CN117918869A - Sport injury intelligent diagnosis and evaluation system based on sEMG technology - Google Patents
Sport injury intelligent diagnosis and evaluation system based on sEMG technology Download PDFInfo
- Publication number
- CN117918869A CN117918869A CN202311752151.8A CN202311752151A CN117918869A CN 117918869 A CN117918869 A CN 117918869A CN 202311752151 A CN202311752151 A CN 202311752151A CN 117918869 A CN117918869 A CN 117918869A
- Authority
- CN
- China
- Prior art keywords
- diagnosis
- semg
- data
- evaluation
- injury
- 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.)
- Pending
Links
- 238000003745 diagnosis Methods 0.000 title claims abstract description 113
- 238000011156 evaluation Methods 0.000 title claims abstract description 73
- 238000005516 engineering process Methods 0.000 title claims abstract description 36
- 208000027418 Wounds and injury Diseases 0.000 title claims description 24
- 230000006378 damage Effects 0.000 title claims description 22
- 208000014674 injury Diseases 0.000 title claims description 21
- 210000002027 skeletal muscle Anatomy 0.000 claims abstract description 72
- 208000029549 Muscle injury Diseases 0.000 claims abstract description 68
- 238000012549 training Methods 0.000 claims abstract description 55
- 208000025978 Athletic injury Diseases 0.000 claims abstract description 25
- 206010041738 Sports injury Diseases 0.000 claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 21
- 238000000034 method Methods 0.000 claims abstract description 9
- 230000005540 biological transmission Effects 0.000 claims abstract description 5
- 238000011282 treatment Methods 0.000 claims description 14
- 238000010801 machine learning Methods 0.000 claims description 7
- 238000011161 development Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 5
- 238000001914 filtration Methods 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 4
- 238000013135 deep learning Methods 0.000 claims description 4
- 206010052428 Wound Diseases 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000004140 cleaning Methods 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims description 3
- 230000004220 muscle function Effects 0.000 claims description 3
- 238000013473 artificial intelligence Methods 0.000 claims description 2
- 230000006735 deficit Effects 0.000 claims 3
- 238000011269 treatment regimen Methods 0.000 abstract description 5
- 230000008569 process Effects 0.000 abstract description 4
- 238000004891 communication Methods 0.000 abstract description 3
- 238000002595 magnetic resonance imaging Methods 0.000 description 8
- 230000000694 effects Effects 0.000 description 7
- 238000012544 monitoring process Methods 0.000 description 5
- 210000003205 muscle Anatomy 0.000 description 5
- 238000002567 electromyography Methods 0.000 description 4
- 238000007689 inspection Methods 0.000 description 4
- 239000000523 sample Substances 0.000 description 4
- 230000001154 acute effect Effects 0.000 description 3
- 230000001684 chronic effect Effects 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 3
- 238000003384 imaging method Methods 0.000 description 3
- 230000003183 myoelectrical effect Effects 0.000 description 3
- 238000004393 prognosis Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 208000029578 Muscle disease Diseases 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000002591 computed tomography Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 230000035876 healing Effects 0.000 description 2
- 230000000877 morphologic effect Effects 0.000 description 2
- 210000000653 nervous system Anatomy 0.000 description 2
- 230000000399 orthopedic effect Effects 0.000 description 2
- 231100000241 scar Toxicity 0.000 description 2
- 210000002966 serum Anatomy 0.000 description 2
- 206010018852 Haematoma Diseases 0.000 description 1
- 101000739754 Homo sapiens Semenogelin-1 Proteins 0.000 description 1
- 206010028289 Muscle atrophy Diseases 0.000 description 1
- 206010052904 Musculoskeletal stiffness Diseases 0.000 description 1
- 239000004642 Polyimide Substances 0.000 description 1
- 102100037550 Semenogelin-1 Human genes 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000037444 atrophy Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 238000011841 epidemiological investigation Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000004118 muscle contraction Effects 0.000 description 1
- 201000000585 muscular atrophy Diseases 0.000 description 1
- 210000002346 musculoskeletal system Anatomy 0.000 description 1
- 230000002232 neuromuscular Effects 0.000 description 1
- 230000005311 nuclear magnetism Effects 0.000 description 1
- 230000035790 physiological processes and functions Effects 0.000 description 1
- 229920001721 polyimide Polymers 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 239000000758 substrate Substances 0.000 description 1
- 210000001519 tissue Anatomy 0.000 description 1
- 238000003325 tomography Methods 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
- 230000002747 voluntary effect Effects 0.000 description 1
Classifications
-
- 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]
- A61B5/397—Analysis of electromyograms
-
- 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
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Artificial Intelligence (AREA)
- Medical Informatics (AREA)
- Physics & Mathematics (AREA)
- Public Health (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Biomedical Technology (AREA)
- Pathology (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Biology (AREA)
- Signal Processing (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Software Systems (AREA)
- Veterinary Medicine (AREA)
- Epidemiology (AREA)
- Computer Networks & Wireless Communication (AREA)
- Primary Health Care (AREA)
- Animal Behavior & Ethology (AREA)
- Databases & Information Systems (AREA)
- Mathematical Physics (AREA)
- Biophysics (AREA)
- Heart & Thoracic Surgery (AREA)
- Psychiatry (AREA)
- Physiology (AREA)
- Fuzzy Systems (AREA)
- Computing Systems (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The invention discloses an intelligent diagnosis and evaluation system for sports injury based on sEMG technology, which relates to the technical field of biomedical engineering, and has the technical key points that: the system comprises a data acquisition end and a diagnosis evaluation end, wherein the data acquisition end comprises a surface electromyographic signal acquisition module and a wireless data transmission module; the diagnosis evaluation end comprises a wireless signal receiving and transmitting unit, a data receiving and processing unit, a diagnosis evaluation module, a database and a diagnosis evaluation result feedback module. The intelligent diagnosis and evaluation system for the sports injury based on the sEMG technology can collect the skeletal surface electromyographic signals of a sporter in real time in the sports training process, and transmit the skeletal surface electromyographic signals collected in real time to a diagnosis and evaluation end by using a wireless communication technology, and the diagnosis and evaluation end is used for realizing intelligent, rapid and accurate diagnosis of the sports training skeletal muscle injury; meanwhile, the system is also convenient for assisting the basic-level medical staff to know the skeletal muscle injury state of the sporter in advance, so that a treatment strategy is conveniently, reasonably and effectively provided, and the skeletal muscle injury of the sports training is effectively reduced.
Description
Technical Field
The invention relates to the technical field of biomedical engineering, in particular to an intelligent diagnosis and evaluation system for sports injury based on sEMG technology.
Background
Sports injury is one of the common injuries of sports trainers, most common is acute and chronic skeletal muscle injury, and directly affects the development of sports training programs. Early accurate diagnosis of skeletal muscle injury is critical to subsequent treatment and disease prognosis.
The incidence of exercise training skeletal muscle injuries is reported differently, skeletal muscle injuries account for over 80% of the sports person's injuries, resulting in 60% of the sports person's inability to participate in exercise training each year. According to epidemiological investigation, it has been shown that more than 2/3 of many sports injuries are due to the progressive formation of cumulative micro-wounds in the musculoskeletal system, with increasing exercise training intensity, the incidence of sports injuries remains high. Through researches, once skeletal muscle is damaged, the skeletal muscle has long healing time, unreliable healing quality, easy formation of hematoma and reparative scar, and the lack of elasticity of scar tissue easily causes muscle stiffness and atrophy, influences the functional recovery of the muscle of a patient, and seriously influences the exercise training effect. The prevention and early rapid and accurate diagnosis of skeletal muscle injury, and the targeted proposal of treatment and rapid rehabilitation have very important effects on reducing incidence of sports injury and guaranteeing and improving health of sports trainees.
