Intelligent drug delivery pain management bracelet

Abstract

The invention discloses an intelligent drug delivery pain management bracelet, and relates to the fields of wearable equipment, transdermal drug delivery and biological signal processing. The bracelet is integrated with a multi-mode biosensor which comprises a temperature sensor, a pressure sensor, a heart rate sensor, a blood oxygen sensor and a skin electric response sensor, and physiological data related to pain can be collected in real time. Through an embedded microcontroller and a machine learning algorithm, a pain intensity evaluation model is constructed, and real-time evaluation of the pain state of a patient is realized. Based on the pain intensity and the physiological state of a patient, a drug release strategy is dynamically adjusted through an intelligent algorithm, and drugs are released painlessly and accurately through a micro-needle transdermal drug delivery technology, so that the patient obtains the optimal treatment effect. And the functions of data storage, historical analysis and remote doctor monitoring can be expanded. The hand ring is convenient to operate, comfortable to use and suitable for various pain management scenes such as chronic pain and postoperative pain, and the personalized level of pain treatment and patient compliance are remarkably improved.

Description

Technical Field

[0001] The present invention relates to a medical auxiliary device, and particularly to an intelligent drug delivery pain management bracelet. Background Art

[0002] Pain management is an important research field in modern medicine. Especially for chronic pain and postoperative pain, which are long-term and complex situations, traditional oral medications or injection treatments have problems such as low drug absorption efficiency, large side effects, and poor patient compliance. In recent years, pain management technologies based on wearable devices and intelligent drug delivery systems have attracted wide attention. These technologies can achieve personalized pain assessment and drug release by real-time monitoring of patients' physiological data and pain signals, which can improve the treatment effect and reduce the risk of drug use.

[0003] Traditional pain management devices usually lack accurate pain assessment models and rely only on patients' subjective feedback, making it difficult to dynamically adjust drug release strategies. The combination of the latest biosensor technology and machine learning algorithms provides new possibilities for the development of intelligent pain management devices. For example, biosensors such as temperature sensors, pressure sensors, heart rate and blood oxygen sensors can collect physiological data related to pain. By comprehensively analyzing this data, the pain status of patients can be accurately evaluated. In addition, transdermal drug delivery technology (such as microneedle technology) can achieve painless and precise drug release, which is an important development direction for current intelligent pain management devices. Summary of the Invention

[0004] The object of the present invention is to provide an intelligent drug delivery pain management bracelet.

[0005] To achieve the above object, the present invention is implemented according to the following technical solution:

[0006] The present invention includes a bracelet housing, characterized in that: a drug delivery system, a biosensor module, data processing and intelligent algorithms, and a user interaction and feedback unit are integrated on the bracelet housing. The drug delivery system uses a transdermal drug delivery system to deliver drugs into the body, and a drug dose control module controls the dose. The biosensor collects physiological signals related to pain in real time through a pain perception sensor, and monitors physiological parameters such as heart rate, blood oxygen, and skin electrical response through a physiological state sensor. The data processing and intelligent algorithms process the biosignals collected in real time through an embedded microcontroller, and analyze the pain status of the patient through machine learning algorithms. Based on the pain intensity and the patient's physiological condition, the intelligent algorithm determines the drug release strategy. The user interaction and feedback unit is used in cooperation with a mobile phone APP through a bracelet interface, and is equipped with intelligent voice and vibration feedback.

[0007] The transdermal drug delivery system is a microneedle array that delivers drugs into the body through the skin, which includes multiple drug storage compartments. The drug dose control module regulates the drug release rate and amount in the form of a micropump, microcurrent, or thermal stimulation.

[0008] The pain perception sensor includes temperature, pressure, pH, and resistance sensors. The data processing and intelligent algorithm construct a pain assessment model using pain scores and physiological data monitored by the sensors. The drug release strategy includes dose size, release frequency, and drug type.

[0009] The user interaction and feedback unit enables users to set pain thresholds, view pain management history records, and receive real-time health reminders and treatment suggestions through an APP. The bracelet is equipped with a vibration module. When the patient's pain intensity exceeds the preset threshold, the bracelet will automatically vibrate to remind the user to adjust the drug dose.

[0010] The beneficial effects of the present invention are:

[0011] The present invention is an intelligent drug delivery pain management bracelet. Compared with the prior art, the present invention has the following technical effects:

[0012] Real-time pain assessment: Based on multi-dimensional physiological data such as temperature, pressure, heart rate, blood oxygen, and skin electrical response, the pain score of the patient is calculated in real time through constructing a weighted model and machine learning algorithm, and the change of pain intensity is evaluated.

[0013] Personalized drug release: By combining intelligent algorithms with pain scores and physiological states, the drug release rate, dose, and frequency are dynamically adjusted to ensure that patients obtain the best treatment effect and avoid drug overdose or insufficiency.

[0014] Efficient and painless drug delivery: Using microneedle transdermal drug delivery technology, it provides efficient and precise drug delivery, while avoiding the pain and discomfort brought by traditional injection methods.

[0015] Data storage and feedback optimization: The bracelet can record the patient's physiological data and drug release information, optimize the personalized treatment plan through historical data analysis, and support doctors to remotely monitor the patient's health status.

[0016] User-friendly experience: Through the mobile phone App, patients can view pain scores, drug release status, and health data at any time, and obtain intelligent reminders and health suggestions.

[0017] The overall solution significantly improves the accuracy and convenience of pain management through the multi-modal fusion of biosensor data and the dynamic regulation of intelligent algorithms, providing an efficient, safe, and personalized solution for chronic pain patients and postoperative recovery patients. Description of the Drawings

[0018] Figure 1 It is the block diagram of the hardware structure of the present invention. Specific embodiments

[0019] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions of this invention are used to explain the present invention, but do not limit the present invention.

[0020] As Figure 1 shown: The present invention includes a bracelet housing, characterized in that: a drug delivery system, a biosensor module, data processing and intelligent algorithms, and a user interaction and feedback unit are integrated on the bracelet housing. The drug delivery system uses a transdermal drug delivery system to deliver drugs into the body, and a drug dose control module controls the dose. The biosensor real-time collects physiological signals related to pain through a pain perception sensor, and monitors physiological parameters such as heart rate, blood oxygen, and skin electrical response through a physiological state sensor. The data processing and intelligent algorithms process the real-time collected biosignals through an embedded microcontroller, and analyze the pain state of the patient through machine learning algorithms. Based on the pain intensity and the physiological condition of the patient, the intelligent algorithm determines the drug release strategy. The user interaction and feedback unit is used in cooperation with a mobile phone APP through the bracelet interface, and is equipped with intelligent voice and vibration feedback.

[0021] The transdermal drug delivery system is a microneedle array that delivers drugs into the body through the skin, which includes multiple drug storage compartments. The drug dose control module adjusts the drug release rate and amount in a way of a micropump, microcurrent or thermal stimulation.

[0022] The pain perception sensor includes temperature, pressure, pH, and resistance sensors. The data processing and intelligent algorithms use pain scores and physiological data monitored by sensors to construct a pain assessment model. The drug release strategy includes dose size, release frequency, and drug type.

[0023] According to different physiological signals (such as temperature, pressure, heart rate, blood oxygen, etc.), a weighted model is designed to estimate the pain intensity P of the patient. This model assumes that the pain intensity has a certain correlation with multiple physiological signals. The pain assessment model uses the following formula:

[0024] P = w1·T + w2·ΔP + w3·HR + w4·SpO2 + w5·EDA

[0025] Where: P: Pain intensity (numerical score, 0 indicates no pain, 10 indicates extreme pain); T: Skin surface temperature (unit: °C); ΔP: Pressure change at the injured site (unit: Pa); HR: Heart rate (unit: beats per minute); SpO2: Blood oxygen saturation (unit: %); EDA: Skin electrical response (unit: μS, reflecting skin conductance changes caused by pressure or anxiety); w1, w2, w3, w4, w5: Weighting coefficients, representing the influence weights of different physiological signals on pain assessment;

[0026] The weighting coefficients are obtained through machine learning model training, and the training data comes from the historical data of different types of pain patients.

[0027] To further improve the accuracy of pain assessment, the machine learning model classifies the pain types of patients, including mild, moderate, and severe. There is a training dataset D = {(x1, y1), (x2, y2),..., (x n , y n )};

[0028] Where: x i = [T i , ΔP i , H R i, SpO 2i , EDA i is the input feature vector, representing the physiological signals of the i-th sample; y i : is the label, representing the pain level of the patient, mild: 1, moderate: 2, severe: 3; The goal is to divide the dataset into different categories through a hyperplane, and the optimization problem can be expressed as:

[0029]

[0030] The constraint conditions are:

[0031] y i (w T x i + b) ≥ 1 - ξ i , ξ i ≥ 0

[0032] Where: w: is the normal vector of the hyperplane, defining the direction of data separation; b: is the bias term; ξ i : is the slack variable, used to handle non-linearly separable cases; C: is the regularization parameter, controlling the balance between the size of the margin and the error;

[0033] By obtaining an optimal decision function:

[0034] f(x) = sign(w T x + b)

[0035] For classifying new physiological signal data x as mild, moderate, or severe pain.

[0036] The drug release strategy includes a drug release rate model, a drug dosage adjustment strategy, and a personalized drug release adjustment model. In the drug release rate model, the release rate R(t) of the drug is dynamically adjusted according to the patient's pain intensity P and physiological state. The change in the drug release rate is represented by the following model:

[0037] R(t) = α · f(P(t)) · g(S(t))

[0038] Where: R(t): The release rate of the drug (unit: mg / h); α: The maximum release rate coefficient of the drug; P(t): The current pain intensity of the patient, obtained based on the pain assessment model; f(P(t)): The relationship function between pain intensity and drug release rate, indicating that the stronger the pain, the higher the drug release rate; A non-linear function is selected For controlling the influence of pain intensity on the drug release rate, where k is a constant; g(S(t)): The influence function of the physiological state, representing the regulatory effect of factors such as heart rate and blood oxygen on drug release; If the patient has a high body temperature, drug metabolism may be faster, so g(S(t)) can be a regulatory factor for temperature, blood oxygen, etc.;

[0039] The drug dosage adjustment strategy is represented using fuzzy control rules:

[0040] R(t) = R base · Fuzzy(P(t), HR(t), SpO2(t))

[0041] Where: Fuzzy(P(t), HR(t), SpO2(t)) is a control function based on fuzzy logic that can dynamically adjust the drug release rate according to the fuzzy rules of different input parameters;

[0042] The personalized drug release adjustment model further optimizes the personalized drug release strategy through a machine learning model. There is a set of training data D = {(x i , y i ), where: x i : Is the input feature, including pain intensity, heart rate, and blood oxygen, and y i : Is the corresponding drug release rate;

[0043] The trained model f drug Is used to predict the drug release rate for new patient data x(t):

[0044] R(t) = f drug (x(t))

[0045] The user interaction and feedback unit enables users to set pain thresholds, view pain management history records, and receive real-time health reminders and treatment suggestions through the APP. The bracelet is equipped with a vibration module. When the pain intensity of the patient exceeds the preset threshold, the bracelet will vibrate automatically to remind the user that they need to adjust the drug dosage.

[0046] Example:

[0047] A 40-year-old patient with chronic pain, suffering from knee arthritis, has a large fluctuation in daily pain intensity, especially the pain intensifies after activities. The patient needs a non-invasive, accurate and convenient pain management solution, avoiding over-reliance on oral painkillers, and at the same time hopes to understand their own health status in real time and obtain personalized treatment suggestions.

[0048] System configuration:

[0049] Drug delivery module: Using a biodegradable microneedle array, realizing painless drug delivery through transdermal administration. Equipped with a micro pump to control the drug release rate, and the drug is a non-steroidal anti-inflammatory drug (NSAIDs), such as ibuprofen.

[0050] Sensor module: Temperature sensor: Monitoring the skin temperature change at the knee joint (such as the temperature increase caused by inflammation). Pressure sensor: Attached around the knee joint to record pressure changes (such as the load-bearing situation during activities). Heart rate and blood oxygen sensor: Installed on the wrist position to monitor heart rate (HR) and blood oxygen saturation (SpO2). Skin electroresponse (EDA) sensor: Used to evaluate the impact of emotions or stress on pain.

[0051] Control and communication module: Embedded microcontroller (such as ARM Cortex-M series).

[0052] Bluetooth module, used to connect to the smartphone App.

[0053] Feedback module: Vibration motor: Used to remind the user of the drug delivery status or abnormal conditions.

[0054] OLED display screen: Displaying real-time pain intensity and drug status.

[0055] App functions: Real-time display of pain scores, drug release rates and health data. Providing historical data analysis and giving personalized health management suggestions.

[0056] Algorithm part: Pain assessment model based on multi-modal sensor data. Intelligent drug release strategy.

[0057] Operation process:

[0058] Device initialization: Wear the bracelet on your wrist and fix it to the knee joint through a flexible sensor patch. The user opens the App, enters basic information (such as age, gender, weight, medical history, etc.), and sets the pain threshold (for example, when the pain score, P, is higher than 6, triggers drug release).

[0059] Real-time data collection and pain assessment: The system starts to collect biological signals in real time, recording the following data every 1 minute:

[0060] T: knee joint skin temperature 37°C; ΔP: joint pressure increased to 200Pa; HR: heart rate 78 beats / min; SpO2: blood oxygen saturation 95%; EDA: skin electrical response increased to 0.8μS;

[0061] The pain score P was calculated using the following formula:

[0062] P=0.4·T+0.3·ΔP+0.2·HR-0.1·SpO2+0.5·EDA

[0063] Where: T = 37°C; ΔP = 200Pa; HR = 78 times / minute; SpO2 = 95%, EDA = 0.8μS; then:

[0064] P=0.4·37+0.3·200+0.2·78-0.1·95+0.5·0.8=15.9

[0065] Drug Release Strategies:

[0066] Drug release strategy initiated based on pain score P:

[0067]

[0068] Assumption: Maximum release rate α = 10 mg / h. Constant k = 0.5; Physiological regulation factor g(S(t)) combines blood oxygen and heart rate changes, assuming g(S(t)) = 0.8; then:

[0069]

[0070] The micropump was activated to release ibuprofen at a rate of R(t) = 7.96 mg / h.

[0071] User reminder and feedback: The App displays the drug release status and current pain score in real time. If P is continuously higher than 8, the system vibrates to remind the user to see a doctor. If the physiological signal is abnormal (such as SpO2 < 90% or HR > 100), the system pauses drug release and sends an alarm.

[0072] Data Storage and Analysis: The system uploads the collected data and drug release records to the cloud for long-term analysis. A daily health report is generated to advise users on appropriate rest or treatment plan adjustment.

[0073] Pain Management: The changing trend of the real-time pain score P can clearly show the pain fluctuations of the patient. After the patient exercises, the pain score rises from 6 to 12, and the score is controlled below 8 through drug release. The drug release has high precision, avoiding the risk of overmedication. Patients report significant pain relief and higher comfort.

[0074] Health Monitoring: Real-time monitoring of heart rate and blood oxygen status reminds patients to avoid excessive exercise or fatigue. Data recording helps doctors understand the treatment effect and disease progression of patients.

[0075] User Experience: The microneedle drug delivery system is painless and comfortable for patients to wear. The App has a friendly interface and is easy to operate, effectively improving patient compliance.

[0076] Expansion Functions and Optimization Directions of the Present Invention:

[0077] Expansion of Drug Types: Other drugs can be selected according to different pain types, such as narcotic analgesics or anti-inflammatory drugs. The multi-warehouse design supports the simultaneous storage of multiple drugs.

[0078] Algorithm Optimization: Optimize the pain assessment model through deep learning, and further improve the prediction accuracy by combining the patient's personalized data. Introduce reinforcement learning to automatically adjust the drug release strategy according to long-term treatment data.

[0079] Integration with Health Platform: Connect to the telemedicine platform, and doctors can view patient data online and adjust treatment plans. Link with smart fitness devices to provide comprehensive health management services.

[0080] The embodiments of the present invention demonstrate the actual application process and effects of the intelligent drug delivery pain management bracelet. By integrating microneedle drug delivery technology, sensor data collection, intelligent algorithms, and user-friendly interaction, accurate and personalized pain management has been successfully achieved. Such a system provides a convenient and effective solution for chronic pain patients, and at the same time has broad market prospects and application value.

[0081] The technical solutions of the present invention are not limited to the limitations of the above specific embodiments. Any technical deformation made according to the technical solutions of the present invention falls within the protection scope of the present invention.

Claims

1. A smart drug delivery pain management bracelet, comprising a bracelet housing, characterized in that: The bracelet shell is integrated with a drug delivery system, a biosensor module, a data processing and intelligent algorithm, and a user interaction and feedback unit. The drug delivery system uses a transdermal drug delivery system to deliver drugs into the body, and a drug dosage control module controls the dosage. The biosensor collects physiological signals related to pain in real time through a pain perception sensor, and monitors heart rate, blood oxygen, and skin electrical response physiological parameters through a physiological state sensor. The data processing and intelligent algorithm processes the biological signals collected in real time through an embedded microcontroller, and analyzes the patient's pain state through a machine learning algorithm. Based on the pain intensity and the patient's physiological condition, the intelligent algorithm determines the drug release strategy. The user interaction and feedback unit is used in conjunction with a mobile phone APP through a bracelet interface, and is equipped with intelligent voice and vibration feedback.

2. The smart drug delivery pain management bracelet according to claim 1, characterized in that: The transdermal drug delivery system is a microneedle array that delivers drugs into the body through the skin, and includes multiple drug storage compartments. The drug dosage control module adjusts the drug release rate and amount by means of a micropump, microcurrent or thermal stimulation.

3. The smart drug delivery pain management bracelet according to claim 2, characterized in that: The pain perception sensors include temperature, pressure, pH, and resistance sensors. The data processing and intelligent algorithm uses pain scores and physiological data monitored by sensors to build a pain assessment model. The drug release strategy includes dosage size, release frequency, and drug type.

4. The smart drug delivery pain management bracelet according to claim 3, characterized in that: The user interaction and feedback unit enables users to set pain thresholds, view pain management history records, and receive real-time health reminders and treatment suggestions through the APP. The bracelet has a built-in vibration module. When the patient's pain intensity exceeds the preset threshold, the bracelet will automatically vibrate to remind the user that the drug dosage needs to be adjusted.

5. The smart drug delivery pain management bracelet according to claim 4, characterized in that: The pain assessment model uses the following formula: P=w1·T+w2·ΔP+w3·HR+w4·SpO2+w5·EDA Where: P: pain intensity; T: skin surface temperature; ΔP: pressure change at the injured part; HR: heart rate; SpO2: blood oxygen saturation; EDA: electrical skin response; w1, w2, w3, w4, w5: weighting coefficients, indicating the influence of different physiological signals on pain assessment; The weighting coefficients are obtained through training of machine learning models, and the training data comes from historical data of different types of pain patients.

6. The smart drug delivery pain management bracelet according to claim 5, characterized in that: The machine learning model classifies the patient's pain type, including mild, moderate, and severe. There is a training data set D = {(x1, y1), (x2, y2), ..., (x n ,y n )}; in: is the input feature vector, representing the physiological signal of the i-th sample; y i : is a label, indicating the patient's pain level, mild: 1, moderate: 2, severe: 3; the goal is to divide the data set into different categories through a hyperplane, and the optimization problem can be expressed as: The constraints are: y i (w T x i +b)≥1-ξ i ,x i ≥0 Where: w: is the normal vector of the hyperplane, defining the direction of data separation; b: is the bias term; ξ i : is a slack variable used to handle nonlinear separable situations; C: is a regularization parameter that controls the balance between the size of the interval and the error; By obtaining an optimal decision function: f(x)=sign(w T x+b) Used to classify new physiological signal data x as mild, moderate or severe pain.

7. The smart drug delivery pain management bracelet according to claim 6, characterized in that: The drug release strategy includes a drug release rate model, a drug dosage adjustment strategy, and a personalized drug release adjustment model. The drug release rate R(t) in the drug release rate model is dynamically adjusted according to the patient's pain intensity P and physiological state. The change in drug release rate is represented by the following model: R(t)=α·f(P(t))·g(St)) Where: R(t): drug release rate; α: drug maximum release rate coefficient; P(t): patient's current pain intensity, obtained based on the pain assessment model; f(P(t)): relationship function between pain intensity and drug release rate, indicating that the stronger the pain, the higher the drug release rate; select nonlinear function Used to control the effect of pain intensity on drug release rate, where k is a constant; g(S(t)): the influence function of physiological state, which indicates the regulatory effect of factors such as heart rate and blood oxygen on drug release; if the patient's body temperature is high, drug metabolism may be faster, so g(S(t)) can be a regulatory factor of temperature, blood oxygen, etc.; The drug dosage regulation strategy is expressed using fuzzy control rules: R(t)=R base ·Fuzzy(P(t),HR(t),SpO2(t)) Among them: Fuzzy (P (t), HR (t), SpO2 (t)) is a control function based on fuzzy logic, which can dynamically adjust the drug release rate according to the fuzzy rules of different input parameters; The personalized drug release regulation model further optimizes the personalized drug release strategy through a machine learning model. There is a set of training data D = {(x i ,y i )}, where: x i : are input features, including pain intensity, heart rate, blood oxygen, y i : is the corresponding drug release rate; The trained model f drug Used to predict drug release rate for new patient data x(t): R(t)=f drug (x(t))。

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