CN117524413A - Motion protection method, device, equipment and medium based on millimeter wave radar - Google Patents
Motion protection method, device, equipment and medium based on millimeter wave radar Download PDFInfo
- Publication number
- CN117524413A CN117524413A CN202410019665.0A CN202410019665A CN117524413A CN 117524413 A CN117524413 A CN 117524413A CN 202410019665 A CN202410019665 A CN 202410019665A CN 117524413 A CN117524413 A CN 117524413A
- Authority
- CN
- China
- Prior art keywords
- motion
- human body
- gesture
- millimeter wave
- wave radar
- 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
- 238000000034 method Methods 0.000 title claims abstract description 46
- 230000036541 health Effects 0.000 claims abstract description 26
- 230000009471 action Effects 0.000 claims abstract description 24
- 230000017531 blood circulation Effects 0.000 claims description 21
- 238000004590 computer program Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 11
- 238000012544 monitoring process Methods 0.000 claims description 11
- 238000012549 training Methods 0.000 claims description 11
- 238000004422 calculation algorithm Methods 0.000 claims description 9
- 238000005259 measurement Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 6
- 230000002441 reversible effect Effects 0.000 claims description 6
- 238000005265 energy consumption Methods 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000013508 migration Methods 0.000 claims description 3
- 230000005012 migration Effects 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 230000006378 damage Effects 0.000 abstract description 5
- 230000001815 facial effect Effects 0.000 abstract description 4
- 238000013473 artificial intelligence Methods 0.000 abstract description 3
- 238000004891 communication Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 9
- 238000007726 management method Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 238000013139 quantization Methods 0.000 description 2
- 208000017667 Chronic Disease Diseases 0.000 description 1
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000013526 transfer learning Methods 0.000 description 1
- 230000000007 visual 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
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
-
- 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
- A61B5/1116—Determining posture transitions
-
- 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
- A61B5/1126—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb using a particular sensing technique
-
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/886—Radar or analogous systems specially adapted for specific applications for alarm systems
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
-
- 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
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
-
- 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
-
- 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
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
-
- 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
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
Abstract
The invention relates to the field of artificial intelligence, and discloses a motion protection method, a motion protection device, electronic equipment and a storage medium based on millimeter wave radar, wherein the method comprises the following steps: recording movement time, recording a figure heart rate curve, and carrying out human body posture feature recognition on the posture point cloud image to obtain a human body posture sequence; identifying the facial and physical characteristics of the person object to obtain a target identification object, and extracting medical health data corresponding to the target identification object; predicting the exercise intensity according to the medical health data and the data in the historical exercise record table by utilizing a pre-trained exercise safety early warning model to obtain an exercise intensity warning value, and calculating an action intensity curve according to the human body posture sequence; and calculating a body bearing value of the action intensity curve and the figure heart rate curve, and generating alarm prompt information when the body bearing value is greater than or equal to the exercise intensity warning value. The invention can estimate the motion bearing capacity of the user and avoid or reduce the damage caused by excessive motion of the user.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a motion protection method and device based on millimeter wave radar, electronic equipment and a readable storage medium.
Background
With the health awareness slowly reaching the masses, more and more people begin to participate in body building, and the number of users participating in various body building exercises is continuously increasing.
However, some middle-aged and elderly or even young people are prone to life hazards when exercising vigorously in the presence of chronic diseases, or do not exercise for a long period of time, and sudden and violent exercise is at risk of injury, and thus, the part of people may need exercise protection monitoring.
Disclosure of Invention
The invention provides a motion protection method, a motion protection device, electronic equipment and a readable storage medium based on millimeter wave radar, which aim to estimate the motion bearing capacity of a user and avoid or reduce the damage caused by excessive motion of the user.
In order to achieve the above object, the present invention provides a motion protection method based on millimeter wave radar, the method comprising:
when a person object appears in a preset motion area, acquiring a person heart rate and gesture point cloud image of the person object by utilizing a pre-constructed millimeter wave radar;
When a preset body movement gesture appears in the gesture point cloud image, recording movement time, recording a character heart rate curve corresponding to the character heart rate, carrying out human gesture feature recognition on the gesture point cloud image to obtain a human gesture sequence, and storing the movement time and the human gesture sequence into a pre-constructed historical movement record table;
identifying the face and body characteristics of the person object to obtain a target identification object, and extracting medical health data corresponding to the target identification object from a pre-constructed family member information base;
predicting the exercise intensity according to the medical health data and the data in the historical exercise record table by utilizing a pre-trained exercise safety early warning model to obtain an exercise intensity warning value;
inquiring the motion quantity of each motion in the human body gesture sequence, counting the total motion quantity, and carrying out average operation in unit time on the total motion quantity counting result according to the motion time to obtain an action intensity curve;
and calculating body bearing values of the action intensity curve and the figure heart rate curve, and generating alarm prompt information when the body bearing values are larger than or equal to the exercise intensity warning value.