Currently, surface myoelectric techniques are an effective means of accurately, rapidly, noninvasively examining exercise training skeletal muscle injuries. For patients with exercise training skeletal muscle injuries, imaging examinations such as Electromyography (EMG), computerized tomography (computed tomography, CT), magnetic resonance imaging (magnetic resonance imaging, MRI) and ultrasound examination (US) and serum biochemical indexes are commonly used in emergency treatment to indirectly reflect the degree of acute and chronic skeletal muscle injuries and prognosis. The traditional EMG has more factors, and the probe needs to be pricked into the body, which belongs to invasive operation. And the electromyographic signals are easy to be influenced by adjacent muscles, environment, noise and the like, and only the change of the electromyographic signals can be observed, so that the morphological information after the muscle injury can not be obtained. MRI can accurately judge the position, degree, range and the like of muscle injury, and is suitable for evaluating muscle diseases. However, MRI is not real-time, and the equipment is large, and it is impossible to perform field and station and training field inspection, and the inspection cost is high.
In recent years, with rapid development of exercise biomechanics, the exercise biomechanics plays an important role in the fields of monitoring, intervention and the like of body efficacy of skeletal muscles and the like. The rapid response and intervention equipment is developed by researching exercise biomechanics abroad, an individual training scheme is formulated by applying biomechanics, virtual simulation, surface myoelectricity and other technologies, and a karn system for damage early warning and risk assessment is developed, so that the incidence rate of training injuries is remarkably reduced. Therefore, how to use the exercise biomechanics technology to realize the intelligent rapid accurate diagnosis of exercise training skeletal muscle injury, assist basic medical workers in reasonably and effectively proposing a treatment strategy, realize monitoring and early warning, big data analysis and scientific training, and effectively reduce the urgent need of exercise training skeletal muscle injury.
Therefore, the invention aims to provide an intelligent diagnosis and evaluation system for sports injury based on sEMG technology so as to solve the problems.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an intelligent diagnosis and evaluation system for sports injury based on sEMG technology.
The technical aim of the invention is realized by the following technical scheme:
The intelligent diagnosis and evaluation system for the sports injury based on the sEMG technology comprises a data acquisition end and a diagnosis and evaluation end, wherein the data acquisition end comprises a surface electromyographic signal acquisition module and a wireless data transmission module;
The diagnosis evaluation end comprises a wireless signal receiving and transmitting unit, a data receiving and processing unit, a diagnosis evaluation module, a database and a diagnosis evaluation result feedback module;
The surface electromyographic signal acquisition module is used for acquiring skeletal surface electromyographic signals of various parts of a human body;
The wireless signal receiving and transmitting unit is used for receiving the skeletal surface electromyographic signals acquired by the surface electromyographic signal acquisition module and transmitting the skeletal surface electromyographic signals to the data receiving and processing unit; the wireless signal receiving and transmitting unit is also used for transmitting data information to the data acquisition end;
the data receiving and processing unit is used for receiving and processing the skeletal surface electromyographic signals and extracting characteristic information in the skeletal surface electromyographic signals;
The diagnosis evaluation module analyzes and judges based on the trained skeletal muscle injury sEMG diagnosis model according to the skeletal surface electromyographic signals and the characteristic information to obtain a diagnosis evaluation result of the sports injury;
the database is used for storing exercise training skeletal muscle injury sEMG data;
The diagnosis evaluation result feedback module displays and interprets the diagnosis evaluation result according to the diagnosis evaluation result of the diagnosis evaluation module, and sends the diagnosis evaluation result to the data acquisition end through the wireless signal receiving and sending unit, so that diagnosis result prompt and diagnosis and treatment auxiliary operation are realized.
Further, the data receiving and processing unit performs band-pass filtering pretreatment of 10-100Hz on the skeletal surface electromyographic signals, analyzes the skeletal surface electromyographic signals subjected to the band-pass filtering pretreatment by utilizing an algorithm to extract time domain and frequency domain characteristic value information, calculates a plurality of parameter values of the time domain and the frequency domain, and diagnoses and evaluates muscle function conditions of a human body.
Furthermore, the algorithm analysis is realized by an upper computer with data processing and operation capabilities.
Further, the database is a skeletal muscle injury sEMG signal database for exercise training, and the construction method of the database comprises the following steps: by collecting sEMG signal data of patients with skeletal muscle injuries of different degrees and combining the development condition of later-stage illness states of the patients and treatment means, a diagnosis database is established.
