CN106971059B

CN106971059B - Wearable equipment based on neural network self-adaptation health monitoring

Info

Publication number: CN106971059B
Application number: CN201710117165.0A
Authority: CN
Inventors: 陈晨
Original assignee: Fuzhou Yunkai Intelligent Technology Co ltd
Current assignee: Chen Chen
Priority date: 2017-03-01
Filing date: 2017-03-01
Publication date: 2020-08-11
Anticipated expiration: 2037-03-01
Also published as: CN106971059A

Abstract

The invention discloses a wearable device based on neural network self-adaptive health monitoring, which comprises: the device comprises a motion state solving module, a motion link judging module, a dimension reduction solving module, a physiological parameter setting module and a physiological parameter collecting module; the motion state solving module collects acceleration data output by the acceleration sensor, filters high-frequency noise through wavelet transformation and divides the motion state of a user; the motion link judgment module is used for subdividing the motion state by combining the angular speed to obtain a motion subdivision link; and the dimension reduction solving module is used for further finely distinguishing partial pathological features by combining the optimized SVM hierarchical level with different hierarchical data. The analysis link of the invention integrates different characteristics to reduce the dimension and sample, thereby greatly reducing the amount of exercise, ensuring the correctness through the key calculation verification link, improving the medical reference value of the obtained physiological parameters of the user, and extracting the power consumption standby capability under the same condition.

Description

Wearable equipment based on neural network self-adaptation health monitoring

Technical Field

The invention relates to the field of intelligent wearing, in particular to wearable equipment based on neural network adaptive health monitoring.

Background

Cardiovascular disease, the most dangerous of human health, is a disease whose etiology involves the tissues that transport blood and the circulatory system including the heart, arterial blood vessels, and venous blood vessels in the human body. The human body pulse contains rich physiological and pathological information related to the cardiovascular system, and the waveform amplitude, period and shape of the pulse can be used as important basis for judging whether the cardiovascular system of the human body is healthy or not.

For a long time, people obtain an electrocardiogram by an electrocardiograph to judge. With the development of wearable technology and telemedicine, people are increasingly not satisfied with obtaining self health information by using large-scale equipment such as an electrocardiograph under the conditions of limiting time and place and limiting physiological activities, and tend to monitor self health status freely, flexibly, continuously and in real time.

The acquisition of the electrocardiogram is divided into four categories in principle:

1. light: the pulse rate is detected by light transmission or reflection of the blood flowing in the blood vessel (pulse), and is usually equal to the heart rate data. The current finger-clipped optical sensor basically uses the visible light transmission principle; whereas pulse light sensors basically use the principle of light reflection.

2. Electricity: the ECG signal is a millivolt level electrical signal generated by the beating of human heart, and the electrocardiogram of physical examination is seen by everyone, and devices such as heart rate belt, electrocardiogram watch and the like which require at least more than two electrodes basically belong to the detection principle.

3. Sound: the heart is usually examined using the ultrasonic doppler principle, which is the common fetal heart monitor.

4. Pressing: the pulse rate is usually detected by using a pressure sensor to detect the weak pressure signal of the pulse beat, and the blood pressure and the pulse rate are mainly calculated in the blood pressure detection with few hand rings.

Among the four methods, the optical detection method has the advantages of convenience and rapidness in implementation, low cost, capability of 24-hour detection and the like, and becomes a mainstream measure of wearable portable watch measurement. Especially, green light is used as a light source to measure the characteristics of the PPG, compared with infrared light and red light, the green light is less interfered by subcutaneous tissues, and the acquired PPG waveform is more complete. Through green light irradiation, because the absorption coefficient and blood concentration of light-absorbing substances such as hemoglobin of human subcutaneous tissues and the like in the whole blood circulation are kept unchanged, the optical path of transmitted light or reflected light is periodically changed along with the action of the heart, the volume of peripheral blood in diastole is minimum, the optical path is minimum, the light absorption is also minimum, the detected light intensity is minimum, the cardiac systole is just opposite, the change of the reflected light intensity is detected and converted into a digital signal, and then the electrocardio pulse wave is depicted.

