor parameters of the motion sensor or before the detection system works, the motion sensor can be calibrated in a manual or automatic mode, namely the sensor parameters are calibrated to a coordinate system used by the data processing module for evaluating the motion state of the measured object, so that the motion state of the measured user can be represented better by the sensor parameters. Calibration may be achieved in a number of ways. For example, the measured object can be stood in a specified direction to calibrate the vertical, lateral horizontal and horizontal forward directions of the measured object, which are vertical to each other. As another example, calibration may be based on the obtained sensor parameters and the predetermined action. The basic calibration method using a motion sensor is well known to those skilled in the art and will not be described in detail herein.
The judgment module determines a first muscle health risk degree according to the first joint action characteristic parameter, determines a second muscle health risk degree according to the second joint action characteristic parameter, and determines a third muscle health risk degree of the muscle to be detected according to the first muscle health risk degree and the second muscle health risk degree.
The judging module can be realized by a processor or an FPGA board and the like. The muscle health risk degree may indicate whether or not there is a muscle health risk or the degree or grade of the muscle health risk, etc. For example, the muscle health risk degree may be represented by 0 and 1, or presence or absence, to indicate whether a muscle health risk exists. As another example, the muscle health risk level may represent the degree or degree of muscle health risk present by 1 to 5 stars or by normal, mild, moderate, poor, severe, and dangerous. Also for example, the muscle health risk level may represent the degree or grade of the presence of a muscle health risk in terms of a percentage.
Determining the muscle health risk according to the joint action characteristic parameters can be achieved in various ways.
In one embodiment, the maximum value of the joint motion characteristic parameter may be compared to one or more predetermined joint motion characteristic parameter thresholds, and the presence of a muscle health issue may be determined based on whether the joint motion characteristic parameter reaches the joint motion characteristic threshold, or the degree or level of muscle health risk may be determined based on the proportional relationship between the maximum value of the joint motion characteristic parameter and the joint motion characteristic threshold.
In another embodiment, the minimum value of the joint motion characteristic parameter may be compared to one or more predetermined joint motion characteristic parameter thresholds, and the presence of a muscle health issue may be determined based on whether the minimum value of the joint motion characteristic parameter reaches the joint motion characteristic threshold, or the degree or level of muscle health risk may be determined based on a proportional relationship between the minimum value of the joint motion characteristic parameter and the joint motion characteristic threshold.
In yet another embodiment, the joint motion characteristic parameter may be compared to one or more predetermined joint motion characteristic parameter thresholds, and a determination may be made as to whether a muscle health issue exists based on whether the joint motion characteristic parameter reaches the joint motion characteristic threshold, or a determination may be made as to the degree or level of muscle health risk existing based on a proportional relationship between the joint motion characteristic parameter and the joint motion characteristic threshold.
The above-mentioned threshold value of the joint motion characteristic can be generated according to a database of joint motion characteristic parameters generated by sampling a large number of people.
According to the invention, the motion sensors worn on different body parts without joints are used for obtaining the joint motion characteristic parameters corresponding to different joints, and the joint motion characteristic parameters of different joints are deduced under the condition that the influence of the motion sensors on the motion of a user to be detected is reduced to the greatest extent. Because the movement of the joints can be influenced by other muscles besides the muscles to be detected, the health state of the muscles to be detected can be reflected from the angle of different muscle combinations according to different muscle health risk degrees determined by the joint movement characteristic parameters of different joints, and the problem of better screening and screening the muscles to be detected is facilitated.
Determining the third muscle health risk level of the muscle to be tested from the first muscle health risk level and the second muscle health risk level may be performed in a variety of ways.
In one embodiment, when any one of the first muscle health risk degree and the second muscle health risk degree is zero risk, the determination module determines that the third muscle health risk degree of the muscle to be tested is zero risk. If the joint motion characteristic parameter corresponding to one joint indicates that a muscle problem exists, the muscle of other body parts which are not connected with the joint motion characteristic parameter and are not the muscle to be measured possibly has a problem, so that the muscle to be measured which can influence the joint motion has no problem as long as the joint motion characteristic parameter corresponding to one joint indicates that no muscle problem exists.
