CN116636862A - Ankle joint stiffness estimation method and system based on musculoskeletal anatomy statistical model - Google Patents

Ankle joint stiffness estimation method and system based on musculoskeletal anatomy statistical model Download PDF

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CN116636862A
CN116636862A CN202310573357.8A CN202310573357A CN116636862A CN 116636862 A CN116636862 A CN 116636862A CN 202310573357 A CN202310573357 A CN 202310573357A CN 116636862 A CN116636862 A CN 116636862A
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muscle
stiffness
tendon
gastrocnemius
soleus
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张琴
陈豫生
张键
熊蔡华
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Huazhong University of Science and Technology
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Huazhong University of Science and Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/10—Pre-processing; Data cleansing
    • 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
    • A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
    • A61B5/1071—Measuring physical dimensions, e.g. size of the entire body or parts thereof measuring angles, e.g. using goniometers
    • 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
    • A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
    • A61B5/1072—Measuring physical dimensions, e.g. size of the entire body or parts thereof measuring distances on the body, e.g. measuring length, height or thickness
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/389—Electromyography [EMG]
    • 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
    • A61B5/4538—Evaluating a particular part of the muscoloskeletal system or a particular medical condition
    • A61B5/4585—Evaluating the knee
    • 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
    • A61B5/4538—Evaluating a particular part of the muscoloskeletal system or a particular medical condition
    • A61B5/4595—Evaluating the ankle
    • 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
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
    • 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
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Abstract

The invention provides an ankle joint rigidity estimation method and system based on a skeletal muscle anatomical statistical model, which belong to the field of human body mechanical impedance measurement, and the method comprises the following steps: inputting the preprocessed surface electromyographic signals into a muscle activation kinetic model to calculate muscle activation signals; inputting human body limb characteristic parameters into a musculoskeletal statistical model to obtain microscopic parameters of a muscle structure; according to the muscle activation signals and the microcosmic parameters of the muscle structure, calculating to obtain the muscle force and the muscle tendon unit force under the specific muscle activation level through a muscle force solving model; according to the muscle force and the microscopic parameters of the muscle structure, calculating the rigidity of the muscle tendon unit through a muscle tendon unit rigidity model; and calculating the joint rigidity through a muscle tendon to joint mapping model according to the muscle tendon unit force, the muscle structure microcosmic parameters and the rigidity of the muscle tendon unit. Compared with the traditional rigidity identification and rigidity estimation method, the method is more convenient and quick, and has high precision.

Description

Ankle joint stiffness estimation method and system based on musculoskeletal anatomy statistical model
Technical Field
The invention belongs to the field of mechanical impedance measurement, and particularly relates to an ankle joint rigidity estimation method and system based on a skeletal muscle anatomic statistical model.
Background
In the existing human body rigidity research, two methods are generally adopted to obtain the rigidity of the ankle joint: one is a stiffness identification method, and the other is a stiffness estimation method. Although the ankle joint rigidity identification method can obtain the characteristics of the human ankle joint rigidity under multiple postures, the identification generally needs to develop a special identification device, and the identification itself needs a certain hardware basis for obtaining the rigidity, and the body position of a subject is generally single and is difficult to expand to other sports scenes due to the limitation of the identification device, so that the rigidity identification method has a large limitation in practical engineering application.
With the development of computer technology in recent years, a data driving model with a prediction function, which is built through a neural network, is increasingly used in engineering application, and although the data driving model has the advantages of simple building process and excellent estimation precision, the use of the black box model is unusual because the joint rigidity is difficult to measure in the estimation application of joint rigidity, the model has the limitations of single use scene, unknown parameter influence effect and the like. Analysis of published rigidity estimation research documents can find that the main current thought is to build a musculoskeletal model according to a rigidity generation mechanism to realize the prediction of the rigidity of joints of a human body. However, it is not difficult to find that the musculoskeletal model accords with the neural transmission principle and the human anatomy structure and has advantages in terms of biological interpretation and universality of the model, but the estimation accuracy of the model is lower and modeling difficulty is higher because the number of submodels forming the estimation model is large and the increase of intermediate variables can cause the amplification of estimation errors.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide an ankle joint rigidity estimation method and system based on a musculoskeletal anatomy statistical model, and aims to solve the problems that the modeling difficulty of the existing model is high, the estimation error is amplified due to the increase of intermediate variables, and the estimation precision of the model is low.
To achieve the above object, in one aspect, the present invention provides an ankle joint stiffness estimation method based on a musculoskeletal anatomy statistical model, comprising the steps of:
s1: measuring the characteristic parameters of the limbs of the human body and the surface electromyographic signals; wherein, human limb characteristic parameters include: ankle angle, knee angle, height and weight; the surface electromyographic signals include: surface electromyographic signals of the tibialis anterior, medial gastrocnemius, lateral gastrocnemius and soleus muscles of the respective joints are driven;
s2: performing high-pass filtering on the surface electromyographic signals from all muscles to remove motion artifacts, and performing full-wave rectification and normalization to complete the preprocessing process of the electromyographic signals; inputting the preprocessed surface electromyographic signals into a muscle activation kinetic model to calculate each muscle activation signal;
s3: inputting human body limb characteristic parameters into a musculoskeletal statistical model, and acquiring microscopic parameters of a muscle structure through the musculoskeletal statistical model;
s4: according to the muscle activation signals and the microcosmic parameters of the muscle structures, calculating the muscle force and the muscle tendon unit force of each muscle at a specific muscle activation level through a muscle force solving model;
s5: according to the micro parameters of the muscle force and the muscle structure, calculating the rigidity of each muscle tendon unit through a muscle tendon unit rigidity model;
s6: according to the muscle force, the microcosmic parameters of the muscle structure and the rigidity of the muscle tendon units, the joint rigidity of the ankle joint driven by the tibialis anterior, the medial gastrocnemius, the lateral gastrocnemius and the soleus is calculated through a muscle tendon-joint mapping model;
wherein the musculoskeletal anatomical statistical model comprises: a muscle force solving model, a musculoskeletal statistics model, a muscle activation dynamics model, a muscle tendon unit stiffness model, and a muscle tendon-joint mapping model.
