CN112022166A - Human body identity recognition method and system based on medical movement disorder feature recognition - Google Patents
Human body identity recognition method and system based on medical movement disorder feature recognition Download PDFInfo
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- 230000005021 gait Effects 0.000 claims description 124
- 208000011644 Neurologic Gait disease Diseases 0.000 claims description 22
- 206010017577 Gait disturbance Diseases 0.000 claims description 17
- 230000037237 body shape Effects 0.000 claims description 16
- 208000012661 Dyskinesia Diseases 0.000 claims description 13
- 206010017585 Gait spastic Diseases 0.000 claims description 12
- 206010044565 Tremor Diseases 0.000 claims description 12
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- 206010008748 Chorea Diseases 0.000 claims description 10
- 208000012601 choreatic disease Diseases 0.000 claims description 10
- 208000004067 Flatfoot Diseases 0.000 claims description 8
- 206010061159 Foot deformity Diseases 0.000 claims description 8
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- 241000489861 Maximus Species 0.000 claims description 6
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- 206010033892 Paraplegia Diseases 0.000 claims description 6
- 206010008129 cerebral palsy Diseases 0.000 claims description 6
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 6
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/117—Identification of persons
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
- G06V40/23—Recognition of whole body movements, e.g. for sport training
Abstract
The invention discloses a human body identity recognition method and system based on medical movement disorder feature recognition, which mainly realize the recognition of human body identity information by extracting human body movement features and body profile features in images, carrying out medical movement disorder feature recognition according to the human body movement features and the body profile features, and comparing the difference between a human body to be detected and a human body to be compared on the human body medical movement disorder features and the body profile features.
Description
Technical Field
The invention relates to a human body identity recognition technology and a portrait identification technology, in particular to a human body identity recognition method and a human body identity recognition system based on medical dyskinesia feature recognition.
Background
The diseases bring troubles to the mind and body of people, and influence the expression of human body posture and action behavior characteristics, otherwise, the identification of human body identity information can be realized by analyzing the human body abnormal posture and action behavior characteristics from the images. The disease and other medical history information is an important component of the human identity information, and can be effectively used for human identity information identification and portrait identification. However, the existing human body identification technology and portrait identification technology do not effectively utilize medical characteristic information reflected in the human body abnormal action behavior characteristics.
Disclosure of Invention
Aiming at the requirements of the fields of human body identification technology, portrait identification technology and the like on new technical and new methods for human body identification, the invention provides a human body identification method and a human body identification system based on medical movement disorder characteristic identification, and aims to solve the problem that medical characteristic information reflected in human body abnormal action behavior characteristics is not effectively utilized in the existing human body identification technology and portrait identification technology.
The invention is realized by the following technical scheme:
a human body identity recognition method based on medical movement disorder feature recognition comprises the following steps:
step a, extracting a human body image to be detected and comparing human body motion characteristics and human body figure contour characteristics in the human body image;
b, according to the human body motion characteristics and the human body shape contour characteristics, performing human body medical motion obstacle characteristic identification on the human body to be detected and the human body to be compared;
step c, comparing the human body to be detected with the difference and the similarity of the human body in the medical dyskinesia characteristics and the body figure profile characteristics of the human body;
and d, identifying the identity of the human body according to the difference and identity comparison result obtained in the step c.
Further, the human motion characteristics comprise motion information characteristics of the head, the limbs and the trunk of the human body.
Further, the human body shape and contour characteristics comprise human body shape, body state, posture and appearance characteristics.
Further, the human medical dyskinesia characteristics include abnormal gait and voluntary motor regulation dysfunction caused by nervous system diseases.
Further, the abnormal gait includes disuse gait, unregulated gait, hemiplegic gait, cerebral palsy gait, parkinson's disease gait, paraplegic gait, prosthetic gait, joint disease gait, spastic gait, stiff gait, flaccidity gait, circling gait, tiptoe gait, scissors gait, panic gait, hallucination gait, lameness gait, pain reduction gait, splayfoot gait, gluteus maximus gait, duck step gait, and snare leg gait.
Further, voluntary motor regulation dysfunction caused by the nervous system disease includes resting tremor, muscular rigidity, bradykinesia, abnormal posture and gait, chorea-like movements, chorea-like hand posture, and dystonia.
A human body identification system based on medical movement disorder feature recognition comprises:
the characteristic extraction module is used for extracting the human body image to be detected and comparing the human body motion characteristic and the human body figure contour characteristic in the human body image;
the medical characteristic identification module is used for carrying out human medical movement obstacle characteristic identification on a human body to be detected and a human body to be compared according to the human movement characteristic and the human body shape contour characteristic;
the characteristic comparison module is used for comparing the difference between the human body to be detected and the human body for comparison on the medical movement disorder characteristic of the human body and the body profile characteristic of the human body;
and the identity recognition module is used for carrying out human identity recognition on the difference and identity comparison result obtained by the characteristic comparison module.
