CN110638449A - Muscle quantitative analysis method based on mechanical work - Google Patents
Muscle quantitative analysis method based on mechanical work Download PDFInfo
- Publication number
- CN110638449A CN110638449A CN201910948198.9A CN201910948198A CN110638449A CN 110638449 A CN110638449 A CN 110638449A CN 201910948198 A CN201910948198 A CN 201910948198A CN 110638449 A CN110638449 A CN 110638449A
- Authority
- CN
- China
- Prior art keywords
- muscle
- muscles
- mechanical work
- gait
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/112—Gait analysis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1107—Measuring contraction of parts of the body, e.g. organ, muscle
-
- 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]
Abstract
The invention relates to a muscle quantification analysis method based on mechanical work, which comprises the following steps: step S1: collecting motion capture data, ground reaction force data and myoelectric data of a subject during walking; step S2: taking the data in the step S1 as the input of open source software opensims, and obtaining a myoelectricity driving musculoskeletal model of the subject by scaling a musculoskeletal model; step S3: utilizing inverse dynamics and residual error reduction of opensims and a muscle calculation control tool to obtain moment arms and muscle strength of a plurality of gait cycles; step S4: taking the average value of more than 10 gait cycle data, resampling to 101 points, then calculating the mechanical work of the muscle by utilizing the muscle force and the arm of force, and drawing a curve; step S5: gait is divided into four phases by ground reaction forces. The change of the mechanical work of the muscles is used for reflecting the contraction mode of the muscles in different stages of gait and the coordination effect among the muscles. The invention can reflect the contraction mode of each muscle and the coordination effect between the muscles.
Description
Technical Field
The invention relates to the field of biomechanics, in particular to a muscle quantitative analysis method based on mechanical work.
Background
Since human body movement is the result of joint movement driven by coordinated contraction between muscles, quantifying the coordination between muscles is very important for the assessment of motor function and the analysis of abnormal gait.
With the development of modern measurement technology, many students in the field analyze muscles by quantifying data such as kinematics, dynamics, force plate and myoelectricity, so as to discuss the contribution of the muscles to aspects such as joint load, gravitational acceleration, body support and advancing under a certain action. The main reason for the human body to generate these actions is the coordination among muscles, but the current research cannot intuitively reflect the muscle coordination relationship.
In the prior art, although the human motion parameters are quantitatively analyzed, the complex coordination relationship among muscles cannot be known. At present, the coordination relationship of muscles is analyzed by quantifying the amplitude of the electrical signals of the surface muscles and calculating the correlation coefficient between the muscles, but how the muscles coordinate with each other to generate joint motion cannot be known.
Disclosure of Invention
In view of the above, the present invention provides a muscle quantification analysis method based on mechanical work, which solves the problem that the existing muscle quantification analysis method cannot intuitively analyze the coordination between muscles.
The invention is realized by adopting the following scheme: a muscle mass analysis method based on mechanical work, comprising the steps of:
step S1: collecting motion capture data, ground reaction force data and myoelectric data of a subject during walking;
step S2: the motion capture data and the myoelectric data are used as input of open source software opensims, and a musculoskeletal model is zoomed to obtain a myoelectric driving musculoskeletal model of the subject;
step S3: utilizing inverse dynamics and residual error reduction of opensims and a muscle calculation control tool to obtain moment arms and muscle strength of a plurality of gait cycles;
step S4: taking the average value of more than 10 gait cycle data, resampling to 101 points, then calculating the mechanical work of the muscle by utilizing the muscle force and the arm of force, and drawing a curve of the change of the mechanical work in one gait;
step S5: the gait is divided into a first double-leg supporting period, a single-leg supporting period, a second double-leg supporting period and a swinging period through the ground reaction force;
step S6: the change of the mechanical work of the muscle is utilized to reflect the contraction mode of the muscle in different stages of gait and the coordination effect among the muscles: when the muscle does work and increases, the muscle contracts centripetally; when the muscle work is reduced, the muscle is centrifugally contracted; in gait, each muscle contracts in different ways, and the coordination relationship between muscles can be divided into synergy and antagonism.