Further preferably, the musculoskeletal statistical model is:
L shank ＝HT(0.247+Π F (sex)×0.01)
L thigh ＝HT(0.232+Π F (sex)×0.017)
(α o ) sol ＝28.3π/180,(α o ) mg ＝9.9π/180,(α o ) lg ＝12π/180,(α o ) ta ＝9.6π/180
wherein ,
wherein ,
wherein HT is the height of the subject; pi (II) F (sex) is a sex parameter of the subject, pi when the subject is male F (sex) is 0, pi when the subject is female F (sex) is 1; l (L) thigh Length of thigh of the subject; l (L) shank Length of the subject's lower leg;optimal muscle fiber length ++representing soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively>(α o ) sol 、(α o ) mg 、(α o ) lg and (αo ) ta Optimal feathered angles of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; alpha SOL 、α MG 、α LG and αTA The feather angles representing the soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively; /> Muscle tendon unit resting length of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively +.> SOL L MT 、 MG L MT 、 LG L MT 、 TA L MT Muscle tendon unit length L of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively MT ，/>To an initial angle theta with the ankle joint 0 A function related to the angular change Δθ; r is R SOL 、R MG 、R LG 、R TA Muscle moment arms R, respectively soleus, medial gastrocnemius, lateral gastrocnemius and tibialis anterior->Moment arm slopes of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively +.>K T Is tendon stiffness; />Normalized tendon stiffness; f (f) l t 、f l s 、f l p W is the width of the muscle; a is a muscle activation signal; x is x 1 ，x 2 ，x 3 ，y 1 A mathematical parameter; calculating muscle microcosmic parameters including resting length of muscle tendon unit according to human body limb parameters>Optimal myofiber length->Optimal feather angle alpha of muscle 0 The feather angle alpha of the muscle, the moment arm R of the muscle and the moment arm slope +.>Wherein, human limb parameters include: height HT, weight M, ankle angle
Further preferably, the muscle activation kinetic model is:
u j (t)＝f a (e j (t))
wherein ,fa Is a neural activation model; u (u) j (t) is a nerve activation signal; e, e j (t) is a surface electromyographic signal; a, a j (t) is an activation signal reflecting the activation intensity of the j-th muscle,is the activation rate of the j-th muscle; c 1 +c 2 Is an activation rate constant corresponding to u j (t)＝1；c 2 Is the deactivation rate constant corresponding to u j (t)＝0；t act Time constant for muscle activation; t is t deact Is the deactivation time constant; ζ is the activation/deactivation scaling factor of the muscle; according to the surface electromyographic signals e j (t) calculating muscle activation signal a j (t)；
The muscle strives to solve the model to be:
SOL V＝2.57HT*M+120
MG V＝1.71HT*M+46.2
LG V＝1.08HT*M+15.7
TA V＝0.796HT*M+36.7
wherein ,FM For muscle force, f l s Is a proportionality coefficient, W is the width of the muscle, alpha 0 Is the optimal feather angle of the muscle, alpha is the feather angle of the muscle,for optimal myofiber length, < >>Is the optimal muscle force; /> Optimal muscular power of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively +.> SOL PCSA、 MG PCSA、 LG PCSA、 TA PCSA is the muscle cross-sectional areas PCSA of the soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; SOL V、 MG V、 LG V、 TA v is the muscle volume V of the soleus muscle, medial gastrocnemius, lateral gastrocnemius and tibialis anterior, respectively; HT is the height of the subject, M is the weight of the subject; according to the optimal myofiber length->Optimal feather angle alpha of muscle 0 Calculation of muscle force F from muscle feathering angle alpha and muscle activation signal a M 。
Further preferably, the muscle tendon unit stiffness model is:
wherein ,
(α o ) sol ＝28.3π/180,(α o ) mg ＝9.9π/180,(α o ) lg ＝12π/180,(α o ) ta ＝9.6π/180
wherein ,fp Is a proportionality coefficient, K p For the passive stiffness of the muscle tendon unit, K MTU For muscle tendon unit stiffness, K M For muscle stiffness, K T For the rigidity of the tendon, the tendon is provided with a plurality of elastic elements, and />Tendon stiffness of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; /> and />Rest length of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively +.> Is the optimal length of the muscle fiber; (alpha) o ) sol 、(α o ) mg 、(α o ) lg and (αo ) ta Optimal feathered angles of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; x is x 4 A mathematical parameter; according to the resting length of the muscle tendon unit>Optimal myofiber length->And muscle force F M Calculation of muscle tendon cell stiffness K MTU 。
Further preferably, the muscle tendon-joint mapping model is:
wherein, when a plurality of muscles act together on the same joint and drive the same joint to move, the subscript j added on the mathematical variable indicates the j-th muscle;for the muscle stiffness of the j-th muscle, +.>For the j-th muscle tendon unit stiffness, +.>For tendon stiffness of the j-th muscle, ΔL j The deformation quantity of the j-th muscle is that of the ankle joint, and theta is that of the ankle joint; k (K) j Stiffness for the j-th muscle; k is joint stiffness; r is R j Moment arm for the j-th muscle; />Moment arm slope for the j-th muscle; />Muscle force for the j-th muscle; according to muscle force F M Muscle tendon cell stiffness K MTU Muscle moment arm R and moment arm slope +.>Calculating the muscle stiffness K of each muscle j And further calculate joint stiffness K.
In another aspect, the present invention provides an ankle joint stiffness estimation system based on a musculoskeletal anatomical statistical model, comprising:
the parameter measurement module is used for measuring the characteristic parameters of the limbs of the human body and the surface electromyographic signals; wherein, human limb characteristic parameters include: ankle angle, knee angle, height and weight; the surface electromyographic signals include: surface electromyographic signals of the tibialis anterior, medial gastrocnemius, lateral gastrocnemius and soleus muscles of the respective joints are driven;
the parameter preprocessing module is used for performing high-pass filtering on the surface electromyographic signals from all muscles to remove motion artifacts, and performing full-wave rectification and normalization processing to complete the preprocessing process of the electromyographic signals;
the calculation module of the muscle activation signals is used for inputting the surface electromyographic signals after pretreatment into a muscle activation dynamics model to calculate each muscle activation signal;
the acquisition module of the microcosmic parameter is used for inputting the characteristic parameters of the limbs of the human body into the musculoskeletal statistical model, and acquiring the microcosmic parameter of the muscle structure through the musculoskeletal statistical model;
the calculation module of the muscle force is used for calculating the muscle force and the muscle tendon unit force of each muscle under a specific muscle activation level through a muscle force solution model according to each muscle activation signal and microscopic parameters of each muscle structure;
the rigidity calculation module of the muscle tendon units is used for calculating the rigidity of each muscle tendon unit through a muscle tendon unit rigidity model according to microscopic parameters of each muscle force and each muscle structure;
and the joint rigidity evaluation module is used for calculating the joint rigidity of the ankle joint driven by the tibiofemoral muscle, the medial gastrocnemius muscle, the lateral gastrocnemius muscle and the soleus muscle together according to the muscle tendon unit force, the microcosmic parameters of the muscle structure and the rigidity of the muscle tendon unit through a muscle tendon-joint mapping model.