Optionally, before the pre-trained exercise safety pre-warning model is utilized, the method further includes:
acquiring a motion safety early warning model comprising a body limit prediction network, an action intensity recognition network and a body bearing value calculation network, and acquiring a pre-constructed medical expert experience sample set;
acquiring a pre-constructed motion quantity monitoring model, and performing migration learning on the motion strength recognition network by using the motion quantity monitoring model;
sequentially extracting a target sample from the medical expert experience sample set, performing network forward calculation on the target sample by using the motion safety early warning model to obtain a predicted bearing value, and obtaining a predicted body limit value output by a body limit prediction network in the motion safety early warning model by using a pre-constructed middle layer network output interface;
calculating a first loss value between a real bearing value label corresponding to the target sample and the predicted bearing value and a second loss value between a real warning value label corresponding to the target sample and the predicted body limit value by using a cross entropy loss algorithm;
according to a gradient descent algorithm, performing minimum network reverse parameter updating operation on the first loss value and the second loss value to obtain an updated motion safety early warning model;
Judging the convergence of the first loss value;
when the first loss value is not converged, returning to the operation step of sequentially extracting a target sample from the medical expert experience sample, and iteratively updating the updated motion safety early warning model;
and stopping the training process when the first loss value converges to obtain a training-completed motion safety early warning model.
Optionally, the acquiring the figure heart rate and the gesture point cloud image of the figure object by using the pre-constructed millimeter wave radar includes:
scanning the blood flow velocity of the person object by utilizing a continuous wave scanning mode of the pre-constructed millimeter wave radar to obtain a blood flow velocity reflected wave;
calculating a target character heart rate of the character object according to the blood flow velocity reflection wave, a preset blood flow velocity and a human heart rate reflection table;
performing human body scanning on the person object by utilizing a frequency modulation continuous wave scanning mode of the millimeter wave radar to obtain a human body posture reflected wave;
and drawing a posture point cloud image according to the human posture reflected wave.
Optionally, the drawing the posture point cloud image according to the reflected wave of the human body posture includes:
Extracting a human body posture signal in the human body posture reflected wave;
denoising the human body posture signal, performing digital-to-analog conversion and fast Fourier transformation to obtain a human body posture frequency signal;
doppler velocity measurement and multi-angle measurement are carried out on the human body posture frequency signals, and space position coordinates of the person object are obtained;
and displaying the spatial position coordinates of the character object in a three-dimensional mode to obtain a gesture point cloud image.
Optionally, the performing human body posture feature recognition on the posture point cloud image to obtain a human body posture sequence includes:
performing feature engineering operation based on the position, direction and posture of the human body on the pre-constructed human body motion feature sample to obtain a motion posture feature type;
performing feature extraction operation on the attitude point cloud image based on the motion attitude feature type to obtain a motion feature sequence set;
and performing feature full-connection classification recognition operation on the motion feature sequence set to obtain a human body posture sequence.
Optionally, the querying the motion amount of each motion in the human body gesture sequence counts the total motion amount, including:
vectorizing human body posture features in the human body posture sequence to obtain a human body posture feature vector;
Performing full-connection classification judgment on the human body posture feature vector to obtain the motion type and the motion times of each motion in the human body posture sequence;
inquiring the energy consumption value of each motion type, and calculating the motion quantity of each motion type according to each motion frequency;
and carrying out sum operation on the motion amounts of all the motion types to obtain the total motion amount.
Optionally, after the generating the alarm prompt information, the method further includes:
when the gesture point cloud image is detected to suddenly present the reverse gesture, telephone notification is carried out on a preset guardian according to the guardian telephone in the basic information of the user.
In order to solve the above problems, the present invention also provides a motion protection device based on millimeter wave radar, the device comprising:
the image heart rate data acquisition module is used for acquiring a figure heart rate and a gesture point cloud image of a figure object by utilizing a pre-built millimeter wave radar when the figure object appears in a preset motion area, recording motion time when a preset body motion gesture appears in the gesture point cloud image, recording a figure heart rate curve corresponding to the figure heart rate, carrying out human gesture feature recognition on the gesture point cloud image to obtain a human gesture sequence, and storing the motion time and the human gesture sequence into a pre-built historical motion record table;
The medical information acquisition module is used for identifying the face and the physical characteristics of the person object to obtain a target identification object, and extracting medical health data corresponding to the target identification object from a pre-constructed family member information base;
the body limit prediction module is used for predicting the exercise intensity according to the medical health data and the data in the historical exercise record table by utilizing a pre-trained exercise safety early warning model to obtain an exercise intensity warning value;
and the bearing value warning module is used for inquiring the motion quantity of each motion in the human body posture sequence, counting the total motion quantity, carrying out average operation in unit time on the total motion quantity counting result according to the motion time to obtain a motion intensity curve, carrying out body bearing value calculation on the motion intensity curve and the figure heart rate curve, and generating warning prompt information when the body bearing value is greater than or equal to the motion intensity warning value.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one computer program; and
And the processor executes the computer program stored in the memory to realize the motion protection method based on the millimeter wave radar.
In order to solve the above-described problems, the present invention also provides a computer-readable storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-described millimeter wave radar-based motion protection method.
