CN113362948B - 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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- CN113362948B CN113362948B CN202110572999.7A CN202110572999A CN113362948B CN 113362948 B CN113362948 B CN 113362948B CN 202110572999 A CN202110572999 A CN 202110572999A CN 113362948 B CN113362948 B CN 113362948B
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- 210000003205 muscle Anatomy 0.000 title claims abstract description 309
- 238000012545 processing Methods 0.000 claims abstract description 17
- 238000012360 testing method Methods 0.000 claims abstract description 17
- 238000001514 detection method Methods 0.000 abstract description 14
- 230000000875 corresponding effect Effects 0.000 description 35
- 210000000689 upper leg Anatomy 0.000 description 28
- 210000001624 hip Anatomy 0.000 description 16
- 238000005452 bending Methods 0.000 description 14
- 238000000034 method Methods 0.000 description 11
- 210000002414 leg Anatomy 0.000 description 10
- 230000001133 acceleration Effects 0.000 description 8
- 244000309466 calf Species 0.000 description 8
- 210000004394 hip joint Anatomy 0.000 description 7
- 210000000629 knee joint Anatomy 0.000 description 7
- 241000489861 Maximus Species 0.000 description 6
- 210000003314 quadriceps muscle Anatomy 0.000 description 6
- 230000001276 controlling effect Effects 0.000 description 4
- 210000002683 foot Anatomy 0.000 description 4
- 210000003127 knee Anatomy 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 210000002027 skeletal muscle Anatomy 0.000 description 3
- 210000003489 abdominal muscle Anatomy 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 210000000617 arm Anatomy 0.000 description 1
- 210000000988 bone and bone Anatomy 0.000 description 1
- 210000002310 elbow joint Anatomy 0.000 description 1
- 210000000245 forearm Anatomy 0.000 description 1
- 210000003128 head Anatomy 0.000 description 1
- 230000003387 muscular Effects 0.000 description 1
- 230000036544 posture Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 210000000323 shoulder joint Anatomy 0.000 description 1
- 230000012232 skeletal muscle contraction Effects 0.000 description 1
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
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Abstract
The present application relates to a system for detecting the state of health of a muscle under test of an object of interest. The detection system comprises an action indication module, a motion sensor, a data processing module and a judging module. A motion sensor worn by the object of interest gathers a plurality of sensor parameters when the object of interest completes a predetermined action indicated by the action indication module. The data processing module obtains different action characteristic parameters corresponding to different muscle combinations comprising the muscle to be tested according to the sensor parameters, and the judging module determines muscle health risk degrees respectively corresponding to the different muscle combinations according to the different action characteristic parameters so as to finally determine the muscle health risk degrees of the muscle to be tested.
Description
Technical Field
The present invention relates to the technical field of detecting a muscular problem of an object to be measured by a sensor worn by the object to be measured.
Background
Muscles are composed of muscle tissue that extends throughout the body of a person and has a number of important roles in the person. When the skeleton muscle wraps the skeleton and a human body is hit or bumped by 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 also support the body and help the person maintain various postures. Skeletal muscle is attached to bone, and all movements of the human body are accomplished by skeletal muscle contraction, without skeletal muscle, without movement.
When a problem occurs in the muscles of the human body, a problem may also occur in the movements of the human body. By wearing the motion sensor by a person, the information acquired by the sensor can be used for acquiring the motion related information of the person to judge whether the motion of the person is abnormal, so that the health state of the muscle of the person is judged and the follow-up rehabilitation treatment for the muscle is considered.
Disclosure of Invention
According to one embodiment of the present invention, a system for detecting a state of health of a muscle under test of an object of interest is provided. The detection system comprises an action indication module, at least one motion sensor, a data processing module and a judging module. The action indication module indicates that the object of interest has completed at least one predetermined action. At least one motion sensor intended to be placed on the object of interest and to obtain a plurality of sensor parameters when the object of interest has completed at least one predetermined action. The data processing module obtains a first action characteristic parameter corresponding to a first muscle combination of the object of interest according to at least one first sensor parameter of the plurality of sensor parameters. The data processing module obtains a second action characteristic parameter corresponding to a second muscle combination of the object of interest according to at least one second sensor parameter of the plurality of sensor parameters. The first muscle group and the second muscle group each comprise a muscle to be tested. The judging module determines the first muscle health risk degree according to the first action characteristic parameter and determines the second muscle health risk degree according to the second action characteristic parameter, so that the third muscle health risk degree of the muscle to be detected is determined according to the first muscle health risk degree and the second muscle health risk degree.