In yet another embodiment, when both the first muscle health risk degree and the second muscle health risk degree are not zero risk, the third muscle health risk degree of the muscle to be tested is determined as the maximum value of the first muscle health risk degree and the second muscle health risk degree. The greater the value of the health risk, the greater the indicated muscle health risk. When different joint action characteristic parameters corresponding to different joints all show that muscle problems exist, the muscle to be measured of the body part connected with the different joints probably has muscle problems. The maximum risk value of different muscle health risk degrees determined according to different joint action characteristic parameters is used as the muscle health risk degree of the muscle to be detected, when the health state of the muscle with problems is improved in a subsequent rehabilitation exercise mode, the muscle rehabilitation exercise can be guided by the exercise with lower requirements on the muscle capacity, so that the muscle with problems is protected to the maximum extent, the muscle with problems is guided or treated by progressive exercise rehabilitation, and the secondary damage of excessive rehabilitation exercise or treatment to the muscle with problems is avoided.
In one embodiment, the first joint motion characteristic parameter is a first angle of flexion and extension of the first joint and the second joint motion characteristic parameter is a second angle of flexion and extension of the second joint. For example, the first joint is a knee joint and the second joint is an ankle joint. For another example, the first joint is a shoulder joint and the second joint is an elbow joint.
The system according to an embodiment of the invention may detect the health state of the calf anterior muscle of the subject of interest.
The action indicating module indicates the tested user to complete the preset action of 'deep squatting', namely, the tested user is required to squat to the limit state that the user can not lower any more.
The detection system comprises a first motion sensor, a second motion sensor and a third motion sensor, wherein the first motion sensor is worn on the lower leg of a first body part of a detected user, the second motion sensor is worn on the upper leg of a second body part of the detected user, and the third motion sensor is worn on the foot of a third body part of the detected user. After the user to be tested begins to squat deeply, the first sensor parameter measured by the first motion sensor is a shank angle value changing along with time, the second sensor parameter measured by the second motion sensor is a thigh angle value changing along with time, and the third sensor parameter measured by the third motion sensor is a foot angle value changing along with time.
The data processing module obtains a first joint motion characteristic parameter of the interested object as a first angle of the first joint knee joint extension and flexion according to the shank angle value of the first sensor parameter and the thigh angle value of the second sensor parameter, namely an included angle between the thigh and the shank in the front forward direction of the interested object. The data processing module obtains a second joint motion characteristic parameter of the interested object as a second angle of flexion and extension of the ankle joint of the second joint according to the shank angle value of the first sensor parameter and the step angle value of the third sensor, namely, an included angle between the shank and the foot in the front forward direction of the interested object.
The judgment module compares a first joint motion characteristic parameter corresponding to a first joint, namely a first knee joint flexion and extension angle with a first knee joint flexion and extension angle threshold value and a second knee joint flexion and extension angle threshold value: if the maximum value of the first angle of flexion and extension of the knee joint is larger than the threshold value of the second knee joint flexion and extension angle, determining that the first muscle health risk degree is zero; determining that the first muscle health risk degree is serious if the maximum value of the first angle of flexion and extension of the knee joint is smaller than the threshold value of the first knee joint flexion and extension angle; and if the maximum value of the first angle of flexion and extension of the knee joint is between the first knee joint flexion and extension angle threshold value and the second knee joint flexion and extension angle threshold value, determining that the first muscle health risk degree is medium.
The judging module compares a second motion characteristic parameter corresponding to a second joint, namely a second ankle flexion and extension angle with the first ankle flexion and extension angle threshold value and the second ankle flexion and extension angle threshold value: if the maximum value of the second angle of the ankle joint flexion and extension is larger than the threshold value of the second ankle joint flexion and extension angle, determining that the second muscle health risk degree is zero; if the maximum value of the second angle of flexion and extension of the ankle joint is smaller than the threshold value of the first ankle joint flexion and extension angle, determining that the second muscle health risk degree is serious; and if the maximum value of the second ankle joint flexion and extension angle is in the middle of the first ankle joint flexion and extension angle threshold value and the second ankle joint flexion and extension angle threshold value, determining that the second muscle health risk degree is medium.
When the first muscle health risk degree is zero, the judging module determines that the third muscle health risk degree of the front side muscle of the lower leg is zero, namely the front side muscle of the lower leg is healthy. When the first muscle health risk degree is medium and the second muscle health risk degree is serious, the judging module determines that the third muscle health risk degree of the front side muscle of the shank is serious.
