Dysphagia monitoring system based on triboelectric effect

Abstract

The invention provides a dysphagia monitoring system based on a triboelectric effect, and relates to the technical field of medical health monitoring. Comprising a triboelectric sensing module used for obtaining electric signals generated by swallowing actions, a signal processing and communication module used for conditioning the electric signals and collecting and transmitting the electric signals to an interactive diagnosis platform, and the interactive diagnosis platform used for displaying the electric signals in real time and embedding a fluctuation algorithm to analyze and judge dysphagia. The problems that in an existing dysphagia monitoring technology, invasiveness is high, operation is complex, and continuous, dynamic and portable monitoring in community and family scenes is difficult to achieve are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical and health monitoring, and particularly to a dysphagia monitoring system based on the triboelectric effect. Background Art

[0002] Dysphagia is a common functional disorder, mainly manifested as difficulties, pain or a sense of suffocation when swallowing food or liquid, and is common in the elderly, patients with neurological diseases (such as stroke, Parkinson's disease and Alzheimer's disease), and people after head and neck tumor treatment. With the aggravation of global aging, dysphagia has become an increasingly serious public health problem. Dysphagia may lead to serious complications such as aspiration and aspiration pneumonia, and even threaten life safety. Traditional dysphagia monitoring methods, including fiberoptic laryngoscopy swallowing function examination, videofluoroscopic swallowing examination, pressure measurement, etc., although they can provide certain diagnostic information, these methods have limitations such as invasiveness, non-portability, inability to continuously and dynamically monitor, and there is a certain subjectivity in the interpretation of examination results by different experts, increasing the uncertainty of diagnosis.

[0003] Currently, with the development of intelligent wearable devices, non-invasive and portable monitoring devices have become a research hotspot for dysphagia monitoring. However, existing wearable devices still face some challenges. For example, monitoring devices based on surface electromyogram signals are often troubled by problems such as signal interference, low signal-to-noise ratio, and complex signal channels, affecting their accuracy and stability. Devices using pressure sensors or strain sensors, although simple in structure and easy to implement, also face the problem of unstable interfaces and require repeated calibration. Wearable devices with inertial measurement units and acoustic sensors, although they can provide richer monitoring data, the complex signal conditioning circuits and data processing systems limit their wide application in continuous monitoring of dysphagia. Summary of the Invention

[0004] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a dysphagia monitoring system based on the triboelectric effect.

[0005] To achieve the above purpose, the present invention provides the following solution:

[0006] A dysphagia monitoring system based on the triboelectric effect, comprising:

[0007] A triboelectric sensing module, a signal processing and communication module, and an interactive diagnosis platform connected in sequence;

[0008] The triboelectric sensing module is used to acquire the electrical signals generated by swallowing actions. The signal processing and communication module is used to condition the electrical signals and collect and transmit them to the interactive diagnosis platform. The interactive diagnosis platform is used to display the electrical signals in real time and embed a fluctuation algorithm to analyze and distinguish swallowing disorders. The fluctuation algorithm is used to calculate the volatility of the electrical signals and determine the result of the swallowing action according to the volatility and a preset dual-threshold judgment mechanism. The results of the swallowing action include normal swallowing and swallowing disorders.

[0009] Preferably, the triboelectric sensing module includes:

[0010] A base layer, a spacer layer, and a dielectric layer stacked from top to bottom;

[0011] The base layer includes a substrate layer and an electrode layer; the dielectric layer includes a support structure and a microstructure; the substrate layer is used to provide mechanical support; the electrode layer is used to transfer the charges required for electrical signals; the spacer layer is used to enable the base layer and the dielectric layer to contact and separate; the dielectric layer is used to friction with the electrode layer and store charges to generate electrical signals.

[0012] Preferably, the signal processing and communication module includes:

[0013] A power management sub-module, a signal conditioning sub-module, and a collection and transmission sub-module;

[0014] The power management sub-module is used to supply power to the operational amplifier and provide a bias voltage for the signal conditioning sub-module. The signal conditioning sub-module is used to convert the electrical signals into initial voltage signals and perform signal amplification, noise suppression, and filtering processing to obtain the final voltage signals. The collection and transmission sub-module is used to perform digital sampling through an analog-to-digital converter and transmit the voltage signals to the interactive diagnosis platform via wireless Bluetooth.