Further preferably, the musculoskeletal statistical model is:
L shank ＝HT(0.247+Π F (sex)×0.01)
L thigh ＝HT(0.232+Π F (sex)×0.017)
(α o ) sol ＝28.3π/180,(α o ) mg ＝9.9π/180,(α o ) lg ＝12π/180,(α o ) ta ＝9.6π/180
wherein ,
wherein ,
wherein HT is the height of the subject; pi (II) F (sex) is a sex parameter of the subject, pi when the subject is male F (sex) is 0, pi when the subject is female F (sex) is 1; l (L) thigh Length of thigh of the subject; l (L) shank Length of the subject's lower leg;optimal muscle fiber length ++representing soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively>(α o ) sol 、(α o ) mg 、(α o ) lg and (αo ) ta Optimal feathered angles of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; alpha SOL 、α MG 、α LG and αTA The feather angles representing the soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively; /> Muscle tendon unit resting length of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively +.> SOL L MT 、 MG L MT 、 LG L MT 、 TA L MT Muscle tendon unit length L of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively MT ，/>To an initial angle theta with the ankle joint 0 A function related to the angular change Δθ; r is R SOL 、R MG 、R LG 、R TA Muscle moment arms R, respectively soleus, medial gastrocnemius, lateral gastrocnemius and tibialis anterior->Moment arm slopes of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively +.>K T Is tendon stiffness; />Normalized tendon stiffness; f (f) l t 、f l s 、f l p W is the width of the muscle; a is a muscle activation signal; x is x 1 ，x 2 ，x 3 ，y 1 A mathematical parameter; calculating muscle microcosmic parameters including resting length of muscle tendon unit according to human body limb parameters>Optimal myofiber length->Optimal feather angle alpha of muscle 0 The feather angle alpha of the muscle, the moment arm R of the muscle and the moment arm slope +.>Wherein, human limb parameters include: height HT, weight M, ankle angle +.>
Further preferably, the muscle activation kinetic model is:
u j (t)＝f a (e j (t))
wherein ,fa Is a neural activation model; u (u) j (t) is a nerve activation signal; e, e j (t) is a surface electromyographic signal; a, a j (t) is an activation signal reflecting the activation intensity of the j-th muscle,is the activation rate of the j-th muscle; c 1 +c 2 Is an activation rate constant corresponding to u j (t)＝1；c 2 Is the deactivation rate constant corresponding to u j (t)＝0；t act Time constant for muscle activation; t is t deact Is the deactivation time constant; ζ is the activation/deactivation scaling factor of the muscle; according to the surface electromyographic signals e j (t) calculating muscle activation signal a j (t)；
The muscle force solving module is as follows:
SOL V＝2.57HT*M+120
MG V＝1.71HT*M+46.2
LG V＝1.08HT*M+15.7
TA V＝0.796HT*M+36.7
wherein ,FM For muscle force, f l s Is a proportionality coefficient, W is the width of the muscle, alpha 0 Is the optimal feather angle of the muscle, alpha is the feather angle of the muscle,for optimal myofiber length, < >>Is the optimal muscle force; /> Respectively soleus muscle, medial gastrocnemius muscle and lateralOptimal muscular force of gastrocnemius and tibialis anterior +.> SOL PCSA、 MG PCSA、 LG PCSA、 TA PCSA is the muscle cross-sectional areas PCSA of the soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; SOL V、 MG V、 LG V、 TA v is the muscle volume V of the soleus muscle, medial gastrocnemius, lateral gastrocnemius and tibialis anterior, respectively; HT is the height of the subject, M is the weight of the subject; according to the optimal myofiber length->Optimal feather angle alpha of muscle 0 Calculation of muscle force F from muscle feathering angle alpha and muscle activation signal a M 。
Further preferably, the muscle tendon unit stiffness model is:
wherein ,
(α o ) sol ＝28.3π/180,(α o ) mg ＝9.9π/180,(α o ) lg ＝12π/180,(α o ) ta ＝9.6π/180
wherein ,fp Is a proportionality coefficient, K p For the passive stiffness of the muscle tendon unit, K MTU For muscle tendon unit stiffness, K M For muscle stiffness, K T For the rigidity of the tendon, the tendon is provided with a plurality of elastic elements, and />Tendon stiffness of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; /> and />Rest length of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively +.> Is the optimal length of the muscle fiber; (alpha) o ) sol 、(α o ) mg 、(α o ) lg and (αo ) ta Optimal feathered angles of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; x is x 4 A mathematical parameter; according to the resting length of the muscle tendon unit>Optimal myofiber length->And muscle force F M Calculation of muscle tendon cell stiffness K MTU 。
Further preferably, the muscle tendon-joint mapping model is:
/>
wherein, subscript j in all formulas represents the j-th muscle;for the muscle stiffness of the j-th muscle, +.>For the j-th muscle tendon unit stiffness, +.>For tendon stiffness of the j-th muscle, ΔL j The deformation quantity of the j-th muscle is that of the ankle joint, and theta is that of the ankle joint; k (K) j Stiffness for the j-th muscle; k is joint stiffness; r is R j Moment arm for the j-th muscle; />Moment arm slope for the j-th muscle; />Muscle force for the j-th muscle; according to muscle force F M Muscle tendon cell stiffness K MTU Muscle moment arm R and moment arm slope +.>Calculating the muscle stiffness K of each muscle j And further calculate joint stiffness K.