In one embodiment, the first joint motion characteristic parameter is a first radius of the first joint wobble and the second joint motion characteristic parameter is a second radius of the second joint wobble. For example, the first joint is a knee joint and the second joint is an ankle joint. For another example, the first joint is a shoulder joint and the second joint is an elbow joint.
The system according to an embodiment of the invention may detect whether there is a problem with the thigh muscle of the object of interest.
The action indicating module indicates the tested user to complete the preset action of 'single leg squat', namely, the tested user is required to squat after one leg is lifted off the ground.
The detection system comprises a first motion sensor, a second motion sensor and a third motion sensor, wherein the first motion sensor is worn on the thigh of a first body part of a detected user, the second motion sensor is worn on the waist of a second body part of the detected user, and the third motion sensor is worn on the calf of a third body part of the detected user. After the tested user begins to squat with one leg, the first sensor parameter measured by the first motion sensor is a thigh angle value changing along with time, the second sensor parameter measured by the second motion sensor is a waist angle value changing along with time, and the third sensor parameter measured by the third motion sensor is a shank angle value changing along with time.
The data processing module obtains a first joint movement characteristic parameter of the interested object as a first radius of the first joint hip joint shaking according to the thigh angle value of the first sensor parameter and the waist angle value of the second sensor parameter. The data processing module obtains a second joint motion characteristic parameter of the interested object as a second radius of the shake of the second joint knee joint according to the thigh angle value of the first sensor parameter and the shank angle value of the third sensor.
The detection system can obtain sensor calibration data of a waist sensor to hip joint distance, thigh length, shank length, thigh sensor to knee joint distance, shank sensor to knee joint distance and the like of an interested object by enabling a detected user to stand upright, sit down and lie down in a sensor calibration process. The detection system also enables the detection person to manually input calibration data such as hip-joint distance of the waist sensor, thigh length, shank length, thigh-to-knee distance of the thigh sensor, and shank-to-knee distance of the shank sensor of the object of interest. The calibration method of the sensor mentioned herein is known to those skilled in the art and will not be described herein.
The data processing module can obtain the projection position of the hip joint (namely the intersection point of the thigh and the waist) on the horizontal ground according to the calibration data of the sensor and the thigh angle value and the waist angle value measured by the sensor, the hip joint has a plurality of projection positions on the horizontal ground along with the shaking of the body of the interested object in the single-leg squatting process, the gravity centers of the plurality of projection positions are used as the origin, and the distance from the hip joint projection point farthest from the origin to the origin is the first radius of the shaking of the hip joint.
The data processing module can obtain the projection position of knee joint (namely thigh and shank intersection point) on the horizontal ground according to the calibration data of the sensor and the thigh angle value and shank angle value measured by the sensor, the knee joint has a plurality of projection positions on the horizontal ground along with the shaking of the interested object in the single-leg squatting process, the gravity centers of the plurality of projection positions are used as the origin, and the distance from the knee joint projection point farthest from the origin to the origin is the second radius of shaking of the knee joint.
The judgment module compares a first joint motion characteristic parameter corresponding to a first joint, namely a first radius of the shaking hip joint, with a hip joint shaking threshold value: determining that the first muscle health risk degree is zero if the first radius of the hip joint wobble is less than a hip joint wobble threshold value; if the first radius of the hip joint wobble is larger than a hip joint wobble threshold value but smaller than twice the hip joint wobble threshold value, determining that the first muscle health risk degree is medium; determining the first muscle health risk as severe if the first radius of hip wobble is greater than twice the hip wobble threshold.
The judgment module compares a second joint motion characteristic parameter corresponding to a second joint, namely a second radius of the knee joint shaking with a knee joint shaking threshold value: determining that the second muscle health risk degree is zero if the second radius of the knee joint swing is less than the knee joint swing threshold value; if the second radius of the knee joint swing is larger than the knee joint swing threshold value but smaller than two times of the knee joint swing threshold value, determining that the second muscle health risk degree is medium; determining that the second muscle health risk is severe if the first radius of knee joint wobble is greater than twice the knee joint wobble threshold.
When the second muscle health risk degree is zero, the judging module determines that the third muscle health risk degree of the thigh muscle is zero, namely the thigh muscle is healthy. When the first muscle health risk degree is medium and the second muscle health risk degree is serious, the judging module determines that the third muscle health risk degree of the thigh muscle is serious.
