CN114947771A - Human body characteristic data acquisition method and device - Google Patents
Human body characteristic data acquisition method and device Download PDFInfo
- Publication number
- CN114947771A CN114947771A CN202210528424.XA CN202210528424A CN114947771A CN 114947771 A CN114947771 A CN 114947771A CN 202210528424 A CN202210528424 A CN 202210528424A CN 114947771 A CN114947771 A CN 114947771A
- Authority
- CN
- China
- Prior art keywords
- human body
- monitoring control
- target human
- data
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
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
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
-
- 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
- A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
- A61B5/1135—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing by monitoring thoracic expansion
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
- A61B5/681—Wristwatch-type devices
-
- 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
Abstract
The application discloses a human body characteristic data acquisition method and a human body characteristic data acquisition device, wherein the method comprises the following steps: the method comprises the following steps that an acquisition device utilizes an ultra wide band UWB radar array to identify a target human body in a signal detectable area in real time, and determines an upper half body three-dimensional model for the identified target human body; determining a chest and abdomen region of the target human body based on the upper half body three-dimensional model, and detecting the chest and abdomen region; determining a contour model of the thoracoabdominal region and monitoring control points in the contour model based on the detected data point set; the monitoring control points comprise respiration monitoring control points and/or heartbeat monitoring control points; and detecting the chest and abdomen area in real time by using a UWB radar array, and determining the respiratory frequency and/or heartbeat frequency of the target human body based on the detected coordinate change condition of the monitoring control point. By the adoption of the method and the device, the comfort of the user to be tested during human body characteristic data acquisition can be improved.
Description
Technical Field
The invention relates to a human body physiological characteristic monitoring technology, in particular to a human body characteristic data acquisition method and a human body characteristic data acquisition device.
Background
At present, for physiological characteristics of heartbeat and respiration of a human body, instruments such as a heart monitor and an electrocardio monitor, a heart rate meter, a respiration bandage, a stethoscope and other devices are generally adopted for monitoring.
In the process of implementing the present invention, the inventor finds that the existing physiological characteristic monitoring method has a problem of poor use experience. The reason for this is that the above monitoring method uses a contact type inspection device, i.e. the device needs to contact with the skin of the human body, and for patients who need to monitor for a long time or people with skin injury not healing or skin allergy, the contact with the inspection device for a long time causes inconvenience or injury. Therefore, the above-mentioned conventional physiological characteristic monitoring method relies on the direct contact between the device and the detection object, so that the detection object may feel uncomfortable or inconvenient when the human characteristic data is collected.
Disclosure of Invention
In view of this, the main objective of the present invention is to provide a method and an apparatus for collecting human body characteristic data, which can improve the comfort of a user to be tested during human body characteristic data collection.
In order to achieve the above purpose, the embodiment of the present invention provides a technical solution:
a human body characteristic data acquisition method comprises the following steps:
the method comprises the following steps that an acquisition device utilizes an ultra wide band UWB radar array to identify a target human body in a signal detectable area in real time, and determines an upper half body three-dimensional model for the identified target human body;
determining a chest and abdomen region of the target human body based on the upper half body three-dimensional model, and detecting the chest and abdomen region; determining a contour model of the thoracoabdominal region and monitoring control points in the contour model based on the detected data point set; the monitoring control points comprise respiration monitoring control points and/or heartbeat monitoring control points;
and utilizing a UWB radar array to detect the chest and abdomen area in real time, and determining the respiratory frequency and/or the heartbeat frequency of the target human body based on the detected coordinate change condition of the monitoring control point.
The embodiment of the present invention further provides a human body characteristic data acquisition apparatus, including:
the target identification unit is used for identifying a target human body in a signal detectable area in real time by using an Ultra Wide Band (UWB) radar array and determining an upper half body three-dimensional model for the identified target human body;
a detection preparation unit, configured to determine a chest and abdomen region of the target human body based on the upper body three-dimensional model, and detect the chest and abdomen region; determining a contour model of the thoracoabdominal region and monitoring control points in the contour model based on the detected data point set; the monitoring control points comprise respiration monitoring control points and/or heartbeat monitoring control points;
and the data acquisition unit is used for utilizing a UWB radar array to detect the chest and abdomen area in real time and determining the respiratory frequency and/or the heartbeat frequency of the target human body based on the detected coordinate change condition of the monitoring control point.
The embodiment of the invention also provides human body characteristic data acquisition equipment, which comprises a processor and a memory;
the memory stores an application program executable by the processor for causing the processor to perform the human body characteristic data acquisition method as described above.
An embodiment of the present invention further provides a computer-readable storage medium, in which computer-readable instructions are stored, and the computer-readable instructions are used for performing the human body characteristic data acquisition as described above.
An embodiment of the present invention further provides a computer program product, which includes a computer program/instruction, and when the computer program/instruction is executed by a processor, the method implements the steps of acquiring the human body characteristic data as described above.
In summary, the human body characteristic data acquisition scheme provided by the embodiment of the invention identifies the detection object by using the ultra-wideband technology, and determines the upper half body three-dimensional model for the identified target human body; then, determining a contour model of a chest and abdomen region and monitoring control points for real-time detection of physiological characteristics of a target human body based on the upper half body three-dimensional model; and finally, carrying out real-time detection on the chest and abdomen region by using the UWB radar array, and determining the respiratory frequency and/or the heartbeat frequency of the target human body based on the detected coordinate change condition of the monitoring control point. So, collection equipment is through utilizing the UWB signal, discerns and fixes a position accurately to the chest abdomen region of target human body for human characteristic data's collection need not rely on the direct contact of check out test set with the detection object, thereby can promote the travelling comfort of being surveyed user when human characteristic data gathers.
Drawings
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an embodiment of the present invention utilizing a UWB radar array to determine a region of emphasis detection;
FIG. 3 is a schematic diagram of a three-dimensional model of an upper half of a body for identifying a target human body according to an embodiment of the invention;
FIG. 4 is a schematic diagram of a contour model of a thoraco-abdominal region obtained by a thin-plate spline bending smoothing process according to an embodiment of the present invention;
FIG. 5 is a schematic view of the fluctuation of the chest and abdomen during human respiration;
FIG. 6 is a schematic diagram of a portion of the steps of the offset compensation process according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating a completion fill for missing data according to an embodiment of the present invention;
FIG. 8 is a diagram illustrating an application of the embodiment of the present invention to scene one;
FIG. 9 is a diagram illustrating an application of the embodiment of the present invention to scene two;
FIG. 10 is a diagram illustrating an application of the embodiment of the present invention to scene three;
FIG. 11 is a schematic diagram of an apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a schematic flow chart of a human body characteristic data acquisition method according to an embodiment of the present invention, and as shown in fig. 1, the embodiment mainly includes:
In this step, the acquisition device is used for identifying a detection target (namely, a target human body) nearby in real time and determining a three-dimensional model of the upper half of the body, so as to determine a contour model of the chest and abdomen region and corresponding monitoring control points for acquiring physiological characteristic data of the target human body based on the model in the subsequent step. So, carry out human discernment and listen through introducing UWB technique, on the one hand can be so that human characteristic collection equipment need not contact with the human body when carrying out human characteristic data acquisition, thereby can promote the travelling comfort that the user used, and because the target human body can not be restricted in fixed usage space by the use scene of equipment, thereby can improve the flexibility of gathering, the application scene has effectively been enlarged, on the other hand can utilize the penetrability of UWB signal to non-metallic medium (clothing, ornaments etc.), ensure the accurate collection to human characteristic.
Specifically, in this step 101, an ultra wide band chirp continuous wave signal is sent to the environment around the device by using the UWB radar array, the signal forms an echo after being reflected by a nearby human body or object, and at this time, the acquisition device can identify nearby human body information based on the echo by using a radar imaging technology and combining with a human body identification algorithm, so that the direction and distance information between the device and the target human body can be obtained. Here, the specific method for identifying the target human body by using the radar imaging technology is known to those skilled in the art, and is not described herein again.
The acquisition device may specifically be a wearable device integrated with a UWB radar array, such as a smart watch, but is not limited thereto.
In one embodiment, the following method may be specifically adopted to determine the three-dimensional model of the upper half body for the identified target human body:
step a1, determining the current key detection area based on the position of the target human body.
Specifically, in this step, the position of the target human body based on the identification is used as the key detection direction of the UWB radar array, and the area of the target human body measured in this direction is used as the current key detection area (as shown in fig. 2).
A2, in the key detection area, sending a signal to the target human body by using a UWB radar array to determine the upper half body three-dimensional model of the target human body; the upper half body three-dimensional model comprises skeleton points of all parts of an upper limb and template data of the upper limb of a human body.
Preferably, in order to improve the accuracy of creating the upper-half three-dimensional model of the target human body, in an embodiment, the following method may be specifically adopted to determine the upper-half three-dimensional model of the target human body:
step a21, in the key detection area, sending UWB linear frequency modulation continuous wave signals to the human body by using a UWB radar array.
Step a22, based on the echo reflected by the target human body to the UWB linear frequency modulation continuous wave signal, removing the fixed clutter in each distance unit of the echo receiving matrix by using an averaging method.
Here, it is necessary to remove the fixed clutter in each range cell in the echo receiving matrix, so as to accurately locate the range cell where the target human body is located in the subsequent step.
Step a23, determining the distance unit where the target human body is located by using a Constant False Alarm Rate (CFAR) detection technology based on the distance unit without the fixed clutter, and generating three-dimensional distance direction and direction receiving matrix data based on the three-dimensional array echo data of the determined direction of the distance unit.
The existing CFAR detection technology is utilized to detect each distance unit with fixed clutter removed to obtain a distance unit where a target human body is located, then three-dimensional array echo data of the direction of the distance unit where the target human body is located is obtained based on the obtained distance unit where the target human body is located, after sampling and quantizing processing is carried out on the three-dimensional array echo data, three-dimensional distance direction-direction receiving matrix data are obtained, and therefore in the subsequent step, the upper half body three-dimensional model of the target human body is obtained based on the three-dimensional distance direction-direction receiving matrix data. The specific method for determining the three-dimensional array echo data and generating the three-dimensional distance direction and orientation direction receiving matrix data in this step is known to those skilled in the art, and is not described herein again.
Step a24, based on the receiving matrix data of the three-dimensional distance direction and the orientation direction, executing data preprocessing for separating different parts of the target human body to obtain skeleton points of each part of the upper limb of the target human body and template data of the upper limb of the human body.
Specifically, in an embodiment, as shown in fig. 3, based on the three-dimensional distance direction-direction receiving matrix data, a random forest algorithm, data preprocessing for presetting a standard human body template, and skeleton information in the standard human body template may be used to perform data preprocessing for separating different parts of the target human body, so as to obtain the upper half body three-dimensional model, which is used as a basic three-dimensional data model for real-time detection of the target human body. In order to improve the accuracy of determining the monitoring control point, the skeleton information in the standard human body template comprises the identification information of a preset breathing detection area and/or a heartbeat detection area.
102, determining a chest and abdomen area of the target human body based on the upper half body three-dimensional model, and detecting the chest and abdomen area; determining a contour model of the thoracoabdominal region and monitoring control points in the contour model based on the detected data point set; the monitoring control points comprise respiration monitoring control points and/or heartbeat monitoring control points.
The method comprises the following steps of determining a contour model of a chest and abdomen region for detecting a target human body in real time and monitoring control points in the contour model.
Preferably, in an embodiment, as shown in fig. 4, the profile model of the thoracoabdominal region may be determined by specifically using the following method:
and performing thin-plate spline (TPS) bending smoothing treatment on the data points in the data point set to obtain a contour model of the chest and abdomen region.
In one embodiment, the following method may be specifically adopted to determine the monitoring control point:
and determining the respiration monitoring control point and/or the heartbeat monitoring control point based on a respiration detection area and/or a heartbeat detection area in a preset standard human body template.
Preferably, in order to improve the accuracy of data acquisition, one or more data points with the largest distance variation with the device can be selected as corresponding respiration or heartbeat monitoring control points within the respiration detection region or heartbeat detection region based on the data point set.
103, detecting the chest and abdomen area in real time by using a UWB radar array, and determining the respiratory frequency and/or the heartbeat frequency of the target human body based on the detected coordinate change condition of the monitoring control point.
In this step, the method is used for detecting the chest and abdomen area of the target human body determined in step 102 in real time, and analyzing the coordinate change condition of the monitoring control point determined in step 102 by using the detection result to obtain the physiological characteristic data of the target human body.
In practical application, in order to improve the accuracy of human body characteristic data acquisition, the static or moving state of a human body can be distinguished for acquisition, and the specific method comprises the following steps:
in one embodiment, when the target human body is in a static state, the following method may be specifically adopted to determine the respiratory frequency and/or the heartbeat frequency data of the target human body:
and determining the reciprocating movement frequency of the monitoring control point in the time range according to the detected coordinate data of the monitoring control point in the appointed time range to obtain the corresponding respiratory frequency and/or heartbeat frequency.
Here, considering that the thorax and abdomen of a human body fluctuate during respiration, the corresponding monitoring control point is displaced. Generally, when a human body breathes, the diaphragm muscle relaxes and contracts, the protruding center moves back and forth, the front and back radial displacement of the thoracic cavity can generate 0-3 cm fluctuation, and the fluctuation amplitude of the thoracic cavity of 1.5-3.5 mm can be generated on the surface of the thoracic cavity in the heart region due to the beating of the heart of the human body. Based on this, the corresponding breathing and/or heartbeat frequency may be obtained based on monitoring the reciprocating frequency of the control point. As shown in fig. 5, when the monitor control point P is moved to point P ', the lung fluctuation variable can be calculated according to the formula, i.e. the distance d ' from P to P '. The fluctuation frequencies of the chest, the abdomen and the heart of the human body, namely the respiratory frequency and the heartbeat frequency, can be obtained through sampling calculation in a period of time.
The time range may be set by a person skilled in the art based on the accuracy requirement of actual measurement, and details are not described herein.
In one embodiment, when the target human body is in a motion state, preferably, in order to improve the accuracy of human body characteristic data acquisition in the motion state, the following method may be specifically adopted to determine the respiratory frequency and/or the heartbeat frequency data of the target human body:
step x1, for each coordinate data of the monitoring control point detected within a specified time range, performing deviation compensation processing on the coordinate data based on three-dimensional rotation data monitored by a motion sensor of the acquisition equipment in real time so as to eliminate position deviation generated by relative motion between the target human body and the acquisition equipment; the three-dimensional rotation data comprises a three-dimensional angle and a three-dimensional displacement of the acquisition device relative to the target human body.
It should be noted that when the human body and the collecting device are in a relative motion state, it is difficult to monitor the respiratory frequency and the heartbeat frequency of the human body in real time compared with a relative rest state. Although the above step 101 can adjust the focus detection area in real time based on the current human body recognition situation to offset the change of angle and position caused by some relative motion, for large relative motion, the deviation caused by relative displacement and angle change cannot be completely offset only by adjusting the detection direction. In order to eliminate the deviation and obtain relatively accurate dynamic monitoring effect, the method adds the data of a motion sensor on the equipment to overcome the deviation caused by relative displacement and angle change. Therefore, compared with the prior art that the UWB equipment can only be fixed at one position for detection, the method can greatly improve the application range of the equipment for collecting the breathing and/or heartbeat frequency by introducing the motion sensor for deviation compensation.
In practical applications, the deviation compensation process in step x1 can be implemented by using an existing method. As shown in fig. 6, assuming that v1(a1, b1, c1) and v1(a1, b1, c1) represent the rotation axis of the capturing device in the human motion state, and θ represents the rotation angle of the capturing device in the human motion state, the offset compensation process can be specifically implemented by a matrix rotation transformation operation using the following steps.
1. One end v1(a1, b1, c1) of the rotating shaft is displaced to the origin along x, y and z axes respectively-x, -y and-z;
2. rotating the axis of rotation by- α degrees along the x-axis to the XOZ plane;
3. rotating the rotation axis to the z-axis by β degrees along the y-axis;
4. rotating the rotating shaft by theta degrees along the y axis;
5. the reverse process of the step 3 is executed, namely the rotating shaft rotates by-beta degrees along the y axis;
6. the reverse process of the step 2 is executed, namely the rotating shaft rotates alpha degrees along the x axis;
7. the reverse of step 1 is performed, i.e. one end v1(a1, b1, c1) with the rotation axis at the origin is displaced from the origin along x, y, z axes, respectively.
The above process can be characterized by the following formula:
(u, v, w) ═ a2, b2, c2) - (a1, b1, c1, and is a unit vector, and a, b, c respectively represent (a1, b1, c1)
Wherein T (-x, -y, -z) represents the process of performing step 1;
rx (- α) represents the process of performing step 2;
ry (β) represents the process of performing step 3;
rz (θ) represents that the process of step 4 is performed;
ry (- β) represents that the process of step 5 is performed;
rx (α) represents performing the process of step 6;
t (x, y, z) represents the process of performing step 7.
And step x2, determining the reciprocating frequency of the monitoring control point in the time range according to the coordinate data after the deviation compensation processing in the specified time range, and obtaining the corresponding respiratory frequency and/or heartbeat frequency.
Here, since the displacement deviation of the monitoring control point with respect to the acquisition device due to the movement of the target human body is effectively eliminated by the deviation compensation processing as described above, the reciprocating movement of the monitoring control point due to the fluctuation of the chest and abdomen can be effectively determined, so that the accuracy of the reciprocating movement frequency of the monitoring control point obtained in the case of movement within a certain time range can be improved.
In practical applications, when a target human body is in a certain posture, the chest and the abdomen may be partially shielded, so that the chest detection is incomplete, and therefore, the feature point is lost, in this case, the feature point is limited by the absence of the detected data, so that the feature data acquisition is inaccurate. To solve the problem, the following method may be further adopted:
in one embodiment, in the process of detecting the thoracoabdominal region in real time, integrity check is performed on a data point set corresponding to the detected thoracoabdominal region, and when a data point is missing, an alarm is triggered; when the number of times of alarming reaches a preset threshold value within a preset time range, searching a matched model from a preset model library based on a data point set currently detected within the time range; and utilizing the searched model to perform complete filling on the data point set in the time range to obtain a corresponding complete data point set.
As shown in fig. 7, if the detected data is missing, the data of the time period corresponding to the missing data is intercepted, a matched and similar model is searched in the model library, and the missing data is filled in a complementary manner, so that relatively accurate breathing and heartbeat frequency is obtained.
In an embodiment, for the model library, based on feature data acquired by a user in different states in a real-time detection process, machine learning training is performed on the models in the different states to construct the model library.
For the threshold, the smaller the value setting is, the more sensitive the completion filling process of the trigger data point is, so that the integrity of the data point set for feature collection can be better guaranteed, but the completion filling process may be too frequent, and the computational overhead is large. Specifically, a suitable threshold may be set by a person skilled in the art based on the accuracy requirement for feature data acquisition in practical application, and by weighing the operation overhead.
For the time range, the time length for limiting the statistics of the alarm times is shorter, and the completion filling processing is more timely when the time length is shorter, so that the integrity of the data point set for feature acquisition can be better guaranteed, but the operation overhead is easily too large, and specifically, a suitable time range can be set by a person skilled in the art based on the requirement on the accuracy of feature data acquisition in practical application, and the operation overhead is balanced.
In one embodiment, in step 103, an abnormality determination may be further performed on the determined breathing frequency and/or heartbeat frequency of the target human body, and if the abnormality of the feature data is detected, the detected user may be prompted by an alarm.
According to the technical scheme, the UWB signals are utilized to accurately identify and position the chest and abdomen region of the target human body, so that the human body characteristic data can be acquired without depending on the direct contact between the detection equipment and a detection object, and the comfort of a detected user can be improved during the acquisition of the human body characteristic data. Based on the characteristics, the method embodiment can be suitable for daily human body monitoring and monitoring of old people, children, patients (especially people suffering from respiratory and sleep disorders) and the like, can generate health reports according to the monitored respiratory and heartbeat frequencies, and is beneficial for users to know the occurrence of abnormal conditions in time. In addition, the method embodiment can also be applied to the vital sign monitoring of the pet, and the health and safety of the pet are concerned. Meanwhile, under virtual scenes such as a metasma and the like, the method embodiment can also be well applied, can be used in virtual images (such as AVATAR head portraits and the like) of video calls, can reflect body changes of both parties in time, and increases interestingness and authenticity.
The following description will exemplarily describe a specific application of the present application with reference to several specific application scenarios:
in a first scenario, a user monitors feature data by using a wearable device (such as a smart watch) in a static state.
As shown in fig. 8, when the user has a rest, the wearable device can be placed nearby without wearing the wearable device, the wearable device penetrates through bedding and clothes through the UWB radar array, the respiratory frequency and the heartbeat frequency of the sleep state of the user are detected by using the above characteristic data acquisition method, and a report is recorded and formed for the user to check, and if an abnormal state (such as apnea) occurs in the monitoring process, an alarm is given in time to remind the user or a guardian to handle the abnormal state in time.
And in a second scenario, the user utilizes wearable equipment (such as a smart watch and a bracelet) to monitor the characteristic data in the motion state.
As shown in fig. 9, in the user motion state, the respiratory frequency and the heartbeat frequency of the user are monitored, and compared with the static state, the relative motion between the wearable device and the user needs to be considered, and the respiratory and heartbeat frequencies can be calculated by using the matrix rotation and movement algorithm and the UWB array detection angle adjustment by relying on the motion sensor of the wearable device and the model data of the body data of the upper limbs of the human body.
For the situation that the movement swing is too large and the UWB array cannot detect the real data due to being shielded by the body, the inertia estimation can be carried out by depending on the data detected before and after and the accumulated data model, so as to compensate the data part lost due to being shielded in a short time.
And thirdly, applying the human body characteristic data in the meta universe and the virtual reality scene.
As shown in fig. 10, the method embodiment described above can be applied to the meta universe and the virtual reality, and since the virtual human does not reflect the vital signs of the human in the real world, the virtual human lacks reality in activities in a virtual scene such as social contact or games, and does not feel the physical changes of the human or the other, such as shortness of breath during running, and acceleration of heartbeat during tension and excitement. By adopting the method, the vital signs (respiratory frequency and heart rate) of the wearer can be monitored in real time, the monitoring values are applied to the virtual scene, so that the virtual person and the real person have the respiratory and heartbeat sensing rates, the virtual image is fuller, and the real experience in the virtual scene can be improved.
Based on the above method embodiment, an embodiment of the present invention provides a human body characteristic data acquisition device, where the device is disposed in an acquisition device, as shown in fig. 11, and includes:
a target identification unit 1101, configured to identify a target human body in a signal detectable region in real time by using an ultra wideband UWB radar array, and determine an upper half-body three-dimensional model for the identified target human body;
a detection preparation unit 1102, configured to determine a chest and abdomen region of the target human body based on the upper body three-dimensional model, and detect the chest and abdomen region; determining a contour model of the thoracoabdominal region and monitoring control points in the contour model based on the detected data point set; the monitoring control points comprise respiration monitoring control points and/or heartbeat monitoring control points;
the data acquisition unit 1103 is configured to utilize the UWB radar array to detect the thoracic and abdominal regions in real time, and determine the respiratory frequency and/or the heartbeat frequency of the target human body based on the detected coordinate change condition of the monitoring control point.
It should be noted that the embodiments of the method and the apparatus are based on the same inventive concept, and because the principles of solving the problems of the method and the apparatus are similar, the apparatus and the method can be implemented by referring to each other, and repeated parts are not described again.
Corresponding to the method embodiment, the embodiment of the application also provides human body characteristic data acquisition equipment, which comprises a processor and a memory; the memory stores an application program executable by the processor, and the application program is used for enabling the processor to execute the human body characteristic data acquisition method. Specifically, a system or an apparatus equipped with a storage medium on which a software program code that realizes the functions of any of the embodiments described above is stored may be provided, and a computer (or a CPU or MPU) of the system or the apparatus is caused to read out and execute the program code stored in the storage medium. Further, part or all of the actual operations may be performed by an operating system or the like operating on the computer by instructions based on the program code. The program code read out from the storage medium may be written into a memory provided in an expansion board inserted into the computer or a memory provided in an expansion unit connected to the computer, and then, based on an instruction of the program code, a CPU or the like mounted on the expansion board or the expansion unit may be caused to perform part or all of the actual operations, thereby implementing the functions of any of the above-described embodiments of the human body characteristic data acquisition method.
The memory may be embodied as various storage media such as an Electrically Erasable Programmable Read Only Memory (EEPROM), a Flash memory (Flash memory), and a Programmable Read Only Memory (PROM). The processor may be implemented to include one or more central processors or one or more field programmable gate arrays, wherein the field programmable gate arrays integrate one or more central processor cores. In particular, the central processor or central processor core may be implemented as a CPU or MCU.
Embodiments of the present application implement a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the human body characteristic data acquisition method as described above.
It should be noted that not all steps and modules in the above flows and structures are necessary, and some steps or modules may be omitted according to actual needs. The execution order of the steps is not fixed and can be adjusted as required. The division of each module is only for convenience of describing adopted functional division, and in actual implementation, one module may be divided into multiple modules, and the functions of multiple modules may also be implemented by the same module, and these modules may be located in the same device or in different devices.
The hardware modules in the various embodiments may be implemented mechanically or electronically. For example, a hardware module may include a specially designed permanent circuit or logic device (e.g., a special purpose processor such as an FPGA or ASIC) for performing specific operations. A hardware module may also include programmable logic devices or circuits (e.g., including a general-purpose processor or other programmable processor) that are temporarily configured by software to perform certain operations. The implementation of the hardware module in a mechanical manner, or in a dedicated permanent circuit, or in a temporarily configured circuit (e.g., configured by software), may be determined based on cost and time considerations.
"exemplary" means "serving as an example, instance, or illustration" herein, and any illustration, embodiment, or steps described as "exemplary" herein should not be construed as a preferred or advantageous alternative. For the sake of simplicity, only the parts relevant to the present invention are schematically shown in the drawings, and do not represent the actual structure as a product. In addition, in order to make the drawings concise and understandable, components having the same structure or function in some of the drawings are only schematically illustrated or only labeled. In this document, "a" does not mean that the number of the relevant portions of the present invention is limited to "only one", and "a" does not mean that the number of the relevant portions of the present invention "more than one" is excluded. In this document, "upper", "lower", "front", "rear", "left", "right", "inner", "outer", and the like are used only to indicate relative positional relationships between relevant portions, and do not limit absolute positions of the relevant portions.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (12)
1. A human body characteristic data acquisition method is characterized by comprising the following steps:
the method comprises the steps that an acquisition device utilizes an ultra wide band UWB radar array to identify a target human body in a signal detectable area in real time, and determines an upper half three-dimensional model for the identified target human body;
determining a chest and abdomen region of the target human body based on the upper half body three-dimensional model, and detecting the chest and abdomen region; determining a contour model of the thoracoabdominal region and monitoring control points in the contour model based on the detected data point set; the monitoring control points comprise respiration monitoring control points and/or heartbeat monitoring control points;
and utilizing a UWB radar array to detect the chest and abdomen area in real time, and determining the respiratory frequency and/or the heartbeat frequency of the target human body based on the detected coordinate change condition of the monitoring control point.
2. The method of claim 1, wherein determining the upper-body three-dimensional model for the identified target human body comprises:
determining a current key detection area based on the position of the target human body;
in the key detection area, transmitting a signal to the target human body by using a UWB radar array to determine an upper half body three-dimensional model of the target human body; the upper half body three-dimensional model comprises skeleton points of all parts of an upper limb and template data of the upper limb of a human body.
3. The method of claim 2, wherein the determining the three-dimensional model of the upper body of the target human body comprises:
in the key detection area, transmitting UWB linear frequency modulation continuous wave signals to the human body by using a UWB radar array;
based on the echo reflected by the target human body to the UWB linear frequency modulation continuous wave signal, removing fixed clutter in each distance unit of an echo receiving matrix by using an averaging method;
determining a distance unit where the target human body is located by using a constant false alarm probability CFAR detection technology based on the distance unit without the fixed clutter, and generating receiving matrix data of a three-dimensional distance direction and an azimuth direction based on three-dimensional array echo data of the determined distance unit in the azimuth direction;
and executing data preprocessing for separating different parts of the target human body based on the receiving matrix data of the three-dimensional distance direction and the orientation direction to obtain skeleton points of all parts of the upper limb of the target human body and template data of the upper limb of the human body.
4. The method of claim 1, wherein determining the profile model for the thoraco-abdominal region comprises:
and carrying out thin-plate spline TPS bending smoothing treatment on the data points in the data point set to obtain a contour model of the chest and abdomen area.
5. The method of claim 1, wherein determining the monitoring control point comprises:
and determining the respiration monitoring control point and/or the heartbeat monitoring control point based on a respiration detection area and/or a heartbeat detection area in a preset standard human body template.
6. The method of claim 1, wherein the determining respiratory and/or heartbeat frequency data of the target human body when the target human body is in a stationary state comprises:
and determining the reciprocating movement frequency of the monitoring control point in the time range according to the detected coordinate data of the monitoring control point in the appointed time range to obtain the corresponding respiratory frequency and/or heartbeat frequency.
7. The method of claim 1, wherein the determining respiratory rate and/or heartbeat rate data of the target human body when the target human body is in motion comprises:
for each coordinate data of the monitoring control point detected within a specified time range, performing deviation compensation processing on the coordinate data based on three-dimensional rotation data monitored by a motion sensor of the acquisition equipment in real time so as to eliminate position deviation generated by relative motion between the target human body and the acquisition equipment; the three-dimensional rotation data comprises a three-dimensional angle and a three-dimensional displacement of the acquisition equipment relative to the target human body;
and determining the reciprocating frequency of the monitoring control point in the time range according to the coordinate data after the deviation compensation processing in the appointed time range to obtain the corresponding respiratory frequency and/or heartbeat frequency.
8. The method of claim 1, further comprising:
in the process of detecting the chest and abdomen area in real time, carrying out integrity check on a data point set corresponding to the detected chest and abdomen area, and triggering an alarm when a data point is missing;
when the number of times of alarming reaches a preset threshold value within a preset time range, searching a matched model from a preset model library based on a data point set currently detected within the time range; and utilizing the searched model to perform complete filling on the data point set in the time range to obtain a corresponding complete data point set.
9. A human body characteristic data acquisition device is characterized by comprising:
the target identification unit is used for identifying a target human body in a signal detectable area in real time by using an ultra-wideband UWB radar array and determining an upper half body three-dimensional model for the identified target human body;
a detection preparation unit, configured to determine a chest and abdomen region of the target human body based on the upper body three-dimensional model, and detect the chest and abdomen region; determining a contour model of the thoracoabdominal region and monitoring control points in the contour model based on the detected data point set; the monitoring control points comprise respiration monitoring control points and/or heartbeat monitoring control points;
and the data acquisition unit is used for utilizing the UWB radar array to detect the chest and abdomen area in real time and determining the respiratory frequency and/or the heartbeat frequency of the target human body based on the detected coordinate change condition of the monitoring control point.
10. The human body characteristic data acquisition equipment is characterized by comprising a processor and a memory;
the memory stores an application program executable by the processor for causing the processor to execute the human body characteristic data acquisition method according to any one of claims 1 to 8.
11. A computer-readable storage medium having computer-readable instructions stored thereon for performing the human body characteristic data acquisition according to any one of claims 1 to 8.
12. A computer program product comprising computer programs/instructions, characterized in that the computer programs/instructions, when executed by a processor, implement the steps of the human characteristic data acquisition according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210528424.XA CN114947771A (en) | 2022-05-16 | 2022-05-16 | Human body characteristic data acquisition method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210528424.XA CN114947771A (en) | 2022-05-16 | 2022-05-16 | Human body characteristic data acquisition method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114947771A true CN114947771A (en) | 2022-08-30 |
Family
ID=82983193
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210528424.XA Pending CN114947771A (en) | 2022-05-16 | 2022-05-16 | Human body characteristic data acquisition method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114947771A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116129525A (en) * | 2023-01-24 | 2023-05-16 | 中国人民解放军陆军防化学院 | Respiratory protection training evaluation system and method |
-
2022
- 2022-05-16 CN CN202210528424.XA patent/CN114947771A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116129525A (en) * | 2023-01-24 | 2023-05-16 | 中国人民解放军陆军防化学院 | Respiratory protection training evaluation system and method |
| CN116129525B (en) * | 2023-01-24 | 2023-11-14 | 中国人民解放军陆军防化学院 | Respiratory protection training evaluation system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| D’Antonio et al. | A markerless system for gait analysis based on OpenPose library | |
| Wang et al. | Rf-ecg: Heart rate variability assessment based on cots rfid tag array | |
| Seifert et al. | Doppler radar for the extraction of biomechanical parameters in gait analysis | |
| US11386998B2 (en) | Systems and techniques for estimating the severity of chronic obstructive pulmonary disease in a patient | |
| CN105595995B (en) | Physiological data detection system, detection device, terminal device, data analysing method | |
| CN107115102A (en) | A kind of osteoarticular function appraisal procedure and device | |
| JP7107264B2 (en) | Human Body Motion Estimation System | |
| Fan et al. | Wearable motion attitude detection and data analysis based on Internet of Things | |
| CN114947771A (en) | Human body characteristic data acquisition method and device | |
| CN112363139A (en) | Human body breathing time length detection method and device based on amplitude characteristics and storage medium | |
| WO2022193851A1 (en) | Method and system for recognizing user actions | |
| Meng et al. | Accuracy improvement on the measurement of human-joint angles | |
| Schipper et al. | Estimation of respiratory rate and effort from a chest-worn accelerometer using constrained and recursive principal component analysis | |
| Zhang et al. | Wital: A COTS WiFi devices based vital signs monitoring system using NLOS sensing model | |
| WO2024012330A1 (en) | Electronic device for evaluating progression of parkinson's disease | |
| Gu et al. | Real-time vital signs monitoring based on COTS WiFi devices | |
| CN111603750A (en) | Motion capture recognition evaluation system and method based on edge calculation | |
| CN114098679B (en) | Vital sign monitoring waveform recovery method based on deep learning and radio frequency sensing | |
| CN114847931A (en) | Human motion tracking method, device and computer-readable storage medium | |
| He et al. | A joint localization assisted respiratory rate estimation using IR-UWB radars | |
| Boccanfuso et al. | Collecting heart rate using a high precision, non-contact, single-point infrared temperature sensor | |
| JP2007121217A (en) | Bodily motion analyzer | |
| US11854214B2 (en) | Information processing apparatus specifying a relationship between a sensor and an object included in image data, and method and non-transitory computer-readable storage medium | |
| Rana et al. | 3-D gait abnormality detection employing contactless IR-UWB sensing phenomenon | |
| CN108143404A (en) | A kind of measurement heart rate data method and system |
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 |