Since the completion of a specific action usually requires the participation of more than one muscle to be tested, an action parameter indicating whether the action of the object of interest is abnormal can only reflect whether a muscle group is healthy or not, but cannot absolutely reflect whether the muscle to be tested, which is only a part of the muscle group, is healthy or not. Under the condition that the influence degree of the muscle to be measured on the action characteristic parameters corresponding to the muscle combinations is not known and no other measuring equipment is used for assisting, by measuring different action characteristic parameters corresponding to different muscle combinations (namely the first muscle combination and the second muscle combination), the muscle health risk degree of the muscle to be measured can be more accurately determined by utilizing the different muscle health risk degrees determined according to the different action characteristic parameters to exclude the influence of the muscle to be measured on the object of interest to finish specific actions due to the fact that the different muscle combinations both comprise the muscle to be measured and also respectively comprise the muscle not to be measured, wherein the other muscle combinations are not included.
According to yet another embodiment of the present invention, when any one of the first muscle health risk level and the second muscle health risk level is zero risk, the determination module determines that the muscle health risk level of the muscle under test is zero risk. If the motion characteristic parameter corresponding to one muscle group indicates that there is a muscle problem, it may be that there is a problem with other muscles of the muscle group other than the muscle to be tested, and therefore, when the motion characteristic parameter corresponding to the other muscle group indicates that there is no muscle problem, it may be determined that the muscle to be tested is free of the problem. In this way, false alarm situations, which may occur when detecting muscle problems of the muscle to be measured belonging to a part of the muscle group with the action parameters corresponding to only one muscle group, can be avoided.
According to still another embodiment of the present invention, when the first muscle health risk level and the second muscle health risk level are both different from zero risk, the third muscle health risk level of the muscle to be tested is determined to be the maximum value of the first muscle health risk level and the second muscle health risk level. When different action characteristic parameters corresponding to different muscle combinations all show that the muscle problem exists, the muscle to be detected which is included in the different muscle combinations has a larger probability of the muscle problem. The maximum risk value of stroke of different muscle health risk degrees determined according to different action characteristic parameters is used as the muscle health risk degree of the muscle to be tested, when the health state of the muscle with problems is improved by using rehabilitation exercises in the follow-up process, the muscle with potential problems is trained by using exercises with lower requirements on muscle capacity, the muscle with problems can be protected to the greatest extent, the muscle with problems is guided by progressive exercise rehabilitation, and damage to the muscle with problems caused by rehabilitation exercises with excessive strength 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 invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic structural diagram of a system for detecting a health state of a muscle to be measured of an object of interest according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Fig. 1 is a schematic structural diagram of a system for detecting a health state of a muscle to be measured of an object of interest according to an embodiment of the present invention.
As shown in fig. 1, according to one embodiment of the present invention, a system for detecting a health state of a muscle under test of a subject of interest is provided. The system comprises an action indication module, at least one motion sensor, a data processing module and a judging module.
The object of interest is a user to be tested who needs to detect whether a problem exists in a muscle, and the user to be tested can be an adult or a child, etc. The muscle to be tested is any one or any combination of a certain muscle, a certain position muscle, a certain body part muscle or a certain muscle group on the tested user, such as thigh back side muscle, hip muscle, calf front side muscle, forearm inner side muscle and the like.
The action indication module indicates that the object of interest has completed at least one predetermined action. The predetermined action is an action that the user under test can perform or can attempt to perform, such as squatting with a single leg, stepping forward, or lifting an arm, etc. The action indication module may select a predetermined action from a pre-stored or established database of predetermined actions based on the muscle under test. For example, the muscle to be tested is a hip muscle, and the action indication module selects a predetermined action corresponding to the hip muscle test from the predetermined action database. For another example, the detection system needs to detect the whole body muscles of the user to be detected, and the action indication module sequentially selects different preset actions corresponding to different muscles from the preset action database according to the detection sequence of the whole body muscles. In addition, the selection of the preset actions can also consider the known health state of the tested user, and select preset actions with proper difficulty from a preset action database.
