CN112386248A - Method, device and equipment for detecting human body falling and computer readable storage medium - Google Patents
Method, device and equipment for detecting human body falling and computer readable storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device and equipment for detecting human body falling and a computer readable storage medium, relates to the technical field of information processing, and aims to solve the problem of high false judgment rate of monitoring human body falling. The method comprises the following steps: acquiring point cloud data of a monitored human body, and performing cluster analysis on the point cloud data to obtain a point cloud group of a human body trunk of the monitored human body; calculating posture information of the human body in a target time period based on the point cloud group of the human body, wherein the posture information at least comprises at least one inclination angle of the human body; and detecting whether the monitored human body falls down or not according to the posture information. The embodiment of the invention can improve the accuracy of monitoring the falling phenomenon of the human body.
Description
Technical Field
The present invention relates to the field of information processing technologies, and in particular, to a method, an apparatus, a device, and a computer-readable storage medium for detecting a human body tumble.
Background
At present, the following detection schemes for human body falls are provided: through setting up alarm device, utilize video monitoring, utilize wearable equipment to detect etc.. However, these methods are not suitable for emergency situations, involve privacy problems, or cannot wear wearable devices due to skin sensitivity problems, and the like, and thus have obstacles in practical application, and therefore, the monitoring misjudgment rate of human body falling is high.
Disclosure of Invention
The embodiment of the invention provides a method, a device and equipment for detecting human body falling and a computer readable storage medium, which aim to solve the problem of high false judgment rate of monitoring human body falling.
In a first aspect, an embodiment of the present invention provides a method for detecting a human fall, including:
acquiring point cloud data of a monitored human body, and performing cluster analysis on the point cloud data to obtain a point cloud group of a human body trunk of the monitored human body;
calculating posture information of the human body in a target time period based on the point cloud group of the human body, wherein the posture information at least comprises at least one inclination angle of the human body;
and detecting whether the monitored human body falls down or not according to the posture information.
Wherein the calculating the posture information of the human trunk in a target time period based on the point cloud group of the human trunk comprises:
obtaining physical characteristic parameters of the point cloud group of the human trunk based on a principal component analysis method;
determining the extending direction of the human body trunk according to the physical characteristic parameters;
and taking the included angle between the extending direction and the zenith direction as the inclination angle of the human body trunk.
Wherein the determining the extending direction of the human body trunk according to the physical characteristic parameters comprises:
calculating the mass center of the human body trunk group according to the physical characteristic parameters;
calculating a covariance matrix according to the centroid;
calculating an eigenvalue and an eigenvector of the covariance matrix;
and taking the direction represented by the eigenvector corresponding to the maximum eigenvalue as the extension direction of the human body trunk.
Wherein, will the contained angle between extending direction and the zenith direction includes as the angle of inclination of human trunk:
calculating an angle between the extension direction and the zenith direction using the following formula:
where ω denotes the angle, dot denotes the dot product operation, εxRepresenting the characteristic vector corresponding to the maximum characteristic value, v representing the zenith direction, | epsilonx| v | each represents εxAnd ν.
Wherein the posture information further comprises at least one angular velocity of the human torso;
the calculating the posture information of the human body trunk in the target time period further comprises:
acquiring the difference between the inclination angle at the first moment and the inclination angle at the second moment in the target time period;
using the quotient of the obtained difference and the length of time between the first time and the second time as the angular velocity of the human torso at the first time.
Wherein the detecting whether the monitored human body falls or not according to the posture information comprises:
when the minimum value of the at least one inclination angle is larger than a first preset value, detecting that the monitored human body falls down; or
And when the maximum value of the at least one angular velocity is greater than a second preset value, detecting that the monitored human body falls down.
The method for acquiring the point cloud data of the monitored human body and performing cluster analysis on the point cloud data to obtain the point cloud group of the human body trunk of the monitored human body comprises the following steps:
acquiring point cloud data of the monitored human body by using millimeter wave radar equipment, and acquiring a candidate group with the most reflecting points from the point cloud data;
and taking the group with the maximum reflection intensity in the candidate groups as the point cloud group of the human body trunk.
In a second aspect, an embodiment of the present invention provides an apparatus for detecting a human fall, including:
the first acquisition module is used for acquiring point cloud data of a monitored human body and carrying out cluster analysis on the point cloud data to obtain a point cloud group of the human body trunk of the monitored human body;
a calculation module, configured to calculate pose information of the human trunk within a target time period based on the point cloud group of the human trunk, where the pose information at least includes at least one inclination angle of the human trunk;
and the detection module is used for detecting whether the monitored human body falls down or not according to the posture information.
Wherein the calculation module comprises:
the first acquisition submodule is used for acquiring physical characteristic parameters of the point cloud group of the human trunk based on a principal component analysis method;
the first determining submodule is used for determining the extending direction of the human body trunk according to the physical characteristic parameters;
and the second determining submodule is used for taking the included angle between the extending direction and the zenith direction as the inclined angle of the human body trunk.
