CN114587298A - Method and system for detecting and separating physiological information of multiple human bodies - Google Patents
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Abstract
Translated fromChinese
Description
Translated fromChinese技术领域technical field
本发明涉及人体生理信号分离的技术领域，尤其涉及一种多人体生理信息检测-分离方法及系统。The present invention relates to the technical field of human physiological signal separation, in particular to a multi-human physiological information detection-separation method and system.
背景技术Background technique
心率是观察生命体征健康状态的重要参数，在医学上心率的检测可以通过心电仪和心超设备等进行，但是这些测量设备往往体积较大，使用时对人有一定的束缚性，因此便利性较差；另一方面，智能手环和腕带等相较于医疗设备尽管有非常好的使用便利性，但是由于腕关节的骨骼和肌肉的运动位移较大，置于这类设备中的传感器难以稳定地获取同一位置的信息，从而设备输出生理信号的失真度较高。与上述两类设备相比，智能胸带是一类能够同时兼顾操作便利性和数据准确率的设备，置入智能胸带中的传感器件可以是温度传感器进行体温的测量、可以是导电电极进行心电参数的测量，还可以是光电或加速度传感器进行心率和心率变化率的测量，因此智能胸带具有较好的应用前景。Heart rate is an important parameter to observe the health status of vital signs. In medicine, the detection of heart rate can be carried out by electrocardiograph and echocardiography equipment, etc. However, these measuring equipments are usually large in size and have a certain restraint on people when they are used, so it is convenient On the other hand, although smart wristbands and wristbands are very convenient to use compared to medical devices, due to the large movement displacement of the bones and muscles of the wrist joint, the It is difficult for the sensor to obtain the information of the same position stably, so the distortion of the physiological signal output by the device is high. Compared with the above two types of devices, the smart chest strap is a kind of device that can take into account the convenience of operation and data accuracy at the same time. The sensor device placed in the smart chest strap can be a temperature sensor to measure body temperature, or a conductive electrode to measure body temperature. The measurement of ECG parameters can also be performed by photoelectric or acceleration sensors to measure the heart rate and the rate of change of heart rate, so the smart chest strap has a good application prospect.
置有加速度传感器的智能胸带，具有成本低、体积小和功耗低的特点，但是由于其极高的测量灵敏度，这类智能胸带所获得的生理信号中还往往混入有呼吸节律和步态信息，如何通过合适的信号处理方法获得准确的心率数据是一项具有挑战性的工作。The smart chest strap with acceleration sensor has the characteristics of low cost, small size and low power consumption, but due to its extremely high measurement sensitivity, the physiological signals obtained by this type of smart chest strap are often mixed with respiratory rhythm and step. How to obtain accurate heart rate data through appropriate signal processing methods is a challenging task.
发明内容SUMMARY OF THE INVENTION
本部分的目的在于概述本发明的实施例的一些方面以及简要介绍一些较佳实施例。在本部分以及本申请的说明书摘要和发明名称中可能会做些简化或省略以避免使本部分、说明书摘要和发明名称的目的模糊，而这种简化或省略不能用于限制本发明的范围。The purpose of this section is to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the application to avoid obscuring the purpose of this section, abstract and title, and such simplifications or omissions may not be used to limit the scope of the invention.
鉴于上述现有存在的问题，提出了本发明。The present invention has been proposed in view of the above-mentioned existing problems.
因此，本发明解决的技术问题是：如何通过合适的信号处理方法获得准确的心率数据。Therefore, the technical problem solved by the present invention is: how to obtain accurate heart rate data through a suitable signal processing method.
为解决上述技术问题，本发明提供如下技术方案：采集人体运动时的混合信号；对所采集信号中的所有信号进行筛选、滤波处理，并分离所述混合信号中的步态信号；将所述步态信号扣除后的信号进行时域分割，将分割后的混合信号的数据作为神经网络模型的输入，并通过调整参数及比较不同参数模型的指标，保存最优的模型；利用训练完成后的模型分离出呼吸信号和心率信号，计算三种信号的频率。In order to solve the above-mentioned technical problems, the present invention provides the following technical solutions: collecting mixed signals during human motion; screening and filtering all the signals in the collected signals, and separating the gait signals in the mixed signals; The signal after deduction of gait signal is divided into time domain, and the data of the divided mixed signal is used as the input of the neural network model, and the optimal model is saved by adjusting the parameters and comparing the indicators of different parameter models; The model separates the respiration signal and the heart rate signal, and calculates the frequencies of the three signals.
