CN212755675U - Physiological condition monitoring equipment based on electroencephalogram signals and bioimpedance data - Google Patents
Physiological condition monitoring equipment based on electroencephalogram signals and bioimpedance data Download PDFInfo
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- CN212755675U CN212755675U CN202020020853.2U CN202020020853U CN212755675U CN 212755675 U CN212755675 U CN 212755675U CN 202020020853 U CN202020020853 U CN 202020020853U CN 212755675 U CN212755675 U CN 212755675U
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Abstract
The utility model relates to a based on brain electrical signal and bioimpedance data physiology situation supervisory equipment, its characterized in that utilizes an embedded equipment, has realized the portable collection of human brain electrical and bioimpedance data to manage on passing to thing networking platform on the data that will gather the gained. The cloud platform can accurately reflect the health condition of a human body, can realize real-time monitoring and management of health data of the data through the cloud platform, is different from the traditional equipment which is expensive to use, low in portability and low in real-time performance, and can realize convenient collection and analysis of the data.
Description
Technical Field
The utility model relates to an intelligent medical system of thing networking, specific physiological status supervisory equipment based on brain electrical signal and bioimpedance data that says so belongs to health management technical field.
Background
The brain, one of the most delicate and complex organs of the human body, contains abundant physiological information and pathological information. The information transmitted by the electroencephalogram signals can be identified through the characteristic signals, the optimal wave band in the electroencephalogram signals is extracted through a method of combining fundamental wave components and harmonic wave components, and then electroencephalogram channels are screened out through the Kolmogorov-Simrnov test. Finally, the classifier is trained and tested through a verification process and then used for distinguishing different types of emotions.
Bioelectrical impedance measurement, or impedance technology for short, is a detection technology for extracting biomedical information related to human physiology and pathology by using the electrical characteristics and change rule of biological tissues and organs. And acquiring related physiological and pathological information by detecting the detected impedance and the change of the body surface. It has the advantages of no wound, no harm, low cost, simple operation, rich functional information, etc. and is easy to be accepted by doctors and patients. Bioimpedance techniques, represented by various impedance and admittance flow diagrams, have been widely used in clinical settings, with ongoing progress.
The brain waves and the bio-impedance can objectively reflect the physiological condition of the human body. Different brain electrical signals can reflect different mental states of human bodies, and even different individuals can be identified according to different brain electrical signals generated by different people under the same stimulation. Different biological impedances can reflect different physiological and pathological states of human body. Physiological responses corresponding to different mental states occur when a person is in different states, and corresponding brain wave and bioimpedance information are different even when the person is in different sleep states. The emotion of a person can be analyzed by acquiring electroencephalogram signals, and emotion processing is carried out; the electrical impedance characteristics of human tissues or organs are obtained by measuring the human bioimpedance, so that the state of the tissues is known, the functions of the organs are evaluated, the diseased tissues are identified, the health condition is evaluated, and early warning is provided for abnormal conditions.
However, although the conventional electroencephalography and bioimpedance measuring apparatus is high in quality and reliable in medical diagnosis, it is complicated to use, and it is required to go to a fixed place (such as a hospital) and, most of the time, to wait for an analysis result after data collection. The cost for use is expensive, the portability is low, the real-time performance is low, and in most cases, data within a period of time are collected, so that the long-term condition of the human body is difficult to judge and predict. In the aspect of emotion classification, although a lot of related researches at home and abroad prove the feasibility of emotion recognition through electroencephalogram, a model with high accuracy is not available all the time. Therefore, at present, it is difficult to realize real-time monitoring and management of the health condition of the user, and more specifically, the electroencephalogram and bioimpedance are used, which have high accuracy, but depend on data collected by large-scale equipment.
SUMMERY OF THE UTILITY MODEL
The utility model aims at providing a physiological condition supervisory equipment based on brain electrical signal and bioimpedance data to prior art not enough, can realize the collection of brain electrical signal and bioimpedance signal to transmit the high in the clouds through wireless module with gathering the gained data, realize that the brain electrical signal is visual and categorised process and bioimpedance data's processing and the real-time supervision and the management of data.