Wherein the first determination submodule includes:
the first calculating unit is used for calculating the mass center of the human body trunk group;
the second calculation unit is used for calculating a covariance matrix according to the centroid;
a third calculation unit, configured to calculate an eigenvalue and an eigenvector of the covariance matrix;
a first determination unit configured to determine a direction indicated by a feature vector corresponding to a maximum feature value as an extending direction of the human trunk;
and the second determining unit is used for taking the included angle between the extending direction and the zenith direction as the inclined angle of the human body trunk.
The second determining unit is specifically configured to calculate an included angle between the extending direction and the zenith direction by using the following formula:
where ω denotes the angle, dot denotes the dot product operation, εxRepresenting the characteristic vector corresponding to the maximum characteristic value, v representing the zenith direction, | epsilonx| v | each represents εxAnd ν.
Wherein the posture information further comprises at least one angular velocity of the human torso; the device further comprises: the second acquisition module is used for acquiring the difference between the inclination angle at the first moment and the inclination angle at the second moment in the target time period; and the third acquisition module is used for utilizing the quotient of the obtained difference and the time length between the first moment and the second moment as the angular speed of the human body trunk at the first moment.
The detection module is specifically configured to detect that the monitored human body falls when a minimum value of the at least one inclination angle is greater than a first preset value; or when the maximum value of the at least one angular velocity is larger than a second preset value, detecting that the monitored human body falls down.
Wherein the first obtaining module comprises:
the first acquisition submodule is used for acquiring the point cloud data of the monitored human body by using millimeter wave radar equipment and acquiring a candidate group with the most reflecting points from the point cloud data;
and the second acquisition sub-module is used for taking the group with the maximum reflection intensity in the candidate groups as the point cloud group of the human body trunk.
In a third aspect, an embodiment of the present invention further provides a human body fall detection apparatus, including: a collector, a processor and a detector;
the collector is used for acquiring point cloud data of a monitored human body, and carrying out cluster analysis on the point cloud data to obtain a point cloud group of the human body trunk of the monitored human body;
the processor is used for calculating posture information of the human body in a target time period based on the point cloud group of the human body, wherein the posture information at least comprises at least one inclination angle of the human body;
and the detector is used for detecting whether the monitored human body falls down or not according to the posture information.
Wherein the processor is further configured to:
obtaining physical characteristic parameters of the point cloud group of the human trunk based on a principal component analysis method;
determining the extending direction of the human body trunk according to the physical characteristic parameters;
and taking the included angle between the extending direction and the zenith direction as the inclination angle of the human body trunk.
Wherein the processor is further configured to:
calculating the mass center of the human body trunk group according to the physical characteristic parameters;
calculating a covariance matrix according to the centroid;
calculating an eigenvalue and an eigenvector of the covariance matrix;
and taking the direction represented by the eigenvector corresponding to the maximum eigenvalue as the extension direction of the human body trunk.
Wherein the processor is further configured to:
calculating an angle between the extension direction and the zenith direction using the following formula:
where ω denotes the angle, dot denotes the dot product operation, εxRepresenting the characteristic vector corresponding to the maximum characteristic value, v representing the zenith direction, | epsilonx| v | each represents εxAnd ν.
Wherein the posture information further comprises at least one angular velocity of the human torso; wherein the processor is further configured to:
acquiring the difference between the inclination angle at the first moment and the inclination angle at the second moment in the target time period;
using the quotient of the obtained difference and the length of time between the first time and the second time as the angular velocity of the human torso at the first time.
Wherein the detector is further configured to:
when the minimum value of the at least one inclination angle is larger than a first preset value, detecting that the monitored human body falls down; or
When the maximum value of the at least one angular velocity is larger than a second preset value, the fact that the monitored human body falls down is detected.
Wherein, the collector is still used for:
acquiring point cloud data of the monitored human body by using millimeter wave radar equipment, and acquiring a candidate group with the most reflecting points from the point cloud data;
and taking the group with the maximum reflection intensity in the candidate groups as the point cloud group of the human body trunk.
In a fourth aspect, an embodiment of the present invention provides an information processing apparatus, including: a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor; the processor is configured to read a program in the memory to implement the steps in the method according to the first aspect.
In a fifth aspect, an embodiment of the present invention provides a computer-readable storage medium for storing a computer program, where the computer program is implemented to implement, when executed by a processor, the steps in the method according to the first aspect.
In the embodiment of the invention, the obtained point cloud data of the monitored human body is subjected to clustering analysis to obtain a point cloud group of the human body trunk. And calculating the posture information of the human trunk in a target time period based on the point cloud group of the human trunk, and detecting whether the monitored human body falls down or not according to the posture information. Because the posture information of the human body has good stability and better distinguishing capability for the falling behavior and other common misjudgment behaviors, the method and the device for monitoring the falling phenomenon of the human body can improve the accuracy of monitoring the falling phenomenon of the human body.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart of a method for detecting a human fall according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of cluster analysis results in an embodiment of the present invention;
fig. 3 is a second flowchart of a method for detecting a human fall according to an embodiment of the present invention;
fig. 4 is a third flowchart of a method for detecting a human fall according to an embodiment of the present invention;
fig. 5 is one of the structural diagrams of the structure of the human body fall detection apparatus according to the embodiment of the present invention;
fig. 6 is a second structural diagram of a structure of a human body fall detection device according to an embodiment of the present invention;
fig. 7 is a third structural diagram of a structure diagram of a human body fall detection device according to an embodiment of the present invention;
fig. 8 is a block diagram of an information processing apparatus provided by an embodiment of the present invention;
fig. 9 is a system framework diagram provided by an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart of a method for detecting a human fall according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:
In this step, first, point cloud data of a monitored human body is acquired by a millimeter wave radar device, and a candidate group having the most reflection points is acquired from the point cloud data. And then, taking the group with the maximum reflection intensity in the candidate group as the point cloud group of the human body trunk.