作为本发明所述的多人体生理信息检测-分离方法的一种优选方案，其中：利用带通滤波器提取所述混合信号中的步态信号。As a preferred solution of the multi-human physiological information detection-separation method of the present invention, the gait signal in the mixed signal is extracted by using a band-pass filter.
作为本发明所述的多人体生理信息检测-分离方法的一种优选方案，其中：将所有采集的数据进行坏点的删除，并将所述呼吸和心率信号进行滤波处理；所述滤波处理采用数字滤波器：通过巴特沃斯低通滤波器消除呼吸信号中微弱的心率信号和身体噪声；通过巴特沃斯高通滤波器消除心率信号的基线漂移。As a preferred solution of the multi-human physiological information detection-separation method according to the present invention, wherein: all collected data are deleted from dead pixels, and the respiration and heart rate signals are filtered; Digital filter: Eliminate the weak heart rate signal and body noise in the respiratory signal through the Butterworth low-pass filter; Eliminate the baseline drift of the heart rate signal through the Butterworth high-pass filter.
作为本发明所述的多人体生理信息检测-分离方法的一种优选方案，其中：采用Conv-Tasnet深度学习网络模型对信号进行分离，包括：将经过数据预处理后的数据作为模型的训练数据，对其进行时域上的分割；调整模型的参数，设置模型的学习率，通过正向传播计算的损失值，进行反向传播，修改权重；若损失值达到预设的指标，则保存模型，否则继续判断是否达到最大迭代次数，若达到最大迭代次数则保存模型，否则继续调整参数。As a preferred solution of the multi-human physiological information detection-separation method of the present invention, wherein: using the Conv-Tasnet deep learning network model to separate the signals, including: using the data after data preprocessing as the training data of the model , segment it in the time domain; adjust the parameters of the model, set the learning rate of the model, carry out back propagation through the loss value calculated by forward propagation, and modify the weight; if the loss value reaches the preset index, save the model , otherwise continue to judge whether the maximum number of iterations is reached, if the maximum number of iterations is reached, save the model, otherwise continue to adjust the parameters.
作为本发明所述的多人体生理信息检测-分离方法的一种优选方案，其中：所述Conv-Tasnet深度学习网络模型，在分离器中加入Bahdanau注意力机制，在所述分离器后加入一个随机移除单元dropout；所述Conv-Tasnet深度学习网络模型采用的梯度下降的方法为AdamW+SAM。As a preferred solution of the multi-human physiological information detection-separation method according to the present invention, wherein: the Conv-Tasnet deep learning network model is added with a Bahdanau attention mechanism in the separator, and a Bahdanau attention mechanism is added after the separator. The unit dropout is randomly removed; the gradient descent method adopted by the Conv-Tasnet deep learning network model is AdamW+SAM.
作为本发明所述的多人体生理信息检测-分离方法的一种优选方案，其中：最优分离模型的选取标准为不同参数训练的模型之间的性能对比得到的最优的模型，包括两种指标：信噪比改善和最大化尺度不变信噪比；选择信噪比改善或最大化尺度不变信噪比最优的模型作为信号分离模型。As a preferred solution of the multi-human physiological information detection-separation method of the present invention, wherein: the selection criterion of the optimal separation model is the optimal model obtained by comparing the performance of models trained with different parameters, including two Indicators: SNR improvement and maximizing scale-invariant SNR; the optimal model for SNR improvement or maximizing scale-invariant SNR is selected as the signal separation model.
作为本发明所述的多人体生理信息检测-分离方法的一种优选方案，其中：对于所述心率信号，采用区间最大值算法，得到心率信号的每一个峰值，通过信号频率计算公式计算得到心率；对于所述呼吸信号和步态信号，通过低通滤波器滤除高频信号，波峰提取法找到区间内波峰个数，并通过信号频率计算公式计算得到呼吸频率和步频。As a preferred solution of the multi-human physiological information detection-separation method of the present invention, wherein: for the heart rate signal, an interval maximum algorithm is used to obtain each peak value of the heart rate signal, and the heart rate is calculated by the signal frequency calculation formula. For described breathing signal and gait signal, filter out high-frequency signal by low-pass filter, find the number of peaks in the interval by wave peak extraction method, and obtain breathing frequency and cadence frequency through the calculation formula of signal frequency.
作为本发明所述的多人体生理信息检测-分离方法的一种优选方案，其中：所述信号频率计算公式为：As a preferred solution of the multi-human physiological information detection-separation method of the present invention, wherein: the signal frequency calculation formula is:
其中，fs是采样频率，Xstart是第一个峰值所在的位置，Xend是最后一个峰值所在的位置，n是这段区间峰值的个数。Among them, fs is the sampling frequency, Xstart is the position of the first peak, Xend is the position of the last peak, and n is the number of peaks in this interval.