The action indication module may be implemented in a variety of ways. For example, the action indication module may be a display screen that indicates that the object of interest has completed 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, which indicates that the object of interest has completed the predetermined action by means of a voice prompt.
At least one motion sensor to be placed on the object of interest and to obtain a plurality of sensor parameters when the object of interest has completed at least one predetermined action, respectively. The motion sensor is a sensor that can measure motion related sensor parameters, such as inertial sensors, gyroscopes or accelerometers, etc. The sensor parameters include any one or any combination of speed, angle, angular speed, acceleration, angular acceleration, and the like. The plurality of sensor parameters may be obtained in a variety of ways. For example, a plurality of sensor parameters may be obtained by performing a plurality of predetermined actions. For another example, a plurality of sensor parameters may be obtained by a plurality of motion sensors.
When the detection system works, the object of interest needs to wear a plurality of motion sensors at different positions or positions on the body, such as any combination of legs, feet, waist, head, arms and the like. The motion sensor may be placed in a position on the subject of interest in a number of ways, such as by securing the motion sensor to the subject's calf with straps, and, for example, by the subject holding the motion sensor by hand.
The data processing module obtains a first motion characteristic parameter corresponding to a first muscle combination of the object of interest according to at least one first sensor parameter of the plurality of sensor parameters, and obtains a second motion characteristic parameter corresponding to a second muscle combination of the object of interest according to at least one second sensor parameter of the plurality of sensor parameters. The first muscle group and the second muscle group each comprise a muscle to be tested.
The data processing module may be implemented by a processor or an FPGA board. The motion characteristic parameter of the corresponding muscle group is a parameter characterizing the motion characteristics of the corresponding body part of the object of interest controlled by the muscle group. Muscle groups may include any combination of muscle blocks, muscles or muscle groups of different body parts. The motion characteristic parameter is a parameter that can represent a motion state or a result of a certain body part of the object of interest in the process of completing a predetermined motion, so as to reflect a muscle combination health state, for example, a frequency of shaking of the certain body part of the object of interest, for example, qu Shenjiao degrees of the certain body part of the object of interest, for example, a motion acceleration of the certain body part of the object of interest, and the like. Muscles of different degrees of health, and the degree and manner in which the action is completed may also be different.
The muscles comprised in a muscle group each have a non-negligible effect on the value of the corresponding motion characteristic parameter when the object of interest has completed the predetermined motion. There is no or negligible effect on the outcome of the motion parameter, a muscle in a certain location, a muscle in a certain body part or a certain group of muscles does not belong to a specific combination of muscles corresponding to a specific motion characteristic parameter. For example, when the tested user performs push-ups, the movement modes of different parts of the arms are different, the muscle combinations for controlling the movement of the big arms are different from the muscle combinations for controlling the movement of the small arms, the action characteristic parameters for representing the movement of the big arms correspond to the muscle combinations for controlling the movement of the big arms, and the action characteristic parameters for representing the movement of the small arms correspond to the muscle combinations for controlling the movement of the small arms.
The first muscle group and the second muscle group are two different muscle groups, namely the two muscle groups simultaneously comprise the muscle to be tested, and at least one muscle group also comprises a certain muscle which is not included in the other muscle group, a muscle at a certain position, a muscle at a certain body part or a certain muscle group. For example, the first muscle group includes thigh front muscles but does not include hip muscles, and the second muscle group includes hip, waist and thigh front muscles. For another example, the first muscle group includes thigh back side muscles and shank back side muscles, excluding foot muscles, and the second muscle group includes shank back side muscles and foot muscles, excluding thigh back side muscles.
The at least one first sensor parameter may be any one or any combination of speed, angle, angular velocity, acceleration, angular acceleration, etc. The at least one second sensor parameter may be any one or any combination of speed, angle, angular velocity, acceleration, angular acceleration, etc. For example, the at least one first sensor parameter comprises an angle and a speed of a certain body part. As another example, the at least one first sensor parameter comprises acceleration of a body part. For example, the at least one second sensor parameter comprises an angular velocity of one body part and an angular velocity of another body part.