According to the embodiment of the invention, firstly, a cloud image of the gesture point of a user is obtained through a millimeter wave radar, so that a human gesture sequence of the user during movement is obtained, the type and the number of times of movement can be identified according to the human gesture sequence by utilizing a movement safety early warning model, so that the movement quantity of the user is calculated, the relation between the two curves is checked together with a pre-obtained figure heart rate curve, and the real-time body bearing value of the user is comprehensively calculated; in addition, the exercise safety early warning model can predict the exercise intensity warning value of the limit of the user through the medical health data and the historical exercise records, so that whether the body bearing value exceeds the exercise intensity warning value is compared, and whether an alarm is needed or not is judged. Therefore, the motion protection method, the motion protection device, the motion protection equipment and the storage medium based on the millimeter wave radar can estimate the motion bearing capacity of the user and avoid or reduce the damage caused by excessive motion of the user.
Drawings
Fig. 1 is a schematic flow chart of a motion protection method based on millimeter wave radar according to an embodiment of the present invention;
fig. 2 and fig. 3 are a flowchart illustrating a detailed implementation of one of steps in a millimeter wave radar-based motion protection method according to an embodiment of the present invention;
fig. 4 is a schematic block diagram of a motion protection device based on millimeter wave radar according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an internal structure of an electronic device for implementing a motion protection method based on millimeter wave radar according to an embodiment of the present invention;
the achievement of the object, functional features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention provides a motion protection method based on millimeter wave radar. The execution subject of the millimeter wave radar-based motion protection method includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiments of the present application. In other words, the millimeter wave radar-based motion protection method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The server may include an independent server, and may also include a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, which is a schematic flow chart of a motion protection method based on millimeter wave radar according to an embodiment of the present invention, in an embodiment of the present invention, the motion protection method based on millimeter wave radar includes:
s1, when a person object appears in a preset motion area, acquiring a person heart rate and gesture point cloud image of the person object by utilizing a pre-constructed millimeter wave radar.
In the embodiment of the invention, the movement area can be a fixed living room, bedroom and other areas, and can also be a yoga mat nearby area and other areas which can be tracked at any time.
The millimeter wave radar is a radar system for detection and measurement by utilizing millimeter waves. Millimeter waves refer to electromagnetic waves with the wavelength of 1-10 mm, have the characteristics of high frequency, high energy, high speed, high precision and the like, can analyze the body actions of a user with low resolution, can recognize the body health data of the user with high precision, such as heartbeat and the like, and are very suitable for monitoring the health of a human body in an indoor complex environment.
In detail, in the embodiment of the present invention, the acquiring, by using the pre-constructed millimeter wave radar, the figure heart rate and the gesture point cloud image of the figure object includes:
Scanning the blood flow velocity of the person object by utilizing a continuous wave scanning mode of the pre-constructed millimeter wave radar to obtain a blood flow velocity reflected wave;
calculating a target character heart rate of the character object according to the blood flow velocity reflection wave, a preset blood flow velocity and a human heart rate reflection table;
performing human body scanning on the person object by utilizing a frequency modulation continuous wave scanning mode of the millimeter wave radar to obtain a human body posture reflected wave;
and drawing a posture point cloud image according to the human posture reflected wave.
In an alternative embodiment of the present invention, when a continuous wave signal emitted by a continuous wave scanning mode of the millimeter wave radar interacts with blood of a human living being, a blood flow velocity reflection wave affected by a blood flow velocity is generated, and therefore, the blood flow velocity reflection wave can reflect the blood flow velocity of the human living being.
Further, the preset blood flow rate and heart rate reflection table refers to a table of one-to-one correspondence between blood flow rate and heart rate of a human under the conventional condition.
Because the heart rate of the human living things has close relation with the blood flow rate, the embodiment of the invention can calculate the heart rate of the target person of the human living things according to the blood flow rate reflection wave and the preset blood flow rate and human heart rate reflection table, ensure the accuracy of the heart rate of the target person and realize the non-contact measurement of the heart rate of the human living things.
Further, when a continuous wave signal emitted by a continuous wave scanning mode of the millimeter wave radar interacts with a human living organism, a human body posture reflected wave influenced by the position of the human body is generated, and therefore, the human body posture reflected wave can reflect the action posture of the human living organism.
In detail, in the embodiment of the present invention, the drawing the posture point cloud image according to the reflected wave of the human body posture includes:
extracting a human body posture signal in the human body posture reflected wave;
denoising the human body posture signal, performing digital-to-analog conversion and fast Fourier transformation to obtain a human body posture frequency signal;
doppler velocity measurement and multi-angle measurement are carried out on the human body posture frequency signals, and space position coordinates of the person object are obtained;
and displaying the spatial position coordinates of the character object in a three-dimensional mode to obtain a gesture point cloud image.
S2, when a preset body movement gesture appears in the gesture point cloud image, recording movement time, recording a character heart rate curve corresponding to the character heart rate, carrying out human gesture feature recognition on the gesture point cloud image to obtain a human gesture sequence, and storing the movement time and the human gesture sequence into a pre-constructed historical movement record table.
The body movement posture in the embodiment of the invention refers to the preset conventional movement starting hand type such as jogging, squatting and the like. When these body movement gestures occur, indicating that the user is starting to perform movements, movement recording and monitoring may be turned on.