为解决上述技术问题，本发明还提供了一种多人体生理信息检测-分离系统，包括：信号采集器，包括加速度传感器、微控制器模块、模数转换单元、无线发送模块，其通过有线或无线的方式进行连接，用于采集人体运动时的混合信号；数据预处理子系统，与所述信号采集器相连接，包括无线接收模块、筛选与滤波模块、步态分离模块，用于对采集的信号进行预处理；信号分离子系统，与所述数据预处理子系统相连接，包括模型训练模块、信号分离模块和频率计算模块，用于构建模型及分离信号。In order to solve the above technical problems, the present invention also provides a multi-human physiological information detection-separation system, including: a signal collector, including an acceleration sensor, a microcontroller module, an analog-to-digital conversion unit, and a wireless sending module, which are connected by wired or The wireless connection is used to collect the mixed signal of human body movement; the data preprocessing subsystem, which is connected to the signal collector, includes a wireless receiving module, a screening and filtering module, and a gait separation module, which is used for collecting The signal is preprocessed; the signal separation subsystem, connected with the data preprocessing subsystem, includes a model training module, a signal separation module and a frequency calculation module for building a model and separating signals.
本发明的有益效果：本发明在设备上较为简单并且对人体的束缚性较小，传感器也有着体积小、质量轻、功耗低等特点；本发明模型训练的复杂度较低和模型较小；本发明减少了信息的损失，增加了模型的鲁棒性，能够更好得避免过拟合，提高了网络的泛化能力。Beneficial effects of the present invention: the present invention is relatively simple in equipment and less restrictive to the human body, and the sensor also has the characteristics of small size, light weight, low power consumption, etc.; the present invention has lower model training complexity and smaller model The invention reduces the loss of information, increases the robustness of the model, can better avoid overfitting, and improves the generalization ability of the network.
附图说明Description of drawings
为了更清楚地说明本发明实施例的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其它的附图。其中：In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort. in:
图1为本发明一个实施例提供的一种多人体生理信息检测-分离方法及系统的基本流程示意图；FIG. 1 is a schematic diagram of a basic flow of a method and system for detecting and separating multiple human physiological information according to an embodiment of the present invention;
图2为本发明一个实施例提供的一种多人体生理信息检测-分离方法及系统的信号分析算法流程示意图；2 is a schematic flowchart of a signal analysis algorithm of a method and system for detecting and separating multiple human physiological information according to an embodiment of the present invention;
图3为本发明一个实施例提供的一种多人体生理信息检测-分离方法及系统的信号采集器佩戴示意图；3 is a schematic diagram of wearing a signal collector of a method and system for detecting and separating multiple human physiological information according to an embodiment of the present invention;
图4为本发明一个实施例提供的一种多人体生理信息检测-分离方法及系统的数据预处理子系统工作流程示意图；4 is a schematic diagram of the workflow of the data preprocessing subsystem of a method and system for detecting and separating multiple human physiological information according to an embodiment of the present invention;
图5为本发明一个实施例提供的一种多人体生理信息检测-分离方法及系统的模型训练流程示意图；5 is a schematic diagram of a model training process of a method and system for detecting and separating multiple human physiological information according to an embodiment of the present invention;
图6为本发明一个实施例提供的一种多人体生理信息检测-分离方法及系统的信号分离流程的信号示意图；6 is a schematic signal diagram of a signal separation process of a method and system for detecting and separating multiple human physiological information according to an embodiment of the present invention;
图7为本发明一个实施例提供的一种多人体生理信息检测-分离方法及系统的模块结构示意图。FIG. 7 is a schematic structural diagram of a module of a method and system for detecting and separating multiple human physiological information according to an embodiment of the present invention.
具体实施方式Detailed ways
为使本发明的上述目的、特征和优点能够更加明显易懂，下面结合说明书附图对本发明的具体实施方式做详细的说明，显然所描述的实施例是本发明的一部分实施例，而不是全部实施例。基于本发明中的实施例，本领域普通人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本发明的保护的范围。In order to make the above objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Example. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
在下面的描述中阐述了很多具体细节以便于充分理解本发明，但是本发明还可以采用其他不同于在此描述的其它方式来实施，本领域技术人员可以在不违背本发明内涵的情况下做类似推广，因此本发明不受下面公开的具体实施例的限制。Many specific details are set forth in the following description to facilitate a full understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein, and those skilled in the art can do so without departing from the connotation of the present invention. Similar promotion, therefore, the present invention is not limited by the specific embodiments disclosed below.
其次，此处所称的“一个实施例”或“实施例”是指可包含于本发明至少一个实现方式中的特定特征、结构或特性。在本说明书中不同地方出现的“在一个实施例中”并非均指同一个实施例，也不是单独的或选择性的与其他实施例互相排斥的实施例。Second, reference herein to "one embodiment" or "an embodiment" refers to a particular feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in various places in this specification are not all referring to the same embodiment, nor are they separate or selectively mutually exclusive from other embodiments.
本发明结合示意图进行详细描述，在详述本发明实施例时，为便于说明，表示器件结构的剖面图会不依一般比例作局部放大，而且所述示意图只是示例，其在此不应限制本发明保护的范围。此外，在实际制作中应包含长度、宽度及深度的三维空间尺寸。The present invention is described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the sectional views showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the present invention. scope of protection. In addition, the three-dimensional spatial dimensions of length, width and depth should be included in the actual production.
同时在本发明的描述中，需要说明的是，术语中的“上、下、内和外”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本发明的限制。此外，术语“第一、第二或第三”仅用于描述目的，而不能理解为指示或暗示相对重要性。At the same time, in the description of the present invention, it should be noted that the orientation or positional relationship indicated in terms such as "upper, lower, inner and outer" is based on the orientation or positional relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention. The invention and simplified description do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operate in a particular orientation, and therefore should not be construed as limiting the invention. Furthermore, the terms "first, second or third" are used for descriptive purposes only and should not be construed to indicate or imply relative importance.
本发明中除非另有明确的规定和限定，术语“安装、相连、连接”应做广义理解，例如：可以是固定连接、可拆卸连接或一体式连接；同样可以是机械连接、电连接或直接连接，也可以通过中间媒介间接相连，也可以是两个元件内部的连通。对于本领域的普通技术人员而言，可以具体情况理解上述术语在本发明中的具体含义。Unless otherwise expressly specified and limited in the present invention, the term "installation, connection, connection" should be understood in a broad sense, for example: it may be a fixed connection, a detachable connection or an integral connection; it may also be a mechanical connection, an electrical connection or a direct connection. The connection can also be indirectly connected through an intermediate medium, or it can be the internal communication between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood in specific situations.
实施例1Example 1
参照图1～6，为本发明的一个实施例，提供了一种多人体生理信息检测-分离方法，包括：1 to 6 , which is an embodiment of the present invention, a method for detecting and separating multiple human physiological information is provided, including:
S1：采集人体运动时的混合信号。S1: The mixed signal of human motion is collected.
需要说明的是，利用信号采集器采集人体运动时的混合信号，其中，如图3所示，信号采集器固定在弹性胸带上，在设计实验采集生理信号时，将信号采集器放置在人体的左胸口处，所采集的信号为受试者的心率、呼吸、步态以及这三种信号的混合信号。It should be noted that a signal collector is used to collect the mixed signals of human body movement. As shown in Figure 3, the signal collector is fixed on the elastic chest strap. When designing an experiment to collect physiological signals, the signal collector is placed on the human body. At the left chest of the subject, the collected signals are the subject's heart rate, respiration, gait, and a mixture of these three signals.
S2：分离混合信号中的步态信号，并对混合信号中的所有信号进行筛选、滤波处理。S2: Separating the gait signal in the mixed signal, and screening and filtering all the signals in the mixed signal.
需要说明的是，如图4所示，将所有采集的数据进行坏点的删除，并将呼吸和心率信号进行滤波处理，其中，滤波处理采用数字滤波器的方法：通过巴特沃斯低通滤波器消除呼吸信号中微弱的心率信号和身体噪声；通过巴特沃斯高通滤波器消除心率信号的基线漂移，再利用带通滤波器将混合信号中的步态信号提取出来。It should be noted that, as shown in Figure 4, all the collected data are deleted with dead pixels, and the respiration and heart rate signals are filtered, wherein the filtering process adopts the method of digital filter: through Butterworth low-pass filtering The device eliminates the weak heart rate signal and body noise in the breathing signal; the baseline drift of the heart rate signal is eliminated by the Butterworth high-pass filter, and the gait signal in the mixed signal is extracted by the band-pass filter.
S3：将步态信号扣除后的信号进行时域分割，将分割后的混合信号的数据作为神经网络模型的输入，并通过调整参数以及比较不同参数模型的指标，保存最优的模型。S3: Perform time domain segmentation on the signal after deduction of the gait signal, use the data of the divided mixed signal as the input of the neural network model, and save the optimal model by adjusting the parameters and comparing the indicators of different parameter models.