Further, the human motion characteristics comprise motion information characteristics of the head, the limbs and the trunk of the human body.
Further, the human body shape and contour characteristics comprise human body shape, body state, posture and appearance characteristics.
Further, the human medical dyskinesia characteristics include abnormal gait and voluntary motor regulation dysfunction caused by nervous system diseases.
Further, the abnormal gait includes disuse gait, unregulated gait, hemiplegic gait, cerebral palsy gait, parkinson's disease gait, paraplegic gait, prosthetic gait, joint disease gait, spastic gait, stiff gait, flaccidity gait, circling gait, tiptoe gait, scissors gait, panic gait, hallucination gait, lameness gait, pain reduction gait, splayfoot gait, gluteus maximus gait, duck step gait, and snare leg gait.
Further, voluntary motor regulation dysfunction caused by the nervous system disease includes resting tremor, muscular rigidity, bradykinesia, abnormal posture and gait, chorea-like movements, chorea-like hand posture, and dystonia.
Compared with the prior art, the human body identity recognition method and system based on the medical movement disorder feature recognition provided by the invention can be used for recognizing the medical movement disorder feature according to the human body movement feature by extracting the human body movement feature and the body profile feature in the image, and comparing the difference between the human body to be detected and the human body to be compared on the medical movement disorder feature and the body profile feature of the human body, so that the identity information of the human body is recognized.
Drawings
Fig. 1 is a flow chart diagram of a human body identity recognition method based on medical dyskinesia feature recognition of the present invention.
Fig. 2 is a schematic diagram of the composition principle of a human body identification system based on medical dyskinesia feature recognition of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the following embodiments and the accompanying drawings.
The embodiment provides a human body identity recognition method based on medical movement disorder feature recognition. The whole work flow is shown in fig. 1, and comprises the following steps:
step a, extracting the human body image to be detected and comparing the human body motion characteristic and the human body figure contour characteristic in the human body image. In this embodiment, the human body image to be compared is usually a human body image with known identity information, and the human body image to be detected is a human body image with unknown identity information and needs to be compared with the human body image to be compared to determine whether the human body image and the human body image to be compared are the same person. The human motion characteristics can comprise motion information characteristics of the head, the limbs and the trunk of the human body. The human body figure and contour characteristics can comprise human body type, body state, posture and appearance characteristics. When the step is specifically implemented, foreground and background separation can be carried out on a target human body in the image through a computer vision field method from the image in the video sequence, the target human body image is continuously tracked in the subsequent video sequence, the head, the limbs and the trunk of the target human body are identified, and the motion information characteristics of the head, the limbs and the trunk of the target human body, the body shape, the posture and the appearance characteristics of the target human body are calculated. For example, the body type, the body state, the posture and the appearance characteristics of the target human body in the image are extracted by a foreground and background separation method or a human body recognition method in the field of computer vision. For another example, the movement speed and direction information of the head, limbs and trunk of the target human body in the video sequence images are calculated by an optical flow method in the computer vision field.
And b, identifying the medical movement disorder characteristics of the human body to be detected and the human body to be compared according to the human body movement characteristics and the human body shape contour characteristics. In this embodiment, according to the human body motion features and the human body shape profile features extracted in step a, medical motion disorder features of the human body to be detected and the human body to be compared are respectively identified. The human body medical dyskinesia characteristics comprise abnormal gait of a human body and random dyskinesia regulation dysfunction caused by nervous system diseases, wherein the abnormal gait comprises disuse gait, irregulability gait, hemiplegic gait, cerebral palsy gait, Parkinson gait, paraplegia gait, artificial limb gait, joint disease gait, spastic gait, stiff gait, relaxation gait, circling gait, pointe gait, scissors gait, panic gait, drop foot gait, lameness gait, pain reduction gait, splayfoot gait, gluteus maximus gait, duck step gait and compass leg gait; disorder of voluntary motor regulation caused by nervous system diseases includes resting tremor, muscular rigidity, bradykinesia, abnormal gait posture, chorea-like movements, chorea-like hand posture, dystonia. Whether the human body image is required to be detected or compared, the step can identify the medical dyskinesia characteristics of the human body according to the human body movement characteristics and the human body figure outline characteristics extracted from the human body image. For example, in step a, the human motion features and the human body contour features extracted from the human body image to be detected have a regular up-down shaking phenomenon at the target human body hand, particularly when the arm is stationary, the palm part has up-down shaking motion, and the motion features are consistent with the hand stationary tremor features caused by the parkinson disease, so that the motion features in the human body image to be detected are identified as the hand stationary tremor features. For another example, in the step a, the gait feature in the human body image for comparison is identified as the pain-reducing gait feature caused by the left hip joint pain if the motion feature and the body contour feature of the target human body walking, which has the motion feature and the body contour feature of the left leg standing phase time shorter than the right leg standing phase time, the left shoulder descending, the trunk inclination, the left lower limb outward rotation and the flexion position, are consistent with the pain-reducing gait feature caused by the left hip joint pain. For another example, when the target human body in the human body video sequence image to be detected is extracted in the step a to walk, the long axes of the two feet are not consistent with the advancing direction, the included angle formed between the inner side of the heel and the advancing direction is more than 10 degrees, and the gait feature is consistent with the gait feature of the shape of the Chinese character 'exo', the gait feature in the human body video sequence image to be detected is identified as the gait feature of the shape of the Chinese character 'exo'.
