CN116087943A - Indoor falling detection method and system based on millimeter wave radar - Google Patents
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Abstract
The disclosure provides an indoor falling detection method and system based on millimeter wave radar, which relate to the technical field of millimeter wave radar signals, and comprise the steps of transmitting continuous frequency modulation millimeter wave radar signals and receiving, and performing FFT operation on the obtained millimeter wave radar signals; performing self-adaptive constant false alarm rate detection on the millimeter wave radar signals after FFT operation, and selecting effective target motion information; filtering the effective target motion information, detecting whether a person exists in the room, if so, acquiring the motion information of the human target by using a deep learning LSTM network, and judging whether a falling event occurs according to the motion information of the human target. By means of the combined calculation of the obtained information of the human body movement and judging whether the human body falls down, the problem that the false alarm rate is high due to single information calculation is avoided.
Description
Technical Field
The disclosure relates to the technical field of millimeter wave radar signals, in particular to an indoor falling detection method and system based on millimeter wave radar.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Population aging has become a worldwide commonplace at present. Especially, the population aging state of China is more serious, and the following characteristics are presented: the old people have huge population, the aging speed is high, and the old care ratio is greatly increased. At that time, the technology and intelligent upgrading of the old supplies are imperative.
It is counted that the falling is the first cause of the death even in the Shanghai of the elderly over 65 years old, and even the elderly with good physical condition at ordinary times, the fall is serious injury caused by 17.7 percent of falling. Current fall detection systems are largely divided into wearable and non-wearable. However, wearable devices need to be charged frequently, and it cannot be guaranteed that the old people wear the devices all the time. For non-wearable detection systems, cameras, thermal infrared or millimeter wave radars are mainly used. However, the use of a camera or infrared is susceptible to optical fibers and shielding, and it is not possible to ensure all-weather detection, and the privacy of the user is easily exposed. However, at present, the problem of low accuracy and high false alarm rate still exists in indoor falling detection by using a millimeter wave radar.
Disclosure of Invention
In order to solve the problems, the disclosure provides an indoor fall detection method and system based on millimeter wave radar, and provides a non-contact indoor fall detection method, which can accurately identify the fall state of indoor personnel, train a classifier by using a deep learning network, establish a human fall judgment mechanism, be suitable for indoor environment, and ensure detection accuracy and reliability.
According to some embodiments, the present disclosure employs the following technical solutions:
an indoor fall detection method based on millimeter wave radar comprises the following steps:
transmitting and receiving continuous frequency modulation millimeter wave radar signals, and performing FFT operation on the obtained millimeter wave radar signals;
performing self-adaptive constant false alarm rate detection on the millimeter wave radar signals after FFT operation, and selecting effective target motion information;
filtering the effective target motion information, detecting whether a person exists in the room, if so, acquiring the motion information of the human target by using a deep learning LSTM network, and judging whether a falling event occurs according to the motion information of the human target.
According to some embodiments, the present disclosure employs the following technical solutions:
indoor fall detection system based on millimeter wave radar includes:
the signal transmitting and recovering module is used for transmitting and receiving continuous frequency modulation millimeter wave radar signals; performing FFT operation on the obtained millimeter wave radar signals;
the signal calculation processing module is used for carrying out FFT operation on the obtained millimeter wave radar signals; performing self-adaptive constant false alarm rate detection on the millimeter wave radar signals after FFT operation, and selecting effective target motion information;
the model judging and detecting module is used for carrying out filtering processing on the effective target motion information, detecting whether a person exists in a room, if so, acquiring the motion information of the human target by using the deep learning LSTM network, and judging whether a falling event occurs according to the motion information of the human target.
According to some embodiments, the present disclosure employs the following technical solutions:
a non-transitory computer readable storage medium for storing computer instructions which, when executed by a processor, implement the millimeter wave radar-based indoor fall detection method.
According to some embodiments, the present disclosure employs the following technical solutions:
an electronic device, comprising: a processor, a memory, and a computer program; the processor is connected with the memory, the computer program is stored in the memory, and when the electronic equipment runs, the processor executes the computer program stored in the memory so as to enable the electronic equipment to execute the indoor falling detection method based on the millimeter wave radar.
Compared with the prior art, the beneficial effects of the present disclosure are:
1. the detection method disclosed by the invention adopts a non-contact mode, has no influence on daily life of a human body, does not need to collect videos, can protect privacy safety of a user, can accurately and rapidly identify the falling state of indoor personnel, has the advantages of easiness in use, safety, comfort in use and the like, is suitable for indoor environments such as home, nursing homes and wards, and ensures the health of daily life of the elderly.
The intelligent bathroom has the advantages of simple elements, convenience in installation, capability of quickly identifying falling states of the solitary old people and timely generating early warning information, easiness in use, safety, comfort in use and the like, is suitable for old people modes such as families, institutions and communities, and improves the safety and the health of the life of the old people.
