CN113164055B - Mobile monitoring device and physiological signal adjusting and processing method - Google Patents

Abstract

The present application discloses a mobile monitoring device and a method for adjusting and processing physiological signals, wherein the mobile monitoring device comprises: a motion sensor, a physiological signal acquisition device, a memory and a processor; wherein the motion sensor, the physiological signal acquisition device, the memory and the processor are connected via a lead wire; the motion sensor is used to acquire the motion signal of a target object; the physiological signal acquisition device is used to acquire the physiological signal of the target object; the memory is used to store an executable program; the processor is used to execute the executable program in the memory that implements the following functions: acquiring the physiological signal and motion signal of the target object; analyzing the motion signal to obtain the motion signal characteristics of the target object, and determining the posture information of the target object based on the motion signal characteristic information; determining the physiological signal characteristics based on the physiological signal; and adjusting the physiological signal characteristics according to the posture indicated by the posture information.

Description

Mobile monitoring equipment, adjustment and processing method of physiological signals

Technical Field

The present application relates to the field of medical equipment, and more specifically, to a mobile monitoring device and a method for adjusting and processing physiological signals.

Background Art

With the development of medical technology and the improvement of people's understanding of medicine, the importance and attention of rapid recovery after surgery have been greatly enhanced and improved. During the postoperative recovery period, medical staff hope that patients can get out of bed and move around more to promote rapid recovery. However, traditional bedside monitoring limits the patient's activity space, and the long and complicated cables cannot allow patients to move comfortably. Therefore, mobile monitoring has become the first choice to meet the needs, playing a role in monitoring and measurement during the rapid recovery period after surgery.

For mobile monitoring, interference may occur due to electrode pulling during activities such as walking, getting in and out of bed, and friction between clothes, which can seriously interfere with the waveform signal and affect the accuracy of physiological signal measurement.

To address the above-mentioned problems, no effective solution has been proposed yet.

Summary of the invention

According to one aspect of an embodiment of the present application, a mobile monitoring device is provided, comprising: a motion sensor, a physiological signal acquisition device, a memory and a processor; wherein the motion sensor, the physiological signal acquisition device, the memory and the processor are connected via a lead wire; the motion sensor is used to acquire the motion signal of a target object; the physiological signal acquisition device is used to acquire the physiological signal of the target object; the memory is used to store an executable program; the processor is used to execute the executable program in the memory that implements the following functions: acquiring the physiological signal and motion signal of the target object; analyzing the motion signal to obtain the motion signal characteristics of the target object, and determining the posture information of the target object based on the motion signal characteristic information; determining the physiological signal characteristics based on the physiological signal; and adjusting the physiological signal characteristics according to the posture indicated by the posture information.

According to one aspect of an embodiment of the present application, a method for adjusting physiological characteristics is provided, including: acquiring physiological signals and motion signals of a target object; analyzing the motion signals to obtain motion signal characteristics of the target object, and determining posture information of the target object based on the motion signal characteristic information; determining physiological signal characteristics based on the physiological signals; and adjusting the physiological signal characteristics according to the posture indicated by the posture information.

According to another aspect of an embodiment of the present application, a method for processing physiological signals is provided, including: acquiring physiological signals and acceleration parameters of a target object; determining motion signal characteristics of the target object based on the acceleration parameters, and determining posture information of the target object based on the motion signal characteristic information; determining physiological signal characteristics based on physiological signals to obtain a physiological signal feature set; using posture information to delete invalid physiological signal characteristics from the physiological signal feature set to obtain a target physiological signal feature set; using characteristics in the target physiological signal feature set to determine the validity of the physiological signal, or the alarm information corresponding to the physiological signal.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

FIG1 is a schematic diagram of the structure of a mobile monitoring device according to an embodiment of the present application;

FIG2 is a schematic diagram of an optional motion signal analysis process according to an embodiment of the present application;

FIG3 is a schematic diagram of an optional process of assisting optimization of physiological signals by motion signals according to an embodiment of the present application;

FIG4 is a flowchart of an optional optimization process of an electrocardiogram signal feature according to an embodiment of the present application;

FIG5 is a flow chart of a method for adjusting a physiological signal according to an embodiment of the present application;

FIG6 is a flow chart of a method for processing a physiological signal according to an embodiment of the present application.

DETAILED DESCRIPTION

In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

In order to better understand the embodiments of the present application, the technical terms involved in the embodiments of the present application are explained as follows:

QRS wave: The change in the depolarization potential and time of the left and right ventricles. The first downward wave is the Q wave, the upward wave is the R wave, and the next downward wave is the S wave.

When performing mobile monitoring on a patient, the patient's activities will interfere with the parameters collected by the mobile monitoring device, thereby affecting the accuracy of the physiological signal measurement, and even affecting the doctor's judgment of the patient's condition, thereby affecting the patient's recovery. To solve the above problems, an embodiment of the present application provides a mobile monitoring device, which may include: a motion sensor and a processor. The motion sensor can collect the motion signal of the target object and extract the motion signal from the motion signal; the processor can analyze the posture information of the target object based on the motion signal, and then adjust the physiological signal based on the posture information or determine whether to alarm based on the adjusted physiological signal. Among them, the above-mentioned motion sensor includes but is not limited to: an acceleration sensor; the physiological signal includes but is not limited to: an electrocardiogram signal, etc. The following is a detailed description.

FIG1 is a schematic diagram of the structure of a mobile monitoring device according to an embodiment of the present application. As shown in FIG1 , the mobile monitoring device includes:

A motion sensor 10, a physiological signal acquisition device 12, a memory 14 and a processor 16; wherein the motion sensor 10, the physiological signal acquisition device 12, the memory 14 and the processor 16 are connected via a lead wire; wherein: the motion sensor 10 is used to acquire the motion signal of the target object; the physiological signal acquisition device 12 is used to acquire the physiological signal of the target object; the memory 14 is used to store an executable program; the processor 16 is used to execute the executable program in the memory 14 to implement the following functions: acquiring the physiological signal and motion signal of the target object; analyzing the motion signal to obtain the motion signal characteristics of the target object, and determining the posture information of the target object based on the motion signal characteristic information; determining the physiological signal characteristics based on the physiological signal; adjusting the physiological signal characteristics according to the posture indicated by the posture information.

The motion sensor 10 can be worn on the body of a target object (e.g., a patient) as part of a wearable device, and the motion signal collected by the motion sensor 10 is transmitted to the processor 16 so as to analyze and identify the posture of the target object. In addition, there can be multiple motion sensors 10. In this case, the processor 16 can aggregate the signals collected by multiple motion sensors 10 to obtain multiple motion signals; and comprehensively determine the posture information based on the multiple motion signals. For example, the average value of the values of the multiple motion signals can be used as the basis for judging the posture information, and different weights can be assigned to the multiple motion signals, and the multiple motion signals can be weighted summed, so as to determine the above posture information according to the weighted sum value.

In addition, the above-mentioned multiple motion signals can be the same type of parameters collected by sensors of the same type, for example, multiple acceleration values collected by multiple acceleration sensors; they can also be different types of parameters collected by different types of sensors, for example, acceleration values and heart rate values collected by acceleration sensors and heart rate sensors.

In some embodiments of the present application, before analyzing the motion signals and physiological signals respectively collected by the motion sensor 10 and the physiological signal acquisition device 12, the collected signals can be filtered to remove noise; after amplifying the noise-filtered signals, the amplified signals are A/D converted, that is, the analog signals are converted into digital signals, thereby obtaining a basis for analysis.

In addition, in order to further ensure the accuracy of the collected physiological signals, the differential or integral signal of the physiological signal can be calculated after filtering and denoising the physiological information, and the final physiological signal can be determined based on the differential or integral signal. Among them, filtering and denoising can be used to filter out the power frequency interference, base drift and high-frequency noise interference of the signal, and the interference information can be filtered out by integrating the signal, making the signal peak information more prominent.

Similarly, to further ensure the accuracy of the collected motion signal, the motion signal can be band-pass filtered to remove base drift and high-frequency noise interference to obtain a more accurate motion signal.

There are many ways to obtain physiological signal characteristics. For example, when determining the characteristics of physiological signals, it can be determined based on the statistical information of some basic measurement information. For example, the physiological signal can be peak searched to calculate the basic measurement information such as the peak amplitude, slope, width, frequency, etc., and then the signal quality SQI and other time domain characteristics of the physiological signal (such as the effectiveness of the peak, the type of peak, the peak-to-peak interval value and the effectiveness of the interval, etc.) can be calculated by combining the statistical information based on the basic measurement information and clinical prior knowledge. The signal can also be Fourier transformed to obtain the total energy TP, low-frequency energy LP, high-frequency energy HP and other characteristic information of the signal. The ratio characteristics between different characteristic information can also be determined based on Fourier transform, that is, the ratio of two physical quantities is used as the physiological signal characteristic.

In some embodiments of the present application, the processor 16 is also used to compare the posture indicated by the posture information with the specified posture, and when the posture indicated by the posture information is the specified posture, optimize the physiological signal characteristics; when the posture indicated by the posture information is not the specified posture, determine the reliability information of the physiological signal, and when the reliability information indicates unreliable, optimize the physiological signal characteristics based on the motion state information of the target object.

Among them, the optimization of physiological signals can be based on the first motion state information under the specified posture, and the physiological signal characteristics can be optimized to obtain the target physiological signal characteristics. Specifically, it can be expressed as adjusting the weight of the physiological signal characteristics according to the first motion state information, and optimizing the physiological signal according to the weight. Among them, there are many optimization methods: for example, it can be expressed as determining the target physiological signal corresponding to the current motion state according to the correspondence between the first motion state and the physiological signal; correcting the physiological signal according to the target physiological signal; for example, when there are multiple physiological signal characteristics, the physiological signal can be optimized by adjusting the weights of different physiological signals. Specifically: the processor is also used to adjust the weight of the physiological signal characteristics according to the following method: determine the invalid physiological signal characteristics in the physiological signal characteristics according to the first motion state information; adjust the weight of the invalid physiological signal characteristics to zero, that is, delete the invalid physiological signal characteristics.

Taking the above-mentioned designated posture as walking posture as an example, when the designated posture is walking posture, the above-mentioned first motion state information includes: the motion intensity of the target object and the step frequency of the target object; at this time, the invalid physiological signal features in the physiological signal features can be determined in the following manner: when the step frequency is greater than the first threshold and the motion intensity belongs to the first level, the heartbeat interval information in the physiological signal features is determined as an invalid physiological signal feature; when the step frequency is greater than the second threshold and less than the first threshold, and the motion intensity belongs to the second level, the interval information of the QRS wave that does not have uniformity in the physiological signal features and does not match the dominant QRS wave is determined as an invalid physiological signal feature; when the step frequency is less than the second threshold and the motion intensity is the third level, the interval information of the QRS wave that has an ECG noise index higher than the specified value, does not have uniformity and does not match the dominant QRS wave is determined as an invalid physiological signal feature; wherein the motion intensities corresponding to the first level, the second level and the third level decrease in sequence.