In detail, in the embodiment of the present invention, the performing human body posture feature recognition on the posture point cloud image to obtain a human body posture sequence includes:
performing feature engineering operation based on the position, direction and posture of the human body on the pre-constructed human body motion feature sample to obtain a motion posture feature type; performing feature extraction operation on the attitude point cloud image based on the motion attitude feature type to obtain a motion feature sequence set; and performing feature full-connection classification recognition operation on the motion feature sequence set to obtain a human body posture sequence.
In the embodiment of the invention, the positions such as the joints of hands and feet are used as key features for detecting the human body posture through feature engineering, the key features are subjected to feature extraction operation to obtain a motion feature sequence set, and finally, recognition and classification are carried out to obtain the human body posture sequence.
In addition, it should be appreciated that the body's ability to withstand the intensity of movement is different for long-term and abrupt exercises, and thus, the movement time and body posture sequences may be stored in a pre-constructed historical movement record table, providing basic data for subsequent assessment of the body's ability to withstand the limits.
And S3, recognizing the face and the body characteristics of the person object to obtain a target recognition object, and extracting medical health data corresponding to the target recognition object from a pre-constructed family member information base.
Wherein, the family member information base contains conventional physical examination information, medical report information, identity information and the like of each family member.
According to the embodiment of the invention, the identity of the user can be identified according to the facial features and the body features, the user can be firstly and rapidly inquired through the body features such as the body type size, and when the body features are insufficient for inquiring, the facial features are acquired for accurately inquiring, so that the target identification object can be rapidly identified.
After the target identification object is determined, the family member information base can be quickly queried, so that the medical health data of the target identification object can be obtained.
S4, predicting the exercise intensity according to the medical health data and the data in the historical exercise record list by utilizing a pre-trained exercise safety early warning model, and obtaining an exercise intensity warning value.
In the embodiment of the invention, the exercise safety early warning model is a Transformer-based neural network model and comprises a physical limit prediction network, an action intensity recognition network, a physical bearing value calculation network and a comparison output network, and is used for predicting the physical limit of a user according to the physical condition and the historical body-building condition of the user, tracking the exercise intensity of the user in real time and predicting the bearing value of the user, so that the influence of excessive exercise on health of the user is avoided.
According to the embodiment of the invention, firstly, the medical health data and the data in the historical motion record table are subjected to word segmentation quantization operation by utilizing a body limit prediction network to obtain a feature vector set, and then the real-time motion intensity warning value is calculated through algorithm parameters obtained by regression fitting between the feature vector set and a pre-constructed motion intensity warning sample.
In the embodiment of the invention, the body limit prediction network, the action intensity recognition network and the body bearing value calculation network are networks needing training, and the comparison output network is a network without training.
Further, referring to fig. 2, before the exercise safety pre-warning model is pre-trained, the method further includes:
s401, acquiring a motion safety early warning model comprising a body limit prediction network, an action intensity recognition network and a body bearing value calculation network, and acquiring a pre-constructed medical expert experience sample set;
s402, acquiring a pre-constructed motion quantity monitoring model, and performing migration learning on the motion strength recognition network by utilizing the motion quantity monitoring model;
s403, sequentially extracting a target sample from the medical expert experience sample set, performing network forward calculation on the target sample by using the motion safety early warning model to obtain a predicted bearing value, and obtaining a predicted body limit value output by a body limit prediction network in the motion safety early warning model by using a pre-constructed middle layer network output interface;
S404, calculating a first loss value between a real bearing value label corresponding to the target sample and the predicted bearing value and a second loss value between a real warning value label corresponding to the target sample and the predicted body limit value by using a cross entropy loss algorithm;
s405, performing a minimum network reverse parameter updating operation on the first loss value and the second loss value according to a gradient descent algorithm to obtain an updated motion safety early warning model;
s406, judging the convergence of the first loss value;
returning to the operation step of S403 when the first loss value does not converge, and performing iterative updating on the updated motion safety early warning model;
and when the first loss value converges, S407, stopping the training process to obtain a training-completed motion safety early warning model.
In the embodiment of the invention, the pre-constructed motion quantity monitoring model is firstly downloaded from the Internet, and then the network parameters of the motion quantity monitoring model are transferred to the action intensity recognition network through transfer learning, so that the model training time is saved, and the training efficiency is improved.
The embodiment of the invention trains the body limit prediction network and the body bearing capacity value calculation network by using the pre-constructed medical expert experience sample set. The medical expert experience sample set comprises experimental observation data, clinical medical data and quantization labels corresponding to the data.
In the embodiment of the invention, the training process and the direction of the network are controlled through a cross entropy loss algorithm and a gradient descent algorithm, the overall training progress of the model is controlled through the convergence of the first loss value of the final output layer, and when the first loss value is not converged, the model training is proved to have stronger progressive effect and can be continued to be trained; when the first loss value converges, the model progress effect is not obvious, and in order to avoid the model over-fitting phenomenon, the training process can be stopped, so that a training-completed motion safety early warning model is obtained.
S5, inquiring the motion quantity of each motion in the human body gesture sequence, counting the total motion quantity, and carrying out average operation in unit time on the total motion quantity counting result according to the motion time to obtain an action intensity curve.
In detail, referring to fig. 3, in the embodiment of the present invention, the querying the motion amounts of each motion in the human body gesture sequence, and counting the total motion amounts includes:
s51, carrying out vectorization processing on the human body posture features in the human body posture sequence to obtain a human body posture feature vector;
s52, performing full-connection classification judgment on the human body posture feature vector to obtain the motion types and the motion times of each motion in the human body posture sequence;
S53, inquiring the energy consumption value of each motion type, and calculating the motion quantity of each motion type according to each motion frequency;
and S54, performing sum operation on the motion amounts of all the motion types to obtain a total motion amount.
In the embodiment of the invention, the motion types and the motion times of human body actions are firstly identified through feature identification, then a pre-constructed database or the Internet is queried to acquire the energy consumption value of each motion, and the motion amounts of each motion type are sequentially overlapped according to the time sequence, so that the total motion amount is obtained.
And S6, calculating body bearing values of the action intensity curve and the figure heart rate curve, and generating alarm prompt information when the body bearing values are greater than or equal to the exercise intensity warning value.
In the embodiment of the invention, the body bearing value is not the simple weight superposition of the action intensity curve and the figure heart rate curve, but the relation association curve between the action intensity curve and the figure heart rate curve is acquired first, and then the body bearing value is calculated comprehensively according to the relation association curve, the action intensity curve, the figure heart rate curve and the weight curve changing along with time, so that the more accurate body bearing value is obtained.
And finally, comparing the body bearing value with the exercise intensity warning value through the comparison output network, and generating warning prompt information when the body bearing value is greater than or equal to the exercise intensity warning value.
In addition, in another embodiment of the present invention, after the generating the alarm prompt information, the method further includes:
when the gesture point cloud image is detected to suddenly present the reverse gesture, telephone notification is carried out on a preset guardian according to the guardian telephone in the basic information of the user.
In the embodiment of the present invention, the preset guardian may be a relative of the user or an emergency center of the hospital.
In an alternative embodiment of the invention, a part of users still move vigorously after hearing the bell alarm, so that the possibility of motion risk is improved, at the moment, the human body posture point cloud image needs to be analyzed, and when the human body posture point cloud image suddenly presents an inverted posture, the guardian of the user needs to dial through according to the guardian telephone in the basic information, so as to ensure the life safety of the user.
According to the embodiment of the invention, firstly, a cloud image of the gesture point of a user is obtained through a millimeter wave radar, so that a human gesture sequence of the user during movement is obtained, the type and the number of times of movement can be identified according to the human gesture sequence by utilizing a movement safety early warning model, so that the movement quantity of the user is calculated, the relation between the two curves is checked together with a pre-obtained figure heart rate curve, and the real-time body bearing value of the user is comprehensively calculated; in addition, the exercise safety early warning model can predict the exercise intensity warning value of the limit of the user through the medical health data and the historical exercise records, so that whether the body bearing value exceeds the exercise intensity warning value is compared, and whether an alarm is needed or not is judged. Therefore, the motion protection method based on the millimeter wave radar can estimate the motion bearing capacity of the user and avoid or reduce the damage caused by excessive motion of the user.
As shown in fig. 4, a functional block diagram of the motion protection device based on millimeter wave radar of the present invention is shown.
The motion protection device 100 based on millimeter wave radar of the present invention may be installed in an electronic device. Depending on the functions implemented, the motion protection device 100 based on millimeter wave radar may include an image heart rate data acquisition module 101, a medical information acquisition module 102, a body limit prediction module 103, and a bearing value alert module 104, which may also be referred to as a unit, a series of computer program segments capable of being executed by a processor of an electronic device and performing a fixed function, which are stored in a memory of the electronic device.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the image heart rate data acquisition module 101 is configured to acquire a person heart rate and a gesture point cloud image of a person object by using a pre-built millimeter wave radar when the person object appears in a preset motion area, record motion time when a preset body motion gesture appears in the gesture point cloud image, record a person heart rate curve corresponding to the person heart rate, and perform human gesture feature recognition on the gesture point cloud image to obtain a human gesture sequence, and store the motion time and the human gesture sequence into a pre-built historical motion record table;
The medical information obtaining module 102 is configured to identify facial features and physical features of the person object, obtain a target identification object, and extract medical health data corresponding to the target identification object from a pre-constructed family member information base;
the body limit prediction module 103 is configured to predict exercise intensity according to the medical health data and the data in the historical exercise record table by using a pre-trained exercise safety pre-warning model, so as to obtain an exercise intensity warning value;
the bearing value warning module 104 is configured to query the motion amounts of each motion in the human body posture sequence, count the total motion amount, perform average operation in unit time on the total motion amount statistics result according to the motion time, obtain a motion intensity curve, calculate a body bearing value for the motion intensity curve and the figure heart rate curve, and generate warning prompt information when the body bearing value is greater than or equal to the motion intensity warning value.
Fig. 5 is a schematic structural diagram of an electronic device for implementing the motion protection method based on millimeter wave radar according to the present invention.
The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a millimeter wave radar based motion protection program.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various types of data, such as codes of motion protection programs based on millimeter wave radar, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing Unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, executes programs or modules stored in the memory 11 (for example, a motion protection program based on millimeter wave radar, etc.), and invokes data stored in the memory 11 to perform various functions of the electronic device and process data.
The communication bus 12 may be a peripheral component interconnect standard (PerIPheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The communication bus 12 is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
Fig. 5 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 5 is not limiting of the electronic device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
Optionally, the communication interface 13 may comprise a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the communication interface 13 may further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The millimeter wave radar based motion protection program stored by the memory 11 in the electronic device is a combination of a plurality of computer programs, which when run in the processor 10, may implement:
When a person object appears in a preset motion area, acquiring a person heart rate and gesture point cloud image of the person object by utilizing a pre-constructed millimeter wave radar;
when a preset body movement gesture appears in the gesture point cloud image, recording movement time, recording a character heart rate curve corresponding to the character heart rate, carrying out human gesture feature recognition on the gesture point cloud image to obtain a human gesture sequence, and storing the movement time and the human gesture sequence into a pre-constructed historical movement record table;
identifying the face and body characteristics of the person object to obtain a target identification object, and extracting medical health data corresponding to the target identification object from a pre-constructed family member information base;
predicting the exercise intensity according to the medical health data and the data in the historical exercise record table by utilizing a pre-trained exercise safety early warning model to obtain an exercise intensity warning value;
inquiring the motion quantity of each motion in the human body gesture sequence, counting the total motion quantity, and carrying out average operation in unit time on the total motion quantity counting result according to the motion time to obtain an action intensity curve;
And calculating body bearing values of the action intensity curve and the figure heart rate curve, and generating alarm prompt information when the body bearing values are larger than or equal to the exercise intensity warning value.
In particular, the specific implementation method of the processor 10 on the computer program may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable medium may be non-volatile or volatile. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
Embodiments of the present invention may also provide a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, may implement:
When a person object appears in a preset motion area, acquiring a person heart rate and gesture point cloud image of the person object by utilizing a pre-constructed millimeter wave radar;
when a preset body movement gesture appears in the gesture point cloud image, recording movement time, recording a character heart rate curve corresponding to the character heart rate, carrying out human gesture feature recognition on the gesture point cloud image to obtain a human gesture sequence, and storing the movement time and the human gesture sequence into a pre-constructed historical movement record table;
identifying the face and body characteristics of the person object to obtain a target identification object, and extracting medical health data corresponding to the target identification object from a pre-constructed family member information base;
predicting the exercise intensity according to the medical health data and the data in the historical exercise record table by utilizing a pre-trained exercise safety early warning model to obtain an exercise intensity warning value;
inquiring the motion quantity of each motion in the human body gesture sequence, counting the total motion quantity, and carrying out average operation in unit time on the total motion quantity counting result according to the motion time to obtain an action intensity curve;
And calculating body bearing values of the action intensity curve and the figure heart rate curve, and generating alarm prompt information when the body bearing values are larger than or equal to the exercise intensity warning value.
Further, the computer-usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
In the embodiments provided in the present invention, it should be understood that the disclosed electronic device, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A millimeter wave radar-based motion protection method, the method comprising:
when a person object appears in a preset motion area, acquiring a person heart rate and gesture point cloud image of the person object by utilizing a pre-constructed millimeter wave radar;
when a preset body movement gesture appears in the gesture point cloud image, recording movement time, recording a character heart rate curve corresponding to the character heart rate, carrying out human gesture feature recognition on the gesture point cloud image to obtain a human gesture sequence, and storing the movement time and the human gesture sequence into a pre-constructed historical movement record table;
identifying the face and body characteristics of the person object to obtain a target identification object, and extracting medical health data corresponding to the target identification object from a pre-constructed family member information base;
Predicting the exercise intensity according to the medical health data and the data in the historical exercise record table by utilizing a pre-trained exercise safety early warning model to obtain an exercise intensity warning value;
inquiring the motion quantity of each motion in the human body gesture sequence, counting the total motion quantity, and carrying out average operation in unit time on the total motion quantity counting result according to the motion time to obtain an action intensity curve;
and calculating body bearing values of the action intensity curve and the figure heart rate curve, and generating alarm prompt information when the body bearing values are larger than or equal to the exercise intensity warning value.
2. The millimeter wave radar-based motion protection method of claim 1, wherein prior to said utilizing the pre-trained motion safety pre-warning model, the method further comprises:
acquiring a motion safety early warning model comprising a body limit prediction network, an action intensity recognition network and a body bearing value calculation network, and acquiring a pre-constructed medical expert experience sample set;
acquiring a pre-constructed motion quantity monitoring model, and performing migration learning on the motion strength recognition network by using the motion quantity monitoring model;
Sequentially extracting a target sample from the medical expert experience sample set, performing network forward calculation on the target sample by using the motion safety early warning model to obtain a predicted bearing value, and obtaining a predicted body limit value output by a body limit prediction network in the motion safety early warning model by using a pre-constructed middle layer network output interface;
calculating a first loss value between a real bearing value label corresponding to the target sample and the predicted bearing value and a second loss value between a real warning value label corresponding to the target sample and the predicted body limit value by using a cross entropy loss algorithm;
according to a gradient descent algorithm, performing minimum network reverse parameter updating operation on the first loss value and the second loss value to obtain an updated motion safety early warning model;
judging the convergence of the first loss value;
when the first loss value is not converged, returning to the operation step of sequentially extracting a target sample from the medical expert experience sample, and iteratively updating the updated motion safety early warning model;
and stopping the training process when the first loss value converges to obtain a training-completed motion safety early warning model.