In general, the above technical solutions conceived by the present invention have the following compared with the prior art
The beneficial effects are that:
compared with the traditional stiffness identification method, the stiffness identification method does not need to be carried out by special equipment, and is more convenient and faster; compared with other rigidity estimation methods, the rigidity estimation method has the advantages that rigidity identification is not needed, the training of an estimation model is not needed, the complexity of rigidity estimation is greatly reduced, the estimation precision is equivalent to the identification precision, and the mechanical impedance characteristics of a human body can be well reflected.
Drawings
FIG. 1 is a schematic diagram of a stiffness identification research experiment scenario provided by an embodiment of the invention;
FIG. 2 is a flowchart of an ankle stiffness estimation method based on a musculoskeletal anatomy statistical model provided by an embodiment of the present invention;
fig. 3 is a graph of the estimation results of an anatomical statistical model provided by an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment is a model precision test case mainly provided by the human lower limb ankle joint rigidity estimation. In stiffness estimation studies, to estimate the accuracy of an anatomical statistical model, the result of the estimation of the anatomical statistical model is typically compared to the recognition value of the subject. According to the invention, the characteristic parameters of the limbs and the surface myoelectricity data of the subject acquired in the identification of the rigidity of the human ankle are used as the input of the anatomical statistical model, and the rigidity data of the ankle identified in the identification of the rigidity of the human ankle is used as a reference standard to evaluate the estimation performance of the anatomical statistical model. The experimental situation of the subject in the human ankle joint rigidity identification study is shown in fig. 1, the subject sits on a table, the foot is fixed on the tool end of the cooperative mechanical arm comprising 6 rotary joints (the tool end of the cooperative mechanical arm with 6 degrees of freedom is positioned at the front lower part of the subject so that the included angle between the ankle joint and the lower leg at the rest moment is 90 degrees), and the included angle of the ankle joint of the subject is changed by adjusting the rotation angle of the tail end joint. Meanwhile, reflecting mark points are posted at the toes, the heels, the ankle joints and the knee joints of the testee, the optical motion capturing system can collect three-dimensional positions of the reflecting mark points so as to obtain displacement information of different mark points, and ankle joint angle information is obtained through calculation processing; the 6-dimensional force/moment sensor fixedly connected with the mechanical arm tool end is used for collecting moment information in the ankle joint disturbance process; and identifying and obtaining the rigidity value of the ankle joint by using a least square method according to the acquired ankle joint angle and moment information.
As shown in fig. 2, the present invention provides an anatomical statistical model for ankle joint stiffness, comprising 5 sub-models, respectively a muscle activation dynamics model, a muscle striving solution model, a musculoskeletal statistical model, a muscle tendon unit stiffness model, and a muscle tendon-joint mapping model;
a. muscle activation kinetic model
The function of the muscle activation kinetic model is to convert the original myoelectric signal into a muscle activation signal; firstly, performing high-pass filtering on an acquired original electromyographic signal to remove motion artifacts, and then performing full-wave rectification and normalization processing to complete the preprocessing process of the electromyographic signal; processing the normalized electromyographic signals by using a nerve activation model to obtain nerve signals, and finally obtaining muscle activation signals according to a muscle activation dynamics model;
nerve activation signal u of jth muscle j (t) and surface electromyographic signals e j The relationship of (t) can be expressed as:
u j (t)＝f a (e j (t)) (1)
wherein ,fa For neural activation models, the neural activation signal u is characterized j (t) and surface electromyographic signals e j Relation of (t), f a Is a direct proportional function and has a slope of 1;
the muscle activation kinetics model has the following expression:
wherein ,aj (t) is an activation signal reflecting the activation intensity of the j-th muscle, c 1 +c 2 Is an activation proportionality constant c 2 Is the deactivation proportionality constant, c 1 、c 2 With muscle activation time constant t act And deactivation time constant t deact The following relationship exists:
wherein ζ is the activation and deactivation proportionality coefficient of the muscle, and ζ=0.5 is taken as the activation time constant t of the muscle act Taking 15ms;
according to the formulas (1) to (3), the surface electromyographic signals e are utilized j (t) calculating the muscle activation signal a j (t)；
b. Muscle strives for solution model
As an important component of the anatomical statistical model, the function of the muscle force solving model is to calculate the contraction force F of the muscle by means of microscopic parameters and movement characteristics of the muscle structure according to the activation degree of the muscle M The method comprises the steps of carrying out a first treatment on the surface of the According to the muscle activation signal a, the calculation is carried out according to the statistical law shown in the formula (4):
wherein ,fl s Is a proportionality coefficient, W is the width of the muscle, alpha is the feather angle of the muscle,for an optimal length of the muscle fiber,is the optimal muscle force;
the scaling factor f in equation (4) l s And the width W of the muscle are calculated by formulas (5) and (6), respectively:
/>
wherein ,for optimal muscle fiber length, α is the feather angle of the muscle, +.>
Optimal myofiber length in equation (4)And the feather angle alpha of the muscle is obtained in a musculoskeletal statistical model; optimal muscular strength->Calculation was performed by the formulas (7) (8) (9):
wherein ,optimal muscle force +/for four muscle Soleus (SOL), medial Gastrocnemius (MG), lateral Gastrocnemius (LG) and Tibialis Anterior (TA), respectively> SOL PCSA、 MG PCSA、 LG PCSA、 TA PCSA is the muscle cross-sectional areas PCSA of the four muscle soleus muscles (SOL), medial gastrocnemius Muscle (MG), lateral gastrocnemius muscle (LG) and tibialis anterior muscle (TA), respectively; />Is the optimal length of the muscle fiber;
SOL V、 MG V、 LG V、 TA v is the muscle volume V of the four muscles, respectively; HT is the height of the subject, M is the weight of the subject;
c. musculoskeletal statistical model
The musculoskeletal statistical model has the function of solving microscopic parameters of a muscle structure according to the characteristic parameters of limbs of a human body, and establishes rules among the height, the weight and the microscopic parameters of the muscle;
the model between the thigh length and the calf length and the height and the sex of the human body is shown in a formula (10):
wherein HT is the height of the subject; pi (II) F Sex is a sex parameter of the subject, pi when the subject is male F Sex is 0, pi when the subject is female F sex is 1; l (L) thigh Length of thigh of the subject; l (L) shank Length of the subject's lower leg;
deep into the internal muscle structure, the acquisition of various muscle structure parameters is the key for solving the muscle force, the muscle rigidity and the joint rigidity; wherein, solving the muscle force requires the activation degree of the muscle, the optimal muscle fiber length, the optimal muscle force, the feather angle of the muscle, the resting length of the muscle tendon unit and the length of the muscle tendon unit; on one hand, the obtaining of the muscle rigidity needs to solve the obtained muscle force, on the other hand, the optimal length of muscle fibers and the resting length of muscle tendon units are needed, and then the rigidity of the muscle tendon units can be converted into the joint rigidity through the spatial relationship between the muscles and joints, which is formed by muscle moment arms;
optimal fiber length of muscleAnd human height HT, the following relationship exists: />
wherein ,optimal fiber length for soleus, medial gastrocnemius, lateral gastrocnemius and tibialis anterior, respectively +.>
The feather angles alpha of four muscles closely related to ankle joint movement in the present invention are respectively:
(α o ) sol ＝28.3π/180,(α o ) mg ＝9.9π/180,(α o ) lg ＝12π/180,(α o ) ta ＝9.6π/180 (12)
wherein ,αSOL 、α MG 、α LG and αTA The feather angles representing the soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively;
length of rest of muscle tendon units in the inventionMuscle tendon unit length L MT There is a relationship with limb parameters as follows:
wherein ,muscle tendon unit resting length ++for four muscle soleus muscle (SOL), medial gastrocnemius Muscle (MG), lateral gastrocnemius muscle (LG) and tibialis anterior muscle (TA), respectively> SOL L MT 、 MG L MT 、 LG L MT 、 TA L MT Muscle tendon unit length L of four muscles respectively MT ，/>Is the angle theta with the ankle joint ankle A related function;
the average value R of moment arm and the change slope of moment arm of muscle in the ankle joint movement rangeThe statistical model expression of (2) is: />
wherein ,
wherein ,
wherein HT is the height of the subject; pi (II) F (sex) is a sex parameter of the subject, pi when the subject is male F (sex) is 0, pi when the subject is female F sex is 1; l (L) thigh Length of thigh of the subject; l (L) shank Length of the subject's lower leg;optimal muscle fiber length ++representing soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively>(α o ) sol 、(α o ) mg 、(α o ) lg and (αo ) ta Optimal feathered angles of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; alpha SOL 、α MG 、α LG and αTA The feather angles representing the soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively; /> Respectively soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anteriorMuscle tendon unit resting length-> SOL L MT 、 MG L MT 、 LG L MT 、 TA L MT Muscle tendon unit length L of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively MT ，/>To an initial angle theta with the ankle joint 0 A function related to the angular change Δθ; r is R SOL 、R MG 、R LG 、R TA The muscle moment arms R of the soleus muscle, the medial gastrocnemius muscle, the lateral gastrocnemius muscle and the tibialis anterior muscle respectively,moment arm slopes of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively +.>K T Is tendon stiffness; />Normalized tendon stiffness; f (f) l t 、f l s 、f l p W is the width of the muscle; a is a muscle activation signal; x is x 1 ，x 2 ，x 3 ，y 1 A mathematical parameter; calculating muscle microcosmic parameters including resting length of muscle tendon unit according to human body limb parameters>Optimal myofiber length->Optimal feather angle alpha of muscle 0 The feather angle alpha of the muscle, the moment arm R of the muscle and the moment arm slope +.>Wherein, human limb parameters include: height HT, weight M, ankle angle +.>
d. Muscle tendon unit stiffness model
The function of the muscle tendon unit stiffness model is to calculate the stiffness of the muscle tendon unit by means of muscle force and muscle structure parameters. In the present invention, the muscle tendon unit is considered to be formed by connecting muscle fibers and tendons in series, and therefore, the rigidity K of the muscle tendon unit MTU Generally expressed as:
wherein ,KM For muscle stiffness, K T Is tendon stiffness;
muscle stiffness K M According to the force F of the muscle M And length of optimal muscle fiberSolving, wherein the specific expression is as follows:
wherein ,
tendon stiffness K of four muscles T Solving is performed according to the following expression:
/>
(α o ) sol ＝28.3π/180,(α o ) mg ＝9.9π/180,(α o ) lg ＝12π/180,(α o ) ta ＝9.6π/180
wherein , and />Rest length of tendons of four muscles respectively +.> and />Length L of optimal fiber of four muscles respectively O ，(α o ) sol 、(α o ) mg 、(α o ) lg 、(α o ) ta Optimal feather angles of four muscles respectively;
e. muscle tendon-joint mapping model
The function of the muscle tendon to joint mapping model is to obtain the relation between the joint rigidity and the muscle tendon unit rigidity, so that the muscle tendon unit rigidity obtained by solving the formula (17) is conveniently converted into the joint rigidity through a spatial relation, and the modeling process of the spatial relation is as follows:
assuming moment T at the joint by the j-th muscle j The method comprises the following steps:
by definition, the value of the joint stiffness K is equal to the derivative of the joint moment T with respect to the joint angle θ, since each muscle tendon unit at the joint contributes K to its stiffness j The calculated expression of (2) is:
the mapping relation between muscle tendon and joint obtained by simplifying the above formula is as follows:
wherein ,for the muscle stiffness of the j-th muscle, +.>For the j-th muscle tendon unit stiffness, +.>For tendon stiffness of the j-th muscle, ΔL j The deformation quantity of the j-th muscle is that of the ankle joint, and theta is that of the ankle joint; k (K) j Stiffness for the j-th muscle; k is joint stiffness; r is R j Moment arm for the j-th muscle; />Is the firstMoment arm slope of j muscles; />Muscle force for the j-th muscle; according to muscle force F M Muscle tendon cell stiffness K MTU Muscle moment arm R and moment arm slope +.>Calculating the muscle stiffness K of each muscle j And further calculate joint stiffness K.
Based on the above description of the musculoskeletal anatomy statistical model, in one aspect, the invention provides an ankle joint stiffness estimation method based on the musculoskeletal anatomy statistical model, which comprises the following steps:
s1: measuring the characteristic parameters of the limbs of the human body and the surface electromyographic signals; wherein, human limb characteristic parameters include: ankle angle, knee angle, height and weight; the surface electromyographic signals include: surface electromyographic signals of the tibialis anterior, medial gastrocnemius, lateral gastrocnemius and soleus muscles of the respective joints are driven;
s2: performing high-pass filtering on the surface electromyographic signals from all muscles to remove motion artifacts, and performing full-wave rectification and normalization to complete the preprocessing process of the electromyographic signals; inputting the preprocessed surface electromyographic signals into a muscle activation kinetic model to calculate each muscle activation signal;
s3: inputting human body limb characteristic parameters into a musculoskeletal statistical model, and acquiring microscopic parameters of a muscle structure through the musculoskeletal statistical model;
s4: according to the muscle activation signals and the microcosmic parameters of the muscle structures, calculating the muscle force and the muscle tendon unit force of each muscle at a specific muscle activation level through a muscle force solving model;
s5: according to microscopic parameters of each muscle force and each muscle structure, calculating the rigidity of each muscle tendon unit through a muscle tendon unit rigidity model;
s6: based on the muscle tendon unit force, the microscopic parameters of the muscle structure and the rigidity of the muscle tendon unit, the joint rigidity of the ankle joint driven by the tibialis anterior, medial gastrocnemius, lateral gastrocnemius and soleus jointly is calculated through a muscle tendon-joint mapping model.
In another aspect, the present invention provides an ankle joint stiffness estimation system based on a musculoskeletal anatomical statistical model, comprising:
the parameter measurement module is used for measuring the characteristic parameters of the limbs of the human body and the surface electromyographic signals; wherein, human limb characteristic parameters include: ankle angle, knee angle, height and weight; the surface electromyographic signals include: surface electromyographic signals of the tibialis anterior, medial gastrocnemius, lateral gastrocnemius and soleus muscles of the respective joints are driven;
the parameter preprocessing module is used for performing high-pass filtering on the surface electromyographic signals from all muscles to remove motion artifacts, and performing full-wave rectification and normalization processing to complete the preprocessing process of the electromyographic signals;
the calculation module of the muscle activation signals is used for inputting the surface electromyographic signals after pretreatment into a muscle activation dynamics model to calculate each muscle activation signal;
the acquisition module of the microcosmic parameter is used for inputting the characteristic parameters of the limbs of the human body into the musculoskeletal statistical model, and acquiring the microcosmic parameter of the muscle structure through the musculoskeletal statistical model;
the calculation module of the muscle force is used for calculating the muscle force and the muscle tendon unit force of each muscle under a specific muscle activation level through a muscle force solution model according to each muscle activation signal and microscopic parameters of each muscle structure;
the rigidity calculation module of the muscle tendon units is used for calculating the rigidity of each muscle tendon unit through a muscle tendon unit rigidity model according to microscopic parameters of each muscle force and each muscle structure;
and the joint rigidity evaluation module is used for calculating the joint rigidity of the ankle joint driven by the tibiofemoral muscle, the medial gastrocnemius muscle, the lateral gastrocnemius muscle and the soleus muscle together according to the muscle tendon unit force, the microcosmic parameters of the muscle structure and the rigidity of the muscle tendon unit through a muscle tendon-joint mapping model.
Calculating to obtain an estimated value of the rigidity of the ankle joint of the human body through the anatomic statistical model under a specific muscle activation level; the identification value and the estimation value of the ankle joint stiffness are combined, the data are plotted in the graph, and the result is shown in fig. 3. It can be seen from fig. 3 that the anatomical statistical model accurately estimates the ankle stiffness of the human body from the surface electromyographic signals at three different levels of tibial anterior muscle activation.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (10)

1. An ankle joint stiffness estimation method based on a musculoskeletal anatomy statistical model is characterized by comprising the following steps:
s1: measuring the characteristic parameters of the limbs of the human body and the surface electromyographic signals; wherein, human limb characteristic parameters include: ankle angle, knee angle, height and weight; the surface electromyographic signals include: surface electromyographic signals of the tibialis anterior, medial gastrocnemius, lateral gastrocnemius and soleus muscles of the respective joints are driven;
s2: performing high-pass filtering on the surface electromyographic signals from all muscles to remove motion artifacts, and performing full-wave rectification and normalization to complete the preprocessing process of the electromyographic signals; inputting the preprocessed surface electromyographic signals into a muscle activation kinetic model to calculate each muscle activation signal;
s3: inputting human body limb characteristic parameters into a musculoskeletal statistical model, and acquiring microscopic parameters of a muscle structure through the musculoskeletal statistical model;
s4: according to the muscle activation signals and the microcosmic parameters of the muscle structures, calculating the muscle force and the muscle tendon unit force of each muscle at a specific muscle activation level through a muscle force solving model;
s5: according to microscopic parameters of each muscle force and each muscle structure, calculating the rigidity of each muscle tendon unit through a muscle tendon unit rigidity model;
s6: according to the muscle tendon unit force, the microcosmic parameters of the muscle structure and the rigidity of the muscle tendon unit, calculating joint rigidity of an ankle joint driven by the tibialis anterior, the medial gastrocnemius, the lateral gastrocnemius and the soleus jointly through a muscle tendon-joint mapping model;
wherein the musculoskeletal anatomical statistical model comprises: a muscle force solving model, a musculoskeletal statistics model, a muscle activation dynamics model, a muscle tendon unit stiffness model, and a muscle tendon-joint mapping model.
2. The ankle joint stiffness estimation method according to claim 1, wherein the musculoskeletal statistical model is:
L shank ＝HT(0.247+Π F (sex)×0.01)
L thigh ＝HT(0.232+Π F (sex)×0.017)
(α o ) sol ＝28.3π/180,(α o ) mg ＝9.9π/180,(α o ) lg ＝12π/180,(α o ) ta ＝9.6π/180
wherein ,
wherein ,
wherein HT is the height of the subject; pi (II) F (sex) is a sex parameter of the subject, pi when the subject is male F (sex) is 0, pi when the subject is female F (sex) is 1; l (L) thigh Length of thigh of the subject; l (L) shank Length of the subject's lower leg;optimal muscle fiber length ++representing soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively>(α o ) sol 、(α o ) mg 、(α o ) lg and (αo ) ta Optimal feathered angles of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; alpha SOL 、α MG 、α LG and αTA The feather angles representing the soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively; /> Muscle tendon unit resting length of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively +.> SOL L MT 、 MG L MT 、 LG L MT 、 TA L MT Muscle tendon unit length L of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively MT ，/>To an initial angle theta with the ankle joint 0 A function related to the angular change Δθ; r is R SOL 、R MG 、R LG 、R TA Muscle moment arms R, respectively soleus, medial gastrocnemius, lateral gastrocnemius and tibialis anterior->Respectively are provided withMoment arm slope for soleus, medial gastrocnemius, lateral gastrocnemius and tibialis anterior +.>K T Is tendon stiffness; />Normalized tendon stiffness; f (f) l t 、f l s 、f l p W is the width of the muscle; a is a muscle activation signal; x is x 1 ，x 2 ，x 3 ，y 1 A mathematical parameter; calculating muscle microcosmic parameters including resting length of muscle tendon unit according to human body limb parameters>Optimal myofiber length->Optimal feather angle alpha of muscle 0 The feather angle alpha of the muscle, the moment arm R of the muscle and the moment arm slope +.>Wherein, human limb parameters include: height HT, weight M, ankle angle +.>
3. The ankle joint stiffness estimation method according to claim 1 or 2, wherein the muscle activation dynamics model is:
u j (t)＝f a (e j (t))
wherein ,fa Is a neural activation model; u (u) j (t) is a nerve activation signal; e, e j (t) is a surface electromyographic signal; a, a j (t) is an activation signal reflecting the activation intensity of the j-th muscle,is the activation rate of the j-th muscle; c 1 +c 2 Is an activation rate constant corresponding to u j (t)＝1；c 2 Is the deactivation rate constant corresponding to u j (t)＝0；t act Time constant for muscle activation; t is t deact Is the deactivation time constant; ζ is the activation/deactivation scaling factor of the muscle; according to the surface electromyographic signals e j (t) calculating muscle activation signal a j (t)；
The muscle strives to solve the model to be:
SOL V＝2.57HT*M+120
MG V＝1.71HT*M+46.2
LG V＝1.08HT*M+15.7
TA V＝0.796HT*M+36.7
wherein ,FM For muscle force, f l s Is a proportionality coefficient, W is the width of the muscle, alpha 0 Is the optimal feather angle of the muscle, alpha is the feather angle of the muscle,for optimal myofiber length, < >>Is the optimal muscle force; /> Respectively soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and anterior tibialOptimal muscular strength of the muscle-> SOL PCSA、 MG PCSA、 LG PCSA、 TA PCSA is the muscle cross-sectional areas PCSA of the soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; SOL V、 MG V、 LG V、 TA v is the muscle volume V of the soleus muscle, medial gastrocnemius, lateral gastrocnemius and tibialis anterior, respectively; HT is the height of the subject, M is the weight of the subject; according to the optimal myofiber length->Optimal feather angle alpha of muscle 0 Calculation of muscle force F from muscle feathering angle alpha and muscle activation signal a M 。
4. The ankle joint stiffness estimation method according to claim 1 or 3, wherein the muscle tendon unit stiffness model is:
wherein ,
(α o ) sol ＝28.3π/180,(α o ) mg ＝9.9π/180,(α o ) lg ＝12π/180,(α o ) ta ＝9.6π/180
wherein ,fp Is a proportionality coefficient, K p For the passive stiffness of the muscle tendon unit, K MTU For muscle tendon unit stiffness, K M For muscle stiffness, K T For the rigidity of the tendon, the tendon is provided with a plurality of elastic elements, and />Tendon stiffness of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; /> and />Rest length of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively +.> Is the optimal length of the muscle fiber; (alpha) o ) sol 、(α o ) mg 、(α o ) lg and (αo ) ta Optimal feathered angles of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; 4 a mathematical parameter; according to the resting length of the muscle tendon unit>Optimal myofiber length->And muscle force F M Calculation of muscle tendon cell stiffness K MTU 。
5. The method of estimating the rigidity of a human joint according to claim 1 or 4, wherein the muscle tendon-joint mapping model is:
wherein, when a plurality of muscles act together on the same joint and drive the same joint to move, the subscript j added on the mathematical variable indicates the j-th muscle;for the muscle stiffness of the j-th muscle, +.>For the j-th muscle tendon unit stiffness, +.>For tendon stiffness of the j-th muscle, ΔL j The deformation quantity of the j-th muscle is that of the ankle joint, and theta is that of the ankle joint; k (K) j Stiffness for the j-th muscle; k is joint stiffness; r is R j Moment arm for the j-th muscle; />Moment arm slope for the j-th muscle; />Muscle force for the j-th muscle; according to muscle force F M Muscle tendon cell stiffness K MTU Muscle moment arm R and moment arm slope +.>Calculating the muscle stiffness K of each muscle j And further calculate joint stiffness K.
6. An ankle stiffness estimation system based on a musculoskeletal anatomy statistical model, comprising:
the parameter measurement module is used for measuring the characteristic parameters of the limbs of the human body and the surface electromyographic signals; wherein, human limb characteristic parameters include: ankle angle, knee angle, height and weight; the surface electromyographic signals include: surface electromyographic signals of the tibialis anterior, medial gastrocnemius, lateral gastrocnemius and soleus muscles of the respective joints are driven;
the parameter preprocessing module is used for performing high-pass filtering on the surface electromyographic signals from all muscles to remove motion artifacts, and performing full-wave rectification and normalization processing to complete the preprocessing process of the electromyographic signals;
the calculation module of the muscle activation signals is used for inputting the surface electromyographic signals after pretreatment into a muscle activation dynamics model to calculate each muscle activation signal;
the acquisition module of the microcosmic parameter is used for inputting the characteristic parameters of the limbs of the human body into the musculoskeletal statistical model, and acquiring the microcosmic parameter of the muscle structure through the musculoskeletal statistical model;
the calculation module of the muscle force is used for calculating the muscle force and the muscle tendon unit force of each muscle under a specific muscle activation level through a muscle force solution model according to each muscle activation signal and microscopic parameters of each muscle structure;
the rigidity calculation module of the muscle tendon units is used for calculating the rigidity of each muscle tendon unit through a muscle tendon unit rigidity model according to microscopic parameters of each muscle force and each muscle structure;
and the joint rigidity evaluation module is used for calculating the joint rigidity of the ankle joint driven by the tibiofemoral muscle, the medial gastrocnemius muscle, the lateral gastrocnemius muscle and the soleus muscle together according to the muscle tendon unit force, the microcosmic parameters of the muscle structure and the rigidity of the muscle tendon unit through a muscle tendon-joint mapping model.
7. The ankle joint stiffness estimation system according to claim 6, wherein the musculoskeletal statistical model is:
L shank ＝HT(0.247+Π F (sex)×0.01)
L thigh ＝HT(0.232+Π F (sex)×0.017)
(α o ) sol ＝28.3π/180,(α o ) mg ＝9.9π/180,(α o ) lg ＝12π/180,(α o ) ta ＝9.6π/180
wherein ,
wherein ,
wherein HT is the height of the subject; pi (II) F (sex) is a sex parameter of the subject, pi when the subject is male F (sex) is 0, pi when the subject is female F (sex) is 1; l (L) thigh Length of thigh of the subject; l (L) shank Length of the subject's lower leg;optimal muscle fiber length ++representing soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively>(α o ) sol 、(α o ) mg 、(α o ) lg and (αo ) ta Optimal feathered angles of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; alpha SOL 、α MG 、α LG and αTA The feather angles representing the soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively; /> Muscle tendon unit resting length of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively +.> SOL L MT 、 MG L MT 、 LG L MT 、 TA L MT Muscle tendon unit length L of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively MT ，/>To an initial angle theta with the ankle joint 0 A function related to the angular change Δθ; r is R SOL 、R MG 、R LG 、R TA Muscle moment arms R, respectively soleus, medial gastrocnemius, lateral gastrocnemius and tibialis anterior->Moment arm slopes of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively +.>K T Is tendon stiffness; />Normalized tendon stiffness; f (f) l t 、f l s 、f l p W is the width of the muscle; a is a muscle activation signal; x is x 1 ，x 2 ，x 3 ，y 1 A mathematical parameter; calculating muscle microcosmic parameters including resting length of muscle tendon unit according to human body limb parameters>Optimal myofiber length->Optimal feather angle alpha of muscle 0 The feather angle alpha of the muscle, the moment arm R of the muscle and the moment arm slope +.>Wherein, human limb parameters include: height HT, weight M, ankle angle +.>
8. The ankle joint stiffness estimation system according to claim 6 or 7, wherein the muscle activation dynamics model is:
u j (t)＝f a (e j (t))
wherein ,fa Is a neural activation model; u (u) j (t) is a nerve activation signal; e, e j (t) is a surface electromyographic signal; a, a j (t) is an activation signal reflecting the activation intensity of the j-th muscle,is the activation rate of the j-th muscle; c 1 +c 2 Is an activation rate constant corresponding to u j (t)＝1；c 2 Is the deactivation rate constant corresponding to u j (t)＝0；t act Time constant for muscle activation; t is t deact Is the deactivation time constant; ζ is the activation/deactivation scaling factor of the muscle; according to the surface electromyographic signals e j (t) calculating muscle activation signal a j (t)；
The muscle force solving module is as follows:
SOL V＝2.57HT*M+120
MG V＝1.71HT*M+46.2
LG V＝1.08HT*M+15.7
TA V＝0.796HT*M+36.7
wherein ,FM For muscle force, f l s Is a proportionality coefficient, W is the width of the muscle, alpha 0 Is the optimal feather angle of the muscle, alpha is the feather angle of the muscle,for optimal myofiber length, < >>Is the optimal muscle force; /> Optimal muscular power of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively +.> SOL PCSA、 MG PCSA、 LG PCSA、 TA PCSA is the muscle cross-sectional areas PCSA of the soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; SOL V、 MG V、 LG V、 TA v is the muscle volume V of the soleus muscle, medial gastrocnemius, lateral gastrocnemius and tibialis anterior, respectively; HT is the height of the subject, M is the weight of the subject; according to the optimal myofiber length->Optimal feather angle alpha of muscle 0 Calculation of muscle force F from muscle feathering angle alpha and muscle activation signal a M 。
9. The ankle joint stiffness estimation system according to claim 6 or 8, wherein the muscle tendon unit stiffness model is:
wherein ,
(α o ) sol ＝28.3π/180,(α o ) mg ＝9.9π/180,(α o ) lg ＝12π/180,(α o ) ta ＝9.6π/180
wherein ,fp Is a proportionality coefficient, K p For the passive stiffness of the muscle tendon unit, K MTU For muscle tendon unit stiffness, K M For muscle stiffness, K T For the rigidity of the tendon, the tendon is provided with a plurality of elastic elements, and />Tendon stiffness of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; /> and />Rest length of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle and tibialis anterior, respectively +.> Is the optimal length of the muscle fiber; (alpha) o ) sol 、(α o ) mg 、(α o ) lg and (αo ) ta Optimal feathered angles of soleus muscle, medial gastrocnemius muscle, lateral gastrocnemius muscle, and tibialis anterior, respectively; 4 a mathematical parameter; according to the resting length of the muscle tendon unit>Optimal myofiber length->And muscle force F M Calculation of muscle tendon cell stiffness K MTU 。
10. The ankle joint stiffness estimation system according to claim 6 or 8, wherein the muscle tendon-joint mapping model is:
wherein, when a plurality of muscles act together on the same joint and drive the same joint to move, the subscript j added to the mathematical variable indicates the j-th muscle;for the muscle stiffness of the j-th muscle, +.>For the j-th muscle tendon unit stiffness, +.>For tendon stiffness of the j-th muscle, ΔL j In the shape of the j th muscleA variable, θ is the ankle angle; k (K) j Stiffness for the j-th muscle; k is joint stiffness; r is R j Moment arm for the j-th muscle; />Moment arm slope for the j-th muscle; />Muscle force for the j-th muscle; according to muscle force F M Muscle tendon cell stiffness K MTU Muscle moment arm R and moment arm slope +.>Calculating the muscle stiffness K of each muscle j And further calculate joint stiffness K. />
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CN117153379A (en) * 2023-10-31 2023-12-01 深圳市前海蛇口自贸区医院 Prediction device for thoracic outlet syndrome
CN117153379B (en) * 2023-10-31 2024-02-20 深圳市前海蛇口自贸区医院 Prediction device for thoracic outlet syndrome

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