And c, comparing the difference between the human body to be detected and the human body to be compared on the medical movement disorder characteristic of the human body and the figure profile characteristic of the human body. For example, if the human body to be detected has the hand resting tremor characteristics for comparison with the hand resting tremor characteristics caused by parkinson's disease, the human body to be detected and the human body to be compared are considered to be consistent in the hand resting tremor characteristics. In this embodiment, since the identity information of the images of the human body to be compared is known, the static tremor characteristics of the hand caused by the parkinson disease of the human body to be compared can be obtained by the methods of step a and step b, or by querying the medical history information of the human body to be compared. For another example, if the contour feature of the lower leg of the human body to be tested has the outward appearance feature of the vein caused by the varicose lower leg, and the lower leg of the human body to be compared also has the outward appearance feature of the vein caused by the varicose lower leg, it can be considered that the outward appearance feature of the vein caused by the varicose lower leg of the human body to be tested and the outward appearance feature of the vein caused by the varicose lower leg of the human body to be compared match. For another example, if the human body to be detected has a "toed-out" gait feature and the human body to be compared does not have the "toed-out" gait feature, it can be considered that the human body to be detected and the human body to be compared have a difference in the "toed-out" gait feature. The comparison of other human body medical movement disorder characteristics and human body figure profile characteristics is in the same way.
And d, identifying the identity of the human body according to the difference and identity comparison result obtained in the step c. The human body to be detected is compared with the human body medical movement disorder characteristic and the human body figure profile characteristic of the human body to be compared so as to determine the coincidence point and the difference point between the human body medical movement disorder characteristic and the human body figure profile characteristic, then the identity comparison of human body images is carried out, the detection content and the detection angle in the medical field can be provided for the identity comparison of the human body images, and the validity and the accuracy of the identity comparison of the human body images are improved. For example, when comparing the identity of the human body image to be detected with the identity of the human body image to be compared, on the basis of comparison by using a traditional comparison method, the difference and identity comparison between the human body to be detected and the human body to be compared on the medical movement disorder characteristics of the human body and the body figure profile characteristics of the human body to be compared are added, the difference and identity comparison results between the human body to be detected and the human body to be compared on the medical movement disorder characteristics of the human body and the body figure profile characteristics of the human body are combined with the human identity comparison result obtained by using the traditional comparison method to consider, so as to determine the final result of the human identity comparison, and whether the human body images are the same person is judged according.
Based on the human body identification method, the invention further provides a human body identity identification system based on the medical movement disorder feature identification. As shown in fig. 2, the human body identification system based on medical movement disorder feature recognition comprises:
the characteristic extraction module 1 is used for extracting human body images to be detected and comparing human body motion characteristics and human body figure contour characteristics in the human body images;
the medical characteristic identification module 2 is used for carrying out human medical movement obstacle characteristic identification on a human body to be detected and a human body to be compared according to the human movement characteristic and the human body shape contour characteristic;
the characteristic comparison module 3 is used for comparing the difference between the human body to be detected and the human body for comparison on the medical movement obstacle characteristic of the human body and the body profile characteristic of the human body;
and the identity recognition module 4 is used for carrying out human identity recognition according to the difference and identity comparison result obtained by the characteristic comparison module 3.