3. The millimeter wave radar-based motion protection method according to claim 1, wherein the acquiring the figure heart rate and gesture point cloud image of the figure object by using the pre-constructed millimeter wave radar comprises:
scanning the blood flow velocity of the person object by utilizing a continuous wave scanning mode of the pre-constructed millimeter wave radar to obtain a blood flow velocity reflected wave;
calculating a target character heart rate of the character object according to the blood flow velocity reflection wave, a preset blood flow velocity and a human heart rate reflection table;
performing human body scanning on the person object by utilizing a frequency modulation continuous wave scanning mode of the millimeter wave radar to obtain a human body posture reflected wave;
and drawing a posture point cloud image according to the human posture reflected wave.
4. The millimeter wave radar-based motion protection method according to claim 3, wherein said drawing a posture point cloud image from the reflected wave of the human body posture includes:
extracting a human body posture signal in the human body posture reflected wave;
denoising the human body posture signal, performing digital-to-analog conversion and fast Fourier transformation to obtain a human body posture frequency signal;
Doppler velocity measurement and multi-angle measurement are carried out on the human body posture frequency signals, and space position coordinates of the person object are obtained;
and displaying the spatial position coordinates of the character object in a three-dimensional mode to obtain a gesture point cloud image.
5. The millimeter wave radar-based motion protection method according to claim 1, wherein the performing human body posture feature recognition on the posture point cloud image to obtain a human body posture sequence comprises:
performing feature engineering operation based on the position, direction and posture of the human body on the pre-constructed human body motion feature sample to obtain a motion posture feature type;
performing feature extraction operation on the attitude point cloud image based on the motion attitude feature type to obtain a motion feature sequence set;
and performing feature full-connection classification recognition operation on the motion feature sequence set to obtain a human body posture sequence.
6. The millimeter wave radar-based motion protection method according to claim 1, wherein the querying the motion amount of each motion in the human body posture sequence, and counting the total motion amount, includes:
vectorizing human body posture features in the human body posture sequence to obtain a human body posture feature vector;
Performing full-connection classification judgment on the human body posture feature vector to obtain the motion type and the motion times of each motion in the human body posture sequence;
inquiring the energy consumption value of each motion type, and calculating the motion quantity of each motion type according to each motion frequency;
and carrying out sum operation on the motion amounts of all the motion types to obtain the total motion amount.
7. The millimeter wave radar-based motion protection method according to claim 1, wherein after the generating the alarm prompt message, further comprising:
when the gesture point cloud image is detected to suddenly present the reverse gesture, telephone notification is carried out on a preset guardian according to the guardian telephone in the basic information of the user.
8. A millimeter wave radar-based motion protection device, the device comprising:
the image heart rate data acquisition module is used for acquiring a figure heart rate and a gesture point cloud image of a figure object by utilizing a pre-built millimeter wave radar when the figure object appears in a preset motion area, recording motion time when a preset body motion gesture appears in the gesture point cloud image, recording a figure heart rate curve corresponding to the figure heart rate, carrying out human gesture feature recognition on the gesture point cloud image to obtain a human gesture sequence, and storing the motion time and the human gesture sequence into a pre-built historical motion record table;
The medical information acquisition module is used for identifying the face and the physical characteristics of the person object to obtain a target identification object, and extracting medical health data corresponding to the target identification object from a pre-constructed family member information base;
the body limit prediction module is used for predicting the exercise intensity according to the medical health data and the data in the historical exercise record table by utilizing a pre-trained exercise safety early warning model to obtain an exercise intensity warning value;
and the bearing value warning module is used for inquiring the motion quantity of each motion in the human body posture sequence, counting the total motion quantity, carrying out average operation in unit time on the total motion quantity counting result according to the motion time to obtain a motion intensity curve, carrying out body bearing value calculation on the motion intensity curve and the figure heart rate curve, and generating warning prompt information when the body bearing value is greater than or equal to the motion intensity warning value.
9. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores computer program instructions executable by the at least one processor to enable the at least one processor to perform the millimeter wave radar-based motion protection method of any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the millimeter wave radar-based motion protection method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410019665.0A CN117524413B (en) | 2024-01-05 | 2024-01-05 | Motion protection method, device, equipment and medium based on millimeter wave radar |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410019665.0A CN117524413B (en) | 2024-01-05 | 2024-01-05 | Motion protection method, device, equipment and medium based on millimeter wave radar |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117524413A true CN117524413A (en) | 2024-02-06 |
| CN117524413B CN117524413B (en) | 2024-03-19 |
Family
ID=89757052
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410019665.0A Active CN117524413B (en) | 2024-01-05 | 2024-01-05 | Motion protection method, device, equipment and medium based on millimeter wave radar |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117524413B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012194863A (en) * | 2011-03-17 | 2012-10-11 | Mitsubishi Motors Corp | Vehicle operation support device |
| CN111208508A (en) * | 2019-12-25 | 2020-05-29 | 珠海格力电器股份有限公司 | Motion quantity measuring method and device and electronic equipment |
| CN111481184A (en) * | 2020-04-24 | 2020-08-04 | 华侨大学 | Multi-target respiration heart rate monitoring method and system based on millimeter wave radar technology |
| CN113869160A (en) * | 2021-09-17 | 2021-12-31 | 特斯联科技集团有限公司 | Comprehensive method and system for face recognition, non-sensory heart rate recognition, running posture and expression recognition |
| CN116008936A (en) * | 2022-11-29 | 2023-04-25 | 四川虹美智能科技有限公司 | Human body track tracking detection method based on millimeter wave radar |
| CN116311539A (en) * | 2023-05-19 | 2023-06-23 | 亿慧云智能科技（深圳）股份有限公司 | Sleep motion capturing method, device, equipment and storage medium based on millimeter waves |
| CN116602663A (en) * | 2023-06-02 | 2023-08-18 | 深圳市震有智联科技有限公司 | Intelligent monitoring method and system based on millimeter wave radar |
-
2024
- 2024-01-05 CN CN202410019665.0A patent/CN117524413B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012194863A (en) * | 2011-03-17 | 2012-10-11 | Mitsubishi Motors Corp | Vehicle operation support device |
| CN111208508A (en) * | 2019-12-25 | 2020-05-29 | 珠海格力电器股份有限公司 | Motion quantity measuring method and device and electronic equipment |
| CN111481184A (en) * | 2020-04-24 | 2020-08-04 | 华侨大学 | Multi-target respiration heart rate monitoring method and system based on millimeter wave radar technology |
| CN113869160A (en) * | 2021-09-17 | 2021-12-31 | 特斯联科技集团有限公司 | Comprehensive method and system for face recognition, non-sensory heart rate recognition, running posture and expression recognition |
| CN116008936A (en) * | 2022-11-29 | 2023-04-25 | 四川虹美智能科技有限公司 | Human body track tracking detection method based on millimeter wave radar |
| CN116311539A (en) * | 2023-05-19 | 2023-06-23 | 亿慧云智能科技（深圳）股份有限公司 | Sleep motion capturing method, device, equipment and storage medium based on millimeter waves |
| CN116602663A (en) * | 2023-06-02 | 2023-08-18 | 深圳市震有智联科技有限公司 | Intelligent monitoring method and system based on millimeter wave radar |
Non-Patent Citations (1)
| Title |
|---|
| 祁富贵等: "基于生物雷达的人体运动状态探测识别技术研究", 医疗卫生装备, no. 05, 15 May 2016 (2016-05-15), pages 120 - 126 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117524413B (en) | 2024-03-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lu et al. | Towards unsupervised physical activity recognition using smartphone accelerometers | |
| Wu et al. | Radar‐based fall detection based on Doppler time–frequency signatures for assisted living | |
| Nooruddin et al. | Sensor-based fall detection systems: a review | |
| Erdogan et al. | A data mining approach for fall detection by using k-nearest neighbour algorithm on wireless sensor network data | |
| Shany et al. | Are we stumbling in our quest to find the best predictor? Over‐optimism in sensor‐based models for predicting falls in older adults | |
| Kang et al. | Real-time elderly activity monitoring system based on a tri-axial accelerometer | |
| CN116311539B (en) | Sleep motion capturing method, device, equipment and storage medium based on millimeter waves | |
| Saho et al. | Micro-Doppler radar gait measurement to detect age-and fall risk-related differences in gait: A simulation study on comparison of deep learning and gait parameter-based approaches | |
| CN116942149B (en) | Lumbar vertebra monitoring method, device, equipment and storage medium based on millimeter wave radar | |
| Zhu et al. | Falling motion detection algorithm based on deep learning | |
| Ghosh et al. | IoT-based freezing of gait detection using grey relational analysis | |
| Tabbakha et al. | A wearable device for machine learning based elderly's activity tracking and indoor location system | |
| CN113990500A (en) | Vital sign parameter monitoring method and device and storage medium | |
| CN117524413B (en) | Motion protection method, device, equipment and medium based on millimeter wave radar | |
| Bobin et al. | Smart cup to monitor stroke patients activities during everyday life | |
| Siddiqui et al. | Integrating federated learning with IoMT for managing obesity in smart city | |
| Ghosh et al. | Detection of human activity by widget | |
| CN115937903A (en) | Fall risk assessment method and device, electronic equipment and storage medium | |
| A Kofahi et al. | A smart real-time IoT-based system for monitoring health of athletes | |
| Ren et al. | Research on risk assessment model of subjective and objective integration in manual handling work | |
| Christal et al. | Heart diseases diagnosis using chaotic Harris Hawk optimization with E-CNN for IoMT framework | |
| Jeewandara et al. | An Efficient Machine Learning Based Elderly Monitoring System | |
| KR20200098289A (en) | System and mehtod for monitering emergency situation | |
| Han et al. | Comparison of machine learning methods for the construction of a standalone gait diagnosis device | |
| Li et al. | The early warning research on nursing care of stroke patients with intelligent wearable devices under COVID-19 |
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 |