Further, the specific process of calculating the mechanical work of the muscle is as follows:
the mechanical work of a muscle is equal to the product of the length of the muscle contraction and the tension it generates when it contracts; taking the average value of the muscle force at two adjacent moments as the tension generated during the muscle contraction at the middle moment, and taking the difference value of the moment arms at the adjacent moments as the length changed in the muscle contraction process; the formula is as follows:
Wi＝Fi*Ri ⑴
Fi＝(fi-0.5+fi-0.5)/2 ⑵
Ri＝ri+0.5-ri-0.5 ⑶
i＝1,2,3,4,5.......100 ⑷
wherein, WiMechanical work done by the muscles at moment i; fiThe muscle strength calculated for the moment i is obtained by taking the muscle strength f of two adjacent time pointsi-0.5And fi+0.5Obtaining the average value of; riMoment arm calculated for moment i, which is equal to moment arms of two adjacent time pointsri-0.5And ri+0.5The difference of (a).
Compared with the prior art, the invention has the following beneficial effects:
the invention intuitively reflects the contraction mode (centrifugal contraction or centripetal contraction) of each muscle and the coordination (cooperation and antagonism) among the muscles through the change condition of the mechanical work of the muscles, thereby carrying out quantitative analysis on the coordination among the muscles and being used for the motion function evaluation of athletes and the analysis of abnormal gaits.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the present embodiment provides a muscle quantification analysis method based on mechanical work, including the following steps:
step S1: collecting motion capture data, ground reaction force data and myoelectric data of a subject during walking;
step S2: the motion capture data and the myoelectric data are used as input of open source software opensims, and a universal musculoskeletal model is zoomed to obtain a myoelectric driving musculoskeletal model of the subject;
step S3: acquiring moment arms and muscle strength of a plurality of gait cycles by using Inverse dynamics (Inverse dynamics) and Residual Reduction (Residual Reduction) and muscle calculation control (calculated muscle control) tools of opensims;
step S4: taking the average value of more than 10 gait cycle data, resampling to 101 points, then calculating the mechanical work of the muscle by utilizing the muscle force and the arm of force, and drawing a curve of the change of the mechanical work in one gait;
step S5: the gait is divided into a first double-leg supporting period, a single-leg supporting period, a second double-leg supporting period and a swinging period through the ground reaction force;
step S6: the change of the mechanical work of the muscle is utilized to reflect the contraction mode of the muscle in different stages of gait and the coordination effect among the muscles: when the muscle contracts centripetally, the mechanical work of the muscle is continuously increased; the mechanical work of the centrifugal contraction of the muscle is continuously reduced and even negative work is done. In gait, the coordination relationship between muscles can be divided into synergy and antagonism according to the different contraction modes of each muscle.
In this embodiment, the specific process of calculating the mechanical work of the muscle is as follows:
the mechanical work of a muscle is equal to the product of the length of the muscle contraction and the tension it generates when it contracts; taking the average value of the muscle force at two adjacent moments as the tension generated during the muscle contraction at the middle moment, and taking the difference value of the moment arms at the adjacent moments as the length changed in the muscle contraction process; the formula is as follows:
Wi＝Fi*Ri ⑴
Fi＝(fi-0.5+fi-0.5)/2 ⑵
Ri＝ri+0.5-ri-0.5 ⑶
i＝1,2,3,4,5.......100 ⑷
wherein, WiMechanical work done by the muscles at moment i; fiThe muscle strength calculated for the moment i is obtained by taking the muscle strength f of two adjacent time pointsi-0.5And fi+0.5Obtaining the average value of; riMoment arm calculated for moment i and equal to moment arm r of two adjacent time pointsi-0.5And ri+0.5The difference of (a).