The human body motion characteristics comprise motion information characteristics of the head, the limbs and the trunk of the human body; the human body shape and contour characteristics comprise human body shape, body state, posture and appearance characteristics; the human body medical dyskinesia characteristics comprise abnormal gait and voluntary movement regulation dysfunction caused by nervous system diseases; the abnormal gaits comprise disuse gaits, unregulated gaits, hemiplegic gaits, cerebral palsy gaits, parkinsonism gaits, paraplegic gaits, prosthetic gaits, joint disease gaits, spastic gaits, stiff gaits, flaccidity gaits, circling gaits, scissors gaits, panic gaits, drop-foot gaits, lameness gaits, pain relief gaits, splayfoot gaits, gluteus maximus gaits, duck step gaits and compass leg gaits; the disorder of voluntary motor regulation caused by nervous system diseases comprises resting tremor, muscular rigidity, bradykinesia, abnormal gait posture, dance-like movement, dance-like hand posture and dystonia.
Each module in the human body identification system based on the medical movement disorder feature recognition corresponds to each step in the human body identification method based on the medical movement disorder feature recognition, and is used for executing each step in the human body identification method based on the medical movement disorder feature recognition, and specific actions executed by each module can refer to each step in the human body identification method based on the medical movement disorder feature recognition.
The above-described embodiments are merely preferred embodiments, which are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (12)
1. A human body identity recognition method based on medical movement disorder feature recognition is characterized by comprising the following steps:
step a, extracting a human body image to be detected and comparing human body motion characteristics and human body figure contour characteristics in the human body image;
b, according to the human body motion characteristics and the human body shape contour characteristics, performing human body medical motion obstacle characteristic identification on the human body to be detected and the human body to be compared;
step c, comparing the difference between the human body to be detected and the human body for comparison on the medical dyskinesia characteristics of the human body and the body profile characteristics of the human body;
and d, identifying the identity of the human body according to the difference and identity comparison result obtained in the step c.
2. The human body identification method based on medical movement disorder feature recognition as claimed in claim 1, wherein the human body movement features comprise movement information features of a head, limbs and a trunk of a human body.
3. The method as claimed in claim 1, wherein the human body figure and contour features include human body shape, posture, appearance features.
4. The method of claim 1, wherein the medical dyskinesia characteristics of the human body include abnormal gait and voluntary movement regulation dysfunction caused by nervous system diseases.
5. The method of claim 4, wherein the abnormal gait includes disuse gait, detuning gait, hemiplegic gait, cerebral palsy gait, Parkinson's gait, paraplegic gait, prosthetic gait, joint disease gait, spastic gait, stiff gait, flaccid gait, circling gait, pointed gait, scissors gait, panic gait, drop foot gait, lameness gait, pain reduction gait, lateral splayfoot gait, medial splayfoot gait, gluteus maximus gait, duck step gait, and snare leg gait.
6. The method of claim 4, wherein the disorder of the nervous system is selected from the group consisting of tremor at rest, muscular rigidity, bradykinesia, gait disorder, chorea-like movements, chorea-like hand gestures, and dystonia.
7. A human body identification system based on medical movement disorder feature recognition is characterized by comprising:
the characteristic extraction module is used for extracting the human body image to be detected and comparing the human body motion characteristic and the human body figure contour characteristic in the human body image;
the medical characteristic identification module is used for carrying out human medical movement obstacle characteristic identification on a human body to be detected and a human body to be compared according to the human movement characteristic and the human body shape contour characteristic;
the characteristic comparison module is used for comparing the difference between the human body to be detected and the human body for comparison on the medical movement disorder characteristic of the human body and the body profile characteristic of the human body;
and the identity recognition module is used for carrying out human identity recognition according to the difference and identity comparison result obtained by the characteristic comparison module.
8. The system of claim 7, wherein the body motion characteristics include body motion information characteristics of the head, limbs and trunk of the human body.
9. The system of claim 7, wherein the body contour features include body shape, posture, and appearance.
10. The system of claim 7, wherein the medical dyskinesia characteristics of the human body include abnormal gait and voluntary motor regulation dysfunction due to neurological disease.
11. The system of claim 10, wherein the abnormal gait includes disuse gait, detuning gait, hemiplegic gait, cerebral palsy gait, parkinson gait, paraplegic gait, prosthetic gait, joint disease gait, spastic gait, stiff gait, flaccid gait, circling gait, pointed gait, scissors gait, panic gait, drop foot gait, lameness gait, pain reduction gait, lateral splayfoot gait, medial splayfoot gait, gluteus maximus gait, duck step gait, and snare leg gait.
12. The system of claim 10, wherein the disorder of nervous system-induced voluntary motor regulation comprises resting tremor, muscular rigidity, bradykinesia, gait abnormalities, chorea-like movements, chorea-like hand gestures, dystonia.
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