需要说明的是，优选的，如图2、5所示，该神经网络模型采用Conv-Tasnet深度学习网络模型，主要结构包括编码器、分离器以及解码器，其训练、生成过程包括：将经过数据预处理子系统处理过的数据作为原始的训练数据，对其进行时域上的分割，其次，调整模型的参数，设置模型的学习率，通过正向传播计算的损失值，进行反向传播，修改权重，若损失值达到预期的指标，则保存模型，否则继续判断是否达到最大迭代次数，若达到最大迭代次数则保存模型，否则继续调整参数。It should be noted that, preferably, as shown in Figures 2 and 5, the neural network model adopts the Conv-Tasnet deep learning network model, and the main structure includes an encoder, a separator and a decoder, and the training and generation process includes: The data processed by the data preprocessing subsystem is used as the original training data, and it is divided in the time domain. Secondly, the parameters of the model are adjusted, the learning rate of the model is set, and the loss value calculated by the forward propagation is used for back propagation. , modify the weight, if the loss value reaches the expected index, save the model, otherwise continue to judge whether the maximum number of iterations has been reached, if the maximum number of iterations is reached, save the model, otherwise continue to adjust the parameters.
进一步的，上述的Conv-Tasnet深度学习网络模型，在分离器中加入Bahdanau注意力机制，减少信息的损失，为避免过拟合，增加模型的鲁棒性，在分离器后加入一个随机移除单元dropout；Conv-Tasnet深度学习网络模型采用的梯度下降的方法为AdamW+SAM，AdamW是在Adam+L2正则化的基础上进行改进的算法，具体的是指优化了Adam对于权重越大的参数但惩罚并不是越大的缺陷，锐度感知最小化(SAM)减少损失值和损失锐度，在其领域内寻找均匀地损失值的参数，提高网络的泛化能力。Further, in the above-mentioned Conv-Tasnet deep learning network model, the Bahdanau attention mechanism is added to the separator to reduce the loss of information. In order to avoid overfitting and increase the robustness of the model, a random removal is added after the separator. Unit dropout; the gradient descent method used by the Conv-Tasnet deep learning network model is AdamW+SAM. AdamW is an improved algorithm based on Adam+L2 regularization. Specifically, it refers to optimizing the parameters of Adam for the larger the weight. But the penalty is not a bigger defect, sharpness-aware minimization (SAM) reduces the loss value and loss sharpness, finds parameters with uniform loss value in its field, and improves the generalization ability of the network.
更进一步的，最优分离模型的选取标准为不同参数训练的模型之间的性能对比得到的最优的模型，包括两种指标：信噪比改善(SDRi)和最大化尺度(SI-SNR)不变信噪比；选择信噪比改善或最大化尺度不变信噪比最优的模型作为信号分离模型。Further, the selection criteria of the optimal separation model is the optimal model obtained by comparing the performance of models trained with different parameters, including two indicators: signal-to-noise ratio improvement (SDRi) and maximum scale (SI-SNR) Invariant signal-to-noise ratio; select the model with the best signal-to-noise ratio improvement or maximize the scale-invariant signal-to-noise ratio as the signal separation model.
S4：利用训练完成后的模型分离出呼吸信号和心率信号，计算三种信号的频率。S4: Use the model after training to separate the breathing signal and the heart rate signal, and calculate the frequencies of the three signals.
需要说明的是，对于心率信号，采用区间最大值算法，得到心率信号的每一个峰值，通过信号频率计算公式计算得到心率；对于呼吸信号和步态信号，通过低通滤波器滤除高频信号，波峰提取法找到区间内波峰个数，并通过信号频率计算公式计算得到呼吸频率和步频。It should be noted that, for the heart rate signal, the interval maximum algorithm is used to obtain each peak of the heart rate signal, and the heart rate is calculated by the signal frequency calculation formula; for the breathing signal and gait signal, the high-frequency signal is filtered out by a low-pass filter. , the peak extraction method finds the number of peaks in the interval, and calculates the breathing frequency and cadence through the signal frequency calculation formula.
优选的，信号频率计算公式为：Preferably, the signal frequency calculation formula is:
其中，fs是采样频率，Xstart是第一个峰值所在的位置，Xend是最后一个峰值所在的位置，n是这段区间峰值的个数。Among them, fs is the sampling frequency, Xstart is the position of the first peak, Xend is the position of the last peak, and n is the number of peaks in this interval.
本发明的生理信号采集装置由有弹性的胸带和加速度传感器组成，在设备上较为简单并且对人体的束缚性较小，传感器也有着体积小、质量轻、功耗低等特点；本发明方法是在动态情况下，分离呼吸、心率和步态信号，通过先将步态信号进行提取，再通过分离模型将呼吸和心率信号分离出来，降低模型训练的复杂度和减小模型大小；本发明的深度学习模型是Conv-Tasnet，在解码器前加入Bahdanau注意力机制，减少了信息的损失，在分离器后面增加dropout层，增加了模型的鲁棒性，能够更好得避免过拟合，梯度下降的方法为AdamW+SAM可以在其领域内寻找均匀地损失值的参数，提高网络的泛化能力。The physiological signal acquisition device of the present invention is composed of an elastic chest strap and an acceleration sensor, which is relatively simple in equipment and less restrictive to the human body, and the sensor also has the characteristics of small size, light weight, low power consumption, and the like; the method of the present invention In a dynamic situation, the breathing, heart rate and gait signals are separated, by first extracting the gait signal, and then separating the breathing and heart rate signals through the separation model, so as to reduce the complexity of model training and reduce the size of the model; the present invention The deep learning model is Conv-Tasnet. The Bahdanau attention mechanism is added before the decoder to reduce the loss of information. The dropout layer is added after the separator, which increases the robustness of the model and can better avoid overfitting. The gradient descent method is AdamW+SAM, which can find parameters with uniform loss value in its field, and improve the generalization ability of the network.
对本方法中采用的技术效果加以验证说明，本实施例采用本方法进行测试，以科学论证的手段以验证本方法所具有的真实效果。The technical effect adopted in this method is verified and explained. In this embodiment, this method is used for testing, and the real effect of this method is verified by means of scientific demonstration.
实验者需要在静态下进行心率以及呼吸信号的采集，在跑步机上，将速度为2.5km/h，并逐级增加速度，每次增加0.5km/h，并采集实验者的呼吸信号、心率信号、步态信号以及三种信号的混合信号，该数据由信号采集器发送给数据预处理子系统，对数据进行筛选、滤波之后，通过训练模块得到最大化尺度不变的信噪比最优的模型。The experimenter needs to collect the heart rate and breathing signal in a static state. On the treadmill, set the speed to 2.5km/h, and increase the speed step by step by 0.5km/h each time, and collect the experimenter's breathing signal and heart rate signal. , gait signal and the mixed signal of the three signals, the data is sent by the signal collector to the data preprocessing subsystem, after the data is screened and filtered, the optimal signal-to-noise ratio that maximizes the scale-invariant signal-to-noise ratio is obtained through the training module. Model.
图1是进行多人体信号分离工作流程图，如图6所示a信号，其信号表示的是信号采集器采集的原始混合信号，将原始的加速度混合信号通过数字带通滤波器的过滤，得到步态信号，如图6所示b信号；然后将步态信号扣除后的混合信号，如图6所示c信号，传输到信号分离模块，通过Conv-Tasnet模型分离出心率和呼吸信号，最后通过下面的方法以及公式，计算出三种信号的频率：对于心率信号，采用区间最大值的算法，找到心率信号的每一个峰值，进而通过公式计算得到心率；对于呼吸和步态信号，通过低通滤波器滤除高频信号，波峰提取法找到区间内波峰个数，进而通过公式计算得到呼吸频率和步频。对于三种信号频率的计算，采用以下公式：Figure 1 is a flow chart of the separation of multiple human body signals. As shown in Figure 6, a signal represents the original mixed signal collected by the signal collector. The original acceleration mixed signal is filtered by a digital band-pass filter to obtain The gait signal, as shown in Fig. 6 b signal; then the mixed signal after deduction of the gait signal, as shown in Fig. 6 c signal, is transmitted to the signal separation module, and the heart rate and respiration signals are separated through the Conv-Tasnet model, and finally The frequencies of the three signals are calculated by the following methods and formulas: for the heart rate signal, use the interval maximum algorithm to find each peak of the heart rate signal, and then calculate the heart rate through the formula; for the respiration and gait signals, use the low The high-frequency signal is filtered out through the filter, and the peak extraction method is used to find the number of peaks in the interval, and then the breathing frequency and cadence are calculated by the formula. For the calculation of the three signal frequencies, the following formulas are used:
其中，fs是采样频率，Xstart是第一个峰值所在的位置，Xend是最后一个峰值所在的位置，n是这段区间峰值的个数。Among them, fs is the sampling frequency, Xstart is the position of the first peak, Xend is the position of the last peak, and n is the number of peaks in this interval.
由上述实验结果可知，本发明提出的基于加速度的人体多生理信号分离方法及系统，采集生理信号的流程相对准确，实现较为简单，与可穿戴设备束缚性小且方便，对信号分离的结果性能评估更为准确。It can be seen from the above experimental results that the acceleration-based human body multi-physiological signal separation method and system proposed by the present invention has relatively accurate process of collecting physiological signals, is relatively simple to implement, and is less binding and convenient with wearable devices. assessment is more accurate.
实施例2Example 2
参照图7为本发明另一个实施例，该实施例不同于第一个实施例的是，提供了一种多人体生理信息检测-分离系统，上述多人体生理信息检测-分离方法依托于本系统实现，其具体包括：7 is another embodiment of the present invention, this embodiment is different from the first embodiment in that a multi-human body physiological information detection-separation system is provided, and the above-mentioned multi-human body physiological information detection-separation method relies on this system implementation, which specifically includes:
信号采集器，包括加速度传感器、微控制器模块、模数转换单元、无线发送模块，其通过有线或无线的方式进行连接，用于采集人体运动时的混合信号，优选的，利用加速度传感器采集人体生理信息；微控制器模块，用来滤除高频噪声并控制信号的发送；数模转换模块，将模拟信号转换为数字信号；无线发送模块，将数字信号发送给数据预处理子系统。A signal collector, including an acceleration sensor, a microcontroller module, an analog-to-digital conversion unit, and a wireless sending module, which are connected in a wired or wireless manner and are used to collect mixed signals when the human body is in motion. Preferably, the acceleration sensor is used to collect the human body Physiological information; microcontroller module, used to filter out high-frequency noise and control signal transmission; digital-to-analog conversion module, convert analog signal into digital signal; wireless transmission module, send digital signal to data preprocessing subsystem.
数据预处理子系统，与信号采集器相连接，包括无线接收模块、筛选与滤波模块、步态分离模块，用于对采集的信号进行预处理，优选的，无线接收模块，用于接收信号采集器传输的数据；筛选、滤波模块，对采集的多生理信号进行坏点的筛选和信号中干扰噪声的滤除；步态分离模块，是将心率、呼吸和步态的混合信号中分离出步态信号。A data preprocessing subsystem, connected with the signal collector, includes a wireless receiving module, a screening and filtering module, and a gait separation module, used for preprocessing the collected signals, preferably, a wireless receiving module, used for receiving signal collection The data transmitted by the controller; the screening and filtering module, the multi-physiological signals collected are screened for dead pixels and the interference noise in the signal; the gait separation module is to separate the mixed signals of heart rate, respiration and gait. status signal.
信号分离子系统，与数据预处理子系统相连接，包括模型训练模块、信号分离模块和频率计算模块，用于构建模型及分离信号，优选的，模型训练模块，其作用是通过训练得到最佳的信号分离模型；信号分离模块，从扣除步态信号的混合信号中，将心率和呼吸信号分离出来；频率计算模块，通过分离出来的信号计算出步频、心率和呼吸频率。The signal separation subsystem, which is connected with the data preprocessing subsystem, includes a model training module, a signal separation module and a frequency calculation module, which are used to build models and separate signals. Preferably, a model training module is used to obtain the best results through training. The signal separation model is based on the signal separation model; the signal separation module separates the heart rate and respiration signals from the mixed signal deducted from the gait signal; the frequency calculation module calculates the cadence, heart rate and respiration frequency through the separated signals.
应当认识到，本发明的实施例可以由计算机硬件、硬件和软件的组合、或者通过存储在非暂时性计算机可读存储器中的计算机指令来实现或实施。所述方法可以使用标准编程技术-包括配置有计算机程序的非暂时性计算机可读存储介质在计算机程序中实现，其中如此配置的存储介质使得计算机以特定和预定义的方式操作——根据在具体实施例中描述的方法和附图。每个程序可以以高级过程或面向对象的编程语言来实现以与计算机系统通信。然而，若需要，该程序可以以汇编或机器语言实现。在任何情况下，该语言可以是编译或解释的语言。此外，为此目的该程序能够在编程的专用集成电路上运行。It should be appreciated that embodiments of the present invention may be implemented or implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in non-transitory computer readable memory. The method can be implemented in a computer program using standard programming techniques - including a non-transitory computer-readable storage medium configured with a computer program, wherein the storage medium so configured causes the computer to operate in a specific and predefined manner - according to the specific Methods and figures described in the Examples. Each program may be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
此外，可按任何合适的顺序来执行本文描述的过程的操作，除非本文另外指示或以其他方式明显地与上下文矛盾。本文描述的过程(或变型和/或其组合)可在配置有可执行指令的一个或多个计算机系统的控制下执行，并且可作为共同地在一个或多个处理器上执行的代码(例如，可执行指令、一个或多个计算机程序或一个或多个应用)、由硬件或其组合来实现。所述计算机程序包括可由一个或多个处理器执行的多个指令。Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes (or variations and/or combinations thereof) described herein can be performed under the control of one or more computer systems configured with executable instructions, and as code that executes collectively on one or more processors (eg, , executable instructions, one or more computer programs or one or more applications), implemented in hardware, or a combination thereof. The computer program includes a plurality of instructions executable by one or more processors.
进一步，所述方法可以在可操作地连接至合适的任何类型的计算平台中实现，包括但不限于个人电脑、迷你计算机、主框架、工作站、网络或分布式计算环境、单独的或集成的计算机平台、或者与带电粒子工具或其它成像装置通信等等。本发明的各方面可以以存储在非暂时性存储介质或设备上的机器可读代码来实现，无论是可移动的还是集成至计算平台，如硬盘、光学读取和/或写入存储介质、RAM、ROM等，使得其可由可编程计算机读取，当存储介质或设备由计算机读取时可用于配置和操作计算机以执行在此所描述的过程。此外，机器可读代码，或其部分可以通过有线或无线网络传输。当此类媒体包括结合微处理器或其他数据处理器实现上文所述步骤的指令或程序时，本文所述的发明包括这些和其他不同类型的非暂时性计算机可读存储介质。当根据本发明所述的方法和技术编程时，本发明还包括计算机本身。计算机程序能够应用于输入数据以执行本文所述的功能，从而转换输入数据以生成存储至非易失性存储器的输出数据。输出信息还可以应用于一个或多个输出设备如显示器。在本发明优选的实施例中，转换的数据表示物理和有形的对象，包括显示器上产生的物理和有形对象的特定视觉描绘。Further, the methods may be implemented in any type of computing platform operably connected to a suitable, including but not limited to personal computer, minicomputer, mainframe, workstation, network or distributed computing environment, stand-alone or integrated computer platform, or communicate with charged particle tools or other imaging devices, etc. Aspects of the invention may be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optically read and/or written storage medium, RAM, ROM, etc., such that it can be read by a programmable computer, when a storage medium or device is read by a computer, it can be used to configure and operate the computer to perform the processes described herein. Additionally, the machine-readable code, or portions thereof, may be transmitted over wired or wireless networks. The invention described herein includes these and other various types of non-transitory computer-readable storage media when such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein. A computer program can be applied to input data to perform the functions described herein, transforming the input data to generate output data for storage to non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the transformed data represents physical and tangible objects, including specific visual depictions of physical and tangible objects produced on the display.
如在本申请所使用的，术语“组件”、“模块”、“系统”等等旨在指代计算机相关实体，该计算机相关实体可以是硬件、固件、硬件和软件的结合、软件或者运行中的软件。例如，组件可以是，但不限于是：在处理器上运行的处理、处理器、对象、可执行文件、执行中的线程、程序和/或计算机。作为示例，在计算设备上运行的应用和该计算设备都可以是组件。一个或多个组件可以存在于执行中的过程和/或线程中，并且组件可以位于一个计算机中以及/或者分布在两个或更多个计算机之间。此外，这些组件能够从在其上具有各种数据结构的各种计算机可读介质中执行。这些组件可以通过诸如根据具有一个或多个数据分组(例如，来自一个组件的数据，该组件与本地系统、分布式系统中的另一个组件进行交互和/或以信号的方式通过诸如互联网之类的网络与其它系统进行交互)的信号，以本地和/或远程过程的方式进行通信。As used in this application, the terms "component," "module," "system," etc. are intended to refer to a computer-related entity, which may be hardware, firmware, a combination of hardware and software, software, or running software. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable, a thread in execution, a program, and/or a computer. As an example, both an application running on a computing device and the computing device may be components. One or more components can exist in a process and/or thread of execution, and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures thereon. These components can be implemented by, for example, having one or more data groupings (eg, data from one component interacting with another component in a local system, a distributed system, and/or in a signaling manner such as the Internet network to interact with other systems) to communicate locally and/or as remote processes.
应说明的是，以上实施例仅用以说明本发明的技术方案而非限制，尽管参照较佳实施例对本发明进行了详细说明，本领域的普通技术人员应当理解，可以对本发明的技术方案进行修改或者等同替换，而不脱离本发明技术方案的精神和范围，其均应涵盖在本发明的权利要求范围当中。It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent substitutions without departing from the spirit and scope of the technical solutions of the present invention should be included in the scope of the claims of the present invention.
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