WO2010013631A1 - Procédé destiné à établir une base de données de tension musculaire, base de données de tension musculaire, et procédé et dispositif de calcul de tension musculaire utilisant une base de données de tension musculaire - Google Patents

Procédé destiné à établir une base de données de tension musculaire, base de données de tension musculaire, et procédé et dispositif de calcul de tension musculaire utilisant une base de données de tension musculaire Download PDF

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WO2010013631A1
WO2010013631A1 PCT/JP2009/063147 JP2009063147W WO2010013631A1 WO 2010013631 A1 WO2010013631 A1 WO 2010013631A1 JP 2009063147 W JP2009063147 W JP 2009063147W WO 2010013631 A1 WO2010013631 A1 WO 2010013631A1
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muscle tension
muscle
joint
data
tension
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Japanese (ja)
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仁彦 中村
克 山根
定裕 高屋
昭彦 村井
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国立大学法人東京大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • 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
    • 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
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing

Definitions

  • the present invention relates to estimation of muscle tension.
  • Non-Patent Document 3 research on the musculoskeletal model used in these studies includes inverse dynamics calculation and simulation using Delp et al.'S musculoskeletal model (Non-Patent Document 3), and research on muscle tension estimation during exercise by Rasmussen et al. 4).
  • Nakamura et al. Constructed a whole body detailed musculoskeletal model and performed high-speed dynamic calculation of somatosensory information in order to perform more precise analysis of somatosensory information such as muscle and tendon tension generated during exercise Patent Document 1).
  • the state of the muscle that drives the whole body is estimated by using inverse kinematics and inverse dynamics calculation for the movement measured by the motion capture system.
  • This whole body detailed musculoskeletal model is constructed from a skeletal model and a musculoskeletal network, and the skeletal model is modeled as a rigid link, muscle, tendon, and ligament as wires that generate tension actively or passively.
  • Yamane et al. Measured the surface myoelectric potential and floor reaction force generated during actual exercise in addition to the motion measurement by the motion capture system, and considered the somatic sensation of the actual human body by considering the muscle tension optimization.
  • the muscle tension is calculated (Patent Document 1, Non-Patent Document 2).
  • Optimization using surface electromyographs by Yamane et al. Solves this redundant problem, and can calculate muscle tension close to the somatosensory information of the actual human body.
  • the method using myoelectric data can obtain a physiologically valid result, there is a problem that it takes time and labor for measurement because it is necessary to attach a large number of electrodes to the subject.
  • the first technical means adopted by the present invention is: A method of constructing a muscle tension database using exercise data acquired for each frame when a subject represented by a musculoskeletal model performs a predetermined exercise and muscle strength of each muscle of the musculoskeletal model,
  • the motion data is specified by joint angle data of each joint of the musculoskeletal model for each frame
  • the muscles that are antagonistic and collaborative to drive each joint of the musculoskeletal model are grouped for each joint to form a muscle group, and the muscle tension ratio of each muscle in each muscle group is obtained using the muscle tension.
  • Generate muscle tension ratio data for each frame By storing the joint angle data and the muscle tension ratio data in association with each frame, a muscle tension database for the predetermined exercise is obtained. This is a method for constructing a muscle tension database.
  • the joint angle data includes a joint angle, a joint angular velocity, and a joint angular acceleration of each joint. Further, only the joint angle and joint angular velocity of each joint may be used as the joint angle data, or a higher-order derivative may be included in the joint angle data.
  • the muscle tension ratio of each muscle is obtained by dividing the muscle tension of each muscle by the sum of the muscle tensions of each muscle group. The muscle tension ratio is not limited to the sum of the muscle tensions of each muscle group divided by the muscle tension of each muscle. In short, muscle tension can be distributed to a plurality of muscles constituting the muscle group. Such a ratio may be used.
  • the stored joint angle data and / or the stored muscle tension ratio data are representative values of a plurality of joint angle data and / or a plurality of muscle tension data. For example, when there are a plurality of joint angle data and muscle tension data acquired based on different motion data, these data may be processed and stored in a database. As a representative value, an average, a median, a trim average, a weighted average, or the like can be used.
  • the joint angle data is acquired by inverse kinematic calculation based on motion data obtained by motion capture of a subject who is performing a predetermined motion.
  • the motion capture system for acquiring motion data is an optical motion capture system in one preferred example, but the motion capture method used for acquiring motion data is not limited to optical.
  • the muscle tension is Myoelectric potential data is acquired by a surface electromyograph attached to a predetermined site of a subject who is performing a predetermined exercise, Calculate the joint torque necessary to realize the measured motion by inverse dynamics calculation, It is acquired by performing optimization calculation using myoelectric potential data and joint torque.
  • the method further includes obtaining reaction force data and using the reaction force data to optimize contact force received from the environment, The joint torque is determined by subtracting the optimized contact force from the generalized force.
  • the muscle tension calculation method includes the first step of optimizing the contact force ⁇ C received from the environment using the obtained reaction force data, the obtained exercise data, and the obtained myoelectric potential data. And a second step of optimizing the muscle tension f using the optimized contact force.
  • the reaction force data is acquired by a force sensor.
  • the reaction force data used is floor reaction force data
  • the floor reaction force data is acquired by a force plate.
  • the floor reaction force data may be acquired by a force sensor attached to the back side of the subject's foot.
  • movement data, this floor reaction force data, and this myoelectric potential data are measured simultaneously.
  • the second technical means adopted by the present invention is: A muscle tension database that associates exercise data with muscle tension ratio data,
  • the motion data is specified for each frame by joint angle data of each joint of the musculoskeletal model,
  • the muscle tension ratio data is obtained by grouping muscles in an antagonistic / cooperative relationship that drive each joint of the musculoskeletal model for each joint to form a muscle group, and for each frame, muscle tension of each muscle in each muscle group. Generated by calculating the ratio,
  • the joint angle data and the muscle tension ratio data are stored in association with each frame, A muscle tension database.
  • the muscle tension ratio of each muscle is obtained by dividing the muscle tension of each muscle by the sum of the muscle tensions of each muscle group.
  • the stored joint angle data and / or the stored muscle tension ratio data are representative values of a plurality of joint angle data and / or a plurality of muscle tension data.
  • the third technical means adopted by the present invention is: A muscle tension acquisition method using the muscle tension database, Obtaining joint angle data of each joint of the musculoskeletal model for each frame from the motion data of the subject expressed by the musculoskeletal model; Inputting joint angle data corresponding to one frame or a plurality of frames into a muscle tension database; Searching joint angle data close to the input joint angle data, and outputting muscle tension ratio data corresponding to one frame or a plurality of frames associated with the searched joint angle data from the muscle tension database; Estimate muscle tension by performing optimization calculation using the output muscle tension ratio data, joint torque calculated by calculating inverse dynamics of the exercise data, This is a method for acquiring muscle tension.
  • a muscle tension acquisition device comprising the muscle tension database, Means for obtaining joint angle data of each joint of the musculoskeletal model for each frame from the motion data of the subject expressed by the musculoskeletal model; Means for inputting joint angle data corresponding to one frame or a plurality of frames into a muscle tension database; Means for searching joint angle data close to the input joint angle data, and outputting muscle tension ratio data corresponding to one frame or a plurality of frames corresponding to the searched joint angle data from the muscle tension database; Means for estimating muscle tension by performing optimization calculation using the output muscle tension ratio data, joint torque calculated by calculating inverse dynamics of the motion data;
  • An apparatus for acquiring muscle tension comprising:
  • a feature of the present invention is that by grasping the relationship between antagonistic muscles / cooperating muscles that drive joints as a ratio of muscle tension, muscles that are antagonistic / cooperating relationships that drive one joint are grouped.
  • the muscle tension ratio of each muscle is associated with the exercise data. Therefore, the present invention is characterized in that the muscle tension ratio data is used in the muscle tension optimization calculation in the inverse dynamics calculation of the musculoskeletal model, “A method for obtaining muscle tension by performing inverse dynamics calculation of a musculoskeletal model, The muscle force is obtained by optimizing the contact force ⁇ C and muscle tension f received from the environment by providing reaction force data, motion data, and muscle tension ratio data associated with the motion data.
  • the acquisition method of muscle tension is.
  • ⁇ G is the generalization force
  • J is the Jacobian of the muscle / tendon / ligament
  • J C is the Jacobian of the contact point.
  • ⁇ G is the generalization force
  • J is the Jacobian of the muscle / tendon / ligament
  • J C is the Jacobian of the contact point.
  • ⁇ g joint torque
  • J Jacobian from joint angle to muscle, tendon, ligament length
  • f muscle tension
  • K g a matrix that maps the muscle tension f to the muscle tension of each joint (each muscle group)
  • K fd f the difference between the muscle tension f and the muscle tension f distributed within the muscle group using the muscle tension ratio
  • K 1 weight for joint torque
  • K 2 weight for muscle tension ratio
  • muscle tension close to the muscle tension actually generated in the human body only by noninvasive motion measurement (motion data obtained from motion capture). Can be calculated. Therefore, muscle tension distribution calculation equivalent to that using surface myoelectric potential can be performed without using a surface myoelectric meter.
  • Musculoskeletal model A detailed whole body musculoskeletal model used in the embodiment of the present invention will be described. As shown in FIG. 1, the designed detailed human body model is composed of a skeletal rigid body model grouped with appropriate fineness and a muscle / tendon / ligament wire model stretched on the skeleton.
  • the skeletal model consists of 206 bones throughout the body. Of these, the skull, hand, and toe are treated as a single rigid body, and the model is composed of a total of 53 links. Between each link is a spherical 3 degrees of freedom joint, except for the tarsal bone-toe toe rotation 1 free joint and the first thoracic vertebra 6 breast joint.
  • the skeletal model has a total of 155 degrees of freedom, adding 6 degrees of freedom for the entire translational rotation.
  • Muscles, tendons, and ligaments are modeled as wires that pass through the start point, end point, and waypoint at each link. Muscles, tendons, and ligaments have the following properties. Muscle: A wire that actively generates tension. Tendon: A passively tensioning wire that connects to muscles and transmits muscle tension to bone. Ligaments: Passive tension wires that connect bones and constrain their relative movement. Differences in muscle, tendon, and ligament functions are modeled as follows. A simple part consisting of a series connection of muscles and tendons is represented by a single muscle wire.
  • tendons such as the upper arm bilateral muscle branch and the branched tendons connect to different bones. Since the start point, end point, and waypoint of the wire are all fixed to the link, a virtual link is placed at this branch point.
  • the virtual link has no mass but transmits tension. The virtual link can move freely so that the force and moment are zero. Wide muscles such as the great pectoral muscle and latissimus dorsi are expressed by a plurality of parallel muscle wires.
  • Such a musculoskeletal model is also described in Patent Document 1, Non-Patent Document 1, and Non-Patent Document 2, and these documents can be referred to.
  • the above-mentioned musculoskeletal model is merely an example, and the musculoskeletal model that can be applied to the present invention is not limited to these.
  • the device for acquiring muscle tension includes a plurality of imaging means (camera) for imaging a subject to which a marker is attached, a floor reaction force measuring means (force plate), and an electromyograph means (myoelectric meter).
  • the computer apparatus includes an arithmetic processing unit that performs various calculations, an input unit, an output unit, a display unit, and a storage unit that stores various data.
  • motion capture data exercise data
  • myoelectric potential myoelectric potential
  • floor reaction force are simultaneously measured and used for optimizing muscle strength, thereby obtaining appropriate muscle strength both mechanically and physiologically.
  • muscle tension is calculated as follows. (1) The motion of the subject is measured by the motion capture system, and time-series data of the three-dimensional position of the marker is obtained. (2) The motion information including the joint angle, the joint angular velocity, and the joint angular acceleration is calculated from the three-dimensional position of the marker by inverse kinematics calculation. (3) The joint torque required to realize the motion is calculated by inverse dynamics calculation using the Newton oiler method or the like. (4) The joint torque obtained in (3) is mapped to the floor reaction force and the tension of the muscle, tendon, and ligament using the relationship between the muscle, tendon, ligament length change obtained from the joint angle and each joint angular velocity.
  • inverse dynamics the tension of muscles, tendons, and ligaments that realize the movement is obtained based on the movement data obtained by the movement measurement.
  • the flow of inverse dynamics calculation is as follows: 1. Calculation of joint torque by inverse dynamics of rigid link system; 2. Calculation of Jacobian for wire length joint value; The joint torque is converted into wire tension. Details will be described below.
  • the inverse dynamics calculation of the rigid link system it is possible to calculate the joint torque ⁇ g necessary for realizing the motion in the skeleton model.
  • muscle, tendon, and ligament tension f equivalent to ⁇ g is obtained by using Jacobian J of muscle, tendon, and ligament length l for joint angle ⁇ g . It is expressed.
  • Jacobian J Li for rheumatoid value of the length l i of wire i is the time derivative of l i, is a matrix that relates the joint velocities according to the following equation.
  • This J Li is calculated by the following procedure.
  • J Li, j be the Jacobian related to the joint velocity of i, j
  • J Li is the sum of J Li, j , that is, It is expressed.
  • J pi, j ⁇ p i, j / ⁇ G , that is, Jacobian related to ⁇ G of ⁇ p i, j , “DE Orin and WW Schrader. Efficient computation of the jacobian for robot manipulators. Inter-national Journal of Robotics Research, Vol. 3, No. 4, pp. 66.75, 1984 ”. J Li can be calculated from the sum of J Li, j thus obtained. Further, J Li is obtained by arranging J Li of all wires in the row direction.
  • Patent Document 1 and Non-Patent Document 2 propose a method of performing optimization using an evaluation function that takes into account the surface myoelectric potential measured during actual exercise (see FIG. 2). . It will be understood by those skilled in the art that several methods have been proposed as optimization calculations used for muscle tension calculation, and optimization calculations that can be applied to the present invention are those described in this specification. It is not limited to.
  • Non-Patent Document 2 Optimizing muscle tension with the following flow (1) When measuring exercise, attach a surface myoelectric meter to the subject and install a floor reaction force meter. Measure potential and floor reaction force. (2) The muscle tension is calculated from the surface myoelectric potential using a physiological muscle model. (3) Using the sum of the difference from the muscle tension calculated in (2) and the error of Equation 2.1 as an evaluation function, the muscle tension is optimized to minimize this.
  • a method for calculating muscle tension from surface myoelectric potential will be described.
  • Stroeve's muscle model which formulates Hill and Wilkie's muscle models.
  • the IEMG obtained by integrating the measurement value from the electromyograph with a certain time width represents the activity u of the motor nerve.
  • the relationship between u and muscle activity a is expressed by the following equation.
  • T is a parameter representing a time delay.
  • the relationship between a and muscle tension f is expressed by the following equation.
  • F max is a maximum muscle tension
  • F l (l) and F v (l (dot)) are functions representing the relationship between normalized muscle strength, muscle length, and muscle length change rate, respectively.
  • F l (l) is approximated by a Gaussian function of the following equation.
  • F v (l (dot)) is approximated by the following equation.
  • K l , V sh , V shl , and V ml are constants, and values indicated by Stroeve are used or identified based on motion capture data.
  • the muscle tension optimization method using mathematical programming is shown.
  • an optimization method using linear programming (see Non-Patent Document 1) is shown. Optimization is performed as follows from the muscle tension value f * calculated from the measurement obtained in Equation 2.11 and the previous bar and the matrix K F that corresponds to the measurement value.
  • ⁇ , ⁇ f max , and f that minimize Z are obtained.
  • a T tau is a T max, a T f all elements are positive constant vector.
  • the muscle tension that is appropriate mechanically can be calculated.
  • Jacobian J the relationship between the generalization force, the tension vector f of muscle, tendon, and ligament and the contact force received from the environment is expressed as follows.
  • ⁇ Ci is the contact force at the contact point i
  • J Ci is the Jacobian at the contact point i.
  • Jacobian J C of the contact point is defined by the following equation.
  • pc is a vector or parameter representing the position / posture of the contact point.
  • the inverse dynamics calculation of the musculoskeletal model is a problem of obtaining f and ⁇ C satisfying the formula (2.20) from ⁇ G.
  • the number of muscles is larger than the number of degrees of freedom, so f is not uniquely determined.
  • f is not uniquely determined.
  • two or more links are in contact with the environment or the like, it becomes an irregularity problem. In order to determine these values, it is necessary to perform optimization by some kind of evaluation function.
  • the generalized force ⁇ G includes a six-axis force that acts on the waist link that is not actually driven. Moreover, the force corresponding to this cannot be generated by the muscular force which is an internal force. Therefore, this is substituted with contact force. If the line corresponding to the 6 degrees of freedom of the waist link is taken out from the formula (2.20), the following formula is obtained.
  • E hip is a matrix for extracting necessary rows. A unique solution exists when there is one link in contact, but it becomes an indefinite problem when there are two or more links.
  • ⁇ * C is a measured contact force
  • K C is a matrix for converting the contact force acting on each link into a coordinate system of the measured contact force. For example, if multiple links are on one force sensor, the total floor reaction force acting on those links is measured, converted by K C.
  • the contact force needs to satisfy certain conditions regarding a normal force, a pressure center point, a friction force, and the like.
  • the force in the normal direction is dealt with by including the following inequality constraint condition.
  • E vert is a matrix for extracting a normal direction component of ⁇ C.
  • Other conditions are approximately considered by including the following expression in the evaluation function.
  • Step 1 Contact force optimization (secondary programming)
  • Step 2 Optimizing muscle tension
  • Step 2 Since Step 2 has already been described, Step 1 will be described here.
  • w H and w C are constant weights.
  • the first term of the evaluation function has the effect of reducing the error in equation (2.21).
  • the correlation between exercise and muscle tension ratio was verified. Specifically, in the same operation, the correlation between the motion and the muscle tension ratio between different speeds and different subjects is verified using the muscle tension data obtained based on the measurement.
  • the measurement of the same movement with different subjects and movement speed will be described.
  • the motions of three subjects were measured using a motion capture system and a surface electromyograph.
  • the measurement patterns are “slow walking ⁇ 3”, “ordinary walking ⁇ 3”, “fast walking ⁇ 3”, and “jogging ⁇ 3”.
  • the surface myoelectric potential was measured by selecting the following eight muscles that act as main and antagonist muscles during leg movement.
  • the muscle tension ratio of each joint is calculated.
  • Biarticular muscles belong to a plurality of groups (that is, one muscle may belong to a plurality of muscle groups).
  • Tables 2 and 3 show the joint groups to which each muscle belongs.
  • the muscle tension ratio at each frame t in each group is calculated.
  • muscles having an antagonistic / cooperative relationship are grouped to form a muscle group for each joint.
  • six muscle groups corresponding to the six joints of the hip joint, the knee joint, and the ankle joint (left and right) are formed, and the muscle tension ratio of each frame t is calculated for each muscle group.
  • the muscle tension ratio of each muscle is obtained by dividing the muscle tension of each muscle by the sum of the muscle tensions of each muscle group (see formula (3.1)).
  • N gruop is the number of groups, and T is the number of frames for which measurement was performed.
  • one period of motion is extracted from each data and normalized in the time direction.
  • one cycle is defined from the moment when the right foot tip part leaves the ground to the next time the right foot tip leaves the ground when the actual walking or jogging operation is observed.
  • One cycle of walking motion is 5 [ms / frame] and corresponds to around 200 frames. Regardless of the speed, scaling was performed so that the motion for one cycle was 200 frames.
  • the straight femoral muscle and the gluteus maxims were selected and compared to see how much the muscle tension ratio is correlated in the same movement.
  • the muscle tension ratio in the groups (knee) and (hip) was given in order to verify how the biarticular muscles should be grouped when constructing the database.
  • the correlation coefficient was used for comparison.
  • [C] Construction of muscle tension database and optimization using muscle tension database As described above, it is confirmed that the muscle tension ratio is correlated between different subjects and different speeds in the same type of exercise (for example, walking). It was done. Therefore, it is possible to apply a detailed muscle tension ratio using surface myoelectric potential or the like to new data of the same type. Therefore, a database for associating muscle tension ratio data and exercise data is constructed for each type of exercise. By using the muscle tension ratio obtained from the database for optimization of muscle tension for new exercise data, it is possible to obtain a muscle tension close to the muscle tension generated in the actual human body from the exercise data alone. The method is shown below.
  • Muscle tension is calculated by optimization using the surface electromyogram described in the previous chapter. (1) The motion is measured using the motion capture, the surface electromyograph, and the floor reaction force meter at the same time. (2) Calculate motion data by inverse kinematics calculation. (3) Calculate the joint torque necessary to realize the measured motion by inverse dynamics calculation. In calculating the joint torque, consider the contact force tau C received from the environment (see the calculation of the above-mentioned .tau. 'G). (4) The calculated joint torque is optimized using the surface myoelectric potential, and the muscle tension is calculated. For the optimization, the above description can be used.
  • joint angular velocities and joint angular accelerations are obtained from all joint angles ⁇ k [t] obtained at step t2 obtained in step (2), and joints are obtained from the joint angles, joint angular velocities, and joint angular accelerations.
  • Angular data m k [t] is created.
  • ⁇ t is a measurement time per frame.
  • muscle tension ratio data G i is calculated from equation 3.1, 3.2, 3.3.
  • all joints correspond to a set of muscle tension ratios of muscle groups that drive the joints (which may be collectively expressed as a vector).
  • the motion data is obtained by arranging frame data (posture data in the frame t) in time series.
  • the posture data in the frame t is based on joint angle data (joint angle, joint angular velocity, joint angular acceleration) of each joint. Identified.
  • the muscle tension ratio data of each joint can be associated with the joint angle data of each joint, and these are stored as a set. If the joint angle data in the frame t is specified, the muscle tension ratio data that is paired with the joint angle data is also specified.
  • a database is created for each exercise (walking, jogging, etc.).
  • Muscle tension ratio data (G 11 , G 12 ,...) Relating to all joints (each muscle group) is stored corresponding to the time series data M 1 of the joint angle data of all joints.
  • the database corresponding to the input motion is calculated by calculating the likelihood that the input motion is generated from a statistical model (Hidden Markov Model) representing a certain type of motion in the database. You can choose.
  • the correspondence between the joint angle data and the muscle tension ratio data can be further saved in various ways.
  • a set of many joint angles, speeds, accelerations, and muscle tension ratios for each frame is stored flat, and when using a database, the joint angles, speeds, and accelerations match best.
  • the muscle tension ratio of the frame to be used can be adopted. In this case, the time interval of each frame does not need to be constant.
  • the correspondence between the joint angle data and the muscle tension ratio data may be a hierarchical database structure having a tree structure (typically a binary tree structure).
  • the top layer of the tree structure is one node that includes all frames of a plurality of motion data, and each frame of motion data is included in one node of each hierarchy of the tree structure
  • each node includes a frame having a state quantity (joint angle / velocity / acceleration) closer to the lower layer from the upper layer.
  • a representative value (average, median, trimmed average, weighted average, etc.) of each node based on the state quantities of a plurality of frames included in each node is obtained.
  • the representative values (average, median, trim average, weighted average, etc.) of the muscle tension ratio of frames entering the same node are obtained. In this case, it is desirable that the time interval of each frame is constant.
  • Muscle tension is calculated by the following method using the database constructed in the previous section. The overall flow is shown in FIG. (1) Capture data is obtained by motion capture. (2) The joint angle, joint angular velocity, and joint angular acceleration are calculated by inverse kinematics calculation. (3) Calculate the joint torque necessary to realize the measured motion from the inverse dynamics calculation. (4) The joint angle, joint angular velocity, and joint angular acceleration are input to the database. The database searches the data closest to the input exercise and outputs the muscle tension ratio G i ′. (5) The muscle tension is optimized using the muscle tension ratio G i ′ obtained from the database.
  • joint angle data M ′ is obtained as in the previous section.
  • M k of the same type of motion as that of the new motion data is searched from the database, and k and s that minimize
  • at each time t are obtained.
  • m ′ [t] is joint angle data at time t of M ′.
  • k corresponds to the type of motion and s corresponds to the frame number.
  • the search for k, s depends on the type of database used. For example, if a database is created for each type of exercise and the database to be used is determined in advance, the distance from the joint angle data of the frame t of the input exercise data is the minimum. A frame s having joint angle data is obtained. Even when the database includes a plurality of types of motion data, the frame s having the joint angle data that minimizes the distance from the joint angle data of the frame t of the input motion data is obtained. For example, if a database is created for each type of exercise and the database to be used is unknown, it is input from a statistical model (hidden Markov model) representing a type of exercise in the database.
  • a statistical model hidden Markov model
  • the motion k corresponding to the input motion can be selected by calculating the likelihood that the generated motion is generated. Then, the frame s having the joint angle data that minimizes the distance from the joint angle data of the frame t of the motion data input from the frames included in the motion k is obtained.
  • D i ⁇ R Ni ⁇ Ni is created from the muscle tension ratio d i ⁇ R Ni of each joint obtained from the database.
  • N i is the number of muscles belonging to group i.
  • D i f i is Group muscle tension muscle tension f i partitioned within the group using muscle tension ratio d i of i and muscle tension f i.
  • a matrix K fd that summarizes D i (difference only in group i) for all groups is: It becomes.
  • N element is the number of all lines.
  • the quadratic programming is solved from Jacobian J and K fd mapping from generalized coordinates to muscles, tendons, and ligament lengths as follows.
  • ⁇ g joint torque
  • J Jacobian from joint angle to muscle, tendon, ligament length
  • f muscle tension
  • K g a matrix that maps the muscle tension f to the muscle tension of each joint (each muscle group)
  • K fd f the difference between the muscle tension f and the muscle tension f distributed within the muscle group using the muscle tension ratio
  • K 1 weight for joint torque
  • K 2 weight for muscle tension ratio
  • physiologically valid muscle tension can be estimated only from motion data that can be measured in a non-contact and non-invasive manner, so that the application range of the musculoskeletal model is expanded.
  • non-contact measurement is indispensable for the robot to estimate the internal state of the human in the human communication of the robot (conventional myoelectric data required a contact electrode).
  • FIG. 4 is a view similar to FIG. 3 and showing a muscle tension database structure according to the present invention. It is a figure which shows muscle tension calculation using the muscle tension database which concerns on this invention.

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  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

L'invention porte sur l’établissement d'une base de données destinée à calculer la tension musculaire physiologiquement appropriée uniquement au moyen de données de mouvement déterminées de manière invasive. L'invention porte sur un procédé destiné à établir une base de données de tension musculaire à l'aide de données musculaires acquises pour chaque image lorsqu'un sujet représenté par un squelette musculaire modélisé effectue un mouvement prédéterminé et par les tensions musculaires du squelette musculaire modélisé. Les données de mouvement sont spécifiées par des données d'angle d'articulation relatives à chaque articulation du squelette musculaire modélisé pour chaque image. Le procédé d’établissement d’une base de données de tension articulaire consiste à déterminer un groupe de muscles en relation antagoniste/coopérative entraînant chacune des articulations du squelette musculaire modélisé; à déterminer le rapport de tension musculaire de chacun des muscles formant chaque groupe de muscles par des tensions musculaires; à créer des données de rapport de tension musculaire pour chaque image; à associer les données d'angle d'articulation aux données de rapport de tension musculaire pour chaque image; à stocker les données d'angle d'articulation et de rapport de tension musculaire associées aux données d'angle d'articulation.
PCT/JP2009/063147 2008-07-27 2009-07-23 Procédé destiné à établir une base de données de tension musculaire, base de données de tension musculaire, et procédé et dispositif de calcul de tension musculaire utilisant une base de données de tension musculaire WO2010013631A1 (fr)

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CN113100789B (zh) * 2021-04-16 2022-10-21 西北工业大学 一种膝关节内外侧受力实时分析系统
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