When the scheme of the embodiment of the invention is utilized, the low-power-consumption millimeter wave radar data does not have identity identification information, so that the privacy of users can be better protected. Wherein the monitored human body may be, for example, a person. Data output by the millimeter wave radar is point cloud data of a human body of a monitored object, and information of each point comprises space three-dimensional information, reflection intensity information and speed information.
And performing cluster analysis on the point cloud data to divide the point cloud data into a plurality of groups. The cluster analysis can be realized based on the space three-dimensional information only, and can also be combined with the reflection intensity information or the speed information. In order to improve accuracy, in practical application, clustering analysis can be performed based on only the classification result of the spatial three-dimensional information. Any clustering method may be used for the clustering.
The trunk is the largest reflector in each part of the human body, so the corresponding point cloud group should have two characteristics: (1) the most reflecting points and (2) the maximum reflecting intensity. Based on the above principle, first, a candidate group with the most reflection points is obtained from the point cloud data, and then, a group with the maximum reflection intensity in the candidate group is used as the point cloud group of the human trunk.
Fig. 2 is a schematic diagram of a cluster analysis result in the embodiment of the present invention.
102, calculating posture information of the human body in a target time period based on the point cloud group of the human body, wherein the posture information at least comprises at least one inclination angle of the human body.
In this step, based on Principal Component Analysis (PCA), physical characteristic parameters of the point cloud group of the human trunk are obtained, and then the extending direction of the human trunk is determined according to the physical characteristic parameters. And then, taking the included angle between the extending direction and the zenith direction as the inclined angle of the human body trunk. Wherein the physical characteristic parameters comprise characteristic vectors and characteristic values of a point cloud group of a human body trunk.
In practical application, after the point cloud group of the human body trunk is determined, the PCA is adopted to obtain the physical characteristic parameters of the point cloud group of the human body trunk, and the extending direction of the human body trunk is determined from three main directions of the physical characteristic parameters. Then, the included angle between the extending direction of the human body and the zenith direction is the inclination angle of the human body.
The specific calculation flow is as follows:
(1) calculating a centroid of the group of human torsos.
A group of point clouds of a human torso, each point cloud represented as: pi＝<xi,yi,zi>The point cloud group is represented as: p1，P2，……，PN. The centroid m (the average of the array in the n-dimensional direction) is calculated as follows:
wherein N represents the number of point clouds in the point cloud group.
(2) And calculating a covariance matrix according to the centroid.
Wherein the covariance matrix C is represented as:
the covariance matrix C represents the interrelationship between the x, y, z coordinate values. If these three coordinate values are not related two by two, then their element on the covariance matrix has a value of 0.
(3) And calculating an eigenvalue and an eigenvector of the covariance matrix.
The characteristic value λ can be determined from | C- λ E | ═ 01，λ2，λ3Then, the solved eigenvalue is brought back to solve the corresponding eigenvector epsilon1，ε2，ε3。
(4) And taking the direction represented by the eigenvector corresponding to the maximum eigenvalue as the extension direction of the human body trunk.
The magnitude of the eigenvalue indicates the degree of expansion and contraction in the corresponding direction, so the maximum eigenvalue max (λ [. lamda. ])1，λ2，λ3) Corresponding feature vector epsilonxRepresenting the direction of extension of the human torso, and the smaller two characteristic values correspond to the cross-sectional direction of the human torso.
(5) And taking the included angle between the extending direction and the zenith direction as the inclination angle of the human body trunk.
Eigenvector εxNamely the main direction, calculating the included angle between the main direction and the zenith direction v (0,0,1) to obtain the body inclination angle, wherein the formula is as follows:
where ω denotes the angle, dot denotes the dot product operation, εxRepresenting the characteristic vector corresponding to the maximum characteristic value, v representing the zenith direction, | epsilonx| v | each represents εxAnd ν.
And 103, detecting whether the monitored human body falls down or not according to the posture information.
Here, in a case where a minimum value of the at least one inclination angle is greater than a first preset value, it is detected that the monitored human body falls down. The first preset value can be set randomly.
Because the posture information of the human body has good stability and better distinguishing capability for the falling behavior and other common misjudgment behaviors, the method and the device for monitoring the falling phenomenon of the human body can improve the accuracy of monitoring the falling phenomenon of the human body.
Referring to fig. 3, fig. 3 is a flowchart of a method for detecting a human fall according to an embodiment of the present invention, and as shown in fig. 3, the method includes the following steps:
301, obtaining point cloud data of a monitored human body, and performing cluster analysis on the point cloud data to obtain a point cloud group of the human body trunk of the monitored human body.
Wherein, the steps 301-302 can refer to the description of the steps 101-102.
In the embodiment of the invention, because the angular speed of the human trunk can effectively distinguish falling and lying for rest, in order to further improve the accuracy of judgment, the posture information can also adopt the angular speed of the human trunk. Thus, on the basis of calculating at least one inclination angle, the posture information further comprises at least one angular velocity of the human torso. After step 302, the method of the embodiment of the present invention further comprises:
and step 303, acquiring the difference between the inclination angle at the first time and the inclination angle at the second time in the target time period.
And step 304, utilizing the quotient of the obtained difference and the time length between the first time and the second time as the angular velocity of the human body trunk at the first time. The time length between the first time and the second time can be arbitrarily set, for example, set to 1 s.
Specifically, the calculation method of the human body trunk angular velocity comprises the following steps:
calculating t1Angle of inclination theta of human body trunk at time1Calculating the next time t2Angle of inclination theta of human body trunk2Then calculate t based on the next formula2Angular velocity omega of human torso at time1：
ω1＝(θ2-θ1)/(t2-t1)
Wherein the time interval t2-t1May be set to 1 second.
And 305, detecting whether the monitored human body falls down.
In this embodiment, it is detected whether the monitored human body falls or not according to the posture information.
Specifically, when the minimum value of the at least one inclination angle is larger than a first preset value, it is detected that the monitored human body falls down; or, detecting that the monitored human body falls down when the maximum value of the at least one angular velocity is greater than a second preset value. The first preset value and the second preset value can be set randomly.
In the embodiment of the invention, the local change of the millimeter wave radar data is strong, and the local change of the data can be caused by instant slight breathing activity. For example, the signal reflected by the head may have only 1 point, and the signal source reflected by the head in the two adjacent images may be transferred from the forehead of the first frame to the cheek of the second frame. For the characteristic that the data of the millimeter wave radar has large variation, the whole information (more information of more points) should be utilized as much as possible, and the influence of local instability is reduced by utilizing the whole stability. The height information is only based on the height information in the point cloud three-dimensional coordinates, and the inclination angle information utilizes the three-dimensional information and the intensity information, so that the stability is higher. The inclination angle of the human body trunk has higher stability, and the posture of the human body can be reflected more accurately. In addition, the information of the relative height of the inclination angle of the human trunk can effectively distinguish common misjudgment actions such as shoelace tying and sitting, so that the accuracy of falling judgment is effectively improved. In the embodiment of the invention, the monitored human body does not need to wear any equipment, so that the user experience is better. Meanwhile, the embodiment of the invention can automatically identify the time and send out the alarm, and the user does not need to send out the call for help, thereby avoiding the problem that the user loses the call for help capability because of falling down.
On the basis of the embodiment, when the human body falls down, prompt information can be output, such as an alarm. And when the inclination angle of the human body obtained at the third moment is smaller than a third preset value, canceling the output of the prompt message, such as canceling the alarm. Wherein, the third preset value can be set arbitrarily.
Referring to fig. 4, fig. 4 is a flowchart of a method for detecting a human fall according to an embodiment of the present invention. In fig. 4, the human body posture is monitored in real time. When the inclination angle of the human body is larger than theta1In time, the fall can be initially screened. And then, continuing subsequent judgment to determine whether the falling phenomenon really occurs.
When any one of the following conditions is satisfied, it can be determined that a fall phenomenon occurs:
(1) a certain time period t before the current time1The maximum value of the angular velocity of the human body trunk is more than omega1；
(2) A certain time period t before the current time2The minimum value of the inclination angle of the human body trunk is more than theta2。
If the falling phenomenon is determined to occur, an alarm can be sent. Then, when the inclination angle of the human body trunk is less than theta3And if so, canceling the alarm.
Referring to fig. 5, fig. 5 is a structural diagram of an apparatus for detecting a human fall according to an embodiment of the present invention, and as shown in fig. 5, the apparatus may include:
the first acquisition module 501 is configured to acquire point cloud data of a monitored human body by using millimeter wave radar equipment, perform cluster analysis on the point cloud data, and obtain a point cloud group of a human body trunk;
a calculation module 502, configured to calculate pose information of the human trunk within a target time period based on the point cloud group of the human trunk, where the pose information at least includes at least one inclination angle of the human trunk;
a detecting module 503, configured to detect whether the monitored human body falls down according to the posture information.
Wherein the calculating module 502 comprises:
the first acquisition submodule is used for acquiring physical characteristic parameters of the point cloud group of the human trunk based on a principal component analysis method; the first determining submodule is used for determining the extending direction of the human body trunk according to the physical characteristic parameters; and the second determining submodule is used for taking the included angle between the extending direction and the zenith direction as the inclined angle of the human body trunk.
Wherein the first determination submodule includes: the first calculating unit is used for calculating the mass center of the human body trunk group; the second calculation unit is used for calculating a covariance matrix according to the centroid; a third calculation unit, configured to calculate an eigenvalue and an eigenvector of the covariance matrix; a first determination unit configured to determine a direction indicated by a feature vector corresponding to a maximum feature value as an extending direction of the human trunk; and the second determining unit is used for taking the included angle between the extending direction and the zenith direction as the inclined angle of the human body trunk.