In the process that the object of interest completes the preset action, the sensor parameters obtained by the motion sensor are a plurality of values in a period of measurement time, and the corresponding action characteristic parameters obtained according to the sensor parameters are a plurality of values in the same period of measurement time. The start time of the measurement time may be determined in a number of ways, such as with the time at which the action indication module indicates that the object of interest has completed the predetermined action as the start time, and such as by an operator of the detection system entering the start time. The end time of the measurement time may be determined in a number of ways, such as detecting the time when the predetermined action is completed as the end time, such as entering the end time by an operator of the detection system, such as indicating the time when the object of interest is to complete the next predetermined action as the end time.
The data processing module can calibrate the motion sensor manually or automatically before the motion characteristic parameters corresponding to the muscle combinations are obtained according to the sensor parameters of the motion sensor or before the detection system works, 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 better represented by the sensor parameters. Calibration may be achieved in a number of ways. For example, the vertical, lateral and horizontal directions of the object to be measured can be aligned by allowing the object to stand in a specified direction. For another example, calibration may be based on the obtained sensor parameters and the predetermined actions. The basic calibration method using a motion sensor is known to those skilled in the art and will not be described in detail herein.
The judging module determines the first muscle health risk degree according to the first action characteristic parameter and determines the second muscle health risk degree according to the second action characteristic parameter, so that the third muscle health risk degree of the muscle to be detected is determined according to the first muscle health risk degree and the second muscle health risk degree.
The determining module may be implemented by a processor or an FPGA board. The muscle health risk degree indicates whether or not there is a muscle health risk or the degree or grade of the muscle health risk, and the like. For example, the muscle health risk level may be expressed as 0 and 1, or both, to indicate whether there is a muscle health risk. As another example, the muscle health risk level may be expressed in terms of 1 to 5 weeks or in terms of normal, mild, moderate, worse, severe and dangerous levels or ratings of muscle health risk. For another example, the muscle health risk level may be expressed in terms of a percentage as a degree or grade of the muscle health risk present.
Determining the health risk level based on the motion feature parameters may be accomplished in a variety of ways.
In one embodiment, the maximum value of the motion characteristic parameter may be compared with one or more predetermined motion characteristic parameter threshold values, and the muscle health risk level may be determined according to whether the motion characteristic parameter reaches the motion characteristic threshold value, or according to a proportional relationship between the maximum value of the motion characteristic parameter and the motion characteristic threshold value.
In another embodiment, the minimum value of the motion feature parameter may be compared with one or more predetermined motion feature parameter threshold values, and the muscle health risk level may be determined based on whether the minimum value of the motion feature parameter reaches the motion feature threshold value, or based on a proportional relationship between the minimum value of the motion feature parameter and the motion feature threshold value.
In yet another embodiment, the motion characteristic parameter may be compared to one or more predetermined motion characteristic parameter threshold values, and the muscle health risk level may be determined based on whether the motion characteristic parameter reaches the motion characteristic threshold value, or based on a proportional relationship between the motion characteristic parameter and the motion characteristic threshold value.
The action feature threshold value can be generated according to an action feature parameter database generated by a large number of crowd samples.
When judging muscle health by the motion characteristics of the object of interest, if the muscles affecting the object of interest to complete the predetermined motion effect are not considered, including other muscles than the muscle to be measured, even if a plurality of motion characteristic parameters for one muscle group are measured, it is difficult to judge the health state of the muscle to be measured, which affects only a part of the muscle groups of the object of interest to complete the predetermined motion, by the motion characteristic parameters alone. The invention can help to eliminate the influence of the non-muscle to be tested on the action characteristic parameters by measuring the different action characteristic parameters corresponding to different muscle combinations, and better screen and screen the problem of the muscle to be tested.
Determining the third muscle health risk level of the muscle under test from the first muscle health risk level and the second muscle health risk level may be achieved in a number 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 determining module determines that the third muscle health risk degree of the muscle to be tested is zero risk, that is, there is no health risk. If the motion characteristic parameter corresponding to one of the muscle groups indicates a muscle problem, it may be that there is a problem with other muscles of the muscle groups other than the muscle to be tested, and thus, whenever the motion characteristic parameter corresponding to one of the muscle groups indicates that the muscle group has no muscle problem, it is indicated that the muscle to be tested included in the muscle group has no problem.