Preferably, in this embodiment, the body movement is a result of muscle contraction that drives the bone to move about the joint; the joint movement needs to be accurate and effective and can be completed only by coordinating a plurality of muscles. According to different contraction modes of muscles in the process of movement, the muscles can be divided into prime muscles, antagonistic muscles and synergistic muscles. The prime muscle is the principal muscle that produces this movement when contracted, with the cooperative muscle contracting in the same manner as the prime muscle, and the antagonistic muscle contracting in the opposite manner as the prime muscle.
Taking quantitative analysis of mechanical work of muscles associated with the ankle joint as an example:
first two-leg support period: healthy people reach the maximum centripetal contraction of tibialis anterior (prime muscle) on heel landing, the other dorsiflexor muscles (extensor digitorum longus and third fibula) are used as synergic muscles to perform centripetal contraction, and the plantaris flexors (soleus, gastrocnemius, tibialis posterior, longus fibula, short fibula and longus digitorum) are used as antagonistic muscles to perform centrifugal contraction; then the ankle joint does plantar flexion movement, the triceps surae (soleus and gastrocnemius) as the prime muscle contracts centripetally to do positive work, the other plantar flexors (tibialis posterior, fibula long, fibula short and toe long flexors) as the cooperative muscles contract centripetally, and the dorsiflexors (tibialis anterior, extensor digitorum longus and third gastrocnemius) as the antagonistic muscles contract centrifugally to do negative work, so that the ankle joint is driven to reach the neutral position. At the end of the first two-leg support period, the body weight and the mind shift towards the leg, the tibialis anterior muscle is used as a motive muscle to perform positive work by centripetal contraction, the other dorsiflexor muscles (extensor digitorum longus and third peroneal muscle) are used as cooperative muscles, and the plantarflexus muscle is used as an antagonistic muscle to perform negative work by centrifugal contraction, so that the ankle joint is driven to perform dorsiflexion movement, and the body is kept balanced.
Single-leg support period: the ankle joint of a healthy person remains dorsiflexed during the single leg support period. The tibialis anterior muscle is used as a prime muscle to contract centripetally, the extensor digitorum longus and the third fibula muscle are used as synergic muscles to contract centripetally, and the plantar flexor (soleus, gastrocnemius, tibialis posterior, fibula longus, fibula short and toe flexor) is used as antagonistic muscles to contract centrifugally. The contralateral side is now the swing phase, causing the body to accelerate forward.
Second two-leg support period: the ankle joint of a healthy person firstly keeps dorsiflexion movement in the second double-leg supporting period, the ankle joint starts plantarflexion movement after the maximum dorsiflexion value is reached, triceps surae (soleus muscle and gastrocnemius muscle) is used as a motive muscle to contract centripetally to perform positive work, the other plantarflexies (tibialis posterior muscle, peroneal long muscle, peroneal short muscle and peroneal long flexor muscle) are used as cooperative muscles to contract centripetally, and the dorsiflexor muscles (tibialis anterior muscle, extensor digitorum and third peroneal muscle) are used as antagonistic muscles to perform negative work in a centrifugal contraction mode, and the maximum plantarflexion angle is reached when toes leave the ground.
A swing period: healthy people move forward during the swing, and the ankle joint mainly takes a task from plantarflexion to a body neutral position. During plantarflexion, the triceps surae (soleus and gastrocnemius) act as the prime muscle to contract centripetally to perform positive work, the other plantarflexia (tibialis posterior, fibula long, fibula short and toe long flexors) act as the cooperative muscle to contract centripetally, and the dorsiflexors (tibialis anterior, extensor digitorum longus and third fibula) act as the antagonistic muscle to perform negative work by centrifugal contraction.