The second determining unit is specifically configured to calculate an included angle between the extending direction and the zenith direction by using the following formula:
where ω denotes the angle, dot denotes the dot product operation, εxRepresenting the characteristic vector corresponding to the maximum characteristic value, v representing the zenith direction, | epsilonx| v | respectivelyRepresents epsilonxAnd ν.
Wherein the posture information further comprises at least one angular velocity of the human torso; as shown in fig. 6, the apparatus further includes: a second obtaining module 504, configured to obtain a difference between a tilt angle at a first time and a tilt angle at a second time in the target time period; a third obtaining module 505, configured to use a quotient of the obtained difference and a time length between the first time and the second time as an angular velocity of the human body trunk at the first time.
The detection module is specifically configured to detect that the monitored human body falls when a minimum value of the at least one inclination angle is greater than a first preset value; or when the maximum value of the at least one angular velocity is larger than a second preset value, detecting that the monitored human body falls down.
Wherein the first obtaining module 501 includes: the first acquisition submodule is used for acquiring the point cloud data of the monitored human body by using millimeter wave radar equipment and acquiring a candidate group with the most reflecting points from the point cloud data; and the second acquisition sub-module is used for taking the group with the maximum reflection intensity in the candidate groups as the point cloud group of the human body trunk.
Since the principle of the device for solving the problem is similar to the method for detecting human body falls in the embodiment of the present invention, the implementation of the device can refer to the implementation of the method, which has similar implementation principle and technical effect, and the implementation principle and the technical effect are not described herein again.
Referring to fig. 7, fig. 7 is a structural diagram of an apparatus for detecting a human fall according to an embodiment of the present invention, and as shown in fig. 7, the apparatus may include: a collector 701, a processor 702, and a detector 703.
The collector 701 is configured to obtain point cloud data of a monitored human body, perform cluster analysis on the point cloud data, and obtain a point cloud group of a human trunk of the monitored human body;
the processor 702 is configured to calculate pose information of the human trunk within a target time period based on the point cloud group of the human trunk, where the pose information at least includes at least one inclination angle of the human trunk;
the detector 703 is configured to detect whether the monitored human body falls down according to the posture information.
The processor 701 is further configured to obtain physical characteristic parameters of the point cloud group of the human trunk based on a principal component analysis method; determining the extending direction of the human body trunk according to the physical characteristic parameters; and taking the included angle between the extending direction and the zenith direction as the inclination angle of the human body trunk.
Wherein the processor 701 is further configured to calculate a centroid of the group of human torsos; calculating a covariance matrix according to the centroid; calculating an eigenvalue and an eigenvector of the covariance matrix; taking the direction represented by the eigenvector corresponding to the maximum eigenvalue as the extension direction of the human body trunk; and taking the included angle between the extending direction and the zenith direction as the inclination angle of the human body trunk.
Wherein the processor 701 is further configured to calculate an included angle between the extending direction and the zenith direction by using the following formula:
where ω denotes the angle, dot denotes the dot product operation, εxRepresenting the characteristic vector corresponding to the maximum characteristic value, v representing the zenith direction, | epsilonx| v | each represents εxAnd ν.
Wherein the posture information further comprises at least one angular velocity of the human torso; the processor 701 is further configured to obtain a difference between the tilt angle at the first time and the tilt angle at the second time in the target time period; using the quotient of the obtained difference and the length of time between the first time and the second time as the angular velocity of the human torso at the first time.
Wherein the detector 703 is further configured to detect that the monitored human body falls when a minimum value of the at least one tilt angle is greater than a first preset value; or when the maximum value of the at least one angular velocity is larger than a second preset value, detecting that the monitored human body falls down.
The collector 701 is further configured to obtain point cloud data of the monitored human body by using millimeter wave radar equipment, and obtain a candidate group with the most reflection points from the point cloud data; and taking the group with the maximum reflection intensity in the candidate groups as the point cloud group of the human body trunk.
Since the principle of the device for solving the problem is similar to the method for detecting human body falls in the embodiment of the present invention, the implementation of the device can refer to the implementation of the method, which has similar implementation principle and technical effect, and the implementation principle and the technical effect are not described herein again.
As shown in fig. 8, an information processing apparatus of an embodiment of the present invention includes: the processor 800, which is used to read the program in the memory 820, executes the following processes:
acquiring point cloud data of a monitored human body, and performing cluster analysis on the point cloud data to obtain a point cloud group of a human body trunk of the monitored human body;
calculating posture information of the human body in a target time period based on the point cloud group of the human body, wherein the posture information at least comprises at least one inclination angle of the human body;
and detecting whether the monitored human body falls down or not according to the posture information.
A transceiver 810 for receiving and transmitting data under the control of the processor 800.
Where in fig. 8, the bus architecture may include any number of interconnected buses and bridges, with various circuits being linked together, particularly one or more processors represented by processor 800 and memory represented by memory 820. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface. The transceiver 810 may be a number of elements including a transmitter and a transceiver providing a means for communicating with various other apparatus over a transmission medium. The processor 800 is responsible for managing the bus architecture and general processing, and the memory 820 may store data used by the processor 800 in performing operations.
The processor 800 is responsible for managing the bus architecture and general processing, and the memory 820 may store data used by the processor 800 in performing operations.
The processor 800 is further configured to read the computer program and perform the following steps:
acquiring a candidate group with the most reflecting points from the point cloud data;
and taking the group with the maximum reflection intensity in the candidate groups as the point cloud group of the human body trunk.
Obtaining physical characteristic parameters of the point cloud group of the human trunk based on a principal component analysis method;
determining the extending direction of the human body trunk according to the physical characteristic parameters;
and taking the included angle between the extending direction and the zenith direction as the inclination angle of the human body trunk.
The processor 800 is further configured to read the computer program and perform the following steps:
calculating a center of mass of the group of human torso;
calculating a covariance matrix according to the centroid;
calculating an eigenvalue and an eigenvector of the covariance matrix;
taking the direction represented by the eigenvector corresponding to the maximum eigenvalue as the extension direction of the human body trunk;
and taking the included angle between the extending direction and the zenith direction as the inclination angle of the human body trunk.
The processor 800 is further configured to read the computer program and perform the following steps:
calculating an angle between the extension direction and the zenith direction using the following formula:
where ω denotes the angle, dot denotes the dot product operation, εxRepresenting the characteristic vector corresponding to the maximum characteristic value, v representing the zenith direction, | epsilonx| v | each represents εxAnd ν.
The posture information further comprises at least one angular velocity of the human torso; the processor 800 is further configured to read the computer program and perform the following steps:
acquiring the difference between the inclination angle at the first moment and the inclination angle at the second moment in the target time period;
using the quotient of the obtained difference and the length of time between the first time and the second time as the angular velocity of the human torso at the first time.
The processor 800 is further configured to read the computer program and perform the following steps:
when the minimum value of the at least one inclination angle is larger than a first preset value, detecting that the monitored human body falls down; or
When the maximum value of the at least one angular velocity is larger than a second preset value, the fact that the monitored human body falls down is detected.
The processor 800 is further configured to read the computer program and perform the following steps:
acquiring point cloud data of the monitored human body by using millimeter wave radar equipment, and acquiring a candidate group with the most reflecting points from the point cloud data;
and taking the group with the maximum reflection intensity in the candidate groups as the point cloud group of the human body trunk.
Referring to fig. 9, fig. 9 is a system framework diagram provided by the embodiment of the present invention, and as shown in fig. 9, the system may include: a sensing module 901, an intelligent analysis module 902 and a service management module 903.
The sensing module 901 may be, for example, a low-power millimeter wave radar device, and is configured to acquire point cloud data of a monitored human body.
The intelligent analysis module 902 may include: a human body posture characteristic analysis submodule and a human body falling judgment submodule. And the human body posture characteristic analysis submodule is used for calculating human body posture characteristics based on millimeter wave radar data. And the human body falling judgment submodule is used for judging a falling state based on the posture characteristics. In the embodiment of the invention, the trunk part and the four limb parts of the human body are segmented based on clustering analysis, the inclination angle and the angular velocity of the human body are calculated by adopting the physical characteristic parameters of the trunk of the human body, and then the falling state is judged based on the information. The intelligent analysis module can be deployed in customized computing equipment and idle computing resources (such as an intelligent home gateway) in a home, or can be a cloud platform, and the analysis result is uploaded to a service platform.
The service platform module 903 includes: and the message forwarding sub-module, the equipment management sub-module and the user management sub-module realize the functions of data management, equipment management, user management and the like. The message forwarding sub-module is used for being responsible for forwarding the message. The result of data analysis is received from the intelligent analysis module, and can be stored in a database through the equipment management submodule, and the user management submodule provides services such as user management, data query, event push and the like for the guardian/mechanism terminal.
Furthermore, a computer-readable storage medium of an embodiment of the present invention stores a computer program executable by a processor to implement:
acquiring point cloud data of a monitored human body, and performing cluster analysis on the point cloud data to obtain a point cloud group of a human body trunk of the monitored human body;
calculating posture information of the human body in a target time period based on the point cloud group of the human body, wherein the posture information at least comprises at least one inclination angle of the human body;
and detecting whether the monitored human body falls down or not according to the posture information.
Wherein the calculating the posture information of the human trunk in a target time period based on the point cloud group of the human trunk comprises:
obtaining physical characteristic parameters of the point cloud group of the human trunk based on a principal component analysis method;
determining the extending direction of the human body trunk according to the physical characteristic parameters;
and taking the included angle between the extending direction and the zenith direction as the inclination angle of the human body trunk.
Wherein the determining the extending direction of the human body trunk according to the physical characteristic parameters comprises:
calculating the mass center of the human body trunk group according to the physical characteristic parameters;
calculating a covariance matrix according to the centroid;
calculating an eigenvalue and an eigenvector of the covariance matrix;
and taking the direction represented by the eigenvector corresponding to the maximum eigenvalue as the extension direction of the human body trunk.
Wherein, will the contained angle between extending direction and the zenith direction includes as the angle of inclination of human trunk:
calculating an angle between the extension direction and the zenith direction using the following formula:
where ω denotes the angle, dot denotes the dot product operation, εxRepresenting the characteristic vector corresponding to the maximum characteristic value, v representing the zenith direction, | epsilonx| v | each represents εxAnd ν.
Wherein the posture information further comprises at least one angular velocity of the human torso;
the calculating the posture information of the human body trunk in the target time period further comprises:
acquiring the difference between the inclination angle at the first moment and the inclination angle at the second moment in the target time period;
using the quotient of the obtained difference and the length of time between the first time and the second time as the angular velocity of the human torso at the first time.
Wherein the detecting whether the monitored human body falls or not according to the posture information comprises:
when the minimum value of the at least one inclination angle is larger than a first preset value, detecting that the monitored human body falls down; or
And when the maximum value of the at least one angular velocity is greater than a second preset value, detecting that the monitored human body falls down.
The method for acquiring the point cloud data of the monitored human body and performing cluster analysis on the point cloud data to obtain the point cloud group of the human body trunk of the monitored human body comprises the following steps:
acquiring point cloud data of the monitored human body by using millimeter wave radar equipment, and acquiring a candidate group with the most reflecting points from the point cloud data;
and taking the group with the maximum reflection intensity in the candidate groups as the point cloud group of the human body trunk.
In the several embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be physically included alone, or two or more units may be integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute some steps of the transceiving method according to various embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (11)
1. A method for detecting a human fall, comprising:
acquiring point cloud data of a monitored human body, and performing cluster analysis on the point cloud data to obtain a point cloud group of a human body trunk of the monitored human body;
calculating posture information of the human body in a target time period based on the point cloud group of the human body, wherein the posture information at least comprises at least one inclination angle of the human body;
and detecting whether the monitored human body falls down or not according to the posture information.
2. The method of claim 1, wherein the calculating pose information of the human torso over a target time period based on the point cloud group of the human torso comprises:
obtaining physical characteristic parameters of the point cloud group of the human trunk based on a principal component analysis method;
determining the extending direction of the human body trunk according to the physical characteristic parameters;
and taking the included angle between the extending direction and the zenith direction as the inclination angle of the human body trunk.
3. The method of claim 2, wherein said determining the direction of extension of the human torso from the physical characteristic parameter comprises:
calculating the mass center of the human body trunk group according to the physical characteristic parameters;
calculating a covariance matrix according to the centroid;
calculating an eigenvalue and an eigenvector of the covariance matrix;
and taking the direction represented by the eigenvector corresponding to the maximum eigenvalue as the extension direction of the human body trunk.
4. The method according to claim 2 or 3, wherein said taking the angle between said extension direction and the zenith direction as the inclination angle of the human torso comprises:
calculating an angle between the extension direction and the zenith direction using the following formula:
where ω denotes the angle, dot denotes the dot product operation, εxRepresenting the characteristic vector corresponding to the maximum characteristic value, v representing the zenith direction, | epsilonx| v | each represents εxAnd ν.
5. The method of claim 2, wherein the pose information further comprises at least one angular velocity of the human torso;
the calculating the posture information of the human body trunk in the target time period further comprises:
acquiring the difference between the inclination angle at the first moment and the inclination angle at the second moment in the target time period;
using the quotient of the obtained difference and the length of time between the first time and the second time as the angular velocity of the human torso at the first time.
6. The method of claim 5, wherein the detecting whether the monitored human body falls or not according to the posture information comprises:
when the minimum value of the at least one inclination angle is larger than a first preset value, detecting that the monitored human body falls down; or
When the maximum value of the at least one angular velocity is larger than a second preset value, the fact that the monitored human body falls down is detected.
7. The method according to claim 1 or 2, wherein the obtaining of the point cloud data of the monitored human body, and the cluster analysis of the point cloud data to obtain the point cloud group of the human body trunk of the monitored human body comprises:
acquiring point cloud data of the monitored human body by using millimeter wave radar equipment, and acquiring a candidate group with the most reflecting points from the point cloud data;
and taking the group with the maximum reflection intensity in the candidate groups as the point cloud group of the human body trunk.
8. A device for detecting a fall of a human body, comprising:
the first acquisition module is used for acquiring point cloud data of a monitored human body and carrying out cluster analysis on the point cloud data to obtain a point cloud group of the human body trunk of the monitored human body;
a calculation module, configured to calculate pose information of the human trunk within a target time period based on the point cloud group of the human trunk, where the pose information at least includes at least one inclination angle of the human trunk;
and the detection module is used for detecting whether the monitored human body falls down or not according to the posture information.
9. A device for detecting a fall of a human body, comprising: a collector, a processor and a detector;
the collector is used for acquiring point cloud data of a monitored human body, and carrying out cluster analysis on the point cloud data to obtain a point cloud group of the human body trunk of the monitored human body;
the processor is used for calculating posture information of the human body in a target time period based on the point cloud group of the human body, wherein the posture information at least comprises at least one inclination angle of the human body;
and the detector is used for detecting whether the monitored human body falls down or not according to the posture information.
10. An information processing apparatus comprising: a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor; it is characterized in that the preparation method is characterized in that,
the processor for reading a program in the memory to implement the steps in the method of any one of claims 1 to 9.
11. A computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the steps in the method of any one of claims 1 to 9.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114442079A (en) * | 2022-01-14 | 2022-05-06 | 北京清雷科技有限公司 | Target object falling detection method and device |
| CN114720977A (en) * | 2022-06-10 | 2022-07-08 | 长沙莫之比智能科技有限公司 | Object falling detection method and device, millimeter wave radar and storage medium |
| TWI774444B (en) * | 2021-06-28 | 2022-08-11 | 萬旭電業股份有限公司 | Millimeter wave radar apparatus detecting fall posture |
| CN114942434A (en) * | 2022-04-25 | 2022-08-26 | 西南交通大学 | Fall attitude identification method and system based on millimeter wave radar point cloud |
| US20230008729A1 (en) * | 2021-07-11 | 2023-01-12 | Wanshih Electronic Co., Ltd. | Millimeter wave radar apparatus determining fall posture |
| CN116338684A (en) * | 2023-05-29 | 2023-06-27 | 长沙莫之比智能科技有限公司 | Human body falling detection method and system based on millimeter wave radar and deep learning |
| CN117173793A (en) * | 2023-11-02 | 2023-12-05 | 浙江大华技术股份有限公司 | Target gesture detection method, computer device and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007151948A (en) * | 2005-12-07 | 2007-06-21 | Kansai Electric Power Co Inc:The | Method and device for determining falling down |
| US20070225947A1 (en) * | 2006-03-27 | 2007-09-27 | Oki Electric Industry Co., Ltd. | Fall detection device |
| CN105979203A (en) * | 2016-04-29 | 2016-09-28 | 中国石油大学(北京) | Multi-camera cooperative monitoring method and device |
| US20160339293A1 (en) * | 2014-01-27 | 2016-11-24 | The Regents Of The University Of Michigan | Imu system for assessing head and torso orientation during physical motion |
| US20190108740A1 (en) * | 2017-10-06 | 2019-04-11 | Tellus You Care, Inc. | Non-contact activity sensing network for elderly care |
| CN113076781A (en) * | 2020-01-03 | 2021-07-06 | 中国移动通信有限公司研究院 | Method, device and equipment for detecting falling and storage medium |
-
2019
- 2019-08-13 CN CN201910743335.5A patent/CN112386248B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007151948A (en) * | 2005-12-07 | 2007-06-21 | Kansai Electric Power Co Inc:The | Method and device for determining falling down |
| US20070225947A1 (en) * | 2006-03-27 | 2007-09-27 | Oki Electric Industry Co., Ltd. | Fall detection device |
| US20160339293A1 (en) * | 2014-01-27 | 2016-11-24 | The Regents Of The University Of Michigan | Imu system for assessing head and torso orientation during physical motion |
| CN105979203A (en) * | 2016-04-29 | 2016-09-28 | 中国石油大学(北京) | Multi-camera cooperative monitoring method and device |
| US20190108740A1 (en) * | 2017-10-06 | 2019-04-11 | Tellus You Care, Inc. | Non-contact activity sensing network for elderly care |
| CN113076781A (en) * | 2020-01-03 | 2021-07-06 | 中国移动通信有限公司研究院 | Method, device and equipment for detecting falling and storage medium |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI774444B (en) * | 2021-06-28 | 2022-08-11 | 萬旭電業股份有限公司 | Millimeter wave radar apparatus detecting fall posture |
| US20230008729A1 (en) * | 2021-07-11 | 2023-01-12 | Wanshih Electronic Co., Ltd. | Millimeter wave radar apparatus determining fall posture |
| CN114442079A (en) * | 2022-01-14 | 2022-05-06 | 北京清雷科技有限公司 | Target object falling detection method and device |
| CN114942434A (en) * | 2022-04-25 | 2022-08-26 | 西南交通大学 | Fall attitude identification method and system based on millimeter wave radar point cloud |
| CN114942434B (en) * | 2022-04-25 | 2024-02-02 | 四川八维九章科技有限公司 | Fall gesture recognition method and system based on millimeter wave Lei Dadian cloud |
| CN114720977A (en) * | 2022-06-10 | 2022-07-08 | 长沙莫之比智能科技有限公司 | Object falling detection method and device, millimeter wave radar and storage medium |
| WO2023236989A1 (en) * | 2022-06-10 | 2023-12-14 | 长沙莫之比智能科技有限公司 | Subject fall detection method and device, millimeter wave radar, and storage medium |
| CN116338684A (en) * | 2023-05-29 | 2023-06-27 | 长沙莫之比智能科技有限公司 | Human body falling detection method and system based on millimeter wave radar and deep learning |
| CN117173793A (en) * | 2023-11-02 | 2023-12-05 | 浙江大华技术股份有限公司 | Target gesture detection method, computer device and storage medium |
| CN117173793B (en) * | 2023-11-02 | 2024-03-01 | 浙江大华技术股份有限公司 | Target gesture detection method, computer device and storage medium |
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