[0015] Preferably, the signal conditioning sub-module includes:

[0016] A transimpedance amplification unit, a voltage biasing unit, a low-pass filtering unit, and a power frequency notch filtering unit;

[0017] The transimpedance amplification unit is used to construct a current-voltage conversion circuit using an operational amplifier to convert electrical signals into initial voltage signals. The voltage biasing unit is used to amplify the initial voltage signals using an operational amplifier and superimpose a DC bias to obtain a first intermediate voltage signal. The low-pass filtering unit is used to suppress the high-frequency noise interference of the first intermediate voltage signal using a second-order Butterworth low-pass filter to obtain a second intermediate voltage signal. The power frequency notch filtering unit is used to filter out the power frequency interference of the second intermediate voltage signal to obtain the final voltage signals.

[0018] Preferably, the interactive diagnosis platform includes:

[0019] A signal display interface and a fluctuation algorithm;

[0020] The signal display interface is used to display in real time the electrical signals transmitted by the signal processing and communication module. The fluctuation algorithm is used to analyze and determine dysphagia based on the electrical signals.

[0021] Preferably, the fluctuation algorithm includes:

[0022] A time series determination sub-module, a volatility calculation sub-module, and a double-threshold determination sub-module;

[0023] The time series determination sub-module is used to determine the time series of the electrical signals. The volatility calculation sub-module is used to calculate the volatility based on the time series. The double-threshold determination sub-module is used to determine the abnormal points according to the volatility and count the number of abnormal points within a preset time to determine the swallowing result.

[0024] Preferably, the expression of the time series is:

[0025] y t = μ + ∈ t ;

[0026] where y t is the time series, t is the moment, μ is the mean of the time series, and ∈ t is the residual of the time series value at the t-th moment.

[0027] Preferably, the calculation expression of the volatility is:

[0028]

[0029] where t is the moment of the time series, is the volatility of the time series at the t-th moment, α0 is the baseline value of the volatility, and α i is the influence coefficient of the squared residual at the t-i moment on the volatility at the t-th moment, and i = 1, 2..q.

[0030] Preferably, the calculation expression of the double-threshold determination is:

[0031]

[0032] where k is the abnormal point number threshold, θ is the volatility abnormal threshold, is the volatility of the time series at the t-th moment, I(·) is the conditional discrimination, taking the value of 1 when the condition in the parentheses is satisfied, otherwise 0, t is the starting moment, and N is the ending moment.

[0033] The present invention discloses the following technical effects:

[0034] The present invention provides a swallowing disorder monitoring system based on the triboelectric effect, including: a triboelectric sensing module, a signal processing and communication module, and an interactive diagnosis platform, which are connected in sequence; the triboelectric sensing module is used to acquire the electrical signals generated by swallowing actions, the signal processing and communication module is used to condition the electrical signals and collect and transmit them to the interactive diagnosis platform, the interactive diagnosis platform is used to display the electrical signals in real time and embed a fluctuation algorithm to analyze and distinguish swallowing disorders, the fluctuation algorithm is used to calculate the volatility of the electrical signals and determine the results of the swallowing actions according to the volatility and a preset dual-threshold judgment mechanism, and the results of the swallowing actions include: normal swallowing and swallowing disorders. The sensing module of the present invention can convert tiny mechanical movements into electrical signals, and its principle is based on the coupling principle of triboelectrification and electrostatic induction. The wearable device based on the triboelectric effect can achieve non-invasive real-time physiological monitoring. By using triboelectric sensors with micro-nano structures, high-sensitivity swallowing monitoring can be realized. Non-invasiveness and high comfort: Flexible materials fit the skin for portable in vitro monitoring; continuous dynamic monitoring: It can track the changes in swallowing function for a long time and support early risk warning; high sensitivity and anti-interference: The micro-structure design is combined with four-stage signal conditioning to improve the signal-to-noise ratio; low cost and easy to promote: No large equipment is required, and it is suitable for primary medical care and home scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in 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, without creative efforts, other drawings can also be obtained based on these drawings.

[0036] Figure 1 It is a schematic structural connection diagram of a swallowing disorder monitoring system based on the triboelectric effect provided by an embodiment of the present invention;

[0037] Figure 2 It is a hierarchical structure diagram of the triboelectric sensing module provided by an embodiment of the present invention;

[0038] Figure 3 It is a structure diagram of the substrate layer and the electrode layer provided by an embodiment of the present invention;

[0039] Figure 4 It is a structure diagram of the dielectric layer support structure and microstructure provided by an embodiment of the present invention;

[0040] Figure 5 It is a flowchart of the signal processing and communication module provided by an embodiment of the present invention;

[0041] Figure 6Schematic diagram of the signal conditioning sub-module provided by the embodiment of the present invention;

[0042] Figure 7 Flowchart of the fluctuation algorithm provided by the embodiment of the present invention.

[0043] Reference numerals:

[0044] 1 - base layer, 2 - gasket layer, 3 - dielectric layer, 1 - 1 - substrate layer, 1 - 2 - electrode layer, 3 - 1 - support structure, 3 - 2 - microstructure, 4 - triboelectric sensing module, 5 - signal processing and communication module, 6 - interactive diagnosis platform. Detailed implementation manners

[0045] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0047] As Figure 1 shown, the present invention provides a swallowing disorder monitoring system based on the triboelectric effect, including:

[0048] A triboelectric sensing module 4, a signal processing and communication module 5, and an interactive diagnosis platform 6 connected in sequence;

[0049] The triboelectric sensing module 4 is used to obtain the electrical signals generated by swallowing actions. The signal processing and communication module 5 is used to condition the electrical signals and collect and transmit them to the interactive diagnosis platform 6. The interactive diagnosis platform 6 is used to display the electrical signals in real time and embed a fluctuation algorithm to analyze and determine swallowing disorders. The fluctuation algorithm is used to calculate the volatility of the electrical signals and determine the result of the swallowing action according to the volatility and a preset double-threshold judgment mechanism. The result of the swallowing action includes: normal swallowing and swallowing disorders.

[0050] Specifically, the working process of the present invention is as follows: When the wearer makes a swallowing motion, the deformation of the laryngeal tissue drives the multi-layer structure of the triboelectric sensing module 4 to undergo contact separation, generating an electrical signal based on the principle of the coupling of triboelectrification and electrostatic induction; the electrical signal is conditioned and then collected as a digital signal by the microcontroller; the digital signal is transmitted to the interactive diagnosis platform 6 in real time via Bluetooth; the interactive diagnosis platform 6 displays the digital signal in real time and analyzes it through the embedded fluctuation algorithm, and finally outputs the evaluation result of the swallowing motion, realizing the closed-loop monitoring from biomechanical signals to clinical diagnosis indicators. The system provided by the present invention is attached to the outside of the cricoid cartilage in the center of the human neck and consists of three parts:

[0051] 1. The triboelectric sensing module 4: realizing the electromechanical conversion of laryngeal motion signals;

[0052] 2. The signal processing and communication module 5: completing signal conditioning and wireless transmission;

[0053] 3. The interactive diagnosis platform 6: providing a visual diagnosis platform;

[0054] Furthermore, as Figure 2 , 3 shown, the triboelectric sensing module 4 includes:

[0055] A base layer 1, a gasket layer 2, and a dielectric layer 3 stacked from top to bottom. The base layer 1 includes a substrate layer 1-1 and an electrode layer 1-2. The dielectric layer 3 includes a support structure 3-1 and a microstructure 3-2;

[0056] The substrate layer 1-1 is used to provide mechanical support, the electrode layer 1-2 is used to transfer the charges required for electrical signal transfer, the gasket layer 2 enables the electrode layer 1-2 to come into contact or separate from the microstructure 3-2 of the dielectric layer, and the microstructure 3-2 of the dielectric layer is used to rub against the electrode layer 1-2 and store charges to generate electrical signals.

[0057] Specifically, when the larynx moves, the electrode layer 1-2 and the microstructure 3-2 of the dielectric layer will undergo contact-separation motion. Contact electrification stage: The electrode layer 1-2 transfers electrons to the microstructure 3-2 of the dielectric layer. Separation induction stage: The change in the distance between the electrode layer 1-2 and the microstructure 3-2 of the dielectric layer will generate electrostatic induction, so charge transfer will generate current, and then the above process will be cycled.

[0058] Furthermore, as Figure 5 , the signal processing and communication module 5 includes:

[0059] A power management sub-module, a signal conditioning sub-module, and an acquisition and transmission sub-module;

[0060] The power management sub-module is used to supply power to the operational amplifier and provide a bias voltage for the signal conditioning sub-module. The signal conditioning sub-module is used to convert the electrical signal into an initial voltage signal and perform signal amplification, noise suppression, and filtering processing to obtain a final voltage signal. The acquisition and transmission sub-module is used to perform digital sampling through an analog-to-digital converter and transmit the voltage signal to the interactive diagnosis platform 6 via wireless Bluetooth.

[0061] Specifically, the power management sub-module integrates a charge pump and a linear voltage regulator for voltage conversion to supply power to the signal conditioning sub-module and the acquisition and transmission sub-module, and at the same time provides a bias voltage for the signal conditioning sub-module.

[0062] Such as Figure 6 , the signal conditioning sub-module includes:

[0063] A transimpedance amplification unit, a voltage biasing unit, a low-pass filtering unit, and a power frequency notch unit;

[0064] The transimpedance amplification unit is used to construct a current-voltage conversion circuit using an operational amplifier to convert a current signal into an initial voltage signal. The voltage biasing unit is used to amplify the initial voltage signal using an operational amplifier and superimpose a DC bias to obtain a first intermediate voltage signal. The low-pass filtering unit is used to suppress high-frequency noise interference of the first intermediate voltage signal using a second-order Butterworth low-pass filter to obtain a second intermediate voltage signal. The power frequency notch unit is used to filter out the power frequency interference of the second intermediate voltage signal to obtain a final voltage signal.

[0065] The acquisition and transmission sub-module uses an analog-to-digital converter and a Bluetooth module to achieve digitization and wireless transmission of the voltage signal.

[0066] Furthermore, the interactive diagnosis platform 6 includes:

[0067] A signal display interface, a fluctuation algorithm;

[0068] The signal display interface is used to display the electrical signal transmitted by the signal processing and communication module 5 in real time. The fluctuation algorithm is used to analyze and determine dysphagia based on the electrical signal.

[0069] Specifically, such as Figure 7 , the fluctuation algorithm includes:

[0070] A time series determination sub-module, a volatility calculation sub-module, and a double-threshold determination sub-module;

[0071] The time series determination sub-module is used to determine the time series of the electrical signal, the volatility calculation sub-module is used to calculate the volatility according to the time series, and the double-threshold determination sub-module is used to determine the abnormal points according to the volatility and count the number of abnormal points within a preset time to determine the swallowing result.

[0072] Specifically, the expression of the time series is:

[0073] y t = μ + ∈ t ;

[0074] where y t is the time series, t is the moment, μ is the mean of the time series, and ∈ t is the residual of the time series value at time t.

[0075] Specifically, the calculation expression of the volatility is:

[0076]

[0077] where t is the moment of the time series, is the volatility of the time series at time t, α0 is the baseline value of the volatility, and α i is the influence coefficient of the squared residual at time t-i on the volatility at time t, i = 1, 2..q.

[0078] Specifically, the calculation expression of the double-threshold determination is:

[0079]

[0080] where k is the abnormal point quantity threshold, θ is the volatility abnormal threshold, is the volatility of the time series at time t, I(·) is the conditional discrimination, taking the value of 1 when the condition in the brackets is satisfied, otherwise 0, t is the starting moment, and N is the ending moment.

[0081] Furthermore, when the double-threshold determination formula is satisfied, it is determined as dysphagia.

[0082] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other.

[0083] In this article, specific examples are used to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A swallowing disorder monitoring system based on the triboelectric effect, characterized in that Including: A triboelectric sensing module, a signal processing and communication module, and an interactive diagnosis platform connected in sequence; The triboelectric sensing module is used to acquire the electrical signals generated by swallowing actions. The signal processing and communication module is used to condition the electrical signals and collect and transmit them to the interactive diagnosis platform. The interactive diagnosis platform is used to display the electrical signals in real time and embed a fluctuation algorithm to analyze and discriminate swallowing disorders. The fluctuation algorithm is used to calculate the volatility of the electrical signals and determine the result of the swallowing action according to the volatility and a preset dual-threshold judgment mechanism. The results of the swallowing action include normal swallowing and swallowing disorders.

2. The swallowing disorder monitoring system based on the triboelectric effect according to claim 1, wherein The triboelectric sensing module includes: A base layer, a gasket layer, and a dielectric layer stacked from top to bottom; The base layer includes a substrate layer and an electrode layer; the dielectric layer includes a support structure and a microstructure; the substrate layer is used to provide mechanical support; the electrode layer is used to transfer the charges required to generate electrical signals; the gasket layer is used to enable the electrode layer and the dielectric layer to contact or separate; the microstructure of the dielectric layer is used to rub against the electrode layer and retain charges to generate electrical signals.

3. The swallowing disorder monitoring system based on the triboelectric effect according to claim 2, wherein The surface of the dielectric layer is designed with a microstructure.

4. A swallowing disorder monitoring system based on the triboelectric effect according to claim 1, characterized in that, The signal processing and communication module includes: A power management sub-module, a signal conditioning sub-module, and a collection and transmission sub-module; The power management sub-module is used to provide a bias voltage for the signal conditioning sub-module. The signal conditioning sub-module is used to convert the electrical signals into initial voltage signals and perform signal amplification, noise suppression, and filtering processing to obtain final voltage signals. The collection and transmission sub-module is used to perform digital sampling through an analog-to-digital converter and transmit the voltage signals to the interactive diagnosis platform through wireless Bluetooth.

5. The swallowing disorder monitoring system based on the triboelectric effect according to claim 4, characterized in that, The signal conditioning sub-module includes: A transimpedance amplification unit, a voltage biasing unit, a low-pass filtering unit, and a power frequency notch filtering unit; The transimpedance amplification unit is used to construct a current-voltage conversion circuit using an operational amplifier to convert electrical signals into initial voltage signals. The voltage biasing unit is used to amplify the initial voltage signals using an operational amplifier and superimpose a DC bias to obtain a first intermediate voltage signal. The low-pass filtering unit is used to suppress the high-frequency noise interference of the first intermediate voltage signal using a second-order Butterworth low-pass filter to obtain a second intermediate voltage signal. The power frequency notch filtering unit is used to filter out the power frequency interference of the second intermediate voltage signal to obtain the final voltage signal.

6. The swallowing disorder monitoring system based on the triboelectric effect according to claim 1, characterized in that, The interactive diagnosis platform includes: A signal display interface, a fluctuation algorithm; The signal display interface is used to display the electrical signals transmitted by the signal processing and communication module in real time. The fluctuation algorithm is used to analyze and discriminate swallowing disorders according to the electrical signals.

7. A swallowing disorder monitoring system based on the triboelectric effect according to claim 1, characterized in that, The fluctuation algorithm includes: A time series determination sub-module, a volatility calculation sub-module, and a dual-threshold determination sub-module; The time series determination sub-module is used to determine the time series of the electrical signals. The volatility calculation sub-module is used to calculate the volatility according to the time series. The dual-threshold determination sub-module is used to determine the abnormal points according to the volatility and count the number of abnormal points within a preset time to determine the swallowing result.

8. A swallowing disorder monitoring system based on the triboelectric effect according to claim 7, characterized in that, The expression of the time series is: y t = μ + ∈ t ; where y t is a time series, t is a moment, μ is the mean of the time series, and ∈ t is the residual of the time series value at time t.

9. A swallowing disorder monitoring system based on the triboelectric effect according to claim 7, characterized in that, The calculation expression of the volatility is as follows: where \(t\) is the moment of the time series, is the volatility of the time series at time \(t\), \(\alpha_0\) is the baseline value of the volatility, \(\alpha\) i is the influence coefficient of the squared residual at time \(t - i\) on the volatility at time \(t\), where \(i = 1, 2, \cdots, q\).

10. A dysphagia monitoring system based on triboelectric effect according to claim 7, characterized in that, The calculation expression of the double-threshold determination is as follows: where k is the threshold of the number of abnormal points, θ is the volatility anomaly threshold, is the volatility of the time series at time t, I(·) is the conditional discrimination, taking the value of 1 if the condition in the parentheses is satisfied, otherwise 0, t is the starting time, and N is the ending time.

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