In yet another embodiment, when neither the first muscle health risk level nor the second muscle health risk level is zero risk, the third muscle health risk level of the muscle under test is determined to be the maximum of the first muscle health risk level and the second muscle health risk level. The greater the value of the health risk, the greater the indicated muscle health risk. When different action characteristic parameters corresponding to different muscle combinations all show that the muscle problem exists, the muscle to be detected which is included in the different muscle combinations has a larger probability of the muscle problem. The maximum risk value of different muscle health risks determined according to different action characteristic parameters is used as the muscle health risk of the muscle to be tested, and the muscle with problems can be protected to the greatest extent when the health state of the muscle with problems is improved in a follow-up rehabilitation exercise mode.
The at least one first sensor parameter and the at least one second sensor parameter may be obtained in a variety of ways.
In one embodiment, the plurality of motion sensors includes a first motion sensor and a second motion sensor. The first motion sensor is intended to be placed at a first body part of the object of interest for obtaining at least one first sensor parameter and the second motion sensor is intended to be placed at a second body part of the object of interest for obtaining at least one second sensor parameter. The first body part and the second body part are two different parts of the subject of interest. Even if only one preset action is completed, when a plurality of motion sensors are respectively placed at different positions on the object of interest, if the motion states of different positions are controlled by different muscle combinations, whether the muscle to be detected has a problem or not can be detected from multiple aspects, and the accuracy of the detection result is improved. Different sensor parameters are obtained through different sensors, so that action characteristic parameters corresponding to different muscle combinations are obtained, the types of actions required to be completed by a tested user for detection can be reduced, and the testing process is faster and simpler.
In another embodiment, the at least one predetermined action includes a first predetermined action and a second predetermined action. The first predetermined action and the second predetermined action are two different predetermined actions. The plurality of motion sensors obtain at least one first sensor parameter when the object of interest completes a first predetermined action and at least one second sensor parameter when the object of interest completes a second predetermined action. When different muscle combinations are required to be mobilized after different preset actions are completed, whether the muscle to be detected has a problem or not can be detected from multiple angles, and accuracy of detection results is improved. By providing different predetermined actions for the user to be tested, different action characteristic parameters corresponding to different muscle combinations can still be obtained under the condition of limited number of motion sensors.
When the object of interest is wearing a first motion sensor and a second motion sensor at both the first body part and the second body part, respectively, in one embodiment the first motion characteristic parameter comprises a first angle of flexion and extension of said first body part and the second motion characteristic parameter comprises a second angle of flexion and extension of the second body part. For example, the first body part is the thigh and the second body part is the calf. For another example, the first body part is a large arm and the second body part is a small arm. For another example, the first body part is a calf and the second body part is a foot.
A system according to one embodiment of the present invention may detect whether there is a problem with the medial popliteal muscle of the tested user.
The action indication module indicates that the tested user finishes the preset action of forward walking.
The detection system includes a first motion sensor for wearing on a thigh of a first body part of a user under test and a second motion sensor for wearing on a calf of a second body part of the user under test. After the user to be tested starts to walk forward, the first sensor parameter measured by the first motion sensor is thigh angle value which changes with time, and the second sensor parameter measured by the second motion sensor is calf angle value which changes with time.
The data processing module obtains a first movement characteristic parameter corresponding to a first muscle combination comprising biceps femoris and medial popliteal cord muscle according to the thigh angle value of the first sensor parameter obtained by the first movement sensor, wherein the first movement characteristic parameter is a first angle of thigh flexion and extension, namely an included angle of the thigh with the horizontal ground in the advancing direction of the tested user. The data processing module obtains a second motion characteristic parameter corresponding to a second muscle combination comprising the osseous thin muscle and the medial popliteal cord muscle according to the shank angle value of the second sensor parameter obtained by the second motion sensor, wherein the second motion characteristic parameter is a second angle of shank flexion and extension, namely an included angle of the shank between the advancing direction of the tested user and the horizontal ground. The first muscle group does not include the gracilis muscle and the second muscle group does not include the biceps femoris muscle.
The judging module compares a first angle corresponding to a first motion characteristic parameter of the first muscle combination, namely 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: if the maximum value of the thigh flexion and extension angles is larger than the second thigh flexion and extension angle threshold value, determining that the first muscle health risk degree is zero; if the maximum value of the thigh flexion and extension angles is smaller than the first thigh flexion and extension angle threshold value, determining that the first muscle health risk degree is serious; if the maximum thigh flexion and extension angle is intermediate the first thigh flexion and extension angle threshold and the second thigh flexion and extension angle threshold, the first muscle health risk is determined to be intermediate.
The judging module compares a second angle corresponding to a second motion characteristic parameter of a second muscle combination, namely a lower leg flexion and extension angle, with a first lower leg flexion and extension angle threshold value and a second lower leg flexion and extension angle threshold value: if the maximum value of the lower leg flexion and extension angles is larger than the second lower leg flexion and extension angle threshold value, determining that the second muscle health risk degree is zero; if the maximum value of the lower leg flexion and extension angles is smaller than the first lower leg flexion and extension angle threshold value, determining that the second muscle health risk degree is serious; if the maximum value of the lower leg flexion and extension angles is intermediate the first lower leg flexion and extension angle threshold and the second lower leg flexion and extension angle threshold, the second muscle health risk is determined to be intermediate.
When the first muscle health risk is zero, the decision module determines that the third muscle health risk of the medial popliteal cord muscle is zero risk, i.e., the medial popliteal cord muscle is healthy. When the first muscle health risk level is medium and the second muscle health risk level is severe, the decision module determines that the third muscle health risk level of the medial popliteal cord muscle is severe.
When the motion indication module indicates that the object of interest has completed the first predetermined motion and the second predetermined motion, in one embodiment the first motion characteristic parameter comprises a third angle at which a third body part of the object of interest flexes and stretches when the object of interest has completed the first predetermined motion, and the second motion characteristic parameter comprises a fourth angle at which the third body part of the object of interest flexes and stretches when the object of interest has completed the second predetermined motion. The first and second predetermined actions may include a variety of actions. For example, the waist of the third body part of the user under test is bent forward and sideways. For another example, the third body part left leg of the user under test is raised forward and raised backward.
A system according to one embodiment of the present invention may detect whether there is a problem with the longus and the trapezius of the head of the user being tested.
The action indication module indicates the tested user to complete the first preset action of head side swinging and the second preset action of head back tilting.
The detection system includes a motion sensor for wearing on the head of a third body part of the user under test. After the user starts to swing the head, the first sensor parameter measured by the motion sensor is an angle value which changes with time. When the user starts to swing backwards, the second sensor parameter measured by the motion sensor is an angle value which changes with time.
The data processing module obtains a third angle of head lateral flexion and extension, namely an included angle between the direction of head lateral swing of the tested user and the horizontal ground, corresponding to a first motion characteristic parameter of a first muscle combination comprising the head trapezius muscle, the longest head muscle and the outside rectus muscle according to an angle value of a first sensor parameter obtained when the tested user swings the head lateral. The data processing module obtains a second motion characteristic parameter corresponding to a second muscle combination comprising the hemithorn muscle, the longest muscle of the head and the trapper muscle of the head according to an angle value of a second sensor parameter obtained when the tested user swings backwards of the head, namely a fourth angle of backward bending and stretching of the head, namely an included angle between the head and the horizontal ground in the forward direction of the tested user. The first muscle group does not include the hemiacantha of the head and the second muscle group does not include the rectus muscle on the lateral side of the head.
The judging module compares a third angle of the first motion characteristic parameter corresponding to the first muscle combination, namely the head side flexion and extension angle, with a head side Qu Shenjiao degree threshold value: if the minimum value of the head side bending and stretching angle is smaller than the head side Qu Shenjiao degree threshold value, determining that the first muscle health risk degree is zero; if the minimum value of the head side flexion-extension angle is more than twice of the head side Qu Shenjiao degree threshold value, determining that the first muscle health risk degree is serious; if the minimum value of the head lateral flexion-extension angle is greater than the head lateral Qu Shenjiao degree threshold but less than twice the head lateral Qu Shenjiao degree threshold, then the first muscle health risk is determined to be moderate.
The judging module compares the fourth angle of the second motion characteristic parameter corresponding to the second muscle combination, namely the backward bending and stretching angle of the head with a backward bending and stretching angle threshold value of the head: if the minimum value of the backward bending and stretching angle of the head is smaller than the backward bending and stretching angle threshold value of the head, determining that the health risk degree of the second muscle is zero; if the minimum value of the backward bending and stretching angle of the head is more than twice of the threshold value of the backward bending and stretching angle of the head, determining that the second muscle health risk degree is serious; if the minimum value of the head dorsiflexion and extension angle is greater than the head dorsiflexion and extension angle threshold value but less than twice the head dorsiflexion and extension angle threshold value, the second muscle health risk is determined to be medium.
When the second muscle health risk is zero, the decision module determines that the third muscle health risk of the trapezius and the longus capitis is zero risk, i.e. the trapezius and the longus capitis are healthy. When the second muscle health risk level is medium, the first muscle health risk level is severe, and the decision module determines that the third muscle health risk level of the trapezius and the longest cephalic muscle is severe.
When the motion indication module indicates that the object of interest has completed the first predetermined motion and the second predetermined motion, in one embodiment the first motion characteristic parameter comprises a fifth angle at which the fourth body part of the object of interest flexes and stretches when the object of interest has completed the first predetermined motion, and the second motion characteristic parameter comprises a sixth angle at which the fifth body part of the object of interest flexes and stretches when the object of interest has completed the second predetermined motion. For example, the fifth angle of flexion and extension of the elbow joint of the fourth body part is measured when the user performs push-up in the first preset action, and the sixth angle of flexion and extension of the shoulder joint of the fifth body part is measured when the user performs straight backward lifting of the arm in the second preset action.
A system according to one embodiment of the invention may detect whether there is a problem with the gluteus maximus and rectus femoris muscle of the tested user.
The action indication module indicates the tested user to finish the first preset action of 'single leg straight lifting' and the second preset action of 'single leg squatting'.
The detection system comprises a third motion sensor for wearing on the waist of a fourth body part of the tested user, a fourth motion sensor for wearing on the thigh of a fifth body part of the tested user, and a fifth motion sensor for wearing on the calf of a sixth body part of the tested user. When the user to be tested starts to lift up straight one leg, i.e. lifts up one leg without bending the knees, the first sensor parameters comprise the time-varying waist angle value measured by the third sensor and the time-varying thigh angle value measured by the fourth sensor. When the user to be tested starts to squat with one leg, the second sensor parameters include the thigh angle value measured by the fourth sensor over time and the calf angle value measured by the fifth sensor over time.
The data processing module obtains a fifth angle of hip joint flexion and extension, namely an included angle of the waist and the thigh in the front forward direction of the tested user, corresponding to a first muscle combination comprising gluteus maximus, rectus femoris and popliteus, according to a waist angle value and a thigh angle value of a first sensor parameter obtained when the tested user lifts a single leg. The data processing module obtains a fifth angle of knee joint flexion and extension, namely an included angle between the waist and the thigh in the front forward direction of the tested user, according to a thigh angle value and a lower leg angle value of a second sensor parameter obtained when the tested user squats on a single leg, wherein the second movement characteristic parameter corresponds to a second muscle combination comprising gluteus maximus, abdominal muscles and rectus femoris. The first muscle group does not include the abdominal muscle and the second muscle group does not include the popliteal muscle.
The judging module compares a fifth angle of the first motion characteristic parameter corresponding to the first muscle combination, namely the hip joint flexion and extension angle, with a hip joint Qu Shenjiao degree threshold value: if the minimum value of the hip joint bending and stretching angle is smaller than the Qu Shenjiao-degree threshold value of the hip joint, determining that the first muscle health risk degree is zero; if the minimum value of the hip joint bending and stretching angle is 1.5 times greater than the threshold value of Qu Shenjiao degrees of the hip joint, determining that the first muscle health risk degree is serious; if the minimum hip flexion and extension angle is greater than the hip Qu Shenjiao degree threshold but less than 1.5 times the hip Qu Shenjiao degree threshold, the first muscle health risk is determined to be mild.