According to a further embodiment of the invention, the second body part is closer to the center of gravity of the object of interest than the first body part, and the data processing module is further adapted to obtain first body part motion characteristic parameters corresponding to the first body part of the object of interest on the basis of the first sensor parameters and to obtain second body part motion characteristic parameters corresponding to the second body part of the object of interest on the basis of the second sensor parameters.
The second body part may be closer to the center of gravity of the object of interest than the first body part in a number of body part combinations: for example, the second body part is a thigh, and the first body part is a calf; for another example, the second body part is the upper arm, the first body part is the lower arm; for another example, the second body part is the thigh and the first body part is the waist.
The body motion characteristic parameter is a parameter that can characterize a motion state or a result of a body part of the subject of interest in a process of completing a predetermined motion, such as a frequency of shaking of the body part of the subject of interest, a flexion and extension angle of the body part of the subject of interest, a motion acceleration of the body part of the subject of interest, and the like. During the process of completing the predetermined action of the object of interest, the sensor parameters obtained by the motion sensor are a plurality of values within a measurement period, and the body part action characteristic parameters obtained according to the sensor parameters are a plurality of values within the same measurement period.
The judgment module is further used for determining a fourth muscle health risk degree according to the first body part action characteristic parameter, determining a fifth muscle health risk degree according to the second body part action characteristic parameter, and determining a third muscle health risk degree of the muscle to be tested according to the first muscle health risk degree, the second muscle health risk degree, the fourth muscle health risk degree and the fifth muscle health risk degree.
Determining the muscle health risk degree according to the body part motion characteristic parameters may be implemented in various ways, similar to the above-described determination of the muscle health risk degree according to the joint motion characteristic parameters, and will not be described herein again.
The first, second and third bodies are connected by first and second joints, respectively. When the second body part is closer to the center of body gravity of the subject of interest than the first body part, the third body part is further away from the center of body gravity of the subject of interest than the first body part. When the first body part has a supporting effect, the motion state of the second body part closer to the center of gravity of the body and the first body part can be related to the health state of the muscle to be measured of the first body part. Therefore, the muscle health risk degree determined by the action characteristic parameters of the body part where the muscle to be detected is located and the body part closest to the gravity center of the body of the user to be detected is introduced, the health risk degree of the muscle to be detected can be determined from the angle of more muscle combinations including the muscle to be detected, and the problem of the muscle to be detected can be screened and screened more accurately.
Determining the health status of the muscle to be tested according to the first, second, fourth and fifth muscle health risk may be accomplished in a number of ways.
In one embodiment, when any one of the first muscle health risk degree, the second muscle health risk degree, the fourth muscle health risk degree and the fifth muscle health risk degree is zero risk, the determination module determines that the third muscle health risk degree of the muscle to be tested is zero risk. If the joint motion characteristic parameter corresponding to one joint or the body part motion characteristic parameter of the body part indicates that there is a muscle problem, it may be that there is a problem with the muscles of other body parts connected to the joint that are not the muscles to be measured, and therefore, if there is a joint motion characteristic parameter corresponding to one joint or a body part motion characteristic parameter of the body part indicating that there is no muscle problem, it indicates that there is no problem with the muscles to be measured that will affect the motion of the joint or the body part.
In yet another embodiment, when none of the first muscle health risk degree, the second muscle health risk degree, the fourth muscle health risk degree, and the fifth muscle health risk degree is zero risk, the third muscle health risk degree of the muscle to be measured is determined as the maximum value among the first muscle health risk degree, the second muscle health risk degree, the fourth muscle health risk degree, and the fifth muscle health risk degree. The greater the value of the health risk, the greater the indicated muscle health risk. When joint action characteristic parameters corresponding to different joints and body part action characteristic parameters of different body parts all show that muscle problems exist, the muscle to be measured which is connected with different joints and supports a first body part close to the body, the body and the mind have muscle problems probably. When the maximum risk value of different muscle health risk degrees is used as the muscle health risk degree of the muscle to be detected, and the health state of the muscle with the problem is improved in a subsequent rehabilitation exercise mode, the exercise with low requirements on muscle capacity is firstly used for guiding muscle rehabilitation exercise training, the muscle with the problem can be guided or treated by exercise rehabilitation in a gradual and gradual mode, and the problem muscle is prevented from being damaged secondarily by excessive rehabilitation exercise or treatment.
In one embodiment, the first body part motion characteristic parameter is a third angle of flexion and extension of the first body part, the second body part motion characteristic parameter is a fourth angle of flexion and extension of the second body part, the first joint motion characteristic parameter is a first angle of flexion and extension of the first joint, and the second joint motion characteristic parameter is a second angle of flexion and extension of the second joint. For example, the first body part is a thigh, the second body part is a waist, the first joint is a hip joint, and the second joint is a knee joint. For another example, the first body part is a lower arm, the second body part is an upper arm, the first joint is an elbow joint, and the second joint is a wrist joint.
A system according to an embodiment of the invention may detect the health status of the muscles of the back thigh of the subject of interest.
The action indicating module indicates the tested user to complete the preset action of squatting, and the tested user only needs to squat to move down the whole gravity center without squatting to the lowest state.
The detection system comprises a first motion sensor, a second motion sensor and a third motion sensor, wherein the first motion sensor is worn on the thigh of a first body part of a detected user, the second motion sensor is worn on the waist of a second body part of the detected user, and the third motion sensor is worn on the calf of a third body part of the detected user. After the user to be tested begins to squat, the first sensor parameter measured by the first motion sensor is a thigh angle value changing along with time, the second sensor parameter measured by the second motion sensor is a waist angle value changing along with time, and the third sensor parameter measured by the third motion sensor is a shank angle value changing along with time.
The data processing module obtains a first joint movement characteristic parameter of the interested object as a first angle of flexion and extension of a hip joint of a first joint according to the thigh angle value of the first sensor parameter and the waist angle value of the second sensor parameter, namely, an included angle between the waist and the thigh in the front forward direction of the interested object. The data processing module obtains a second joint motion characteristic parameter of the interested object as a second angle of the second joint knee joint flexion and extension according to the thigh angle value of the first sensor parameter and the shank angle value of the third sensor, namely an included angle between the thigh and the shank in the front forward direction of the interested object. The data processing module obtains a first body part motion characteristic parameter of the interested object as a third angle of thigh flexion and extension according to the thigh angle value of the first sensor parameter. The data processing module obtains a second body part motion characteristic parameter of the interested object as a fourth angle of the waist bending and extending according to the thigh angle value of the second sensor parameter.
The judgment module compares a first joint motion characteristic parameter corresponding to a first joint, namely a first knee joint flexion and extension angle with a first knee joint flexion and extension angle threshold value and a second knee joint flexion and extension angle threshold value: if the maximum value of the first angle of flexion and extension of the knee joint is larger than the threshold value of the second knee joint flexion and extension angle, determining that the first muscle health risk degree is zero; determining that the first muscle health risk degree is serious if the maximum value of the first angle of flexion and extension of the knee joint is smaller than the threshold value of the first knee joint flexion and extension angle; and if the maximum value of the first knee joint flexion and extension angle is between the first knee joint flexion and extension angle threshold value and the second knee joint flexion and extension angle threshold value, determining that the first muscle health risk degree is medium.
The judging module compares a second motion characteristic parameter corresponding to a second joint, namely a second ankle flexion and extension angle with the first ankle flexion and extension angle threshold value and the second ankle flexion and extension angle threshold value: if the maximum value of the second angle of flexion and extension of the ankle joint is larger than the threshold value of the second angle of flexion and extension of the ankle joint, determining that the second muscle health risk degree is zero; if the maximum value of the second angle of flexion and extension of the ankle joint is smaller than the threshold value of the first ankle joint flexion and extension angle, determining that the second muscle health risk degree is serious; and if the maximum value of the second ankle joint flexion and extension angle is in the middle of the first ankle joint flexion and extension angle threshold value and the second ankle joint flexion and extension angle threshold value, determining that the second muscle health risk degree is medium.
The judging module compares a first body part motion characteristic parameter corresponding to the first body part, namely a third thigh flexion and extension angle with a first thigh flexion and extension angle threshold value and a second thigh flexion and extension angle threshold value: determining that the fourth muscle health risk degree is zero if the maximum value of the third thigh flexion-extension angle is larger than the second thigh flexion-extension angle threshold value; determining that the fourth muscle health risk is severe if the maximum value of the third thigh flexion-extension angle is smaller than the first thigh flexion-extension angle threshold value; and if the maximum value of the third angle of thigh flexion and extension is intermediate between the first thigh flexion and extension angle threshold and the second thigh flexion and extension angle threshold, determining that the fourth muscle health risk is medium.
The judgment module compares the second body part motion characteristic parameter corresponding to the second body part, namely the fourth waist flexion and extension angle with the first waist flexion and extension angle threshold value and the second waist flexion and extension angle threshold value: if the maximum value of the fourth angle of waist flexion and extension is larger than the threshold value of the second waist flexion and extension angle, determining that the fifth muscle health risk degree is zero; determining that the fifth muscle health risk degree is serious if the maximum value of the fourth angle of waist flexion and extension is smaller than the first waist flexion and extension angle threshold value; and if the maximum value of the fourth angle of waist flexion and extension is intermediate between the first waist flexion and extension angle threshold and the second waist flexion and extension angle threshold, determining that the fifth muscle health risk is medium.
When the fourth muscle health risk degree is zero, the judging module determines that the third muscle health risk degree of the back thigh muscle is zero, namely the back thigh muscle is healthy. When the first muscle health risk degree is slight, the second muscle health risk degree is medium, the fourth muscle health risk degree is severe, and the fifth muscle health risk degree is medium, the judgment module determines that the third muscle health risk degree of the muscle on the back side of the thigh is severe.
In one embodiment, the first body part motion characteristic parameter is a third radius of the first body part roll, the second body part motion characteristic parameter is a fourth radius of the second body part roll, the first joint motion characteristic parameter is a first radius of the first joint roll, and the second joint motion characteristic parameter is a second radius of the second joint roll. For example, the first body part is a thigh, the second body part is a waist, the first joint is a hip joint, and the second joint is a knee joint. For another example, the first body part is the forearm, the second body part is the forearm, the first joint is the elbow joint, and the second joint is the wrist joint.
The system according to an embodiment of the invention may detect whether there is a problem with the thigh muscle of the object of interest.
The action indicating module indicates the tested user to complete the preset action of single-leg squatting, namely, the tested user is required to lift one leg off the ground and then squats.
The detection system comprises a first motion sensor, a second motion sensor and a third motion sensor, wherein the first motion sensor is worn on the thigh of a first body part of a detected user, the second motion sensor is worn on the waist of a second body part of the detected user, and the third motion sensor is worn on the calf of a third body part of the detected user. After the tested user begins to squat with one leg, the first sensor parameter measured by the first motion sensor is a thigh angle value changing along with time, the second sensor parameter measured by the second motion sensor is a waist angle value changing along with time, and the third sensor parameter measured by the third motion sensor is a shank angle value changing along with time.
The data processing module obtains a first joint movement characteristic parameter of the interested object according to the thigh angle value of the first sensor parameter and the waist angle value of the second sensor parameter, and the first joint movement characteristic parameter is a first radius of a projection circle of the first joint hip joint shake on the horizontal ground. The data processing module obtains a second joint motion characteristic parameter of the interested object as a second radius of the shake of the second joint knee joint according to the thigh angle value of the first sensor parameter and the shank angle value of the third sensor. The data processing module obtains a first body part motion characteristic parameter of the interested object according to the thigh angle value of the first sensor parameter, and the first body part motion characteristic parameter is a third radius of thigh shaking of the first body part. The data processing module obtains a second body part motion characteristic parameter of the object of interest as a fourth radius of the second body part waist shake according to the waist angle value of the second sensor parameter.
The detection system can obtain sensor calibration data of a waist sensor to hip joint distance, thigh length, shank length, thigh sensor to knee joint distance, shank sensor to knee joint distance and the like of an interested object by enabling a detected user to stand upright, sit down and lie down in the sensor calibration process. The detection system also enables the detection person to manually input calibration data such as hip-joint distance of the waist sensor, thigh length, shank length, thigh-to-knee distance of the thigh sensor, and shank-to-knee distance of the shank sensor of the object of interest. The calibration method of the sensor mentioned herein is known to those skilled in the art and will not be described herein.
The data processing module can obtain the projection position of the hip joint (namely the intersection point of the thigh and the waist) on the horizontal ground according to the calibration data of the sensor and the thigh angle value and the waist angle value measured by the sensor, the hip joint has a plurality of projection positions on the horizontal ground along with the shaking of the body of the interested object in the single-leg squatting process, the gravity centers of the plurality of projection positions are used as the origin, and the distance from the hip joint projection point farthest from the origin to the origin is the first radius of the shaking of the hip joint.
The data processing module can obtain the projection position of knee joint (namely thigh and shank intersection point) on the horizontal ground according to the sensor calibration data and the thigh angle value and shank angle value measured by the sensor, the knee joint has a plurality of projection positions on the horizontal ground along with the shaking of the single-leg squat body of the interested object, the gravity centers of the plurality of projection positions are used as the origin, and the distance from the knee joint projection point farthest from the origin to the origin is the second radius of shaking of the knee joint.
The data processing module can obtain the projection position of the first motion sensor worn on the thigh on the horizontal ground according to the calibration data of the sensor and the thigh angle value measured by the sensor, along with the shaking of the body of the interested object in the single-leg squatting process, the first sensor has a plurality of projection positions on the horizontal ground, the gravity centers of the plurality of projection positions are used as the origin, and the distance from the projection point farthest from the origin to the origin is the third radius of the shaking thigh.
The data processing module can obtain the projection position of the second motion sensor worn on the waist on the horizontal ground according to the calibration data of the sensor and the thigh angle value measured by the sensor, along with the shaking of the body of the interested object in the single-leg squatting process, the second motion sensor has a plurality of projection positions on the horizontal ground, the gravity centers of the plurality of projection positions are used as the origin, and the distance from the projection point farthest from the origin to the origin is the third radius of the shaking waist.
The judgment module compares a first joint motion characteristic parameter corresponding to a first joint, namely a first radius of the shaking hip joint, with a hip joint shaking threshold value: determining the first muscle health risk level as zero if the first radius of the hip joint wobble is less than a hip joint wobble threshold; if the first radius of the hip joint wobble is larger than a hip joint wobble threshold value but smaller than a value 1.5 times of the hip joint wobble threshold value, determining that the first muscle health risk degree is medium; determining the first muscle health risk as severe if the first radius of hip wobble is greater than 1.5 times the hip wobble threshold value.
The judgment module compares a second joint movement characteristic parameter corresponding to a second joint, namely a second radius of the shake of the knee joint, with a knee joint shake threshold value: determining that the second muscle health risk degree is zero if the second radius of the knee joint swing is less than the knee joint swing threshold value; if the second radius of the knee joint swing is larger than the knee joint swing threshold value but smaller than 1.5 times of the knee joint swing threshold value, determining that the second muscle health risk degree is medium; determining the second muscle health risk as severe if the first radius of knee joint wobble is greater than 1.5 times the knee joint wobble threshold value.
The judging module compares a first body part motion characteristic parameter corresponding to a first body part, namely a third radius of thigh shaking, with a thigh shaking threshold value: determining that the fourth muscle health risk is zero if the third radius of thigh tremor is less than the thigh tremor threshold; if the third radius of the thigh shaking is larger than the thigh shaking threshold but smaller than 1.5 times of the thigh shaking threshold, determining that the fourth muscle health risk degree is medium; determining the fourth muscle health risk as severe if the third radius of thigh wobble is greater than 1.5 times the thigh wobble threshold.
The judgment module is with the second health position motion characteristic parameter that corresponds the second health position, and the fourth radius that the waist rocked promptly is rocked the threshold value with the waist and is compared: if the fourth radius of the waist shaking is smaller than the waist shaking threshold value, determining that the fifth muscle health risk degree is zero; if the fourth radius of the waist shaking is larger than the waist shaking threshold value but smaller than a value 1.5 times of the waist shaking threshold value, determining that the fifth muscle health risk degree is medium; determining that the fifth muscle health risk is severe if the fourth radius of the lumbar prominence is greater than 1.5 times the threshold value of the lumbar prominence.
When the fifth muscle health risk degree is zero, the judgment module determines that the third muscle health risk degree of the thigh muscle is zero, namely the thigh muscle is healthy. When the first muscle health risk degree is slight, the second muscle health risk degree is medium, the fourth muscle health risk degree is slight, the fifth muscle health risk degree is serious, and the judgment module determines that the third muscle health risk degree of the thigh muscle is serious.
It is noted that, in this document, relational terms such as "first" and "second", and the like, may be used solely to distinguish one module, entity, parameter, or operation from another module, entity, parameter, or operation without necessarily requiring or implying any actual such relationship or order between such modules, entities, parameters, or operations. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Accordingly, the invention is not to be limited to the embodiments shown herein. It will be appreciated that those skilled in the art can make various changes or modifications to the embodiments without departing from the spirit and scope of the application, and that such changes or modifications fall within the scope of the application.
Claims (11)
1. A system for detecting a health state of a muscle under test of a first body part of a subject of interest, comprising:
an action indication module for indicating the object of interest to complete a predetermined action;
a first motion sensor intended to be placed at the first body part of the object of interest for obtaining first sensor parameters when the object of interest completes the predetermined action;
a second motion sensor to be placed at a second body part of the object of interest for obtaining second sensor parameters when the object of interest performs the predetermined action, the first body part and the second body part being connected by a first joint of the object of interest;
a third motion sensor to be placed at a third body part of the object of interest for obtaining third sensor parameters when the object of interest performs the predetermined action, the first body part and the third body part being connected by a second joint of the object of interest;
a data processing module to:
obtaining a first joint action characteristic parameter corresponding to the first joint according to the first sensor parameter and the second sensor parameter;
obtaining a second joint action characteristic parameter corresponding to the second joint according to the first sensor parameter and the third sensor parameter;
a determination module to:
determining a first muscle health risk degree according to the first joint action characteristic parameter;
determining a second muscle health risk degree according to the second joint action characteristic parameter;
and determining a third muscle health risk degree of the muscle to be detected according to the first muscle health risk degree and the second muscle health risk degree.
2. The system of claim 1, wherein the decision module is further configured to,
and when any one of the first muscle health risk degree and the second muscle health risk degree is zero risk, determining that the third muscle health risk degree is zero risk.
3. The system of claim 1, wherein the decision module is further configured to,
determining the third muscle health risk degree as a maximum of the first muscle health risk degree and the second muscle health risk degree when neither the first muscle health risk degree nor the second muscle health risk degree is zero risk.
4. The system of any one of claims 1 to 3, wherein the first joint motion characteristic parameter is a first angle of flexion and extension of the first joint and the second joint motion characteristic parameter is a second angle of flexion and extension of the second joint.
5. The system of any one of claims 1 to 3, wherein the first joint motion characteristic parameter is a first radius of the first joint wobble and the second joint motion characteristic parameter is a second radius of the second joint wobble.
6. The system of claim 1, wherein the second body part is closer to a center of gravity of the object of interest than the first body part,
the data processing module is further configured to:
obtaining first body part action characteristic parameters of the first body part corresponding to the object of interest according to the first sensor parameters;
obtaining a second body part motion characteristic parameter of the second body part corresponding to the object of interest according to the second sensor parameter;
the determination module is further configured to:
determining a fourth muscle health risk degree according to the first body part action characteristic parameter;
determining a fifth muscle health risk according to the second body part action characteristic parameter;
determining the third muscle health risk from the first muscle health risk, the second muscle health risk, the fourth muscle health risk, and the fifth muscle health risk.
7. The system of claim 6, wherein the decision module is further configured to,
and when any one of the first muscle health risk degree, the second muscle health risk degree, the fourth muscle health risk degree and the fifth muscle health risk degree is zero risk, determining that the third muscle health risk degree is zero risk.
8. The system of claim 6, wherein the decision module is further configured to,
when none of the first muscle health risk degree, the second muscle health risk degree, the fourth muscle health risk degree, and the fifth muscle health risk degree is zero risk, determining that the third muscle health risk degree is the maximum value among the first muscle health risk degree, the second muscle health risk degree, the fourth muscle health risk degree, and the fifth muscle health risk degree.
9. The system of any one of claims 6 to 8, wherein the first body part motion characteristic parameter is a third angle of flexion and extension of the first body part, the second body part motion characteristic parameter is a fourth angle of flexion and extension of the second body part, the first joint motion characteristic parameter is a first angle of flexion and extension of the first joint, and the second joint motion characteristic parameter is a second angle of flexion and extension of the second joint.
10. The system of any one of claims 6 to 8, wherein the first body part motion characteristic parameter is a third radius of the first body part wobble, the second body part motion characteristic parameter is a fourth radius of the second body part wobble, the first joint motion characteristic parameter is a first radius of the first joint wobble, and the second joint motion characteristic parameter is a second radius of the second joint wobble.
11. The system of claim 1, wherein the first motion sensor, the second motion sensor, and the third motion sensor are inertial sensors.
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