CN112336341A - Human body falling detection system based on inertia and position sensors - Google Patents
Human body falling detection system based on inertia and position sensors Download PDFInfo
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Abstract
The invention discloses a human body falling detection system based on inertia and position sensors, which comprises an MCU module and an MEMS device, wherein the MEMS device is a triaxial accelerometer and a triaxial gyroscope sensor; the MCU module is executed according to the following steps: electrifying the system to work and establishing variables; acquiring observation data and temperature of each dimension of the MEMS device; resolving the temperature correction coefficient for the observation data of the MEMS device, and reducing the deviation caused by the temperature; a statement of a buffer amount; frame refreshing, wherein the total acceleration value is calculated according to a mathematical vector modulo acc = (x ^2+ y ^2+ z ^ 2) ^ 1/2; carrying out data frame skipping derivation operation, and resolving and outputting a falling state; and (3) judging the alarm quantity threshold, observing a change curve caused by the total model length in the state of the last group of data frames, and separating out the following observed quantities through the attitude angle: the change value of the euler angle is a non-falling state caused by natural action and the change of the euler angle is a falling-like state caused by an acceleration semi-sinusoidal curve.
Description
Technical Field
The invention relates to the technical field of measurement and control application, in particular to a human body falling detection system based on inertial and position sensors.
Background
Because the activities of daily behaviors of a human body are complex, the tumble judging algorithm based on the prior threshold has misjudgment of different degrees in practical application, the tumble behavior is difficult to accurately judge simply based on the information of the acceleration sensor, and the scheme of integrating six-axis MEMS devices adopted in the industry at present is a popular scheme. In the micro-machining technology, the electron beam lithography technology is the best pattern making technology at present, under the laboratory environment, the electron beam can be focused into a beam spot with the size of 2 nm, the electron beam lithography technology has important application value in the aspects of micro-machining and preparation of nano devices, and simultaneously, the electron beam lithography technology plays an important role in the three-dimensional microstructure machining technology along with the development of the micro-electro-mechanical system (MEMS) technology. However, the price of a six-axis MEMS device increases geometrically with the increase in performance, for example, performance is higher than that of a typical six-axis device: the price of RMPU6050 should be about 35 yuan/PCS in the secondary market in the prosperous situation, but the output of the gyroscope is jagged (not in a monotonous linear form) and only can be used for low-end posture measurement (toys and the like), so that the gyroscope is not suitable for being worn by a human body, while the price of the secondary market of the mature ADI RMXRS 646 single-shaft gyroscope is 500 yuan/PCS, a six-shaft system is required to be made, and the price of a single set of equipment is not easy to bear by a person.
At present, the problems of the industry technology are as follows: (1) the large-size application occasion is limited, and generally, instruments which can be used for human engineering and bionics scientific research experiments are large in size and heavy in weight, large and complex in interface, and must be cut out as daily consumer electronics; (2) the algorithm is complex and occupies the resources of the overall controller; (3) the price of a single set of finished products must be accepted by the public due to the balanced selection of the MEMS device from cost to test precision.
Disclosure of Invention
The invention aims to provide a human body falling detection system based on inertia and position sensors, which can conveniently detect the falling state of a human body, and has low cost and simple processing procedure.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the human body falling detection system based on the inertia and position sensors comprises an MCU module and an MEMS device, wherein the MEMS device is a triaxial accelerometer and a triaxial gyroscope sensor, and the triaxial accelerometer and the triaxial gyroscope sensor are connected to an input interface of the MCU module;
the MCU module is executed according to the following steps:
s1, electrifying the system to work, and establishing variables;
s2, acquiring observation data and temperature of each dimension of the MEMS device;
s3, resolving the temperature correction coefficient for the MEMS device observation data, and reducing the deviation caused by the temperature;
s4, declaring the buffer storage amount, wherein the contents of the two-dimensional arrays are the total acceleration value and the measured inclination angle, and each array forms a frame;
s5, refreshing a frame, and calculating the total acceleration value according to a mathematical vector modulus acc = (x ^2+ y ^2+ z ^ 2) ^ 1/2;
s6, carrying out derivation operation on the skip data frame, and resolving and outputting a falling state;
s7, judging the alarm quantity threshold, observing a change curve caused by the total model length in the state of the last group of data frames, and separating all observed quantities of attitude angles: the change value of the euler angle is a non-falling state caused by natural action and the change of the euler angle is a falling-like state caused by an acceleration semi-sinusoidal curve.
The invention has the advantages that the acceleration characteristic is an important parameter in the human motion characteristic, when a human body normally walks, the acceleration periodically and regularly changes, and when the human body falls down, the acceleration can be violently changed, and the change of the acceleration can reflect the change of the human motion state, so that the acceleration characteristic is monitored by adopting a six-axis sensor combining three-axis acceleration with a three-axis gyroscope; the invention provides a synchronous control method for measuring the three-dimensional motion of the MEMS microstructure according to the synchronous measurement requirement of a finished product single machine, realizes the automatic acquisition and report of the human body posture, and has low cost; a falling detection logic based on threshold judgment of different falling stages is constructed; through system experiment verification, the detection accuracy rate of the falling detection algorithm to common falling conditions is over 95%, and the falling detection algorithm can meet the requirement of detecting the falling dangerous conditions of old people.
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The invention is further illustrated with reference to the following figures and examples:
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a control flow diagram of the present invention;
fig. 3 is a block diagram of an implementation algorithm of the present invention.
Detailed Description
With reference to fig. 1 to 3, the invention provides a human body fall detection system based on inertia and position sensors, which comprises an MCU module 1 and an MEMS device 2, wherein the MEMS device 2 is a three-axis accelerometer and a three-axis gyroscope sensor, and the three-axis accelerometer and the three-axis gyroscope sensor are connected to an input interface of the MCU module 1;
the MCU module is executed according to the following steps:
s1, electrifying the system to work, and establishing variables;
s2, acquiring observation data and temperature of each dimension of the MEMS device;
s3, resolving the temperature correction coefficient for the MEMS device observation data, and reducing the deviation caused by the temperature;
s4, declaring the buffer storage amount, wherein the contents of the two-dimensional arrays are the total acceleration value and the measured inclination angle, and each array forms a frame;
s5, refreshing a frame, and calculating the total acceleration value according to a mathematical vector modulus acc = (x ^2+ y ^2+ z ^ 2) ^ 1/2;
s6, carrying out derivation operation on the skip data frame, and resolving and outputting a falling state;
s7, judging the alarm quantity threshold, observing a change curve caused by the total model length in the state of the last group of data frames, and separating all observed quantities of attitude angles: the change value of the euler angle is a non-falling state caused by natural action and the change of the euler angle is a falling-like state caused by an acceleration semi-sinusoidal curve.
The MEMS device collects data according to posture change of a trainer, the SOC processing data with the MCU module as a main body is used for preprocessing and calculating data, the data mainly comprise correction parameters of temperature offset and correction coefficients in a set algorithm, the individualized coefficients are burnt into an E2PROM of the SOC after being subjected to batch normalization calculation by a production industrial personal computer, a sensor outputs bus signals in real time along with the movement of the position of a human body, switching threshold values are carried out through the algorithm, complex matrix operation is simplified, operation efficiency is greatly improved, detection algorithm judgment is provided for accidental falling of the old to detect the falling behavior of the human body, and finally judgment of the falling behavior of the trainer is achieved.
The MCU module outputs the result to the upper computer through an upper computer interface.
For the six-axis fusion algorithm and process:
the three-axis accelerometer and the three-axis gyroscope sensor can calculate postures, the postures calculated by long-term data are credible due to the fact that the three-axis accelerometer is sensitive to disturbance of vibration, the three-axis gyroscope sensor is insensitive to vibration, but the gyroscope can drift when being used for a long time, and therefore complementation needs to be carried out, short-term data are collected from the three-axis gyroscope sensor, and long-term data are collected from the three-axis accelerometer.
The program of a single product measures zero positions of six axes in the previous engineering reset work, so that actually the initial zero positions are subtracted from the outputs of the six axes to obtain true values, and each part in the fusion calculation needs to use the true values, which are as follows:
# defineKp 10.0f// KKi here is for adjusting the accelerometer to correct the velocity of the gyroscope
#defineKi 0.008f
# defiehalfT 0.001f// half of the sample period for calculating the angular increment when solving the quaternion differential equation
floatq0 = 1, q1 =0, q2 =0, q3 = 0// initial attitude quaternion, derived from the transform quaternion formula
floatexin =0, eyInt =0, ezInt = 0// the component of the acceleration of gravity measured by the current accelerometer on three axes
// integral of the error with the component of gravity on three axes calculated from the current attitude
voidmiuuupdate (float gx, float gy, float gz, float ax, float ay, float az)// g table gyroscope, a table adding meter
{
float q0temp, q1temp, q2temp, q3temp,/quaternion temporary storage variable, used for solving differential equation
float norm// norm of a modulus or quaternion of a vector
float vx, vy, vz// component of gravity on three axes calculated from the current attitude
float ex, ey, ez;/component of the acceleration of gravity measured by the current accelerometer in three axes
Error from the component of gravity on three axes calculated from the current attitude
First calculate the values obtained
float q0q0 = q0*q0;
float q0q1 = q0*q1;
float q0q2 = q0*q2;
float q1q1 = q1*q1;
float q1q3 = q1*q3;
float q2q2 = q2*q2;
float q2q3 = q2*q3;
float q3q3 = q3*q3;
if (ax ay az =0)// when the addition meter is in a free-fall state, no attitude calculation is performed because a case where the denominator is infinite occurs
return;
norm = sqrt (ax + ay + az) and// unitized accelerometer,
ax = ax /norm;
ay = ay / norm;
az = az / norm;
v/calculate the components of gravity in three axes with the current pose,
the third column of the direction cosine matrix expressed by quaternion and converted from the n-system of reference coordinates to the b-system of carrier coordinates is
vx=2*(q1q3-q0q2)；
vy = 2*(q0q1 + q2q3);
vz = q0q0 - q1q1 - q2q2 + q3q3 ;
V/calculating the error between the measured gravity and the calculated gravity, the vector outer product may indicate this error
ex = (ay*vz - az*vy) ;
ey = (az*vx - ax*vz) ;
ez = (ax*vy - ay*vx) ;
exInt = exInt + ex Ki// integrating the error
eyInt = eyInt + ey * Ki;
ezInt = ezInt + ez * Ki;
// adjusted gyroscope measurements
gx = gx + Kp ex + exInt// post-compensating the error PI to the gyroscope, i.e. compensating for zero drift
gy = gy + Kp*ey + eyInt;
gz = gz + Kp + ezInt// there gz is shifted due to no correction by the observer, and is represented by an integral self-increment or self-decrement
V. updating of attitude, i.e. solving quaternion differential equations
q0temp = q 0// storing the current value for calculation
q1temp=q1;//
q2temp=q2;
q3temp=q3;
Adopting a first-order Picard decomposition method, and the related knowledge can be found in inertial device and inertial navigation system
q0 = q0temp + (-q1temp*gx - q2temp*gy -q3temp*gz)*halfT;
q1 = q1temp + (q0temp*gx + q2temp*gz -q3temp*gy)*halfT;
q2 = q2temp + (q0temp*gy - q1temp*gz +q3temp*gx)*halfT;
q3 = q3temp + (q0temp*gz + q1temp*gy -q2temp*gx)*halfT;
// unitized quaternions do not stretch when rotated in space, only the angle of rotation, like orthogonal transformation in linear algebra
norm = sqrt(q0*q0 + q1*q1 + q2*q2 + q3*q3);
q0 = q0 / norm;
q1 = q1 / norm;
q2 = q2 / norm;
q3 = q3 / norm;
// quaternion to Euler Angle conversion
Where the YAW heading angle is directly replaced by gyroscope integration since the accelerometer has no correction to it
Q_ANGLE.Z = GYRO_I.Z; // yaw
Q_ANGLE.Y = asin(-2 * q1 * q3 + 2 * q0* q2)*57.3; // pitch
Q_ANGLE.X = atan2(2 * q2 * q3 + 2 * q0 * q1,-2 * q1 * q1 - 2 * q2* q2 + 1)* 57.3; // roll
}
The experimental study is carried out on typical MEMS microstructures such as a silicon micro gyroscope, a micro mirror, a micro resonator array, an AFM micro cantilever beam and the like, a satisfactory measuring effect is obtained, the effectiveness and the practicability of an MEMS microstructure dynamic testing system are verified, and the system can carry out MEMS microstructure static measurement and periodic excitation measurement; the MEMS microstructure can be visually measured in out-of-plane motion and in-plane motion:
the method comprises the following steps: optical etching of the uniaxial dimension, planar resonator and vibration pickup patterns;
step two: stacking a bulk silicon process Z-axis vibrator and a resonator;
step three: basic excitation and accurate control of temperature and pressure are carried out through a loading device, and static and dynamic performance tests of the MEMS microstructure under different environmental conditions are realized;
the performance indexes of the system mainly comprise a measuring frequency range of 0-250 kHz, the highest excitation frequency of more than 20kHz, the measuring precision of the off-plane motion of 10mm and the measuring precision of the in-plane motion of 15 mm; for static profile measurements, the indicated value error is less than + -2%, and the indicated value variability is better than 1%.
The IP of the main control MCU is cut, a compact main control board structure is designed, only I2C communication and serial port communication parts are needed to be reserved, mature small packaging is selected, a chip die part is subjected to OTP burning before packaging, an E2PROM is externally hung on a control variable adjusting part (such as communication ID, sensor offset correction value and the like),
choose popular, the meaning method semiconductor STM32F103 core of low price for use as MCU module master control, reserved serial interface and SPI/I2C interface, theoretically all digital output type IMUs of adaptation (including triaxial/six axles/nine axles), the module of adaptation according to engineering needs includes singly not to be restricted to: MAX3488 (for RS422 communication), Bluetooth module HC-05, AD7799 (corresponding to some analog quantity gyroscopes), ATMLH338 (PROM, for storing variables such as compensation coefficient), its PCBA typical size is about 40mmX30mm, help to make all kinds wear the apparatus, the small-scale PCBA that the main control panel made through binding technique can reach 20mmX20mm size even more.
The output of the triaxial accelerometer and the triaxial gyroscope sensor fluctuates, which is mainly limited to the cost limit value of the selected MEMS device, so that the filtering processing is required on software. In practical use, there are two modes of mean filtering and median filtering: the mean filtering method comprises the steps of sampling n +2 data, removing the maximum data and the minimum data, and adding the rest data and dividing by n to obtain an average value; the median filtering method is to sample k (k is an odd number), and after the array is arranged by bubbling, the (k + 1)/2 th data is taken as the median. According to the working mechanism of the microcontroller, the implementation of median filtering saves hardware resources comparatively, but the result of mean filtering is comparatively smooth. The subjective stm32f103 has strong calculation capability, but a part of design median filtering needs to be reserved in consideration of resource occupation. It should be noted that the purpose of these filters is limited to improve the precision and smooth the processed data, rather than implementing the analog features of the traditional filter, such as the indexes of amplitude-frequency characteristics, phase-frequency characteristics, out-of-band attenuation, etc., and reserving a great amount of computing resources, the filtered data can implement higher data throughput, and a step of threshold judgment and timing trigger are added to the data stream for fall detection.
The foregoing is merely a preferred embodiment of the invention and is not intended to limit the invention in any manner; those skilled in the art can make numerous possible variations and modifications to the present teachings, or modify equivalent embodiments to equivalent variations, without departing from the scope of the present teachings, using the methods and techniques disclosed above. Therefore, any simple modification, equivalent replacement, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the scope of the protection of the technical solution of the present invention.
Claims (1)
1. The human body falling detection system based on the inertia and position sensors is characterized by comprising an MCU module and an MEMS device, wherein the MEMS device comprises a three-axis accelerometer and a three-axis gyroscope sensor, and the three-axis accelerometer and the three-axis gyroscope sensor are connected to an input interface of the MCU module;
the MCU module is executed according to the following steps:
s1, electrifying the system to work, and establishing variables;
s2, acquiring observation data and temperature of each dimension of the MEMS device;
s3, resolving the temperature correction coefficient for the MEMS device observation data, and reducing the deviation caused by the temperature;
s4, declaring the buffer storage amount, wherein the contents of the two-dimensional arrays are the total acceleration value and the measured inclination angle, and each array forms a frame;
s5, refreshing a frame, and calculating the total acceleration value according to a mathematical vector modulus acc = (x ^2+ y ^2+ z ^ 2) ^ 1/2;
s6, carrying out derivation operation on the skip data frame, and resolving and outputting a falling state;
s7, judging the alarm quantity threshold, observing a change curve caused by the total model length in the state of the last group of data frames, and separating all observed quantities of attitude angles: the change value of the euler angle is a non-falling state caused by natural action and the change of the euler angle is a falling-like state caused by an acceleration semi-sinusoidal curve.
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| CN112907895A (en) * | 2021-04-15 | 2021-06-04 | 江南造船(集团)有限责任公司 | Boats and ships personnel falling perception method, device, system, medium and electronic equipment |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104224182A (en) * | 2014-03-31 | 2014-12-24 | 桂林电子科技大学 | Method and device for monitoring human tumbling |
| CN105336102A (en) * | 2015-11-25 | 2016-02-17 | 青岛理工大学 | Fall detecting and locating method and device |
| CN105466422A (en) * | 2015-12-02 | 2016-04-06 | 爱芽（北京）科技有限公司 | Algorithm for detecting position variation of toothbrush in mouth |
| CN105632101A (en) * | 2015-12-31 | 2016-06-01 | 深圳先进技术研究院 | Human body anti-tumbling early warning method and system |
| CN109087482A (en) * | 2018-09-18 | 2018-12-25 | 西安交通大学 | A kind of falling detection device and method |
| CN109550219A (en) * | 2018-11-30 | 2019-04-02 | 歌尔科技有限公司 | A kind of determination method, system and the mobile device of motion information |
| CN111643093A (en) * | 2020-01-14 | 2020-09-11 | 天津理工大学 | Animal motion sign monitoring system |
-
2020
- 2020-10-27 CN CN202011161687.9A patent/CN112336341A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104224182A (en) * | 2014-03-31 | 2014-12-24 | 桂林电子科技大学 | Method and device for monitoring human tumbling |
| CN105336102A (en) * | 2015-11-25 | 2016-02-17 | 青岛理工大学 | Fall detecting and locating method and device |
| CN105466422A (en) * | 2015-12-02 | 2016-04-06 | 爱芽（北京）科技有限公司 | Algorithm for detecting position variation of toothbrush in mouth |
| CN105632101A (en) * | 2015-12-31 | 2016-06-01 | 深圳先进技术研究院 | Human body anti-tumbling early warning method and system |
| CN109087482A (en) * | 2018-09-18 | 2018-12-25 | 西安交通大学 | A kind of falling detection device and method |
| CN109550219A (en) * | 2018-11-30 | 2019-04-02 | 歌尔科技有限公司 | A kind of determination method, system and the mobile device of motion information |
| CN111643093A (en) * | 2020-01-14 | 2020-09-11 | 天津理工大学 | Animal motion sign monitoring system |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112907895A (en) * | 2021-04-15 | 2021-06-04 | 江南造船(集团)有限责任公司 | Boats and ships personnel falling perception method, device, system, medium and electronic equipment |
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