The judging module compares a sixth angle of the second motion characteristic parameter corresponding to the second muscle combination, namely the knee joint bending and stretching angle, with a Qu Shenjiao-degree threshold value of the knee joint: if the minimum value of the knee joint bending and stretching angle is smaller than the Qu Shenjiao-degree threshold value of the knee joint, determining that the second muscle health risk degree is zero; if the minimum value of the knee joint bending and stretching angle is 1.5 times greater than the Qu Shenjiao degree threshold value of the knee joint, determining that the second muscle health risk degree is serious; if the minimum knee flexion and extension angle is greater than the knee Qu Shenjiao degree threshold but less than 1.5 times the knee Qu Shenjiao degree threshold, then the second muscle health risk is determined to be mild.
When the second muscle health risk level is zero, the determination module determines that the third muscle health risk level of the gluteus maximus and the rectus femoris muscle is zero risk, i.e., the gluteus maximus and the rectus femoris muscle are healthy. When the second muscle health risk level is severe and the first muscle health risk level is mild, the decision module determines that the third muscle health risk level of the gluteus maximus and rectus femoris is severe.
It should be noted that, in this document, relational terms such as "first" and "second", and the like are used solely to distinguish one from another module, entity, parameter or action without necessarily requiring or implying any actual such relationship or order between such modules, entities, parameters or actions. Moreover, 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 phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the 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. Thus, the present invention will not be limited to the embodiments shown herein. It will be appreciated that numerous variations and modifications may be made to the embodiments by those skilled in the art without departing from the spirit and principles of the present application, but that such variations and modifications fall within the scope of the present application.
Claims (9)
1. A system for detecting a state of health of a muscle under test of an object of interest, comprising:
-an action indication module for indicating that the object of interest has completed at least one predetermined action;
-at least one motion sensor to be placed on said object of interest, respectively, for obtaining a plurality of sensor parameters when said object of interest has completed said at least one predetermined action;
-a data processing module for:
-obtaining a first motion characteristic parameter corresponding to a first muscle combination of the object of interest from at least one first sensor parameter of the plurality of sensor parameters, the first muscle combination comprising the muscle to be tested;
-obtaining a second motion characteristic parameter corresponding to a second muscle combination of the object of interest from at least one second sensor parameter of the plurality of sensor parameters, the second muscle combination comprising the muscle to be tested;
-a decision module for:
-determining a first muscle health risk level from the first motion characteristic parameter;
-determining a second muscle health risk level from the second motion characteristic parameter;
-determining a third muscle health risk level of the muscle under test from the first muscle health risk level and the second muscle health risk level.
2. The system of claim 1, wherein the determination module is further configured to,
-determining that the third muscle health risk level is zero risk when either of the first muscle health risk level and the second muscle health risk level is zero risk.
3. The system of claim 1, wherein the determination module is further configured to,
-determining that the third muscle health risk level is the largest of the first and second muscle health risk levels when neither the first nor the second muscle health risk level is zero risk.
4. The system of claim 1, wherein the at least one motion sensor comprises a first motion sensor and a second motion sensor,
-said first motion sensor is intended to be placed at a first body part of said object of interest for obtaining said at least one first sensor parameter;
-the second motion sensor is intended to be placed at a second body part of the object of interest for obtaining the at least one second sensor parameter.
5. The system of claim 4, wherein the first motion characteristic parameter comprises a first angle of flexion and extension of the first body part and the second motion characteristic parameter comprises a second angle of flexion and extension of the second body part.
6. The system of any one of claims 1 to 3, wherein the at least one predetermined action comprises a first predetermined action and a second predetermined action,
the at least one motion sensor is further configured to:
-obtaining the at least one first sensor parameter when the object of interest has completed the first predetermined action;
-obtaining the at least one second sensor parameter when the object of interest completes the second predetermined action.
7. The system of claim 6, wherein the first motion characteristic parameter comprises a third angle at which a third body part of the object of interest flexes and stretches when the object of interest completes the first predetermined motion, and the second motion characteristic parameter comprises a fourth angle at which the third body part flexes and stretches when the object of interest completes the second predetermined motion.
8. The system of claim 6, wherein the first motion characteristic parameter comprises a fifth angle at which a fourth body part of the subject is flexed when the subject completes the first predetermined motion, and the second motion characteristic parameter comprises a sixth angle at which the fifth body part of the subject is flexed when the subject completes the second predetermined motion.
9. The system of claim 1, wherein the at least one motion sensor is an inertial sensor.
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