Preferably, the present embodiment intuitively reflects the contraction mode (centrifugal contraction or centripetal contraction) of each muscle and the coordination (cooperation, antagonism) therebetween through the change of the mechanical work of the muscle, so as to quantitatively analyze the coordination between the muscles, and can be used for the athletic function assessment, abnormal gait analysis, and the like of athletes.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (2)
1. A muscle mass analysis method based on mechanical work is characterized in that: the method comprises the following steps:
step S1: collecting motion capture data, ground reaction force data and myoelectric data of a subject during walking;
step S2: the motion capture data and the myoelectric data are used as input of open source software opensims, and a musculoskeletal model is zoomed to obtain a myoelectric driving musculoskeletal model of the subject;
step S3: utilizing inverse dynamics and residual error reduction of opensims and a muscle calculation control tool to obtain moment arms and muscle strength of a plurality of gait cycles;
step S4: taking the average value of more than 10 gait cycle data, resampling to 101 points, then calculating the mechanical work of muscles by utilizing the muscle force and the arm of force, and drawing a curve of the change of the mechanical work of each muscle in one gait cycle;
step S5: the gait is divided into a first double-leg supporting period, a single-leg supporting period, a second double-leg supporting period and a swinging period through the ground reaction force;
step S6: the change of the mechanical work of the muscle is utilized to reflect the contraction mode of the muscle in different stages of gait and the coordination effect among the muscles: when the muscle is working positively, the muscle contracts centripetally; when the muscle does negative work, the muscle centrifugally contracts; in gait, each muscle contracts in different ways, and the coordination among the muscles is divided into synergy and antagonism.
2. The method for analyzing muscle mass based on mechanical work according to claim 1, wherein:
the specific process of calculating the mechanical work of the muscle is as follows:
the mechanical work of a muscle is equal to the product of the length of the muscle contraction and the tension it generates when it contracts;
the average value of the muscle force at two adjacent moments is taken as the tension generated when the muscle contracts at the middle moment,
taking the difference value of the moment arms at adjacent moments as the length of the change in the muscle contraction process; the formula is as follows:
Wi＝Fi*Ri ⑴
Fi＝(fi-0.5+fi-0.5)/2 ⑵
Ri＝ri+0.5-ri-0.5 ⑶
i＝1,2,3,4,5.......100 ⑷
wherein, WiMechanical work done by the muscles at moment i; fiThe muscle strength calculated for the moment i is obtained by taking the muscle strength f of two adjacent time pointsi-0.5And fi+0.5Obtaining the average value of; riMoment arm calculated for moment i and equal to moment arm r of two adjacent time pointsi-0.5And ri+0.5The difference of (a).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910948198.9A CN110638449B (en) | 2019-09-30 | 2019-09-30 | Muscle quantitative analysis method based on mechanical work |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910948198.9A CN110638449B (en) | 2019-09-30 | 2019-09-30 | Muscle quantitative analysis method based on mechanical work |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110638449A true CN110638449A (en) | 2020-01-03 |
| CN110638449B CN110638449B (en) | 2021-05-18 |
Family
ID=69012284
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910948198.9A Active CN110638449B (en) | 2019-09-30 | 2019-09-30 | Muscle quantitative analysis method based on mechanical work |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110638449B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114431832A (en) * | 2021-12-27 | 2022-05-06 | 同济大学 | Quantitative analysis method for muscle energy consumption |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE4418475A1 (en) * | 1994-05-20 | 1995-11-30 | T & T Medilogic Medizintechnik | Method and measuring arrangement for analyzing human gait |