Health sign data that wearable healthy intelligent watch gathered are only as health analysis reference in the industry at present, do not replace traditional non-wearing formula domestic medical instrument and popularize fast on a large scale, have following reason:

1. the accuracy problem is as follows: the existing wearable equipment does not consider that different physiological parameters are acquired under different motion states, so that the physiological parameters with medical reference values can be obtained.

Currently, the health detection device measures the heart rate or electrocardiogram of the user in a stationary state. However, in practice, the physiological parameters under different motion states need to be acquired for different patients, that is, the acquisition of many health signs has higher requirements on the state environment of the acquirer. For example, the collection of the electrocardiogram requires the collection of the human body in a calm state, and if the human body is required to rest for 10 minutes after exercise, the obtained heart rate data has medical reference value; dynamic heart rate exercise heart rate data measured 3 minutes after the start of exercise has medical reference value.

Meanwhile, for some special cases, the physiological parameters should be acquired in a specific gait cycle. In the prior art, physiological parameters are collected according to gait states. For example, the prediction and diagnosis of stroke (stroke) and parkinson are related to the behavior of the action state in different steps of the gait cycle. Therefore, the motion state, period and motion fine characteristics need to be acquired finely.

2. The problem of electric quantity: due to constraints on volume, weight, and system performance, power supply capacity is one of the most important factors that limit widespread use. The watch bracelet power consumption standby time of taking green glow collection at present is very limited originally, and prior art gathers the detail characteristic of motion state comprehensively, and the operand that brings, consumption are also bigger, reduce wearable equipment's duration.

In summary, the prior art does not take the motion state of the user into consideration when collecting physiological parameters; meanwhile, the prior art can only carry out simple motion state acquisition such as step counting and the like, and does not acquire the difference of motion links and motion subtle characteristics. In addition, when the prior art collects the detailed characteristics of the motion state, the operation amount is large, and the power consumption is high.

Disclosure of Invention

In view of the foregoing defects of the prior art, the technical problem to be solved by the present invention is to provide a wearable device based on neural network adaptive health monitoring, aiming to solve the problem that the prior art does not consider the motion state of the user when acquiring physiological parameters; the invention identifies the movement links and movement subtle characteristics and acquires corresponding physiological parameter information, thereby facilitating the analysis of the health state of the user by doctors or other personnel. Meanwhile, the motion state is acquired in a grading mode, the data processing operation amount is reduced, the power consumption of the wearable equipment is reduced, and the cruising ability is improved.

In order to achieve the above object, the present invention provides a wearable device based on neural network adaptive health monitoring, comprising:

the motion state solving module is used for acquiring acceleration data output by the acceleration sensor at regular time, filtering high-frequency noise through wavelet transformation and dividing the motion state of a user;

the motion link judgment module is used for subdividing the motion state by combining the angular speed to obtain a motion subdivision link;

the dimensionality reduction solving module is used for further finely distinguishing partial pathological features by using the optimized SVM hierarchical level and combining different hierarchical data;

the physiological parameter setting module is used for setting physiological parameters to be acquired under different motion states and gaits;

and the physiological parameter acquisition module is used for acquiring corresponding physiological parameters under the appointed motion state and gait according to the physiological parameters required to be acquired.

Further, the motion segment determination module is configured to:

acquiring acceleration values of the acceleration sensor in the directions of the x, y and z axes

、

、

And solving the acceleration signal vector mode

(ii) a Collecting angular velocity values of the angular velocity sensor in the directions of three axes of x, y and z

、

、

And solving the angular velocity signal vector mode

(ii) a The above-mentioned

，

；

By vector mode of acceleration signal

Vector mode of angular velocity signal

And establishing an identification model which is perfect in self-adaptation, and subdividing the motion state to obtain a motion subdivision link.