2. According to the working method of the detection system, the sound sensor is used for identifying the indoor human body target, so that false alarm events of falling detection are reduced, alarm caused by movement of animals in the detection process is avoided, and detection accuracy and reliability are guaranteed.
3. The deep learning network training classifier is used, the classifier is trained by using the established data set, the performance and accuracy of the classifier are improved, and the accuracy of human motion information detection is guaranteed.
4. The human body falling judgment mechanism is established, whether falling occurs or not is judged through joint calculation of the obtained information (speed, acceleration, distance, angle and the like) of human body movement, the problem that the false alarm rate is high due to single information calculation is avoided, the falling detection accuracy rate is improved, and the false alarm rate is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the exemplary embodiments of the disclosure and together with the description serve to explain the disclosure, and do not constitute an undue limitation on the disclosure.
FIG. 1 is a workflow diagram of an embodiment of the present disclosure;
fig. 2 is a flow chart of a fall detection mechanism according to an embodiment of the disclosure;
FIG. 3 is a diagram of a VI-CFAR structure in accordance with an embodiment of the present disclosure;
fig. 4 is a LSTM network configuration diagram of an embodiment of the present disclosure.
The specific embodiment is as follows:
the disclosure is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments in accordance with the present disclosure. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Example 1
An embodiment of the present disclosure provides an indoor fall detection method based on millimeter wave radar, as shown in fig. 1, including:
step 1: starting detection equipment, transmitting and receiving continuous frequency modulation millimeter wave radar signals, transmitting the millimeter wave signals into a processor, and performing FFT operation on the millimeter wave radar signals in three dimensions of speed, distance and angle.
The specific steps of carrying out FFT operation on the obtained millimeter wave radar signals are as follows:
step 1.1: performing FFT operation on each chirp dimension of the echo to obtain a distance-pulse diagram;
step 1.2: performing FFT operation on the distance FFT result in chirp dimension to obtain a distance-Doppler graph, and extracting the peak value to obtain the beat frequency (f) of the indoor target I ) And Doppler frequency (f D )；
Step 1.3: calculating echo time delay through beat frequency, so as to calculate the distance of an indoor target; the beat frequency calculation formula is as follows:
wherein f B For beat frequency between the transmitted wave and the echo, f D For Doppler frequency, f I For the frequency of the intermediate frequency signal, K represents the gradient R of Chirp as the target distance and c as the electromagnetic wave velocity.
Step 1.4: the speed of the moving target in the target room is calculated through the Doppler frequency, and the Doppler frequency calculation formula is as follows:
wherein f D For doppler frequency, v denotes the target velocity and λ denotes the electromagnetic wave wavelength.
Step 1.5: the phase difference of the echoes arriving at the different RX antennas is used to calculate the angle of the target. Performing FFT operation on FFT results of two dimensions of distances and speeds of a plurality of RX in an RX dimension to obtain a distance-Doppler-azimuth graph; wherein the phase difference between two RX's calculation formula is:
where ω is the phase difference between the two receive antennas and θ is the angle of the target relative to the radar.
Step 2: the step of adaptively detecting the constant false alarm rate of the millimeter wave radar signal after the FFT operation and selecting effective target motion information is that, as shown in figure 3,
step 2.1: the signal is transmitted into a square law detector and processed;
step 2.2: transmitting the signal output by the square law detector into a VI-CFAR detector for processing, and obtaining reference units A and B;
step 2.3: calculating the average value of reference cells A and B
And the square of the mean value of the reference cell is determined>
And VI and sum of A and B are calculated as sum of unit values; wherein the calculation formula of VI is as follows:
where n is the reference cell number.
Step 2.4: judging clutter background and selecting CFAR strategy;
step 2.5: and inputting the unit to be detected into a comparator for detection, and selecting effective target motion information.
Further, step 3: and filtering the effective target motion information, namely filtering the signal subjected to VI-CFAR by using a Singer alpha beta gamma Kalman filter.
The filtering processing comprises the following steps:
step 3.1: defining an indoor target as a random acceleration model, wherein the formula of the model is as follows:
wherein,,
representing a second-order target random acceleration parameter; w (n) is a gaussian random variable with zero mean and unit variance; sigma (sigma) m Is the standard deviation of motion; ρ m Is an agitation correlation coefficient.
Wherein w (n) is a gaussian random variable with zero mean and unit variance; sigma (sigma) m Is the motorized standard deviation; wherein the agonism correlation coefficient ρ m The formula of (2) is:
wherein τ m Is the relative time of the target acceleration due to the target motion, T is the target motion time.
Step 3.2: defining an autocorrelation function of the model, wherein the function formula is as follows:
wherein sigma a Is the standard deviation.