Further, the specific method for constructing the database comprises the following steps:
A. Data collection
Critical data for patients with varying degrees of skeletal muscle injury are collected, including: general conditions, wound factors, injury levels, clinical manifestations, sEMG signal changes and auxiliary examination and examination;
B. Data processing
C, carrying out data cleaning, sample equalization and standardization treatment on the data collected in the step A;
C. Database generation
And extracting key data of skeletal muscle injury sEMG signal change of exercise training, judging skeletal muscle injury variables through a machine learning algorithm, respectively obtaining accuracy, recall rate, precision and F value calculated by each algorithm under different grouping index sets, comparing the results to obtain a key index model when a diagnosis result is optimal, and generating an exercise training skeletal muscle injury sEMG signal database.
Further, the skeletal muscle injury sEMG diagnostic model utilizes artificial intelligence deep learning to perform machine learning training by exercise training data in a skeletal muscle injury sEMG signal database.
Further, feature information in the skeletal surface electromyographic signals adopts a supervised learning mode to extract skeletal muscle injury sEMG signal features, and machine learning and training are performed through signal data of a skeletal muscle injury sEMG signal database.
Further, the surface electromyographic signal acquisition module is used for acquiring skeletal surface electromyographic signals of the waist, the back and the four limbs of the human body.
According to the scheme, when exercise training is carried out, the data acquisition end can be used for acquiring the skeletal surface electromyographic signals of the sporter in real time, and the skeletal surface electromyographic signals acquired in real time are wirelessly transmitted to the diagnosis evaluation end for diagnosis and evaluation, so that the exercise training skeletal muscle injury can be intelligently, rapidly and accurately diagnosed and evaluated, the diagnosis evaluation result can be fed back to the data acquisition end, and diagnosis result prompt and diagnosis and auxiliary operation can be carried out on the sporter; meanwhile, the diagnosis and evaluation result is also convenient for assisting the basic-level medical staff to reasonably and effectively put forward a treatment strategy, monitoring and early warning, big data analysis and scientific training are convenient to realize, and skeletal muscle injury in exercise training is effectively reduced.
The significance of solving the technical problems is that:
For patients with exercise training skeletal muscle injury, imaging examinations such as electromyography, electronic computer tomography, magnetic resonance imaging and ultrasonic examination and serum biochemical indexes are commonly used in emergency treatment to indirectly reflect the degree and prognosis of acute and chronic skeletal muscle injury. The traditional EMG has more factors, and the probe needs to be pricked into the body, which belongs to invasive operation. And the electromyographic signals are easy to be influenced by adjacent muscles, environment, noise and the like, and only the change of the electromyographic signals can be observed, so that the morphological information after the muscle injury can not be obtained. MRI can accurately judge the positions, the degree, the range and the like of muscle injury, and is suitable for evaluating muscle diseases. However, MRI is not real-time, and the equipment is large, and it is impossible to perform field and station and training field inspection, and the inspection cost is high. In recent years, with rapid development of exercise biomechanics, the exercise biomechanics plays an important role in the fields of monitoring, intervention and the like of body efficacy of skeletal muscles and the like. Therefore, the invention uses the sport biomechanics technology, realizes the intelligentized, rapid and accurate diagnosis of sport training skeletal muscle injury through the designed sport injury intelligentized diagnosis and evaluation system based on the sEMG technology, can assist basic medical workers to reasonably and effectively put forward a treatment strategy, realizes monitoring and early warning, big data analysis and scientific training, and simultaneously is an urgent requirement for effectively reducing sport training skeletal muscle injury.
In summary, compared with the prior art, the invention has the following beneficial effects:
1. The intelligent diagnosis and evaluation system for the sports injury based on the sEMG technology can collect the skeletal surface electromyographic signals of a sporter in real time in the sports training process, and transmit the skeletal surface electromyographic signals collected in real time to a diagnosis and evaluation end by using a wireless communication technology, and the diagnosis and evaluation end is used for realizing intelligent, rapid and accurate diagnosis of the sports training skeletal muscle injury;
2. The intelligent diagnosis and evaluation system for the sports injury based on the sEMG technology can send the diagnosis and evaluation result to the data acquisition end through the wireless signal receiving and sending unit, so that the diagnosis result prompt and diagnosis and auxiliary operation are realized, and meanwhile, auxiliary basic medical workers can know the skeletal muscle injury state of a sporter in advance, so that a relief strategy is conveniently, reasonably and effectively provided, and the sports training skeletal muscle injury is effectively reduced;
3. the sEMG technology-based intelligent diagnosis and evaluation system for the sports injury is more intelligent, rapid and accurate in diagnosis and evaluation of the sports injury compared with the prior art, and high in stability.
Drawings
FIG. 1 is a schematic illustration of an sEMG technology based exercise injury intelligent diagnostic assessment system provided by the present invention;
FIG. 2 is a schematic illustration of an sEMG technology based exercise injury intelligent diagnostic assessment system provided by the present invention;
Fig. 3 is a schematic diagram of the system for intelligent diagnosis and evaluation of sports injury based on sEMG technology.
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 implementation of the present invention will be described in detail below with reference to specific embodiments.
The same or similar reference numerals in the drawings of the present embodiment correspond to the same or similar components; in the description of the present invention, it should be understood that, if there is an azimuth or positional relationship indicated by terms such as "upper", "lower", "left", "right", etc., based on the azimuth or positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but it is not indicated or implied that the apparatus or element referred to must have a specific azimuth, be constructed and operated in a specific azimuth, and thus terms describing the positional relationship in the drawings are merely illustrative and should not be construed as limitations of the present patent, and specific meanings of the terms described above may be understood by those skilled in the art according to specific circumstances.
The scheme provided by the embodiment of the invention is as follows: the intelligent diagnosis and evaluation system for the sports injury based on the sEMG technology is shown by referring to fig. 1, and comprises a data acquisition end and a diagnosis and evaluation end, wherein the data acquisition end comprises a surface electromyographic signal acquisition module and a wireless data transmission module, and the surface electromyographic signal acquisition module is used for acquiring skeletal surface electromyographic signals of waist, back and limb parts of a human body.
The surface electromyographic signal acquisition module and the wireless data transmission module are integrated on the flexible extensible electrode sheet of the polyimide substrate and are used for acquiring the surface electromyographic signal by contacting with skin and wirelessly transmitting the surface electromyographic signal to the diagnosis evaluation end.
The diagnosis evaluation end comprises a wireless signal receiving and transmitting unit, a data receiving and processing unit, a diagnosis evaluation module, a database and a diagnosis evaluation result feedback module, wherein the database is used for storing exercise training skeletal muscle injury sEMG data. The wireless signal receiving and transmitting unit receives the skeletal surface electromyographic signals acquired by the surface electromyographic signal acquisition module and transmits the skeletal surface electromyographic signals to the data receiving and processing unit; the wireless signal receiving and transmitting unit is also used for transmitting data information to the data acquisition end; the data receiving and processing unit receives and processes the skeletal surface electromyographic signals and extracts characteristic information in the skeletal surface electromyographic signals.
The diagnosis evaluation module is used for analyzing and judging based on the trained skeletal muscle injury sEMG diagnosis model according to the skeletal surface electromyographic signals and the characteristic information to obtain a diagnosis evaluation result of the sports injury. The diagnosis evaluation result feedback module displays and interprets the diagnosis evaluation result according to the diagnosis evaluation result of the diagnosis evaluation module, and sends the diagnosis evaluation result to the data acquisition end through the wireless signal receiving and sending unit, so that diagnosis result prompt and diagnosis and treatment auxiliary operation are realized. In the embodiment, in the construction of the skeletal muscle injury sEMG diagnosis model, training injury occurrence scenes are not less than 3, and data are collected not less than 500 times, so that a training injury auxiliary diagnosis model with multi-position, multi-scene and multi-injury diagnosis effects is constructed.
The data receiving and processing unit performs band-pass filtering pretreatment of 10-100Hz on the skeletal surface electromyographic signals, analyzes and extracts time domain and frequency domain characteristic value information in the skeletal surface electromyographic signals subjected to the band-pass filtering pretreatment by utilizing an algorithm, calculates a plurality of parameter values of the time domain and the frequency domain, and diagnoses and evaluates muscle function conditions of a human body.
The algorithm analysis is realized by an upper computer with data processing and operation capabilities.
Wherein, the database is a skeletal muscle injury sEMG signal database for exercise training, and the construction method of the database is as follows: by collecting sEMG signal data of patients with skeletal muscle injuries of different degrees and combining the development condition of later-stage illness states of the patients and treatment means, a diagnosis database is established. The specific method comprises the following steps:
A. Data collection
Critical data (using a variety of approaches such as basic research and routine clinical work) for patients with varying degrees of skeletal muscle injury are collected, including: general conditions, injury factors, injury degree, clinical manifestations, sEMG signal changes, other auxiliary examination, and the like;
B. Data processing
C, carrying out data cleaning, sample equalization and standardization treatment on the data collected in the step A;
C. Database generation
And extracting key data of skeletal muscle injury sEMG signal change of exercise training, judging skeletal muscle injury variables through a machine learning algorithm, respectively obtaining accuracy, recall rate, precision and F values calculated by various algorithms under different grouping index sets, comparing the results to obtain a key index model when a diagnosis result is optimal, and generating an exercise training skeletal muscle injury sEMG signal database.
The skeletal muscle injury sEMG diagnosis model is a motion injury intelligent diagnosis feature model based on the sEMG technology, which is constructed by performing machine training learning through mass data of a skeletal muscle injury sEMG signal database by using an artificial intelligent deep learning technology.
Extraction of sEMG signal features for skeletal muscle injury: extracting sEMG signal characteristics of skeletal muscle injury based on clinical orthopedics, training injury rehabilitation expert opinions and related research bases by adopting a supervised learning mode; machine training learning is performed through massive signal data of the skeletal muscle injury sEMG signal database.
In this embodiment, the motion injury intelligent diagnosis model based on the sEMG technology improves the speed and accuracy of diagnosis of skeletal muscle injury degree by constructing the skeletal muscle injury sEMG diagnosis feature model through machine training and deep learning.
In addition, for the exercise injury intelligent diagnosis model based on the SEMG technology, in clinical application, the features of the skeletal muscle injury sEMG diagnosis model in the scheme of the embodiment can be continuously optimized by comparing the sEMG intelligent diagnosis model in the prior art with other imaging diagnosis results such as clinical orthopedics specialists, nuclear magnetism and the like, so that the accuracy and stability of the model in the scheme of the embodiment are improved, and the confidence and application conditions of the model are obtained.
In the scheme of the embodiment, the surface myoelectricity technology (sEMG) utilized by the flexible and extensible electrode plate at the data acquisition end can objectively reflect physiological state information of skeletal muscles and nervous systems, and the damage state of the skeletal muscles can be objectively and effectively reflected by acquiring useful information from the surface myoelectric signals and quantifying the information. The surface electromyographic signals are obtained by amplifying, displaying and recording the human body electrical signals through the surface electrodes after the human body electrical signals are changed when the neuromuscular system performs voluntary and involuntary activities, and the obtained results are voltage time series signals (shown in figure 2), and are an important method for noninvasively detecting the muscle activities on the body surface. The electromyogram detected by using the surface electromyogram signals is a waveform diagram reflecting the bioelectrical activity law of the muscle nervous system, and the electrical effect of muscle contraction is recorded. Because the amplitude of the sEMG electric signal is very weak, the amplitude is generally hundreds of microvolts, the average value can reach about 1mV, and the generation of the surface myoelectric action potential has strong randomness and belongs to random non-stationary signals, so that the mode has good safety (as shown in figure 3).
In summary, through the above embodiment of the present invention, the intelligent diagnosis and evaluation system for sports injury based on sEMG technology realizes real-time acquisition of skeletal surface electromyographic signals of a sporter in a sports training process, and transmits the real-time acquired skeletal surface electromyographic signals to a diagnosis and evaluation end by using a wireless communication technology, and then the diagnosis and evaluation end realizes intelligent, rapid and accurate diagnosis of sports training skeletal muscle injury; meanwhile, the diagnosis and evaluation result is sent to the data acquisition end through the wireless signal receiving and sending unit, so that diagnosis result prompt and diagnosis and treatment auxiliary operation can be realized, and the auxiliary basic unit medical staff can know the skeletal muscle injury state of the sporter in advance, so that a treatment strategy is conveniently, reasonably and effectively provided, and the skeletal muscle injury of the sports training is effectively reduced.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (8)
1. The intelligent diagnosis and evaluation system for the sports injury based on the sEMG technology is characterized by comprising a data acquisition end and a diagnosis and evaluation end, wherein the data acquisition end comprises a surface electromyographic signal acquisition module and a wireless data transmission module;
The diagnosis evaluation end comprises a wireless signal receiving and transmitting unit, a data receiving and processing unit, a diagnosis evaluation module, a database and a diagnosis evaluation result feedback module;
The surface electromyographic signal acquisition module is used for acquiring skeletal surface electromyographic signals of various parts of a human body;
The wireless signal receiving and transmitting unit is used for receiving the skeletal surface electromyographic signals acquired by the surface electromyographic signal acquisition module and transmitting the skeletal surface electromyographic signals to the data receiving and processing unit; the wireless signal receiving and transmitting unit is also used for transmitting data information to the data acquisition end;
the data receiving and processing unit is used for receiving and processing the skeletal surface electromyographic signals and extracting characteristic information in the skeletal surface electromyographic signals;
The diagnosis evaluation module analyzes and judges based on the trained skeletal muscle injury sEMG diagnosis model according to the skeletal surface electromyographic signals and the characteristic information to obtain a diagnosis evaluation result of the sports injury;
the database is used for storing exercise training skeletal muscle injury sEMG data;
The diagnosis evaluation result feedback module displays and interprets the diagnosis evaluation result according to the diagnosis evaluation result of the diagnosis evaluation module, and sends the diagnosis evaluation result to the data acquisition end through the wireless signal receiving and sending unit, so that diagnosis result prompt and diagnosis and treatment auxiliary operation are realized.
2. The intelligent diagnosis and evaluation system for sports injury based on sEMG technology according to claim 1, wherein the data receiving and processing unit performs 10-100Hz band-pass filtering pretreatment on the skeletal surface electromyographic signals, analyzes the band-pass filtered skeletal surface electromyographic signals to extract time domain and frequency domain characteristic value information by using algorithm, calculates a plurality of parameter values of the time domain and the frequency domain, and diagnoses and evaluates the muscle function condition of the human body.
3. The system for intelligent diagnosis and evaluation of sports injury based on sEMG technology according to claim 2, wherein the algorithm analysis is implemented by an upper computer with data processing and operation capabilities.
4. The system for intelligent diagnosis and evaluation of motor injuries based on the sEMG technology according to claim 3, wherein the database is a motor training skeletal muscle injury sEMG signal database, and the construction method of the database is as follows: by collecting sEMG signal data of patients with skeletal muscle injuries of different degrees and combining the development condition of later-stage illness states of the patients and treatment means, a diagnosis database is established.
5. The sEMG technology based sports injury intelligent diagnosis and evaluation system according to claim 4, wherein the specific method for constructing the database is as follows:
A. Data collection
Critical data for patients with varying degrees of skeletal muscle injury are collected, including: general conditions, wound factors, injury levels, clinical manifestations, sEMG signal changes and auxiliary examination and examination;
B. Data processing
C, carrying out data cleaning, sample equalization and standardization treatment on the data collected in the step A;
C. Database generation
And extracting key data of skeletal muscle injury sEMG signal change of exercise training, judging skeletal muscle injury variables through a machine learning algorithm, respectively obtaining accuracy, recall rate, precision and F value calculated by each algorithm under different grouping index sets, comparing the results to obtain a key index model when a diagnosis result is optimal, and generating an exercise training skeletal muscle injury sEMG signal database.
6. The system for intelligent diagnostic assessment of motor impairment based on sEMG technology according to claim 5, wherein the skeletal muscle impairment sEMG diagnostic model uses artificial intelligence deep learning for machine learning training by data in a motor training skeletal muscle impairment sEMG signal database.
7. The intelligent diagnosis and evaluation system for motor injury based on sEMG technology according to claim 6, wherein the characteristic information in the skeletal surface electromyographic signals adopts a supervised learning mode to extract the characteristics of skeletal muscle injury sEMG signals, and performs machine learning and training by the signal data of the skeletal muscle injury sEMG signal database.
8. The sEMG technology based intelligent diagnosis and evaluation system for sports injury according to claim 7, wherein the surface electromyographic signal acquisition module is used for acquiring skeletal surface electromyographic signals of the waist, the back and the four limbs of the human body.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311752151.8A CN117918869A (en) | 2023-12-19 | 2023-12-19 | Sport injury intelligent diagnosis and evaluation system based on sEMG technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311752151.8A CN117918869A (en) | 2023-12-19 | 2023-12-19 | Sport injury intelligent diagnosis and evaluation system based on sEMG technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117918869A true CN117918869A (en) | 2024-04-26 |
Family
ID=90751569
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311752151.8A Pending CN117918869A (en) | 2023-12-19 | 2023-12-19 | Sport injury intelligent diagnosis and evaluation system based on sEMG technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117918869A (en) |
-
2023
- 2023-12-19 CN CN202311752151.8A patent/CN117918869A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ibitoye et al. | Mechanomyography and muscle function assessment: A review of current state and prospects | |
| CN109222969A (en) | A kind of wearable human upper limb muscular movement fatigue detecting and training system based on Fusion | |
| Maura et al. | Literature review of stroke assessment for upper-extremity physical function via EEG, EMG, kinematic, and kinetic measurements and their reliability | |
| CN105054927A (en) | Biological quantitative assessment method for active participation degree in lower limb rehabilitation system | |
| JPH08501713A (en) | How to measure the effects of joints and related muscles | |
| CN105468908A (en) | Gait analysis method capable of carrying out auxiliary screening on knee osteoarthritis | |
| CN106650195A (en) | Gait analysis method for assisting in screening meniscus injuries | |
| Zhou et al. | Surface electromyogram analysis of the direction of isometric torque generation by the first dorsal interosseous muscle | |
| CN113609975A (en) | Modeling method for tremor detection, hand tremor detection device and method | |
| Burden | Surface electromyography | |
| Li et al. | Detection of muscle fatigue by fusion of agonist and synergistic muscle semg signals | |
| CN114748079A (en) | Wearable myoelectric method for online evaluation of muscle movement fatigue degree | |
| CN118078312B (en) | Muscle function evaluation system of forearm amputation patient residual limb based on electromyographic signals | |
| CN113171121A (en) | Multi-physical-field-coupling-based skeletal muscle system disease diagnosis device and method | |
| CN117918869A (en) | Sport injury intelligent diagnosis and evaluation system based on sEMG technology | |
| Takada et al. | Nonlinear analysis for evaluation of age-related muscle performance using surface electromyography | |
| Kallenberg et al. | Reproducibility of MUAP properties in array surface EMG recordings of the upper trapezius and sternocleidomastoid muscle | |
| Awad et al. | Compare EMG signals by using Myo-Ware muscle sensor and Myo-Trace device for measuring the electrical activity of the muscles | |
| CN112580587A (en) | Bone joint damage information evaluation system and evaluation method based on vibration noise signals | |
| Majid et al. | Performance assessment of the optimum feature extraction for upper-limb stroke rehabilitation using angular separation method | |
| Freeborn et al. | Bioimpedance alterations of knee site tissues during dynamic activity of varying intensity | |
| CN115736955A (en) | System for evaluating degree of disuse muscular atrophy after skeletal joint injury based on surface myoelectricity | |
| Lee et al. | Muscle Condition Measurement System using Non-Invasive Electromyography Signal | |
| Piseru et al. | Advancing Towards Clinical Validation of an Innovative System for Restoring Upper Limb Control in Individuals with Neurological Disorders | |
| Li et al. | Research on Fatigue Assessment Algorithm based on ECG and Multi-source Physiological Signals |
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 |