The utility model adopts the following technical proposal: a physiological condition monitoring device based on electroencephalogram signals and bioimpedance data is characterized in that: the electroencephalogram acquisition device comprises an embedded microprocessor (1), a local display device and an interface (2) thereof, an Internet of things module (3), an electroencephalogram acquisition device wireless transceiving module interface (4) and a biological impedance acquisition module (5), wherein the local display device and the interface (2) thereof, the Internet of things module (3), the electroencephalogram acquisition device wireless transceiving module interface (4) and the biological impedance acquisition module (5) are connected with the embedded microprocessor (1) through a circuit in a PCB (printed circuit board), the electroencephalogram acquisition device is communicated with the electroencephalogram acquisition device wireless transceiving module interface (4) on the PCB through an internal wireless communication module, and data are processed by using a cloud server; the system architecture of the device comprises a sensing layer, a transmission layer and a control layer, wherein the sensing layer refers to a part of the device used for acquiring data, the transmission layer refers to a part of the device used for transmitting the data to a cloud server, and the control layer refers to a part of the device used for operation and control.
Furthermore, the sensing layer is composed of electroencephalogram signal acquisition equipment and a biological impedance detection module, and is used for acquiring data required by physiological condition monitoring equipment of electroencephalogram signals and biological impedance data, wherein the data are the electroencephalogram signals and the biological impedance data.
Furthermore, two-channel equipment is adopted to respectively acquire electroencephalogram data of the left hemisphere and the right hemisphere of the brain.
Furthermore, the biological impedance acquisition module selects a measured impedance range through mode switching, and the switchable impedance measurement ranges are 1k omega-10 k omega and 10k omega-1M omega.
Further, the electroencephalogram acquisition device and the embedded device communicate wirelessly, and the adopted communication protocol includes, but is not limited to, a UART communication protocol.
Furthermore, the communication mode between the embedded device and the cloud server is public network communication, and the adopted communication protocol is not limited to the MQTT protocol.
Furthermore, the deployment quantity of the embedded equipment, namely the terminal and the cloud end, is at least one.
Furthermore, the cloud server classifies the electroencephalogram signals by adopting a neural network model, namely a multilayer perceptron.
The utility model discloses the innovation point that compares with current system lies in:
1. a system with MIMO characteristics typical of the internet of things,
the utility model discloses with the help of the data interactive characteristic of thing networking platform, designed a system of MIMO formula, terminal and high in the clouds all can deploy a plurality ofly, and this makes this system more nimble, can deal with more application scenes, and the manpower and materials of having saved the data collection process just can convenient and fast ground monitor data flow whether normal, have improved the data utilization rate.
2. A neural network classifier capable of performing migration learning (TL) and Lifetime Learning (LL),
the utility model discloses utilize a neural network model to build a classifier that can carry out classification to the eigenvalue that draws. Moreover, through the improvement, the classifier can perform transfer learning and lifetime learning, namely after a model is generated, a new data stream of known classes can be input to be retrained, so that the accuracy of the classifier can be continuously enhanced, and the classifier can be continuously learned.
3. An electroencephalogram signal and embedded equipment interaction framework which is completely and autonomously designed and researched,
the utility model discloses have the very high brain computer interface of expansibility and the mutual frame of embedded controller, and not only combine BCI technique and thing networking and just reserved other interfaces more at the beginning of the design, the user can be through the different classification algorithm of loading, makes the utility model discloses can accomplish more functions, realize "brain accuse" equipment.
4. An embedded device which is designed and built independently,
the utility model discloses a development during the collection equipment based on embedded processor, realized the miniaturized portable purpose of the brain electricity that will be expensive and heavy originally and bio-impedance signal acquisition equipment to utilize MQTT communication protocol and cloud platform to realize public network communication, broken through traditional LAN technical limitation.
Drawings
The present invention will be further described with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of the embedded device system of the present invention.
Fig. 2 is a schematic diagram of the system of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention is provided, but it should be understood that the scope of the present invention is not limited by the embodiments.
The physiological condition monitoring equipment based on the electroencephalogram signal and the bioimpedance data is mainly divided into the following parts:
1. the embedded equipment part:
as shown in fig. 1, 1 is an embedded microprocessor development board, 2 is a local display device and an interface thereof, 3 is an internet of things module, 4 is an electroencephalogram acquisition module and an interface thereof, and 5 is a bioimpedance acquisition module.
a. A sensing layer: as a specific embodiment, the sensing layer is composed of a TGAM brain wave detection module manufactured based on a ThinkGear ASIC series special chip and a bioimpedance detection module based on AD 5933.
The TGAM module comprises a ThinkGear ASIC chip which is a highly integrated single-chip electroencephalogram sensor, can output three Neurosky eSense parameters, can perform analog-to-digital conversion, can detect abnormal states of poor contact, and can filter out electric eye noise and 50/60hz alternating current interference. After the analysis of the TGAM, the original brain wave signal, the brain wave frequency spectrum and the quality of the brain wave signal are output to the local embedded system through the serial port.
AD5933 is a high precision impedance converter system solution that combines an on-board frequency generator with a 12-bit, 1 MSPS analog-to-digital converter (ADC). The frequency generator allows the excitation of an external complex impedance at a known frequency. The response signal from the impedance is sampled by an on-board ADC and a Discrete Fourier Transform (DFT) is processed by an on-board DSP engine. The DFT algorithm returns real (R) and imaginary (I) data words at each output frequency. After calibration, the magnitude of the impedance and the relative phase of the impedance at each frequency point along the sweep can be easily calculated. This data will also be output to the local embedded device via the serial port.
Therefore, the electroencephalogram signal and the bioimpedance information are obtained and further processed.
b. A transmission layer: as specific implementation scheme, the utility model discloses utilize BLK-MD-HC-05 bluetooth module, pass the data of TGAM module to embedded treater on, embedded treater carries out preliminary processing earlier to the data that TGAM module and AD5933 gathered, judge simultaneously whether dataflow and equipment state are normal, if all normal, then rethread thing networking module, utilize MQTT agreement to upload to thing networking OneNet platform with data, through the TFT serial ports screen input wifi account number that has programmed, information such as password, STM32F429 acquires this information through serial ports communication protocol, and communicate with RT-Thread module through the SPI agreement, make equipment and high in the clouds realize internet access. Whether the data flow, the TGAM module and the AD5933 chip are abnormal or not can be monitored through a serial port screen.
c. A control layer: as specific implementation scheme, the utility model discloses a 32 series microcontroller is singlechip (specific model is STM32F 429), as embedded data processing core. In addition, in order to improve the use efficiency of the embedded device, an RT-thread operating system is also mounted on the device in the implementation, the acquired data is firstly subjected to primary processing, and then an embedded program which is developed on the device and is used for reprocessing the electroencephalogram signals acquired by the TGAM module and the data returned by the AD5933 is executed. Therefore, noise and clutter in the signals are removed, and the acquired signals are uploaded to the cloud through the OneNet platform. And the high in the clouds has then used the neural network model based on Python development to carry out the processing of data, the utility model discloses a multilayer perceptron (MLP) algorithm improves it slightly to carry out the mood classification, the bioimpedance data then adopts the formula in the available paper, handles the initial data of chip output earlier, recycles the formula and calculates, thereby obtains the analysis result. Meanwhile, a programmable TFT serial port screen is used as a display device, the wireless account password input device is networked, and the data stream, the TGAM module and the AD5933 chip are monitored through the serial port screen.
2. Cloud part:
the cloud acquires and monitors data flow through the OneNet by means of the OneNet platform of the Internet of things, and executes a program of the cloud to process the collected signals. The utility model discloses a multilayer perceptron (MLP) algorithm improves it slightly to carry out the mood classification, and can realize study and migration study lifelong, thereby accomplish at the in-process that uses, improve categorised rate of accuracy. The bioimpedance data adopts a formula in an existing paper, the original data output by the chip is processed, and then the formula is used for calculation, so that an analysis result is obtained. And performing Fourier expansion on the obtained electroencephalogram signals to obtain signals of each frequency domain, and displaying the signals in real time. Calculating the fatigue of a human body by utilizing theta waves and beta waves, extracting characteristic values of data of all wave bands and inputting the characteristic values into a neural network classifier so as to realize emotion classification, and displaying results (only for reference) through a cloud platform; the biological impedance data is subjected to preliminary denoising, and the average value of 100 data points is taken as human impedance data and input into a formula, so that indexes of human body fat percentage, tissue water content and the like are obtained. The UI interface of the platform is provided with normal indexes, so that comparison by a user is facilitated.
As an optimization scheme of the system, the electroencephalogram acquisition equipment can adopt two TGAM chips to respectively acquire signal data of the left and right hemispheres of the brain so as to improve the accuracy of emotion classification.
The utility model discloses a physiological condition supervisory equipment based on EEG signal and bioimpedance data utilizes embedded equipment to solve EEG signal and bioimpedance data's portable collection, utilizes internet of things to solve the difficult problem of real-time supervision, management, analysis user health status, utilizes the neural network algorithm that has improved to realize the real-time study and the lifelong study of model, makes the accuracy performance of model more and more high, has extremely important meaning to the intelligent management of health data.
The above description is only for the specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto, and any similar changes or substitutions should be covered by the scope of the present invention, therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (9)
1. A physiological condition monitoring device based on electroencephalogram signals and bioimpedance data is characterized in that: the electroencephalogram acquisition device comprises an embedded microprocessor (1), a local display device and an interface (2) thereof, an Internet of things module (3), an electroencephalogram acquisition device wireless transceiving module interface (4) and a biological impedance acquisition module (5), wherein the local display device and the interface (2) thereof, the Internet of things module (3), the electroencephalogram acquisition device wireless transceiving module interface (4) and the biological impedance acquisition module (5) are connected with the embedded microprocessor (1) through a circuit in a PCB (printed circuit board), the electroencephalogram acquisition device is communicated with the electroencephalogram acquisition device wireless transceiving module interface (4) on the PCB through an internal wireless communication module, and data are processed by using a cloud server; the system architecture of the device comprises a sensing layer, a transmission layer and a control layer, wherein the sensing layer refers to a part of the device used for acquiring data, the transmission layer refers to a part of the device used for transmitting the data to a cloud server, and the control layer refers to a part of the device used for operation and control.
2. The EEG and bioimpedance data based physiological condition monitoring device of claim 1, wherein: the sensing layer is composed of an electroencephalogram signal acquisition device and a biological impedance detection module, and is used for acquiring data required by the physiological condition monitoring device of electroencephalogram signals and biological impedance data, wherein the data are the electroencephalogram signals and the biological impedance data.
3. The EEG and bioimpedance data based physiological condition monitoring device of claim 2, wherein: the brain electricity collecting equipment collects signals of the forehead area of the brain through the electrodes.
4. The EEG and bioimpedance data based physiological condition monitoring device of claim 3, wherein: two-channel equipment is adopted to respectively acquire electroencephalogram data of left and right hemispheres of the brain.
5. The EEG and bioimpedance data based physiological condition monitoring device of claim 2, wherein: the biological impedance acquisition module selects a measured impedance range through mode switching, and the switchable impedance measurement ranges are 1k omega-10 k omega and 10k omega-1M omega.
6. The EEG and bioimpedance data based physiological condition monitoring device of claim 1, wherein: the communication mode between the electroencephalogram acquisition equipment and the embedded equipment is wireless communication, and the adopted communication protocol comprises but is not limited to a UART (universal asynchronous receiver/transmitter) communication protocol.
7. The EEG and bioimpedance data based physiological condition monitoring device of claim 1, wherein: the communication mode between the embedded device and the cloud server is public network communication, and the adopted communication protocol is not limited to the MQTT protocol.
8. The EEG and bioimpedance data based physiological condition monitoring device of claim 7, wherein: the deployment quantity of the embedded equipment, namely the terminal and the cloud end, is at least one.
9. The EEG and bioimpedance data based physiological condition monitoring device of claim 1, wherein: the cloud server classifies the electroencephalogram signals by adopting a neural network model, namely a multilayer perceptron.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111012339A (en) * | 2020-01-07 | 2020-04-17 | 南京邮电大学 | Physiological condition monitoring equipment based on electroencephalogram signals and bioimpedance data |
| CN115067921A (en) * | 2022-06-14 | 2022-09-20 | 杭州永川科技有限公司 | External circulation operation nerve function damage prediction system based on electroencephalogram impedance data |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111012339A (en) * | 2020-01-07 | 2020-04-17 | 南京邮电大学 | Physiological condition monitoring equipment based on electroencephalogram signals and bioimpedance data |
| CN115067921A (en) * | 2022-06-14 | 2022-09-20 | 杭州永川科技有限公司 | External circulation operation nerve function damage prediction system based on electroencephalogram impedance data |
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