Step 3.3: defining a transfer matrix of the filter as:
wherein,,
step 3.4: the signals are then filtered using a model and matrix function.
Step 4: the method for detecting whether the indoor person exists comprises the steps of judging whether the indoor person exists by using a sound sensor, judging that an indoor target is a human body after voice conversation or other human body behavior sounds are detected, and otherwise judging that no person exists in the indoor.
Step 5: training a classifier by using a deep learning network, and acquiring motion information of an indoor effective human body target by using the classifier; the motion information of the human body target comprises speed, acceleration, distance and angle.
As shown in fig. 4, the specific steps are as follows:
step 5.1: the configuration of LSTM is set, including forget gate, input gate, cell status and output gate. The calculation formula of the forgetting door is as follows:
f t ＝σ(W f ·[h t-1 ,x t ]+b f ) (9)
wherein W is f Is weight, h t-1 For the last output, x t For current input, σ is forgetting the gate, b f Is a set parameter.
The input gate calculation formula is:
i t ＝σ(W i ·[h t-1 ,x t ]+b i ) (10)
W i same as W f The same is a weight, b i Same as b f As is a set parameter.
Is a new candidate vector, and tanh is the tanh layer in the network.
The cell state calculation formula is:
wherein C is t For a new cell state, C t-1 Is the last cell state.
The output gate calculation formula is:
o t ＝σ(W o [h t-1 ,x t ]+b o )(13)
h t ＝o t *tanh(C t )(14)
wherein W is o Is weight, b o Is a parameter. h is a t Is a new output.
Step 5.2: inputting the data set for calculating the target motion information into an LSTM network training classifier;
step 5.3: the distance, speed and angle information obtained after the signals are processed are used as input data to be input into an LSTM network, and the movement information of the target is obtained;
step 6: judging whether a falling event occurs or not by utilizing the human motion information obtained by the classifier;
as shown in fig. 2, the specific steps are as follows:
step 6.1: obtaining acceleration by using the obtained human body speed;
step 6.2: setting an acceleration threshold, judging that no falling event occurs if the acceleration of the human body is lower than the threshold, and performing the next judgment if the acceleration exceeds the threshold;
step 6.3: if the acceleration of the human body exceeds the set acceleration threshold, continuously detecting whether the human body has a sudden deceleration action (the speed of the human body suddenly decreases when the human body collides with the ground), if the sudden deceleration action does not occur, judging that no falling event occurs, and if the detected human body has the sudden deceleration, continuously detecting;
step 6.4: continuously judging whether the time from acceleration to deceleration of the human body is less than 3 seconds, if the time of the whole event is not less than 3 seconds, judging that no falling event occurs, and if the time of the whole event is less than 3 seconds, continuously detecting;
step 6.5: and calculating the change of the height point of the gravity center of the human body, and judging that a falling event occurs if the change of the height point of the human body from acceleration to deceleration is larger than 50 cm.
Example 2
In one embodiment of the present disclosure, there is provided an indoor fall detection system based on millimeter wave radar, including:
the signal transmitting and recovering module is used for transmitting and receiving continuous frequency modulation millimeter wave radar signals; performing FFT operation on the obtained millimeter wave radar signals;
the signal calculation processing module is used for carrying out FFT operation on the obtained millimeter wave radar signals; performing self-adaptive constant false alarm rate detection on the millimeter wave radar signals after FFT operation, and selecting effective target motion information;
the model judging and detecting module is used for carrying out filtering processing on the effective target motion information, detecting whether a person exists in a room, if so, acquiring the motion information of the human target by using the deep learning LSTM network, and judging whether a falling event occurs according to the motion information of the human target.
The signal transmitting and recovering module comprises detecting equipment which is used for transmitting and recovering continuous frequency modulation millimeter wave signals;
the signal calculation processing module comprises a processor and is used for carrying out FFT operation on the obtained millimeter wave radar signals; performing self-adaptive constant false alarm rate detection on the millimeter wave radar signals after FFT operation, and selecting effective target motion information;
the signal calculation processing module also comprises a sound sensor which is used for judging whether a person exists indoors or not and acquiring sound signals.
The device also comprises a square law detector and a Singer alpha beta gamma Kalman filter, and is used for carrying out self-adaptive constant false alarm rate detection on the signals after FFT operation, selecting effective target motion information and carrying out filtering processing on the signals.
Example 3
An embodiment of the present disclosure provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium is configured to store computer instructions that, when executed by a processor, implement the steps of the indoor fall detection method based on millimeter wave radar.
Example 4
In one embodiment of the present disclosure, there is provided an electronic device including: a processor, a memory, and a computer program; the processor is connected with the memory, the computer program is stored in the memory, and when the electronic equipment runs, the processor executes the computer program stored in the memory, so that the electronic equipment executes the steps of the indoor fall detection method based on the millimeter wave radar.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the specific embodiments of the present disclosure have been described above with reference to the drawings, it should be understood that the present disclosure is not limited to the embodiments, and that various modifications and changes can be made by one skilled in the art without inventive effort on the basis of the technical solutions of the present disclosure while remaining within the scope of the present disclosure.
Claims (10)
1. The indoor falling detection method based on the millimeter wave radar is characterized by comprising the following steps of:
transmitting and receiving continuous frequency modulation millimeter wave radar signals, and performing FFT operation on the obtained millimeter wave radar signals;
performing self-adaptive constant false alarm rate detection on the millimeter wave radar signals after FFT operation, and selecting effective target motion information;
filtering the effective target motion information, detecting whether a person exists in the room, if so, acquiring the motion information of the human target by using a deep learning LSTM network, and judging whether a falling event occurs according to the motion information of the human target.
2. An indoor fall detection method based on millimeter wave radar as defined in claim 1, wherein the process of performing FFT operation on the acquired millimeter wave radar signal comprises: performing FFT operation on millimeter wave radar signals in three dimensions of speed, distance and angle, and performing FFT operation on each chirp dimension of echo to obtain a distance-pulse diagram; and performing FFT operation on the distance FFT result in the chirp dimension to obtain a distance-Doppler graph, and extracting the peak value to obtain the beat frequency and Doppler frequency of the indoor target.
3. The indoor fall detection method based on millimeter wave radar according to claim 1, wherein the step of performing adaptive constant false alarm rate detection on the millimeter wave radar signal after FFT operation and selecting effective target motion information comprises the steps of:
s1: the signal is transmitted into a square law detector and processed;
s2: transmitting the signal output by the square law detector into a VI-CFAR detector for processing, and obtaining reference units A and B;
s3: calculating the reference units A and B, judging clutter background and selecting CFAR strategy;
s4: and inputting the unit to be detected into a comparator for detection, and selecting effective target motion information.
4. The indoor fall detection method based on millimeter wave radar as claimed in claim 1, wherein the filtering processing of the effective target motion information is performed by: and filtering the signals subjected to VI-CFAR by using a Singer alpha beta gamma Kalman filter.
5. The indoor fall detection method based on millimeter wave radar as set forth in claim 4, wherein the step of performing the filtering process is:
defining an indoor target as a random acceleration model and an autocorrelation function of the model, then defining a transfer matrix of a filter, and filtering signals by using the model and the matrix function.
6. The indoor fall detection method based on millimeter wave radar according to claim 1, wherein the method for detecting whether a person exists in the room is to use a sound sensor to determine whether a person exists in the room, and determine that the indoor target is a human body after detecting that a voice conversation or other human body behavioral sounds exist in the room, and otherwise determine that no person exists in the room.
7. An indoor fall detection method based on millimeter wave radar as claimed in claim 1, wherein the movement information of the human target includes speed, acceleration, distance and angle.
8. Indoor fall detecting system based on millimeter wave radar, its characterized in that includes:
the signal transmitting and recovering module is used for transmitting and receiving continuous frequency modulation millimeter wave radar signals; performing FFT operation on the obtained millimeter wave radar signals;
the signal calculation processing module is used for carrying out FFT operation on the obtained millimeter wave radar signals; performing self-adaptive constant false alarm rate detection on the millimeter wave radar signals after FFT operation, and selecting effective target motion information;
the model judging and detecting module is used for carrying out filtering processing on the effective target motion information, detecting whether a person exists in a room, if so, acquiring the motion information of the human target by using the deep learning LSTM network, and judging whether a falling event occurs according to the motion information of the human target.
9. A non-transitory computer-readable storage medium storing computer instructions that, when executed by a processor, implement the millimeter wave radar-based indoor fall detection method of any of claims 1-7.
10. An electronic device, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory, which processor executes the computer program stored in the memory when the electronic device is running, to cause the electronic device to perform the method of performing millimeter wave radar based indoor fall detection as claimed in any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| 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 |
| CN117831224A (en) * | 2024-02-29 | 2024-04-05 | 深圳市迈远科技有限公司 | Fall alarm method, device, equipment and medium based on millimeter radar |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| 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 |
| CN116602663B (en) * | 2023-06-02 | 2023-12-15 | 深圳市震有智联科技有限公司 | Intelligent monitoring method and system based on millimeter wave radar |
| CN117831224A (en) * | 2024-02-29 | 2024-04-05 | 深圳市迈远科技有限公司 | Fall alarm method, device, equipment and medium based on millimeter radar |
| CN117831224B (en) * | 2024-02-29 | 2024-05-24 | 深圳市迈远科技有限公司 | Fall alarm method, device, equipment and medium based on millimeter radar |
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