It can be seen that different step frequencies and different exercise intensities correspond to different optimization strategies for optimizing physiological signal characteristics. As shown in Figure 3, after identifying the motion parameter characteristics, the motion parameters are classified to obtain the first type of motion characteristics, the second type of motion characteristics and the third type of motion characteristics, wherein when the step frequency exceeds the first step frequency threshold and the exercise intensity exceeds the first intensity threshold, the weight of this type of feature is directly adjusted (it can be adjusted to 0); for the second type of motion characteristics (the step frequency is less than the first step frequency threshold and greater than the second step frequency threshold, and the exercise intensity is less than the first intensity threshold and greater than the second intensity threshold), the weight of the time domain or frequency domain characteristics can be modified, and the SQI level or threshold can be changed; for the third type of motion characteristics (the step frequency is less than the second step frequency threshold, and the exercise intensity is less than the second intensity threshold), the weight of the time domain or frequency domain characteristics is modified, and the SQI level or threshold is changed. For example, when the step frequency exceeds 90 and the exercise intensity is high, the weight of the time domain/frequency domain features is directly adjusted (can be set to 0), and the level or threshold of the physiological signal quality index (Signal Quality Index, referred to as SQI) can also be changed; when the step frequency is lower than 90 but higher than 60, and the exercise intensity is medium, the weight of the time domain features or frequency domain features can be adjusted (set to 0-100 under different conditions), and the level or threshold of the physiological signal quality index SQI can be changed under certain conditions; when the step frequency is lower than 60 and the exercise intensity is weak, the weights of some time domain features or frequency domain features can be adjusted.

It should be noted that in some embodiments of the present application, the division of step frequency may not be limited to specific numbers. For example, the step frequency range corresponding to the specific walking form may be determined based on the specific walking form; the above-mentioned exercise intensity may not be divided into strong, medium, and weak, but directly distinguished by a predetermined threshold; in addition, in the walking posture, the step frequency and exercise intensity may not be used for level division, and fast walking, normal walking, slow walking and other types may be used for distinction. At this time, the corresponding weight and other information may be determined according to the walking type.

When the posture indicated by the above posture information does not belong to the specified posture, the reliability information of the physiological signal needs to be considered. In some embodiments of the present application, the reliability information of the physiological signal can be determined by the processor 16: determine the weight of the physiological signal feature; determine the target reliability index of the physiological signal based on the weight of the physiological signal feature and the reliability index corresponding to the physiological signal feature; compare the target reliability index and the preset threshold; determine the reliability information based on the comparison result, wherein when the target reliability index is greater than the preset threshold, the reliability information is determined to be reliable; when the target reliability index is less than the preset threshold, the reliability information is determined to be unreliable.

For example: statistics are collected on the acquired SQI and time domain/frequency domain features, the weights of multiple features are voted and scored, the reliability score is obtained, and the threshold is set to classify the reliability level, and finally normalized to reliable/unreliable. Taking signal quality, QRS wave matching and QRS validity as examples, the weight of signal quality is 50%, the weight of QRS wave matching is 30%, and the weight of QRS wave validity is 20%. If the signal quality is good, 10 points are scored (this score will be adjusted according to different thresholds, similar to the following), if the QRS wave matching is good, 10 points are scored, if the QRS wave is valid, 10 points are scored, and the total score is calculated to be 10. If the total score is greater than the threshold (for example, 7), the physiological signal is determined to be reliable, otherwise it is unreliable.

Based on the above analysis, when the posture of the target object is unclear, if the reliability of the physiological signal is relatively high, the signal characteristics and alarms are not adjusted. If the ECG reliability is low, the parameters are optimized using the motion state. When the motion state is 0, no change is made. When the motion state is greater than 0, different optimization strategies are used for different parameters. Taking the ECG as an example, the validity of the ECG heartbeat interval is judged in combination with the motion state, and arrhythmias are shielded.

In some embodiments of the present application, when adjusting the weight of the above-mentioned physiological signal, the target weight can be determined according to different motion signals to achieve the adjustment of the weight. Specifically, the above-mentioned first motion state information includes: at least one evaluation index for evaluating different motion signals in the first motion state; for each evaluation index in the evaluation index of different parameters, each evaluation index is compared with the corresponding threshold value to obtain at least one comparison result; the target weight of the physiological signal feature is determined according to at least one comparison result; and the weight of the physiological signal feature is adjusted to the target weight. It should be noted that the above-mentioned different motion signals refer to different values of the same type of parameters or values of different types of parameters. For the latter, for example, different step frequencies and different exercise intensities correspond to different weights. Further, for example: when the step frequency exceeds 90 and the exercise intensity is high, the weight of the time domain/frequency domain feature is directly adjusted (can be set to 0); when the step frequency is lower than 90, but exceeds 60, and the exercise intensity is medium, the weight of the time domain/frequency domain feature can be adjusted (set to 0-100 under different conditions); when the step frequency is lower than 60 and the exercise intensity is weak, the weight of some time domain/frequency domain features can be adjusted.

In some embodiments of the present application, before optimizing the physiological signal based on the posture information, a preliminary optimization can also be performed based on the motion state information of the target object. In this case, the processor 16 is also used to obtain the second motion state information of the target object before obtaining the posture information of the target object; optimize the physiological signal characteristics based on the second motion state information to obtain the initial physiological signal characteristics; and optimize the initial physiological signal characteristics again using the first motion state information to obtain the target physiological signal characteristics. That is, in this embodiment, the physiological signal of the target object is optimized twice: 1. Optimization based on the motion state; 2. Optimization based on the posture information. This processing method can make the detection result of the physiological signal more accurate.

The second motion state information includes, but is not limited to, the target object's motion intensity, the target object's step frequency, etc., but is not limited thereto. In addition, it should be noted that the first motion state information and the second motion state information can be the same or different, but the first motion state information is used to adjust the physiological signal characteristics in combination with the posture information, while the second motion state information is used alone as a basis for adjusting the physiological signal characteristics.

In some embodiments of the present application, in order to prevent false alarms, different alarm thresholds are set for different postures, for example: the processor is also used to determine the alarm threshold corresponding to the posture information after optimizing the physiological signal characteristics; compare the index corresponding to the optimized physiological signal characteristics with the alarm threshold; when the posture indicated by the posture information is not the specified posture, and the index corresponding to the physiological signal characteristics is greater than the alarm threshold, an alarm is issued; when the posture indicated by the posture information is the specified posture, and the index corresponding to the physiological signal characteristics is greater than the alarm threshold, the alarm is rejected. For example, when the posture indicated by the posture information is lying or sitting, no alarm is issued, and when the above-mentioned specified posture is a walking state and the above-mentioned conditions are met (the index corresponding to the physiological signal characteristics is greater than the alarm threshold), an alarm is issued.

Taking walking posture as an example, the thresholds of various alarms can be adjusted accordingly for different step frequencies and different intensities, and the conditions are stricter when judging whether the signal characteristics are valid and outputting parameter alarms. For example, as shown in Figure 4, when the step frequency exceeds 90 and the exercise intensity is high, the ECG heartbeat interval is directly set to invalid (to reduce heart rate jumps and heart rate alarms), the QRS classification is normal (to reduce ventricular arrhythmia alarms), and the SQI level/threshold can also be changed, and all ECG arrhythmia alarms can be directly set to invalid; when the step frequency is lower than 90 but higher than 60, and the exercise intensity is medium, the ECG QRS wave interval that does not have uniformity and does not match the dominant QRS wave can be set to invalid (to reduce heart rate jumps and heart rate alarms). alarm), and the QRS wave is classified as normal (reducing ventricular arrhythmia alarm); when the cadence is lower than 60 and the exercise intensity is low, only the interval of the ECG QRS wave with a relatively high ECG noise index, no uniformity, and no match with the dominant QRS wave is invalid (reducing heart rate jumps and heart rate alarms), and the QRS wave is classified as normal (reducing ventricular arrhythmia alarm); when it is identified as running, the monitoring mode is directly changed to running mode. In this mode, turn off the ST/QT switch, turn off the breathing monitoring, turn off the monitoring of intermediate arrhythmias, etc.

It should be noted that in the embodiment of the present application, it is necessary to perform alarm judgment based on the adjusted physiological signal. For example, after eliminating invalid QRS wave intervals, the correct heart rate is calculated using the valid intervals; after re-judging the QRS wave type, the arrhythmia alarm is output using the determined QRS wave type.

In some embodiments of the present application, the walking posture can be determined in the following manner: obtaining a motion signal when the target object is in a walking posture, the motion signal including: peak statistical information of the motion signal of the target object within a preset time period, and vector direction information of the motion signal; determining the number of identical peak information in the peak statistical information; determining that the target object is in a repeated motion form when the number is greater than a first threshold; and determining that the target object is in a walking posture based on the vector direction information when the target object is in a repeated motion form. For example, obtaining search wave information and amplitude information, and statistically analyzing the mean, variance and other information of the time domain feature information; judging the existence of a repeated motion form based on the statistical number of search peaks, and then judging the walking posture based on the direction information of the motion sensor; and calculating the number of search peaks within a period of time, and calculating the walking frequency based on the number of search peaks.

In some embodiments of the present application, the posture information may also include: a static posture; the static posture is determined by: obtaining a direction vector and a motion intensity when the target object is in a static posture; matching the direction vector with a preset direction vector to obtain a matching result; when the matching result indicates that the direction vector is consistent with the preset direction vector and the motion intensity is less than a second threshold, determining that the target object is in a static posture. The static posture includes but is not limited to: the target object is in a lying state or a sitting state.

As shown in FIG2 , the process of analyzing the motion signal includes the following processing steps:

Step S202, collect motion signals through a motion sensor (such as an acceleration sensor), and extract time domain features based on the motion signals, the time domain features include: search information, amplitude information, statistical time domain feature information, mean, variance and other information; wherein, after collecting the motion signal, two independent processes will be executed: steps S204-S208 and steps S210-S214.

Step S204, perform peak search processing on the signal and count the peak information, and then go to step S206.

Step S206, based on the number of peaks counted, it is determined that there is a repeated motion pattern, and based on the direction information of the motion sensor, it is determined that the current object is in a walking posture;

Step S208, calculate the step frequency and walking intensity, and update the threshold for judging the exercise intensity. Count the number of search peaks within a period of time to calculate the walking frequency; based on the statistical SVM (support vector machine) value, use the adaptive threshold to identify the exercise intensity;

Step S210, calculate the acceleration value and determine the direction vector. Based on the accelerometer with a fixed direction, the direction vector when lying down/sitting can be calculated;

Step S212, the direction vector of the motion signal is matched with the direction vector of the lying posture. The calculated direction vector of the accelerometer is matched with the direction vector of the lying posture or the sitting posture.

Step S214, determining the posture of the target object according to the matching result. According to the matching result and the identified motion intensity, judging whether the target object is in a lying state or a sitting state, wherein when the match is found and the motion intensity is lower than a certain threshold, it is determined to be in a lying state or a sitting state.

In addition, the above optimization is performed for the physiological signals collected when the target object is moving (for example, walking). In some embodiments of the present application, when it is determined that the target object is in a lying or sitting posture, the physiological signals can be optimized by combining the exercise intensity in the lying or sitting posture and the reliability of the physiological signals. The specific optimization process can be found in the relevant description above and will not be repeated here.

In some embodiments of the present application, the above-mentioned posture information is obtained in the following manner: obtaining an acceleration signal of the target object through an accelerometer set on the wearable device; determining the motion signal characteristics of the target object based on the acceleration signal, and determining the third motion state information based on the motion signal characteristics; determining the posture information through the third motion state information.

It should be noted that the information included in the first motion state information, the second motion state information and the third motion state information in the embodiment of the present application may be completely the same or partially the same.

In some embodiments of the present application, in order to prevent erroneous measurements, some conditions for triggering measurements can be set, for example: the processor is also used to determine the posture information of the target object based on the acceleration signal of the target object when the timing time is reached; when the posture information indicates that it is in a first posture and the motion intensity exceeds a first threshold, the measurement of the physiological signal of the target object is suspended and the timing is restarted; when the first preset time after the restart of timing is reached and the posture information is a second posture, the measurement of the physiological signal of the target object is started, and the motion intensity of the first posture is higher than the motion intensity of the second posture.

The processor is also used to stop measuring when the diastolic pressure of the target object is detected within a preset detection period after starting to measure the physiological signals of the target object; re-collect the acceleration signal when the diastolic pressure is not detected within the preset detection period, and re-determine the posture of the target object based on the re-collected acceleration signal; stop measuring when the re-determined posture is the first posture and the time for maintaining the first posture reaches a second preset time length.

Taking the physiological signal as a mechanical physiological signal as an example, a non-invasive blood pressure (NIBP) signal is selected as the physiological signal from the mechanical physiological signal. The motion sensor is an accelerometer, and the collected motion signal is an acceleration signal.

The process of using motion status to assist NIBP measurement is as follows:

1. When the timing clock meets the measurement conditions, the acceleration signal analysis function is used to output the human body posture. When it is recognized as fast walking, the measurement is delayed and the timing is restarted;

2. Start measurement when the user is walking slowly, lying down or sitting down;

3. During the pressure platform search period, if the diastolic pressure is directly found, the measurement ends; if the diastolic pressure is not found, the movement posture is recognized again and the movement time is counted. When the movement time exceeds the threshold, the measurement is abandoned and the measurement ends;

4. If there is no exercise or the exercise duration does not reach the threshold, use the pulse signal to mark the platform time and execute step 3.

In some embodiments of the present application, the motion sensor 10 and the processor 16 are integrated into an independent device; or, the motion sensor 10 and the physiological signal acquisition device 12 are integrated into an independent device.

Based on the mobile monitoring device provided in the embodiment of the present application, the movement collected by the motion sensor can be used to judge the human body posture, and the process of physiological signal analysis can be optimized according to the human body posture, thereby improving the accuracy of physiological signal measurement and reducing erroneous parameter output and false alarms.

In the embodiment of the present application, the acceleration information collected by the acceleration sensor at the collar can be used for posture recognition: different postures such as walking, lying, and sitting can be recognized; among which different step frequencies and different intensities can be recognized for the walking posture, so it involves the method of using an accelerometer to recognize the posture. Therefore, based on posture recognition, a comprehensive decision-making method is given: a. When the walking posture is recognized, the thresholds of each alarm can be adjusted accordingly for different step frequencies and different intensities, and the conditions are stricter when judging whether the signal characteristics are valid and outputting parameter alarms. b. When it is recognized that it is lying or sitting, although there is a motion state, the signal characteristics and alarms are not easily corrected; c. When the posture is unclear, the motion state is applied to optimize the physiological parameters.

The following is a detailed description of the workflow of the mobile monitoring device in conjunction with FIG5. The principles of the workflow are as follows: obtaining the physiological signals and motion signals of the target object; analyzing the motion signals to obtain the motion signal characteristics of the target object; determining the posture information of the target object based on the motion signal characteristic information; determining the physiological signal characteristics based on the physiological signals; and adjusting the physiological signal characteristics according to the posture indicated by the posture information. As shown in FIG5, the workflow includes:

Step S502, acquiring physiological signals and motion signals of the target object through a physiological signal acquisition device and a motion sensor respectively;

Step S504, the processor analyzes the motion signal to obtain motion signal characteristics of the target object;

Step S506, the processor determines the posture information of the target object according to the motion signal feature information;

Step S508, the processor determines a physiological signal feature based on the physiological signal;

Step S510, determining whether the posture indicated by the posture information is a designated posture; if the determination result is yes, proceed to step S512, otherwise proceed to step S514;

Step S512, optimizing the physiological signal characteristics;

Step S514, determine the reliability information of the physiological signal, and when the reliability information indicates unreliable, optimize the physiological signal features according to the motion state information of the target object. The optimization can be performed according to the motion level indicated by the motion state information, and the motion level includes but is not limited to: motion intensity level, movement speed level, etc.

Step S516, calculating physiological parameters according to the optimized physiological signal characteristics;

Step S518, determining the validity of the abnormal physiological parameter alarm, wherein the abnormal physiological parameter alarm is an alarm generated when an abnormal physiological parameter is detected.

In some embodiments of the present application, the reliability information of the physiological signal can be determined in the following manner: determine the weight of the physiological signal feature; determine the target reliability index of the physiological signal based on the weight of the physiological signal feature and the reliability index corresponding to the physiological signal feature; compare the target reliability index and a preset threshold; determine the reliability information based on the comparison result, wherein when the target reliability index is greater than the preset threshold, the reliability information is determined to be reliable; when the target reliability index is less than the preset threshold, the reliability information is determined to be unreliable.

When optimizing the physiological signal features, the physiological signal features can be optimized based on the first motion state information under the specified posture to obtain the target physiological signal features. Specifically: the weight of the physiological signal features is adjusted according to the first motion state information. Among them, the invalid physiological signal features in the physiological signal features are determined according to the first motion state information; the weight of the invalid physiological signal features is adjusted to zero, that is, the invalid physiological signal features are deleted.

In some optional embodiments of the present application, the above-mentioned designated posture includes: walking posture; the first motion state information includes: the motion intensity of the target object, the step frequency of the target object; when the step frequency is greater than the first threshold and the motion intensity belongs to the first level, the heartbeat interval information in the physiological signal feature is determined as an invalid physiological signal feature; when the step frequency is greater than the second threshold and less than the first threshold, and the motion intensity belongs to the second level, the interval information of the QRS wave that does not have uniformity in the physiological signal feature and does not match the dominant QRS wave is determined as an invalid physiological signal feature; when the step frequency is less than the second threshold and the motion intensity is the third level, the interval information of the QRS wave that has an ECG noise index higher than the specified value, does not have uniformity and does not match the dominant QRS wave is determined as an invalid physiological signal feature; wherein the motion intensities corresponding to the first level, the second level and the third level decrease in sequence.

In some optional embodiments of the present application, the weight of the physiological signal feature can be adjusted according to different motion states, that is, the first motion state information includes: at least one evaluation index for evaluating different motion signals in the first motion state; for each evaluation index of the evaluation indexes of different parameters, each evaluation index is compared with the corresponding threshold value to obtain at least one comparison result; the target weight of the physiological signal feature is determined based on the at least one comparison result; and the weight of the physiological signal feature is adjusted to the target weight.

In addition, to ensure the optimization effect, the physiological signal characteristics can be optimized twice: before obtaining the posture information of the target object, the second motion state information of the target object is obtained; the physiological signal characteristics are optimized based on the second motion state information to obtain the initial physiological signal characteristics; and the initial physiological signal characteristics are optimized again using the first motion state information under the above-mentioned specified posture to obtain the target physiological signal characteristics.

In addition to being used to calculate the final physiological parameters, the optimized physiological signal characteristics can also be used to determine the effectiveness of the alarm, for example: determine the alarm threshold corresponding to the posture information; compare the indicator corresponding to the optimized physiological signal characteristics with the alarm threshold; when the posture indicated by the posture information is not the specified posture and the indicator corresponding to the physiological signal characteristics is greater than the alarm threshold, an alarm is issued; when the posture indicated by the posture information is the specified posture and the indicator corresponding to the physiological signal characteristics is greater than the alarm threshold, the alarm is rejected.

Taking the above-mentioned designated posture as a walking posture as an example, the walking posture is determined in the following manner: obtaining a motion signal of the target object when it is in a walking posture, the motion signal including: peak statistical information of the motion signal of the target object within a preset time period, and vector direction information of the motion signal; determining the number of identical peak information in the peak statistical information; when the number is greater than a first threshold, determining that the target object is in a repeated motion form; and when determining that the target object is in a repeated motion form, determining that the target object is in a walking posture based on the vector direction information.

For another example, when the above-mentioned specified posture is a static posture, the static posture is determined in the following manner: obtaining the direction vector and the motion intensity when the target object is in a static posture; matching the direction vector with a preset direction vector to obtain a matching result; when the matching result indicates that the direction vector is consistent with the preset direction vector and the motion intensity is less than a second threshold, determining that the target object is in a static posture.

In some embodiments of the present application, acceleration parameters of the target object are obtained by an accelerometer provided on the wearable device; and motion signal characteristics of the target object are determined based on the acceleration parameters.

FIG6 is a flow chart of a method for processing physiological signals according to an embodiment of the present application. As shown in FIG6 , the method includes:

Step S602, obtaining physiological signals and acceleration parameters of the target object;

Step S604, determining the motion signal characteristics of the target object based on the acceleration parameter, and determining the posture information of the target object according to the motion signal characteristic information;

Step S606, determining physiological signal features based on the physiological signal to obtain a physiological signal feature set;

Step S608, deleting invalid physiological signal features from the physiological signal feature set using the posture information to obtain a target physiological signal feature set;

Step S610: determining the validity of the physiological signal or the alarm information corresponding to the physiological signal using the features in the target physiological signal feature set.

It should be noted that the preferred implementation of the embodiment shown in FIG. 6 can refer to the relevant descriptions of the embodiments shown in FIG. 1 to FIG. 5 , which will not be repeated here.

Based on the above solution provided in the embodiment of the present application, the following effects can be achieved:

1. Identify the source of interference. Based on human posture recognition, identify the source of interference, reduce false alarms, and improve parameter accuracy.

2. Improve the correlation between physiological parameter interference and motion signals. For example, if a person is identified as moving and the identified posture is walking, if there is interference with physiological parameters and a false alarm occurs, it can be basically confirmed that it is caused by walking and the false alarm is blocked, and the access condition will not be purely dependent on the reliability of the ECG signal.

3. Reduce false corrections when it is not relevant. When the person is identified as lying or sitting still, even if the acceleration movement changes greatly due to hand tremors, the alarm will not be corrected easily to avoid missed alarms.

The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims (27)

1. A mobile monitoring device, comprising: the device comprises a motion sensor, a physiological signal acquisition device, a memory and a processor; wherein, the motion sensor, the physiological signal acquisition device, the memory and the processor are connected through a lead wire;

the motion sensor is used for collecting motion signals of a target object;

the physiological signal acquisition device is used for acquiring physiological signals of the target object;

The memory is used for storing executable programs;

the processor is configured to execute an executable program in the memory that implements the following functions:

Acquiring physiological signals and motion signals of the target object;

Analyzing the motion signal to obtain the motion signal characteristics of the target object, and determining the gesture information of the target object according to the motion signal characteristic information;

Determining a physiological signal characteristic based on the physiological signal;

adjusting the physiological signal characteristic according to the gesture indicated by the gesture information, comprising:

Comparing the gesture indicated by the gesture information with a designated gesture, and optimizing the physiological signal characteristics when the gesture indicated by the gesture information is the designated gesture; and when the gesture indicated by the gesture information is not the appointed gesture, determining reliability information of the physiological signal, and when the reliability information indicates unreliability, optimizing the physiological signal characteristics according to the motion state information of the target object.

2. The mobile monitoring device of claim 1, wherein the processor is further configured to determine the reliability information of the physiological signal by:

Determining weights of the physiological signal features;

determining a target reliability index of the physiological signal according to the weight of the physiological signal characteristic and the reliability index corresponding to the physiological signal characteristic;

comparing the target reliability index with a preset threshold;

Determining the reliability information according to a comparison result, wherein the reliability information is determined to be reliable when the target reliability index is greater than a preset threshold; and when the target reliability index is smaller than the preset threshold value, determining that the reliability information is unreliable.

3. The mobile monitoring device of claim 1, wherein the processor is further configured to optimize the physiological signal characteristic based on the first motion state information at the specified pose to obtain a target physiological signal characteristic.

4. The mobile monitoring device of claim 3, wherein the processor is further configured to adjust the weight of the physiological signal characteristic in accordance with the first motion state information.

5. The mobile monitoring device of claim 4, wherein the processor is further configured to adjust the weight of the physiological signal feature in accordance with:

determining invalid ones of the physiological signal features according to the first motion state information;

the weight of the inactive physiological signal feature is adjusted to zero.

6. The mobile monitoring device of claim 5, wherein the designated gesture comprises: walking posture; the first motion state information includes: the motion intensity of the target object and the step frequency of the target object;

determining invalid ones of the physiological signal features according to the first motion state information, comprising:

When the step frequency is larger than a first threshold value and the motion intensity belongs to a first grade, determining heartbeat interval information in the physiological signal characteristics as the invalid physiological signal characteristics;

Determining interval information of QRS waves, which are not uniform and matched with dominant QRS waves, in the physiological signal characteristics as the ineffective physiological signal characteristics when the step frequency is greater than a second threshold and less than the first threshold and the motion intensity belongs to a second level;

When the step frequency is smaller than a second threshold value and the motion intensity is of a third level, taking interval information of the QRS wave which is higher than a specified value, does not have uniformity and does not match the dominant QRS wave as the invalid physiological signal characteristic;

wherein, the motion intensities corresponding to the first level, the second level and the third level are sequentially reduced.

7. The mobile monitoring device of claim 4, wherein the first motion state information comprises: at least one evaluation index for evaluating different motion signals in the first motion state; adjusting the weight of the physiological signal feature according to the first motion state information, including:

Comparing each evaluation index with a corresponding threshold value for each evaluation index of the evaluation indexes of different parameters to obtain at least one comparison result;

determining a target weight of the physiological signal feature according to the at least one comparison result; and

And adjusting the weight of the physiological signal characteristic to the target weight.

8. The mobile monitoring device of claim 3, wherein the processor is further configured to obtain second motion state information of the target object prior to obtaining pose information of the target object; optimizing the physiological signal characteristics according to the second motion state information to obtain initial physiological signal characteristics; and re-optimizing the initial physiological signal characteristic by using the first motion state information to obtain the target physiological signal characteristic.

9. The mobile monitoring device of claim 1, wherein the processor is further configured to determine an alert threshold corresponding to the pose information after optimizing the physiological signal characteristics; comparing the index corresponding to the optimized physiological signal characteristics with the alarm threshold; when the gesture indicated by the gesture information is not a specified gesture and the index corresponding to the physiological signal characteristic is greater than the alarm threshold value, alarming; and rejecting alarm when the gesture indicated by the gesture information is the appointed gesture and the index corresponding to the physiological signal characteristic is larger than the alarm threshold.

10. The mobile monitoring device of claim 1, wherein the processor is further configured to determine pose information for the target object based on the acceleration signal of the target object when the timing time is reached; suspending measurement of the physiological signal of the target object and restarting timing when the posture information indicates that the target object is in the first posture and the motion intensity exceeds a first threshold; and when a first preset time length after restarting timing is reached and the gesture information is a second gesture, starting to measure the physiological signal of the target object, wherein the motion intensity of the first gesture is higher than that of the second gesture.

11. The mobile monitoring device of claim 10, wherein the processor is further configured to stop the measurement when the diastolic pressure of the subject is detected within a preset detection period after the measurement of the physiological signal of the subject is started; the acceleration signal is acquired again when the diastolic pressure is not detected in the preset detection period, and the gesture of the target object is determined again based on the acquired acceleration signal; and stopping measurement when the re-determined gesture is the first gesture and the time for keeping the first gesture reaches a second preset duration.

12. The mobile monitoring device of claim 1, wherein the gesture information comprises: walking posture; the walking posture is determined by:

acquiring a motion signal of the target object in a walking posture, wherein the motion signal comprises: peak statistical information of a motion signal of the target object and vector direction information of the motion signal in a preset time period;

determining the number of identical peak information in the peak statistics; determining that the target object is in a repetitive motion configuration when the number is greater than a first threshold; and determining that the target object is in the walking posture based on the vector direction information when the target object is determined to be in the repetitive motion state.

13. The mobile monitoring device of claim 1, wherein the gesture information comprises: a stationary posture; the processor is further configured to determine the stationary pose by:

acquiring a direction vector and a motion strength when the target object is in a static attitude;

matching the direction vector with a preset direction vector to obtain a matching result;

And when the matching result indicates that the direction vector is consistent with a preset direction vector and the motion intensity is smaller than a second threshold value, determining that the target object is in the static attitude.

14. The mobile monitoring device of any of claims 1 to 13, wherein the motion sensor and the processor are integrated in a single device; or the motion sensor and the physiological signal acquisition device are integrated in a single device.

15. A method of adjusting a physiological characteristic, comprising:

acquiring physiological signals and motion signals of a target object;

Analyzing the motion signal to obtain the motion signal characteristics of the target object, and determining the gesture information of the target object according to the motion signal characteristic information;

Determining a physiological signal characteristic based on the physiological signal;

adjusting the physiological signal characteristic according to the gesture indicated by the gesture information, comprising:

judging whether the gesture indicated by the gesture information is a designated gesture, and optimizing the physiological signal characteristics when the judgment result is yes; and when the judgment result is negative, determining the reliability information of the physiological signal, and when the reliability information indicates unreliable, optimizing the characteristics of the physiological signal according to the motion state information of the target object.

16. The method of claim 15, wherein determining reliability information for the physiological signal comprises:

Determining weights of the physiological signal features;

determining a target reliability index of the physiological signal according to the weight of the physiological signal characteristic and the reliability index corresponding to the physiological signal characteristic;

comparing the target reliability index with a preset threshold;

Determining the reliability information according to a comparison result, wherein the reliability information is determined to be reliable when the target reliability index is greater than a preset threshold; and when the target reliability index is smaller than the preset threshold value, determining that the reliability information is unreliable.

17. The method of claim 15, wherein optimizing the physiological signal characteristic comprises:

and optimizing the physiological signal characteristics based on the first motion state information under the appointed gesture to obtain target physiological signal characteristics.

18. The method of claim 17, wherein optimizing the physiological signal characteristic based on the first motion state information at the specified pose comprises: and adjusting the weight of the physiological signal characteristic according to the first motion state information.

19. The method of claim 18, wherein adjusting the weight of the physiological signal characteristic in accordance with the first motion state information comprises:

determining invalid ones of the physiological signal features according to the first motion state information;

the weight of the inactive physiological signal feature is adjusted to zero.

20. The method of claim 19, wherein the specifying a gesture comprises: walking posture; the first motion state information includes: the motion intensity of the target object and the step frequency of the target object;

determining invalid ones of the physiological signal features according to the first motion state information, comprising:

When the step frequency is larger than a first threshold value and the motion intensity belongs to a first grade, determining heartbeat interval information in the physiological signal characteristics as the invalid physiological signal characteristics;

Determining interval information of QRS waves, which are not uniform and matched with dominant QRS waves, in the physiological signal characteristics as the ineffective physiological signal characteristics when the step frequency is greater than a second threshold and less than the first threshold and the motion intensity belongs to a second level;

When the step frequency is smaller than a second threshold value and the motion intensity is of a third level, taking interval information of the QRS wave which is higher than a specified value, does not have uniformity and does not match the dominant QRS wave as the invalid physiological signal characteristic;

wherein, the motion intensities corresponding to the first level, the second level and the third level are sequentially reduced.

21. The method of claim 17, wherein the first motion state information comprises: at least one evaluation index for evaluating different motion signals in the first motion state; adjusting the weight of the physiological signal feature according to the first motion state information, including:

Comparing each evaluation index with a corresponding threshold value for each evaluation index of the evaluation indexes of different parameters to obtain at least one comparison result;

determining a target weight of the physiological signal feature according to the at least one comparison result; and

And adjusting the weight of the physiological signal characteristic to the target weight.

22. The method of claim 17, wherein prior to acquiring the pose information of the target object, the method further comprises:

acquiring second motion state information of the target object;

Optimizing the physiological signal characteristics according to the second motion state information to obtain initial physiological signal characteristics;

And re-optimizing the initial physiological signal characteristic by using the first motion state information to obtain the target physiological signal characteristic.

23. The method of claim 15, wherein after optimizing the physiological signal characteristic, the method further comprises:

Determining an alarm threshold corresponding to the gesture information; comparing the index corresponding to the optimized physiological signal characteristics with the alarm threshold; when the gesture indicated by the gesture information is not a specified gesture and the index corresponding to the physiological signal characteristic is greater than the alarm threshold value, alarming; and rejecting alarm when the gesture indicated by the gesture information is the appointed gesture and the index corresponding to the physiological signal characteristic is larger than the alarm threshold.

24. The method of claim 15, wherein the gesture information comprises: walking posture; the walking posture is determined by:

acquiring a motion signal of the target object in a walking posture, wherein the motion signal comprises: peak statistical information of a motion signal of the target object and vector direction information of the motion signal in a preset time period;

determining the number of identical peak information in the peak statistics; determining that the target object is in a repetitive motion configuration when the number is greater than a first threshold; and determining that the target object is in the walking posture based on the vector direction information when the target object is determined to be in the repetitive motion state.

25. The method of claim 15, wherein the gesture information comprises: a stationary posture; the stationary attitude is determined by:

acquiring a direction vector and a motion strength when the target object is in a static attitude;

matching the direction vector with a preset direction vector to obtain a matching result;

And when the matching result indicates that the direction vector is consistent with a preset direction vector and the motion intensity is smaller than a second threshold value, determining that the target object is in the static attitude.

26. The method according to any one of claims 15 to 25, wherein,

Acquiring physiological signals and motion signals of a target object, comprising: acquiring acceleration parameters of the target object through an accelerometer arranged on the wearable equipment;

Analyzing the motion signal to obtain the motion signal characteristics of the target object, including: and determining the motion signal characteristics of the target object based on the acceleration parameters.

27. A method of processing a physiological signal, comprising:

acquiring physiological signals and acceleration parameters of a target object;

determining the motion signal characteristics of the target object based on the acceleration parameters, and determining the gesture information of the target object according to the motion signal characteristic information;

Determining physiological signal characteristics based on the physiological signals to obtain a physiological signal characteristic set;

Deleting invalid physiological signal characteristics from the physiological signal characteristic set by utilizing the gesture information to obtain a target physiological signal characteristic set, wherein the determining mode of the invalid physiological signal characteristics comprises the following steps:

When the step frequency of the target object is larger than a first threshold value and the motion intensity of the target object belongs to a first grade, determining heartbeat interval information in the physiological signal characteristics as the invalid physiological signal characteristics;

Determining interval information of QRS waves, which are not uniform and matched with dominant QRS waves, in the physiological signal characteristics as the ineffective physiological signal characteristics when the step frequency is greater than a second threshold and less than the first threshold and the motion intensity belongs to a second level;

When the step frequency is smaller than a second threshold value and the motion intensity is of a third level, taking interval information of the QRS wave which is higher than a specified value, does not have uniformity and does not match the dominant QRS wave as the invalid physiological signal characteristic;

wherein the motion intensities corresponding to the first level, the second level and the third level are sequentially reduced;

And determining the physiological signal or the validity of alarm information corresponding to the physiological signal by using the characteristics in the target physiological signal characteristic set.