Furthermore, the motion link judgment module further comprises a filtering unit, wherein the filtering unit performs wavelet transformation operation of three steps of wavelet decomposition, high-frequency wavelet coefficient processing and wavelet reconstruction on the acquired data of the acceleration sensor and the angular velocity sensor, discretizes time domain signals in each direction, decomposes mixed signals of various frequency components into different frequency bands, and then processes the mixed signals according to different characteristics of various seed signals on the frequency domain and frequency bands; acquiring gait data with high signal to noise ratio;

the wavelet transform adopts a hard threshold value method, and the wavelet coefficient is

The threshold value is

；

The above-mentioned

。

Further, the dimension reduction solving module is configured to:

acquiring physiological parameter data of a user, and reducing the dimension of the physiological parameter data of the user;

acquiring acceleration/angular velocity values of the foot of a user in three directions, taking a standard deviation of the whole sample population, taking N as a sample amount, training a classifier, and identifying gait samples by the classifier; comprehensively calculating the degree x of deviation of certain gait from the tidying crowd, wherein

(ii) a Wherein, a, b and c are acceleration/angular velocity values of three directions of the user respectively;

respectively representing the average acceleration/angular velocity values of the crowd in the x, y and z axes in space;

inputting the personal gait samples of N types registered in the database into a classifier for training, judging which type is (1, N) according to an input value, if the input value exceeds the range of (1, N), newly registering the type of N +1, and then updating the classifier again;

and on the basis of different motion division, the same motion is subdivided again, and a classification result is determined by adopting a voting mode.

Further, the dimension reduction solving module is further configured to:

the raw data collected by the sensor is standardized by each element in the matrix

Subtract the mean of the column

Then divided by the standard deviation of the column

Such that each variable is normalized to a matrix X with a mean of 0 and a variance of 1, i.e.

；

Wherein,

，

，

(ii) a i represents the basic information attributes of the body of the population collecting the samples; j represents the attribute of the disease type of the population collecting the samples;

to obtain

；

Solving a correlation coefficient matrix:

where R is a real symmetric matrix (i.e.

) Wherein r is a correlation coefficient; p is the total attribute of the population of the collected samples, i.e. the sum of i + j;

solving the cumulative contribution rate:

；

wherein

All attributes of the population for which samples were collected; p is all attributes in the sample library;

a value that is a single attribute;

if the accumulated contribution rate reaches more than 50%, a ratio height method is adopted, the characteristic value vector of the highest contribution rate is left as a fixed working sample set, and the rest is discarded;

calculating a score matrix, using the remaining characteristic values as new variable principal components, and calculating the score matrix by using the following formula

(ii) a Wherein n corresponds to the basic information attributes of the body of the population collecting the samples; m corresponds to the attribute of the disease type of the population collecting the samples; p is all attributes in the sample library;

wherein X is an original data matrix, U is a principal component load, and a score matrix F is a result obtained after dimensionality reduction;

fitness function of SVM classifier for continuous training subdivision and continuous training subdivision by combining big data

Wherein

And dividing the sample into correct rates for the SVM classifier.

Further, the dimension reduction solving module is further configured to:

calculating by using the output data of the acceleration sensor and adopting a first condition, a second condition and a third condition, and judging the gait of the human motion by using median filtering;

the first condition is: the accelerometer outputs a synthesized amplitude value, and if the synthesized amplitude value is between the given upper threshold value and the given lower threshold value, the human body is judged to be static; otherwise, judging the motion of the person;

the accelerometer output composite amplitude is:

(ii) a Wherein k represents the kth sensor point; b denotes the b-th sensor in a certain sensor spot;

the upper and lower thresholds are respectively:

，

；

the first condition is expressed as:

the second condition is: if the local variance output by the accelerometer is lower than a given threshold value, the human body is judged to be static; otherwise, judging the motion of the person;

the local variance of the accelerometer output is:

(ii) a Wherein k represents the basic information attributes of the body of the population collecting the samples; s represents the half-window sample number; b denotes the b-th sensor;

wherein

For the interval, the accelerometer synthesizes an output average value of the amplitude, and the expression is as follows:

；

s is the number of half-window samples, and a given threshold is defined as:

the second condition is represented as:

；

the third condition is: the angular velocity sensor outputs an angular velocity composite amplitude, and if the angular velocity composite amplitude is lower than a given threshold value, the human body is judged to be static; otherwise, judging the motion of the person;

define the composite amplitude of the gyroscope output as:

the given thresholds are:

the third condition is represented as:

。

further, the wearable device based on the neural network adaptive health monitoring further comprises a model adaptive perfecting module; the model adaptation refinement module is configured to:

reading a new input sample, and calculating the recognition rate of the SVM classifier according to a cross verification method;

if the current recognition rate of the training is higher than or equal to the original recognition rate, setting the parameters of the training as the optimal parameters; otherwise, a selection operation, a crossover operation and/or a mutation operation are executed, and the training parameters are further optimized.

The invention has the beneficial effects that: the scheme overcomes the two contradictions, on one hand, fine motion characteristics are comprehensively collected in the preprocessing process, on the other hand, only limited different sensor functions are called in different specific judging processes through self-adaptive algorithm matching, different specific characteristic calculation is extracted, different characteristic dimension reduction sampling is integrated in an analyzing link, the amount of exercise is greatly reduced, and the accuracy is guaranteed through a key calculation verification link. Therefore, the detailed characteristics of the motion state are comprehensively collected, the calculated amount and the power consumption are greatly reduced, the medical reference value is improved, and the power consumption standby capability is extracted under the same condition.

Drawings

FIG. 1 is a block diagram of the architecture of an embodiment of the present invention;

fig. 2 is a schematic diagram of human motion hierarchy division in the present embodiment;

FIG. 3 is a schematic view of the user walking geometry in the present embodiment;

FIG. 4 is a schematic diagram of three condition gait detection in the present embodiment;

fig. 5 is a schematic flow chart of adaptive refinement of the model in this embodiment.

Detailed Description

The invention is further illustrated by the following examples in conjunction with the accompanying drawings:

health sign data that wearable healthy intelligent watch gathered only refer to as health analysis in the industry at present, do not have the domestic medical instrument of the non-wearing formula of large-scale substitution and popularize fast, have two important reasons:

1. the accuracy problem is as follows: the acquisition of many health signs has higher requirements on the state environment of an acquirer, for example, the acquisition of an electrocardiogram requires the acquisition of a human body in a calm state, and if the human body is required to rest for 10 minutes after exercise, the acquired heart rate data has medical reference value; the exercise heart rate data measured 3 minutes after the exercise starts have medical reference value; the prediction and diagnosis of stroke (cerebral apoplexy), Parkinson and the like are related to the behavior and other conditions of the action states of different links of the gait cycle. Therefore, the motion state, period and motion fine characteristics need to be acquired finely.

The wearable bracelet of industry can only carry out motion state collection such as simple meter step at present, does not gather to the difference of motion link, the slight characteristic of motion. At present, wearable health acquisition equipment authenticated by CFDA medical instruments is not available, and can only be used as reference.

2. The problem of electric quantity: due to constraints on volume, weight, and system performance, power supply capacity is one of the most important factors that limit widespread use. The watch bracelet power consumption standby time of taking green light collection at present is very limited originally, if the detailed characteristic of the motion state is gathered in a comprehensive way, the operand, the consumption of bringing will be huge more.

The scheme overcomes the two contradictions, on one hand, fine motion characteristics are comprehensively collected in the preprocessing process, on the other hand, only limited different sensor functions are called in different specific judging processes through self-adaptive algorithm matching, different specific characteristic calculation is extracted, different characteristic dimension reduction sampling is integrated in an analyzing link, the operation amount is greatly reduced, and the accuracy is guaranteed through a key calculation verification link.

Thereby, the following steps are achieved: the detailed characteristics of the motion state are comprehensively collected, the calculated amount and the power consumption are greatly reduced, the medical reference value is improved, and the power consumption standby capability is extracted under the same condition.

As shown in fig. 1, in a first embodiment of the present invention, there is provided a wearable device for adaptive health monitoring based on a neural network, including:

the motion state solving module is used for acquiring acceleration data output by the acceleration sensor at regular time, filtering high-frequency noise through wavelet transformation and dividing the motion state of a user, wherein the motion state comprises a first cyclic action and a second non-cyclic action; the first cyclical action comprises walking and running; the second non-cyclic action comprises jumping, standing up, sitting down and squatting down; the walking comprises going upstairs, going downstairs and walking on flat ground;

The invention obtains physiological parameters under different states so as to better evaluate the health state; the heart rate under static state is measured when the patient goes to the hospital at ordinary times; in addition, some diseases do not show well in a static state, such as heart disease, and the physiological state is similar to that of a normal person without strenuous exercise, and the symptoms are drastically different after strenuous exercise.

For the application of fall detection and common human behavior analysis, the acceleration sensor can well distinguish the motion and static states of human behaviors, similar motion behaviors are difficult to distinguish, and the characteristics are that the distinguishing degree is large and the confusion degree is also large. The wrist angular velocity is combined to further distinguish, but the acceleration and the angular velocity are calculated simultaneously, the calculation amount is large, the timeliness and the electric quantity power consumption are affected, and therefore the calculation amount and the accuracy are considered by adopting a mode of hierarchical classification, dimension reduction classification and key verification. As shown in fig. 2, the human motion may be divided into different levels.

In this embodiment, the motion state solving module firstly uses the acceleration sensor data to perform a first-level motion judgment on the intelligent wristwatch three-axis acceleration sensor, and collects the acting forces in the x, y and z directions according to a sampling frequency of 100Hz (the walking frequency of a person is generally 110 steps/minute (1.8Hz), the running frequency does not exceed 5Hz, and the sampling frequency of 100Hz can accurately reflect the acceleration change.

In this embodiment, the motion state solving module counts the frequency of occurrence of the peak through the pair of trajectories secondly. In horizontal movement of the user, the vertical and forward accelerations may exhibit periodic variations. In the walking and foot-receiving action, the gravity center is upward, and only one foot touches the ground, the vertical acceleration tends to increase in a positive direction, then the gravity center is moved downwards, and the two feet touch the bottom, and the acceleration is opposite. The horizontal acceleration decreases when the foot is retracted and increases when the stride is taken, as shown in fig. 3.

It is worth mentioning that in walking exercise, the acceleration generated by vertical and forward motion is approximately sinusoidal with time and has a peak at a certain point where the acceleration in the vertical direction changes most.

And finally, the motion state solving module is used for filtering the data. Because the electromagnetic interference in the circuit is a main interference source in the acquisition process, the electromagnetic interference is high-frequency noise; the human motion is mainly low-frequency signals within 50Hz, and the wavelet transform threshold method is selected. For such interference, we add a threshold and a step frequency decision to the detection to filter, that is, the time interval between two adjacent steps is at least greater than 0.2 seconds, and filter high frequency noise.

In this embodiment, the motion segment determining module is configured to:

、

、

And solving the acceleration signal vector mode

、

、

And solving the angular velocity signal vector mode

(ii) a The above-mentioned

，

；

By vector mode of acceleration signal

Vector mode of angular velocity signal

Specifically, the motion link judgment module is used for subdividing motion links and detecting falling by combining angular speed.

Since the acceleration is suitable for the judgment of the motion with definite direction, the judgment of the incapability of falling detection, motion cycle links, splayfoot and the like needs to be carried out by using the angular velocity.

Based on the principle of a kinematic algorithm, four gait event time phases are detected: a gait cycle is divided into two phases, a "support phase" and a "swing phase".

Perry doctors at the national rehabilitation center for RLA, California, USA put forward the RLA staging method according to the occurrence sequence of the walking cycle; the support period is divided into 5 stages; the stride period is broken down into 3 epochs.

1. First touchdown, initialcontact: as the starting point of the walking cycle and the support period; the moment when the heel or other parts of the sole of the foot first contact the ground. The first landing mode for normal people walking is heel landing.

2. Load bearing reaction period, loadingresponse: the foot bottom is in full contact with the ground for a period of time after the heel is grounded; namely, when the heel at one side is grounded and the toe of the lower limb at the opposite side is lifted off; is the process of transferring the center from the heel to the sole. This phase is 0-15% of the gait cycle.

3. Mid-stance, mid-stance: when the finger is lifted from the lower limb at the opposite side to the trunk right above the leg at the side; the center of gravity is now directly above the support surface. This period is 15% -40% of the gait cycle:

4. late stance, terminalstance: the fingers are from the time the support heel lifts off to the time the contralateral lower limb heel lands. This period is 40% -50% of the gait cycle.

5. In the early stage of stepping, pre-swing: the fingers are held for a period of time from heel-strike of the contralateral lower limb until toe-off support. This period is 50% -60% of the gait cycle.

6. Initial step, initial stroke: from the point at which the supporting leg lifts to the point at which the knee joint reaches maximum flexion. This period is 60% -70% of the gait cycle.

7. Mid-swing: from the maximum flexion swing of the knee joint to when the lower leg is perpendicular to the ground. This period is 70% -85% of the gait cycle.

8. At the end of the step, terminating: the lower leg, which is perpendicular to the ground, swings forward until the heel lands again. This period is 85% -100% of the gait cycle.

In this embodiment, the links may be divided by time in the exercise cycle, and certainly, each exercise link may be determined by artificially setting the time of each link.

In the present embodiment, an acceleration signal vector mode and an angular velocity signal vector mode are used as input features of the model.

The analysis module builds a recognition model with perfect self-adaptation, mainly carries out modeling through an acceleration sensor rule, gives a motion state recognition result through the operation of a genetic operator, and is used for analyzing and managing remote health big data.

The handheld device has a low amplitude and a quick twitching state, or what is commonly called hand trembling, or a mischief user wants to simulate walking by quickly and repeatedly shaking the device for a short time, and the accurate value of step counting can be influenced if the interference data are not eliminated.

Carrying out wavelet transformation operations of three steps of wavelet decomposition, high-frequency wavelet coefficient processing and wavelet reconstruction on the collected data in each direction, discretizing time domain signals in each direction, decomposing mixed signals of various frequency components into different frequency bands, and then processing according to different characteristics of each seed signal on a frequency domain and frequency bands; and acquiring gait data with high signal to noise ratio.

Falls are characterized by large acceleration and peak angular velocity because the SVM peak due to collisions with low-lying objects during falls is larger than most common procedures of walking, ascending stairs, etc. in daily activities. However, the process of the human body movement behavior has complexity and randomness, and great misjudgment can be brought by using single acceleration related information to judge the occurrence of the human body falling behavior. Information thresholding using a combination of SVMA and SVMW can distinguish between falls and low intensity motions that produce smaller SVM peaks. Through analyzing the experimental result data SVMA and SVMW in the human body falling process and other daily life behavior processes, the acceleration signal vector modulus threshold value for identifying falling is SVMAT =20m/s2, and the angular velocity signal vector modulus threshold value is SVMWT =4 rad/s.

In this embodiment, the motion link determination module further includes a filtering unit, where the filtering unit performs wavelet transform operations of three steps of wavelet decomposition, high-frequency wavelet coefficient processing, and wavelet reconstruction on the acquired data of the acceleration sensor and the angular velocity sensor, discretizes time domain signals in each direction, decomposes mixed signals of multiple frequency components into different frequency bands, and then processes the mixed signals according to different characteristics of each seed signal in the frequency domain and according to frequency bands; acquiring gait data with high signal to noise ratio;

The threshold value is

；

The above-mentioned

。

In this embodiment, the dimension reduction solving module is configured to:

collecting usersTaking the standard deviation of the whole sample population by using the acceleration/angular velocity values in three directions, wherein N is the sample size, training a classifier, and identifying gait samples by using the classifier; comprehensively calculating the degree x of deviation of certain gait from the tidying crowd, wherein

And the center-to-center distance of the crowd boundary is characterized in that the definition of the center-to-center distance of the boundary is the distance from the boundary point to the centroid.

In this embodiment, in order to eliminate the influence of different dimensions and different orders of magnitude between data, the multidimensional signal needs to standardize the original data to make it comparable, and each element in the proof passes through

Subtract the mean of the column

Then divided by the standard deviation of the column

Such that each variable is normalized to a matrix X with a mean of 0 and a variance of 1.

In this embodiment, the dimension reduction solving module is further configured to:

for original numberAccording to the standardization process, by each element in the testification

Subtract the mean of the column

Then divided by the standard deviation of the column

；

Wherein,

，

，

to obtain

；

Solving a correlation coefficient matrix:

where R is a real symmetric matrix (i.e.

solving the cumulative contribution rate:

；

wherein

a value that is a single attribute;

calculating to obtain a partial matrix, taking the remaining characteristic value as a new variable principal component, and calculating to obtain the partial matrix by using the following formula;

the method not only uses the characteristic of high calculation speed of the fixed working sample set method, but also avoids the problems that the number of the vectors exceeds the scale of the working sample set, and the algorithm only optimizes one part of the support vectors and has range limitation. The abnormal people are selected by the method.

Wherein

And dividing the sample into correct rates for the SVM classifier.

As shown in fig. 4, in the present embodiment, the gait of the movement of the human body can be effectively determined by using the output data of the accelerometer, using a three-condition (C1, C2 and C3) determination algorithm, and using a median filtering method, where the state "0" represents movement and the state "1" represents rest.

calculating a first condition, a second condition and a third condition by using the output data of the acceleration sensor, and judging the gait of the human motion by using median filtering;

the accelerometer output composite amplitude is:

the upper and lower thresholds are respectively:

，

；

the first condition is expressed as:

the local variance of the accelerometer output is:

(ii) a Wherein k represents the body basic information genus of the population collecting the samplesSex; s represents the half-window sample number; b denotes the b-th sensor;

wherein

；

s is the number of half-window samples, which is typically defined to have a value of 15. The given threshold is defined as:

the second condition is represented as:

；

define the composite amplitude of the gyroscope output as:

；

the given thresholds are:

the third condition is represented as:

。

and logic is adopted among the 3 conditions, namely the gait is considered to be in an absolute static state only when the judgment results of the 3 conditions are all 1. And then, by a median filtering method, the continuously output motion result can be effectively judged, noise points are eliminated, and effective and reasonable gait detection data are obtained.

The wearable device based on the neural network adaptive health monitoring provided in the embodiment further comprises a model adaptive perfecting module; the model adaptation refinement module is configured to:

Specifically, with the increase of the sample size, the SVM classifier can be adaptively and continuously optimized and perfected:

(1) sampling calculation of motion state

During the judgment, the standard deviation of one state is larger, and the standard deviation of the other state is smaller and exactly balanced, so that no abnormality is found, and random sampling verification is performed again.

and (4) inputting a new sample every time, and calculating the recognition rate of the SVM classifier according to the cross verification method principle.

(2) And for the characteristic values of the samples which are not found to be abnormal, using an SVM classifier fitness function to divide the accuracy of the samples for the SVM classifier. The parallel execution process is simulated by maintaining a plurality of groups and appropriately controlling the interaction between the groups, thereby improving the execution efficiency of the algorithm even without using a parallel computer.

As shown in fig. 5, each time a new sample is input, the recognition rate of the SVM classifier is calculated according to the principle of the cross validation method, fitness evaluation is performed, the termination value of the genetic algorithm is not set, the termination condition adopts a proportion method, if the recognition rate of training is higher than the existing one, the training parameter is set as the optimal parameter, otherwise, the training parameter is further optimized by performing operations such as selection, crossing and mutation.

To sum up, the health sign data that wearable healthy intelligent watch gathered is only as health analysis reference in the industry at present, does not replace traditional non-wearing formula domestic medical instrument and popularize fast on a large scale, has two important reasons:

The invention overcomes the two contradictions, on one hand, the fine motion characteristics are comprehensively collected in the preprocessing process, on the other hand, only limited different sensor functions are called in different specific judging processes through self-adaptive algorithm matching, different specific characteristic calculation is extracted, the analysis link integrates different characteristics to reduce the dimension and sample, the amount of exercise is greatly reduced, and the accuracy is ensured through the key calculation verification link. Thereby, the following steps are achieved: the detailed characteristics of the motion state are comprehensively collected, the calculated amount and the power consumption are greatly reduced, the medical reference value is improved, and the power consumption standby capability is extracted under the same condition.

The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims

1. A wearable device based on neural network adaptive health monitoring, comprising:

the physiological parameter acquisition module is used for acquiring corresponding physiological parameters under the appointed motion state and gait according to the physiological parameters required to be acquired;

the dimension reduction solving module is configured to acquire physiological parameter data of a user and reduce dimensions of the physiological parameter data of the user;

acquiring acceleration/angular velocity values of a user in three directions, taking a standard deviation of the whole sample population, taking N as a sample amount, training a classifier, and identifying gait samples by using the classifier; comprehensively calculating the degree x of deviation of a certain gait from the whole crowd, wherein

the method comprises the steps that personal N-class gait samples are registered in a database, the samples are input into a classifier for training, which class is (1, N) is judged according to input values, if the class exceeds the range of (1, N), a class N +1 is newly registered, and then the classifier is updated again;

on the basis of different motion division, the same motion is subdivided again, and a voting mode is adopted to determine a classification result;

the dimensionality reduction solving module is further configured to normalize the raw data collected by the sensor by each element in the matrix

Subtract the mean of the column

Then divided by the standard deviation of the column

；

Wherein,

，

to obtain

；

Solving a correlation coefficient matrix:

where R is a real symmetric matrix (i.e.

solving the cumulative contribution rate:

wherein

a value that is a single attribute;

Wherein

Dividing the sample into correct rates for the SVM classifier;

the dimensionality reduction solving module is also configured to utilize the output data of the acceleration sensor, adopt a first condition, a second condition and a third condition to carry out operation, and utilize median filtering to judge the gait of the human motion;

the first condition is: the acceleration sensor outputs a composite amplitude value, and if the composite amplitude value is between the upper threshold value and the lower threshold value, the human body is judged to be static; otherwise, judging the motion of the person;

the output composite amplitude of the acceleration sensor is as follows:

the upper and lower thresholds are respectively:

，

；

the first condition is expressed as:

the second condition is: if the local variance output by the acceleration sensor is lower than a given threshold value, the human body is judged to be static; otherwise, judging the motion of the person;

the local variance of the acceleration sensor output is:

wherein

For the interval acceleration sensor, the output average value of the synthesized amplitude value is expressed as follows:

；

s is the number of half-window samples, and a given threshold is defined as:

the second condition is represented as:

；

the composite amplitude of the angular velocity sensor output is defined as:

the given thresholds are:

the third condition is represented as:

。

2. the neural network adaptive health monitoring-based wearable device of claim 1, wherein: the motion link judging module is configured to:

、

、

And solving the acceleration signal vector mode

、

、

And solving the angular velocity signal vector mode

(ii) a The above-mentioned

，

；

By vector mode of acceleration signal

Vector mode of angular velocity signal

3. The neural network adaptive health monitoring-based wearable device of claim 2, wherein: the motion link judging module also comprises a filtering unit, wherein the filtering unit carries out wavelet transformation operation of three steps of wavelet decomposition, high-frequency wavelet coefficient processing and wavelet reconstruction on the collected data of the acceleration sensor and the angular velocity sensor, discretizes time domain signals in all directions, decomposes mixed signals of various frequency components into different frequency bands, and then processes the mixed signals according to different characteristics of various seed signals on the frequency domain and frequency bands; acquiring gait data with high signal to noise ratio;

The threshold value is

；

The above-mentioned

。

4. The neural network adaptive health monitoring-based wearable device of claim 1, wherein: the system also comprises a model self-adaptive perfecting module; the model adaptation refinement module is configured to: