Intelligent paper diaper flexible sensing data analysis method and system

Abstract

The present application relates to the technical field of intelligent wear and medical health, in particular to a flexible sensing data analysis method and system for intelligent paper diaper, which comprises collecting urine electric signals through a flexible sensor array integrated with nano sensitive materials, removing motion and environmental noise interference by using an adaptive filtering algorithm, and converting weak signals into biochemical index concentrations such as urine sugar and uric acid; further combining multi-dimensional data such as urination duration and water consumption, inputting into a chronic disease evolution model based on machine learning for analysis, and realizing real-time monitoring and early warning of chronic disease risks such as diabetes and kidney disease. The present application solves the problems of traditional urine detection, such as large environmental interference, data lag and lack of continuous monitoring, greatly improving the intelligentization and precision level of nursing for disabled people.

Description

Technical Field

[0001] This invention relates to the field of smart wearable and medical health technology, and in particular to a method and system for analyzing flexible sensing data of smart diapers. Background Technology

[0002] With the increasing aging of the population, the demand for care for disabled elderly, infants, and patients with chronic diseases such as diabetes and kidney disease is becoming increasingly urgent. Urine, as an important metabolic product of the human body, contains biomarkers such as urinary glucose, uric acid, and dopamine, which can directly reflect the body's health status. However, traditional urine testing relies on offline analysis in hospitals, which is cumbersome and time-consuming. Existing smart diaper technologies are mostly limited to simple wetness (humidity) alarms and often use rigid substrates such as PCBs, resulting in a strong sense of discomfort when worn. More importantly, in complex wearable environments, the weak electrochemical signals collected by sensors are easily interfered with by external factors such as human movement friction and changes in ambient temperature and humidity, leading to low detection sensitivity and high false positive rates, making it difficult to meet the needs of accurate online detection of biochemical indicators in clinical nursing.

[0003] Furthermore, existing technologies have significant shortcomings in deep data mining and application, lacking mechanisms for long-term tracking and correlation analysis of urine test data. Most current products can only display single test values ​​and cannot combine multi-dimensional data such as urination duration and water intake to build disease development models, resulting in an inability to effectively identify the potential evolutionary patterns of chronic diseases. Due to the lack of targeted signal processing algorithms and disease early warning models, existing monitoring methods struggle to issue timely risk warnings before conditions such as diabetes and kidney disease worsen, failing to provide healthcare professionals and families with practically meaningful health management advice, thus limiting the in-depth application of intelligent nursing devices in precision medicine and elderly care. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a method and system for analyzing flexible sensing data from smart diapers. The technical solution is as follows: A method for analyzing flexible sensor data from smart diapers includes the following steps: S1: A flexible sensor array integrated on the smart diaper collects analog electrical signals during urination in real time; the flexible sensor array is made of nano-sensitive materials and is used to respond to biochemical indicators in urine. S2: Preprocess the acquired analog electrical signals, including signal amplification, analog-to-digital conversion, and noise removal through adaptive filtering algorithms to obtain a digitized sensor data sequence; S3: Based on a preset electrochemical response model, the sensing data sequence is converted into corresponding urine biochemical index concentration data; the biochemical index includes at least urine glucose, uric acid and dopamine concentrations; S4: Obtain the user's auxiliary physiological data, including urination duration and water intake data, and use the auxiliary physiological data and the urine biochemical index concentration data as multidimensional feature inputs; S5: Input the multidimensional features into a pre-built chronic disease evolution model for analysis; the model is a machine learning model trained based on the correlation between historical urine test data and disease progression. S6: Determine the risk level of chronic diseases based on the model output results. When the detection data deviates from the normal range or meets the characteristics of disease deterioration, generate health warning information and send it to the user terminal.

[0005] As a further aspect of the present invention, in step S2, the removal of noise interference specifically includes: To address the deformation of flexible sensors during wear and interference from the external environment, a differential signal transmission scheme combined with an adaptive filtering algorithm is adopted to filter out non-specific electrical signal noise generated by friction, humidity changes, and human movement, and to extract effective target biomarker response signals.

[0006] As a further aspect of the present invention, in step S3, the electrochemical response model includes specific calibration parameters for a specific biomarker; The parameters are set based on the electrochemical properties of the nanoenzyme complex or molecularly imprinted polymer modification layer on the surface of the flexible sensor. They are used to distinguish the specific potential response differences of potassium ions, sodium ions, glucose and uric acid in urine, and to dynamically compensate the concentration calculation results in combination with ambient temperature data.

[0007] As a further aspect of the present invention, in step S5, the construction process of the chronic disease evolution model includes: Collect a large amount of historical urine test data related to diabetes and kidney disease; Big data analytics techniques were used to uncover potential correlations and patterns of change among urine biochemical index concentrations, urination duration, and water intake data. By training algorithms through supervised learning, an evolutionary prediction model that can reflect the progression of a disease from its early stages to its worsening stages can be established.

[0008] A smart diaper flexible sensing data analysis system, the system comprising: The data acquisition module is used to drive the flexible sensor array integrated on the diaper to collect the electrical signals generated when urine comes into contact with the electrodes; The signal processing module, connected to the data acquisition module, is used for pre-amplification, filtering and noise reduction, and analog-to-digital conversion of weak electrical signals; The data analysis module is used to receive processed data, calculate the concentration of biochemical indicators using an electrochemical response model, and call a chronic disease evolution model to evaluate multidimensional feature data. The early warning interaction module is used to generate risk warnings based on the assessment results and transmit the results to external terminals via a wireless communication unit.

[0009] As a further embodiment of the present invention, the flexible sensor array connected to the data acquisition module adopts a superhydrophobic flexible microelectrode array design. The surface of the microelectrode is modified with graphene-doped enzyme polymer molecules or a titanium dioxide film with highly reactive crystal orientation to achieve highly sensitive capture of micromolar biochemical indicators in urine.

[0010] As a further aspect of the present invention, the signal processing module is configured with a weak signal acquisition circuit, which includes a differential amplifier and a shielding circuit, for improving the signal-to-noise ratio and eliminating signal drift caused by external electromagnetic interference and substrate deformation in complex urine detection environments.

[0011] As a further embodiment of the present invention, the system further includes a wireless data transmission unit and a power management unit; The wireless data transmission unit adopts low-power Bluetooth or Wi-Fi protocol and supports real-time remote data upload. The power management unit is configured to support external battery power supply or connection to a self-powered module based on a biofuel cell / triboelectric nanogenerator.

[0012] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: 1. This invention significantly improves the accuracy and anti-interference capability of urine detection, solving the problem of signal instability of flexible sensors in dynamic wearable environments. By employing a differential signal transmission scheme and adaptive filtering algorithm, this system can effectively filter out noise interference caused by diaper deformation, friction, and environmental changes. Combined with an electrochemical response model targeting specific biomarkers (such as modification with highly reactive crystal-oriented materials), it achieves highly sensitive online detection of indicators such as urine glucose and uric acid at the micromolar level. This allows smart diapers to not only comfortably fit the human body but also provide reliable data that meets national technical standards in complex real-world usage scenarios, greatly improving the stability of detection. 2. This invention further constructs a chronic disease evolution and early warning model, realizing a leap from single data monitoring to proactive health management. By integrating multi-dimensional data such as urine biochemical concentration, urination duration, and water intake, and utilizing machine learning algorithms to deeply mine the potential correlations between data, it can accurately simulate the development process of chronic diseases such as diabetes and kidney disease. Once the monitoring data deviates from the normal range or conforms to the characteristics of disease deterioration, the system can issue a timely risk warning, providing a scientific basis for early intervention in hospitals, nursing homes, and home care, effectively improving the care level and quality of life of disabled people, and has significant clinical application value and social benefits. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating the method of the present invention.

[0014] Figure 2 This is a schematic diagram of the module structure of the system of the present invention.

[0015] Figure 3 This is a schematic diagram of the signal processing flow and data analysis architecture of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] Example 1 This embodiment focuses on illustrating the hardware structure and key sensor design of the system described in this invention, which forms the basis for subsequent data analysis. (See also...) Figure 2 The present invention provides a smart diaper flexible sensing data analysis system, which mainly consists of a data acquisition module, a signal processing module, a data analysis module, an early warning interaction module, a wireless data transmission unit, and a power management unit.

[0018] Specifically, the core of the system lies in a flexible sensor array deeply integrated with the diaper itself. (See also...) Figure 3The physical sensing layer of this flexible sensor array employs a superhydrophilic and hydrophobic flexible microelectrode array design. In terms of fabrication, a highly biocompatible flexible polymer (such as PI or PET) is selected as the substrate to ensure comfort when applied to human skin. To achieve high-sensitivity capture of micromolar-level biochemical indicators in urine, the microelectrode surface undergoes special functional modifications. Specifically, for urine glucose detection, the electrode surface is modified with graphene-doped enzyme polymer molecules, utilizing the high conductivity of graphene and the specific catalytic effect of enzymes to enhance signal response; for specific biomarkers (such as uric acid or dopamine), a titanium dioxide thin film with a highly reactive (001) crystal orientation is used, a material structure that significantly improves electrochemical specificity and sensitivity.

[0019] In terms of circuit connections, the data acquisition module drives the sensor, while the signal processing module is equipped with a dedicated weak signal acquisition circuit. (See also...) Figure 3 The circuit processing layer integrates a differential amplifier and shielding circuitry (e.g., a Faraday cage design) to address the complex environment of urine testing. The shielding circuitry isolates external electromagnetic interference, while the differential amplifier suppresses common-mode noise. Furthermore, the power management unit is designed for a hybrid power supply mode, supporting both micro-battery power and providing interfaces for connecting self-powered modules based on biofuel cells (utilizing urine chemical energy) or triboelectric nanogenerators (utilizing human movement mechanical energy), thus enabling long-term system operation.

[0020] By constructing a hardware system based on a superhydrophobic microelectrode array and nanomaterials, the problems of low sensitivity and strong foreign body sensation in traditional diaper sensors are solved at the physical level. In particular, the application of graphene and highly active titanium dioxide film, combined with differential amplification and shielding circuit design, provides a high signal-to-noise ratio and high-fidelity original signal foundation for subsequent data analysis, ensuring that the system can stably acquire microvolt-level bioelectrical signals even in complex physical wear environments.

[0021] Example 2 Based on the hardware system constructed in Example 1, this example details how to extract effective information from the acquired analog signals and convert it into specific biochemical indicator concentrations, i.e., corresponding... Figure 1 Steps S1 to S3 and Figure 3 The algorithm and model layers in the process.

[0022] See Figure 1 When the user urinates, the flexible sensor array collects analog electrical signals in real time (step S1). Because human movement (such as turning over or walking) causes deformation of the diaper, and because humidity changes drastically during urination, the raw signal often contains a large amount of non-specific noise. Therefore, in step S2, the system performs a rigorous preprocessing procedure. Combined with... Figure 3The preprocessing module employs a differential signal transmission scheme, performing differential operations on the signals from the working electrode and the reference electrode to cancel out common-mode drift caused by substrate bending. Subsequently, an adaptive filtering algorithm is applied to automatically adjust the filter parameters based on the statistical characteristics of environmental noise, accurately filtering out high-frequency noise generated by friction and contact instability, resulting in a clean digital sensing data sequence.

[0023] The next step, S3, involves index conversion based on a pre-defined electrochemical response model. This model is not a simple linear conversion but incorporates specific calibration parameters for particular biomarkers. These parameters are pre-determined and set based on the electrochemical properties of the nanoenzyme complex or molecularly imprinted polymer modification layer mentioned in Example 1. For example, the model can distinguish the response differences of potassium ions, sodium ions, glucose, and uric acid in urine at different potentials. More importantly, the model also incorporates temperature sensor data to dynamically compensate for the concentration calculation results, eliminating the influence of changes in body temperature or urine temperature on the electrochemical reaction rate, thereby calculating accurate concentrations of urinary glucose, uric acid, and dopamine.

[0024] By combining differential signal transmission with adaptive filtering algorithms, the problem of severe artifacts in flexible electronic devices under dynamic usage scenarios is effectively overcome, significantly improving anti-interference capabilities. Simultaneously, the introduction of an electrochemical response model incorporating specific calibration parameters and temperature compensation solves the problem of multi-component cross-interference, enabling the system to convert ambiguous electrical signals into clinical-grade biochemical indicator data with an accuracy ≥85% that meets national testing standards, achieving true "precision detection."

[0025] Example 3 Building upon the aforementioned embodiments that achieve accurate indicator detection, this embodiment further elaborates on how to utilize this data for in-depth health analysis and disease early warning. Figure 1 Steps S4 to S6 and Figure 3 The disease evolution early warning module in the system.

[0026] Single biochemical indicator data often fails to fully reflect the complex progression of chronic diseases. Therefore, refer to... Figure 1 In step S4, this system not only utilizes sensor data but also acquires auxiliary physiological data from the user through system-linked auxiliary devices or manual user input. This data specifically includes urination duration (determined by sensor response duration) and water intake. The system combines this data with urine glucose and uric acid concentrations to form a multidimensional feature vector.

[0027] Subsequently, step S5 is executed, inputting the aforementioned multidimensional features into the pre-constructed chronic disease evolution model. (See also...) Figure 3The algorithm layer of this model utilizes big data analytics to train a supervised learning model (such as a random forest or LSTM network) based on the correlation between massive amounts of historical urine test data and disease progression (e.g., early, middle, and late stages of diabetes). The model can uncover trends in urine biochemical index concentrations over time, as well as the potential nonlinear relationships between these trends and water intake and urination patterns.

[0028] Finally, in step S6, the system determines the current chronic disease risk level based on the model output. When the test data deviates from the normal physiological range (e.g., persistently elevated urine glucose that does not match dietary intake), or conforms to a specific disease deterioration pattern (e.g., uric acid fluctuations accompanied by abnormal urination, indicating a risk of gout or kidney disease), the early warning interaction module will immediately generate graded health warning information and send it to the guardian's mobile app or hospital management terminal via the wireless communication unit.

[0029] By constructing a multi-dimensional data fusion model for the evolution of chronic diseases, this invention overcomes the limitations of traditional diapers that can only perform "single-point detection," enabling dynamic tracking and prediction of the development trends of chronic diseases such as diabetes and kidney disease. This proactive early warning mechanism based on big data and machine learning can keenly capture early signals of disease deterioration, providing personalized health management advice and timely medical intervention for the elderly and disabled patients, thereby effectively reducing the risk of serious complications and greatly improving the level of care in smart elderly care.

[0030] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for analyzing flexible sensor data from smart diapers, characterized in that, Includes the following steps: S1: A flexible sensor array integrated on the smart diaper collects analog electrical signals during urination in real time; the flexible sensor array is made of nano-sensitive materials and is used to respond to biochemical indicators in urine. S2: Preprocess the acquired analog electrical signals, including signal amplification, analog-to-digital conversion, and noise removal through adaptive filtering algorithms to obtain a digitized sensor data sequence; S3: Based on a preset electrochemical response model, the sensing data sequence is converted into corresponding urine biochemical index concentration data; the biochemical index includes at least urine glucose, uric acid and dopamine concentrations; S4: Obtain the user's auxiliary physiological data, including urination duration and water intake data, and use the auxiliary physiological data and the urine biochemical index concentration data as multidimensional feature inputs; S5: Input the multidimensional features into a pre-built chronic disease evolution model for analysis; the model is a machine learning model trained based on the correlation between historical urine test data and disease progression. S6: Determine the risk level of chronic diseases based on the model output results. When the detection data deviates from the normal range or meets the characteristics of disease deterioration, generate health warning information and send it to the user terminal.

2. The method for analyzing flexible sensing data of smart diapers according to claim 1, characterized in that, In step S2, the removal of noise interference specifically includes: To address the deformation of flexible sensors during wear and interference from the external environment, a differential signal transmission scheme combined with an adaptive filtering algorithm is adopted to filter out non-specific electrical signal noise generated by friction, humidity changes, and human movement, and to extract effective target biomarker response signals.

3. The method for analyzing flexible sensing data of smart diapers according to claim 1, characterized in that, In step S3, the electrochemical response model includes specific calibration parameters for a particular biomarker; The parameters are set based on the electrochemical properties of the nanoenzyme complex or molecularly imprinted polymer modification layer on the surface of the flexible sensor. They are used to distinguish the specific potential response differences of potassium ions, sodium ions, glucose and uric acid in urine, and to dynamically compensate the concentration calculation results in combination with ambient temperature data.

4. The method for analyzing flexible sensing data of smart diapers according to claim 1, characterized in that, In step S5, the construction process of the chronic disease evolution model includes: Collect a large amount of historical urine test data related to diabetes and kidney disease; Big data analytics techniques were used to uncover potential correlations and patterns of change among urine biochemical index concentrations, urination duration, and water intake data. By training algorithms through supervised learning, an evolutionary prediction model that can reflect the progression of a disease from its early stages to its worsening stages can be established.

5. A flexible sensing data analysis system for intelligent diapers, characterized in that, The system is used in the smart diaper flexible sensing data analysis method according to any one of claims 1-4, the system comprising: The data acquisition module is used to drive the flexible sensor array integrated on the diaper to collect the electrical signals generated when urine comes into contact with the electrodes; The signal processing module, connected to the data acquisition module, is used for pre-amplification, filtering and noise reduction, and analog-to-digital conversion of weak electrical signals; The data analysis module is used to receive processed data, calculate the concentration of biochemical indicators using an electrochemical response model, and call a chronic disease evolution model to evaluate multidimensional feature data. The early warning interaction module is used to generate risk warnings based on the assessment results and transmit the results to external terminals via a wireless communication unit.

6. The intelligent diaper flexible sensing data analysis system according to claim 5, characterized in that, The flexible sensor array connected to the data acquisition module adopts a superhydrophobic flexible microelectrode array design. The surface of the microelectrode is modified with graphene-doped enzyme polymer molecules or a titanium dioxide film with highly reactive crystal orientation to achieve highly sensitive capture of micromolar biochemical indicators in urine.

7. The intelligent diaper flexible sensing data analysis system according to claim 5, characterized in that, The signal processing module is equipped with a weak signal acquisition circuit, which includes a differential amplifier and a shielding circuit, to improve the signal-to-noise ratio and eliminate signal drift caused by external electromagnetic interference and substrate deformation in complex urine testing environments.

8. The intelligent diaper flexible sensing data analysis system according to claim 5, characterized in that, The system also includes a wireless data transmission unit and a power management unit; The wireless data transmission unit adopts low-power Bluetooth or Wi-Fi protocol and supports real-time remote data upload. The power management unit is configured to support external battery power supply or connection to a self-powered module based on a biofuel cell / triboelectric nanogenerator.

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