| CN1286962A (en) * | 2000-10-09 | 2001-03-14 | 清华大学 | Real-time body gait image detecting method |
| US7369896B2 (en) * | 2002-10-24 | 2008-05-06 | Lockheed Martin Corporation | Systems and methods for treating movement disorders |
| CN102670306A (en) * | 2011-02-17 | 2012-09-19 | 美国博通公司 | Bio-medical unit used in vivo, system thereof and bio-medical application program |
| US20150305672A1 (en) * | 2010-09-21 | 2015-10-29 | Somaxis Incorporated | Methods for assessing and optimizing muscular performance including a frequency based, amplitude adjusted root mean square protocol |
| CN106955109A (en) * | 2017-03-21 | 2017-07-18 | 深圳大学 | gait behavior record analyzer, method and system |
| CN107106846A (en) * | 2015-02-12 | 2017-08-29 | 赫尔实验室有限公司 | System and method for aiding in gait intervention and Prevention of fall |
| CN107397649A (en) * | 2017-08-10 | 2017-11-28 | 燕山大学 | A kind of upper limbs exoskeleton rehabilitation robot control method based on radial base neural net |
| US20180243121A1 (en) * | 2017-02-28 | 2018-08-30 | Jacek Marek Dygut | Foot orthosis with comprehensive method for correcting deformities of the transverse arch of the foot in cases of static transverse flatfoot compounded by hallux valgus, with possible preventive and post-operative applications. |
| CN109346176A (en) * | 2018-08-27 | 2019-02-15 | 浙江大学 | One kind is based on kinesiology modeling and the modified muscle Cooperative Analysis method of surface electromyogram signal |
| CN109662718A (en) * | 2019-01-22 | 2019-04-23 | 北京城市系统工程研究中心 | Motor function assessment system relevant to the elderly's muscle performance |
| CN109789543A (en) * | 2016-07-22 | 2019-05-21 | 哈佛大学校长及研究员协会 | Control for wearable system optimizes |
| CN109984763A (en) * | 2019-04-10 | 2019-07-09 | 福州大学 | Method based on artificial neural network intelligent predicting human synovial torque |
| CN110013370A (en) * | 2019-05-24 | 2019-07-16 | 国家康复辅具研究中心 | A kind of method and apparatus of determining artificial leg to line effect |
| CN110313918A (en) * | 2019-07-17 | 2019-10-11 | 军事科学院系统工程研究院军需工程技术研究所 | A kind of gait phase recognition methods and system based on plantar pressure |
-
2019
- 2019-09-30 CN CN201910948198.9A patent/CN110638449B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE4418475A1 (en) * | 1994-05-20 | 1995-11-30 | T & T Medilogic Medizintechnik | Method and measuring arrangement for analyzing human gait |
| CN1286962A (en) * | 2000-10-09 | 2001-03-14 | 清华大学 | Real-time body gait image detecting method |
| US7369896B2 (en) * | 2002-10-24 | 2008-05-06 | Lockheed Martin Corporation | Systems and methods for treating movement disorders |
| US20150305672A1 (en) * | 2010-09-21 | 2015-10-29 | Somaxis Incorporated | Methods for assessing and optimizing muscular performance including a frequency based, amplitude adjusted root mean square protocol |
| CN102670306A (en) * | 2011-02-17 | 2012-09-19 | 美国博通公司 | Bio-medical unit used in vivo, system thereof and bio-medical application program |
| CN107106846A (en) * | 2015-02-12 | 2017-08-29 | 赫尔实验室有限公司 | System and method for aiding in gait intervention and Prevention of fall |
| CN109789543A (en) * | 2016-07-22 | 2019-05-21 | 哈佛大学校长及研究员协会 | Control for wearable system optimizes |
| US20180243121A1 (en) * | 2017-02-28 | 2018-08-30 | Jacek Marek Dygut | Foot orthosis with comprehensive method for correcting deformities of the transverse arch of the foot in cases of static transverse flatfoot compounded by hallux valgus, with possible preventive and post-operative applications. |
| CN106955109A (en) * | 2017-03-21 | 2017-07-18 | 深圳大学 | gait behavior record analyzer, method and system |
| CN107397649A (en) * | 2017-08-10 | 2017-11-28 | 燕山大学 | A kind of upper limbs exoskeleton rehabilitation robot control method based on radial base neural net |
| CN109346176A (en) * | 2018-08-27 | 2019-02-15 | 浙江大学 | One kind is based on kinesiology modeling and the modified muscle Cooperative Analysis method of surface electromyogram signal |
| CN109662718A (en) * | 2019-01-22 | 2019-04-23 | 北京城市系统工程研究中心 | Motor function assessment system relevant to the elderly's muscle performance |
| CN109984763A (en) * | 2019-04-10 | 2019-07-09 | 福州大学 | Method based on artificial neural network intelligent predicting human synovial torque |
| CN110013370A (en) * | 2019-05-24 | 2019-07-16 | 国家康复辅具研究中心 | A kind of method and apparatus of determining artificial leg to line effect |
| CN110313918A (en) * | 2019-07-17 | 2019-10-11 | 军事科学院系统工程研究院军需工程技术研究所 | A kind of gait phase recognition methods and system based on plantar pressure |
Non-Patent Citations (2)
| Title |
|---|
| 广州工伤康复中心，广州工伤康复医院: "《康复评定》", 31 December 2003 * |
| 纪仲秋、李建设: "《运动生物力学》", 30 June 2001 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114431832A (en) * | 2021-12-27 | 2022-05-06 | 同济大学 | Quantitative analysis method for muscle energy consumption |
| CN114431832B (en) * | 2021-12-27 | 2023-10-20 | 同济大学 | Quantitative analysis method for muscle energy consumption |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110638449B (en) | 2021-05-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Van Dijk et al. | Evaluation of the achilles ankle exoskeleton | |
| Lee et al. | Time-varying ankle mechanical impedance during human locomotion | |
| US11498203B2 (en) | Controls optimization for wearable systems | |
| US9221177B2 (en) | Neuromuscular model-based sensing and control paradigm for a robotic leg | |
| JP5761832B2 (en) | Operation assistance device and tuning control method for operation assistance device | |
| Sartori et al. | Modeling the human knee for assistive technologies | |
| Miller et al. | Sensitivity of maximum sprinting speed to characteristic parameters of the muscle force–velocity relationship | |
| Dzeladini et al. | Effects of a neuromuscular controller on a powered ankle exoskeleton during human walking | |
| CN110638449B (en) | Muscle quantitative analysis method based on mechanical work | |
| Chowdhury et al. | Estimation of forces and moments of lower limb joints from kinematics data and inertial properties of the body by using inverse dynamics technique | |
| Nuckols et al. | Automated detection of soleus concentric contraction in variable gait conditions for improved exosuit control | |
| JP2019084130A (en) | Walking motion evaluation apparatus, walking motion evaluation method, and program | |
| Shorter et al. | Ankle mechanical impedance during the stance phase of running | |
| Chen et al. | Validating model-based prediction of biological knee moment during walking with an exoskeleton in crouch gait: potential application for exoskeleton control | |
| Biao et al. | Design and control of a flexible exoskeleton to generate a natural full gait for lower-limb rehabilitation | |
| Park et al. | Performance estimation of the lower limb exoskeleton for plantarflexion using surface electromyography (sEMG) signals | |
| Mouzo et al. | Leg-orthosis contact force estimation from gait analysis | |
| González-Mendoza et al. | Upper limb musculoskeletal modeling for human-exoskeleton interaction | |
| Pfeifer et al. | Model-based estimation of active knee stiffness | |
| Peng et al. | A practical EMG-driven musculoskeletal model for dynamic torque estimation of knee joint | |
| Kapti et al. | Wearable acceleration sensor application in unilateral trans-tibial amputation prostheses | |
| Cao et al. | An inverse dynamic approach for quantitative muscle force estimation during human standing-up process | |
| Zabre-Gonzalez et al. | Closed-loop future prediction of continuous ankle kinematics and kinetics using residual muscle signals of transtibial amputees | |
| Lee | Task-dependent modulation of multi-dimensional human ankle stiffness | |
| Yang et al. | The effects of walking speed and hardness of Terrain on the foot-Terrain interaction and driving torque for planar human walking |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |