Wearable pain relieving equipment and remote monitoring system thereof

Abstract

The invention discloses wearable pain relieving equipment and a remote monitoring system thereof. The wearable pain relieving equipment comprises a multi-mode physiological sensor array, a data fusion processing module, a control module, a transcutaneous electrical stimulation output module, a communication module and the remote monitoring system. The multi-mode sensor array collects myoelectricity, skin conductance, body surface temperature, heart rate, brain oxygen and other pain related physiological parameters, the data fusion module generates a pain estimated value, the control module dynamically adjusts the stimulation intensity based on a PID + differential compensation algorithm, and the transcutaneous electrical stimulation module outputs current to intervene in pain. The communication module uploads data to a remote monitoring system, and medical staff can monitor and remotely adjust parameters in real time through a cloud platform. The system realizes real-time monitoring, intelligent control and remote intervention of pain, has the advantages of personalized treatment, data safety, expandability and the like, and is suitable for scenes of postoperative pain management, chronic neuralgia and the like.

Description

Technical Field

[0001] The present invention relates to a medical auxiliary device, and in particular to a wearable pain relief device and a remote monitoring system thereof. Background Art

[0002] Pain is a common symptom in clinical medicine, and traditional pain management methods mainly rely on drug therapy (such as opioids) or physical therapy (such as hot compresses and massage). However, drug therapy has problems such as addiction, tolerance and side effects, while traditional physical therapy often lacks real-time monitoring and personalized adjustment capabilities. Although existing transcutaneous electrical stimulation devices can relieve pain to a certain extent, they have the following limitations: 1. It is impossible to monitor pain-related physiological indicators in real time, and it is difficult to dynamically adjust the stimulation parameters; 2. Lack of multimodal sensor fusion, and it is impossible to fully assess the pain state; 3. There is no remote monitoring function, and patients need to frequently travel to and from the hospital, and medical staff cannot intervene in real time; 4. The stimulation parameters are fixed and cannot be intelligently adjusted according to individual differences and dynamic changes in pain. Therefore, there is an urgent need for a wearable pain relief device that can monitor in real time, intelligently control, and remotely intervene to meet the needs of modern pain management. Summary of the Invention

[0003] The object of the present invention is to provide a wearable pain relief device and a remote monitoring system thereof.

[0004] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0005] The wearable pain relief device of the present invention includes a multimodal physiological sensor array, a data fusion processing module, a control module, a transcutaneous electrical stimulation output module, a communication module and a remote monitoring system integrated on a flexible material. The multimodal physiological sensor array is used to collect physiological parameters related to pain of the user. The data fusion processing module is used to perform weighted fusion on the physiological parameters to generate a pain estimation value for evaluating the user's pain state. The control module is used to generate a stimulation output control signal based on the error between the pain estimation value and the target pain value; the transcutaneous electrical stimulation output module is used to control the current output to the user's body surface to intervene in pain relief according to the stimulation output control signal. The communication module is used to upload sensor data, pain estimation value and stimulation control parameters to the remote monitoring system.

[0006] The multimodal physiological sensor array includes an electromyographic sensor, a skin temperature sensor, a skin impedance / conductivity sensor, and a near-infrared sensor, and collects the pain-related physiological parameters including electromyographic signals, skin conductance, body surface temperature, heart rate, and near-infrared brain oxygen signals.

[0007] The remote monitoring system of the wearable pain relief device described in the present invention includes a real-time operating system, a user interaction interface, and a remote control module. The real-time operating system is used to manage task scheduling and adjust stimulation parameters based on sensor data, pain estimation values, and stimulation control parameters; the user interaction interface displays the device status, real-time pain relief effects, and physiological data curves in real time, allowing medical staff to view the status, data fluctuations, and abnormal conditions of the wearable pain relief device in real time. The remote control module allows users to manually adjust personalized stimulation parameters; when the system detects abnormal physiological indicators or equipment failure, it immediately notifies relevant personnel to intervene.

[0008] The control method of the wearable pain relief device of the present invention comprises the following steps:

[0009] S1: Collect the user's pain-related physiological signals;

[0010] S2: Normalize and map physiological signals and calculate pain estimation;

[0011] S3: Calculate the output stimulation intensity based on the error between the pain estimate of the user's pain state and the target pain threshold;

[0012] S4: The stimulation intensity is output to the transcutaneous electrical stimulation output module to output transcutaneous electrical stimulation to relieve pain;

[0013] S5: Upload physiological signals and control data to the remote monitoring system.

[0014] The beneficial effects of the present invention are:

[0015] The present invention is a wearable pain relief device and a remote monitoring system thereof. Compared with the prior art, the present invention has the following significant advantages:

[0016] Multimodal real-time monitoring: By integrating multimodal sensors such as electromyography, skin conductance, body surface temperature, heart rate, and brain oxygen, it comprehensively captures pain-related physiological signals and improves the accuracy of pain assessment;

[0017] Intelligent closed-loop control: Based on PID+ differential compensation algorithm and nonlinear activation function, it dynamically adjusts the stimulation intensity to achieve precise intervention for pain relief;

[0018] Remote monitoring and intervention: Through the cloud platform and user interface, medical staff can view device status and physiological data in real time, remotely adjust stimulation parameters, and improve treatment efficiency;

[0019] Personalized treatment: Supports users to manually adjust stimulation parameters and optimize treatment plans through long-term trend analysis to meet individual needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a diagram of the equipment system architecture of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. The exemplary embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0022] like Figure 1 As shown: The wearable pain relief device of the present invention includes a multimodal physiological sensor array, a data fusion processing module, a control module, a transcutaneous electrical stimulation output module, a communication module and a remote monitoring system integrated on a flexible material, wherein the multimodal physiological sensor array is used to collect physiological parameters related to pain of the user, the data fusion processing module is used to perform weighted fusion on the physiological parameters to generate a pain estimation value for evaluating the user's pain state, the control module is used to generate a stimulation output control signal based on the error between the pain estimation value and the target pain value; the transcutaneous electrical stimulation output module is used to control the current output to the user's body surface for pain relief intervention according to the stimulation output control signal, and the communication module is used to upload sensor data, pain estimation value and stimulation control parameters to the remote monitoring system.

[0023] The multimodal physiological sensor array includes an electromyographic sensor, a skin temperature sensor, a skin impedance / conductivity sensor, and a near-infrared sensor. The physiological parameters related to pain collected include electromyographic signals, skin conductance, body surface temperature, heart rate, and near-infrared brain oxygen signals. The fusion formula for weighted fusion of the physiological parameters by the data fusion processing module is:

[0024]

[0025] Where: φ(m i (t)) represents the normalized output function of sensor i at time t, α i (t) represents the dynamic weight of sensor i, n ≥ 2, and P(t) represents the pain estimation value of the user's pain state.

[0026] The control module generates the stimulation output control signal according to the following calculation method:

[0027] Set a target pain threshold P target , calculate the error signal:

[0028] E(t)=P(t)-P target

[0029] Where: E(t) is the error signal of the system at time t. If E(t)>0, it means that the current pain index is higher than the target and stimulation needs to be increased; if E(t)<0, the opposite is true.

[0030] Stimulus intensity calculation:

[0031] Using PID+differential compensation:

[0032]

[0033] Where: I(t) represents the stimulus intensity at time t, I0 is the basic stimulus intensity; γ is the proportional gain, which controls the strength of the direct influence of the error; λ is the differential gain, which reflects the response to the rate of change of the error; Δt is the sampling time interval;

[0034] Determine the final stimulus amplitude using nonlinear activation compensation:

[0035]

[0036] Where: S(t) is the final stimulus amplitude; S min and S max are the minimum and maximum stimulus amplitudes allowed, respectively; η controls the steepness of the activation function; μ is used to balance the contribution of the error and its rate of change; θ is the activation threshold; Approximately

[0037] Perform exponential smoothing on the pain index:

[0038]

[0039] in: is the smoothed pain index; β is the smoothing factor, which controls the weight distribution of new and old data.

[0040] The remote monitoring system of the wearable pain relief device described in the present invention includes a real-time operating system, a user interaction interface, and a remote control module. The real-time operating system is used to manage task scheduling and adjust stimulation parameters based on sensor data, pain estimation values, and stimulation control parameters; the user interaction interface displays the device status, real-time pain relief effects, and physiological data curves in real time, allowing medical staff to view the status, data fluctuations, and abnormal conditions of the wearable pain relief device in real time. The remote control module allows users to manually adjust personalized stimulation parameters; when the system detects abnormal physiological indicators or equipment failure, it immediately notifies relevant personnel to intervene.

[0041] The control method of the wearable pain relief device of the present invention comprises the following steps:

[0042] S1: Collect the user's pain-related physiological signals;

[0043] S2: Normalize and map physiological signals and calculate pain estimation;

[0044] S3: Calculate the output stimulation intensity based on the error between the pain estimate of the user's pain state and the target pain threshold;

[0045] S4: The stimulation intensity is output to the transcutaneous electrical stimulation output module to output transcutaneous electrical stimulation to relieve pain;

[0046] S5: Upload physiological signals and control data to the remote monitoring system.

[0047] Example:

[0048] For patients with chronic postoperative nerve pain (e.g., after spinal surgery, diabetic neuropathy)

[0049] Target functions: Real-time monitoring of pain physiological signals, intelligent control of transcutaneous electrical stimulation to relieve pain, remote doctor platform viewing and intervention, support for adaptive algorithms and long-term trend analysis; hardware components are as follows:

[0050] Multimodal physiological sensor array:

[0051]

[0052]

[0053] Data fusion processing module, control module:

[0054]

[0055] Transcutaneous electrical stimulation output module:

[0056]

[0057] Communication module:

[0058]

[0059] power supply:

[0060]

[0061] Remote monitoring system:

[0062] deploy content cloud platform AWS+Node-REDDashboard+DynamoDB Function Data visualization, model optimization interface, remote update of strategy parameters Doctor's side You can check the pain index, receive alarms, and remotely set stimulation plans through the web page. User App Android / iOS, functions include manual start / stop, trend chart viewing, pain self-assessment, etc.

[0063] Run the scenario:

[0064] 1. The user wears the device and starts daily use;

[0065] 2. The device collects EMG / GSR / temperature / NIRS data and inputs them into the fusion algorithm:

[0066] P(t)=Σα i (t)·φ(m i (t))

[0067] 3. Pain index P(t)>P target :

[0068] Call the control formula (PID+nonlinear activation):

[0069]

[0070] 4. Update the parameters of the transcutaneous electrical stimulation output module (for example, increase the frequency from 10 Hz to 80 Hz, and adjust the pulse width from 100 μs to 400 μs);

[0071] 5. Data is uploaded to the cloud platform of the remote monitoring system in real time, and doctors can remotely monitor or fine-tune control parameters;

[0072] 6. If a decline in long-term therapeutic efficacy is detected, the remote monitoring system will recommend a new stimulation frequency or electrode placement pattern through an optimization algorithm.

[0073] Experimental data:

[0074] Changes in pain index: Experimental subjects: 20 patients with postoperative chronic neuropathic pain (after spinal surgery).

[0075] Experimental conditions: Wear the device and use it continuously for 7 days, 8 hours a day.

[0076] Pain index (NRS): dropped from an average of 7.2 to 3.1, with a pain relief rate of 85% (the relief rate of traditional transcutaneous electrical stimulation devices is about 60%).

[0077] Response time: The average response time from the device detecting pain signals to outputting stimulation was 1.8 seconds.

[0078] Dynamic adjustment of stimulation parameters:

[0079] Initial stimulation intensity: frequency 10 Hz, pulse width 100 μs.

[0080] After dynamic adjustment: the frequency is adjusted to 80 Hz and the pulse width is adjusted to 400 μs (based on the error between the pain estimate and the target pain threshold).

[0081] Adjustment effect: Pain index decreased by 28% within 30 minutes after adjustment.

[0082] Remote monitoring effect:

[0083] Medical staff intervention: Through the remote monitoring system, doctors discovered abnormally elevated pain indexes in two patients within three hours and adjusted the stimulation parameters in a timely manner. The pain index returned to normal within one hour after the adjustment.

[0084] User satisfaction: 85% of patients said the device was comfortable to wear and provided significant pain relief.

[0085] Compared with traditional methods:

[0086] Traditional transcutaneous electrical stimulation devices: pain relief rate is about 40%, response time is about 5 seconds, and there is no remote monitoring function.

[0087] The device of the present invention has a pain relief rate of 57%, a response time of 1.8 seconds, and supports real-time remote monitoring and intervention.

[0088] Experimental conclusion:

[0089] Experimental results show that this device reduces the pain index (NRS) from an average of 7.2 to 3.1 in patients with postoperative chronic neuralgia, with an efficacy rate of 85%, significantly outperforming traditional transcutaneous electrical stimulation devices (60% efficacy). This invention significantly improves pain relief by using multimodal sensors to monitor pain-related physiological signals in real time and combining it with an intelligent control algorithm to dynamically adjust stimulation parameters. The introduction of a remote monitoring system enables medical staff to intervene in real time, further enhancing the safety and efficiency of treatment.

[0090] The relationship between the pain estimate P(t) and the stimulus intensity I(t):

[0091] Pain estimate range: 0 to 10 (in line with common pain rating standards, such as the Visual Analog Scale (VAS)). Stimulation intensity range: 0 to 80 mA (referring to hardware specifications, maximum output current is 80 mA). Relationship model: Stimulation intensity increases with pain estimate, but there is a saturation effect (i.e., when the pain estimate reaches a certain value, the stimulation intensity no longer increases significantly).

[0092] The Sigmoid function (S-shaped curve) is used to describe the relationship between the pain estimate and the stimulus intensity. The formula is as follows:

[0093]

[0094] Where: I(t): stimulus intensity (unit: mA); I max : Maximum stimulation intensity (80 mA); P(t): Pain estimation value (range: 0 to 10); P0: The middle point of the curve (assumed to be 5, indicating that when the pain estimation value is 5, the stimulation intensity reaches half of the maximum value); k: The steepness of the curve (assumed to be 1, indicating the rising rate of the curve).

[0095] Curve characteristics:

[0096] Low pain estimate area (0 to 3): The stimulation intensity is low and changes slowly. For example, when the pain estimate is 2, the stimulation intensity is approximately 10mA.

[0097] Medium pain estimation range (3 to 7): The stimulation intensity increases rapidly. For example, when the pain estimation increases from 5 to 6, the stimulation intensity increases from 40 mA to 60 mA.

[0098] High pain estimate range (7 to 10): The stimulation intensity tends to saturate and approaches the maximum value. For example, when the pain estimate is 9, the stimulation intensity is about 75mA; when the pain estimate is 10, the stimulation intensity is close to 80mA.

[0099] Dynamic adjustment mechanism:

[0100] Initial state: When the pain estimate is low (such as 2), the stimulation intensity is low (about 10 mA) to avoid overstimulation.

[0101] Dynamic adjustment: When the pain estimate increases (such as from 5 to 7), the stimulation intensity rises rapidly (from 40mA to 60mA) to effectively relieve pain.

[0102] Saturation protection: When the pain estimate is extremely high (such as 9 to 10), the stimulation intensity is close to the maximum value (75 to 80mA) to prevent overload and discomfort to the patient.

[0103] Simulated data:

[0104] Pain estimate: P(t) = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

[0105] Stimulus intensity: I(t) = [0, 5, 10, 15, 25, 40, 60, 70, 75, 78, 80]

[0106] When the pain estimate ranges from 0 to 5, the stimulation intensity gradually increases, as expected. When the pain estimate exceeds 7, the stimulation intensity approaches saturation to ensure safety. When the patient's pain estimate reaches 7, the device automatically adjusts the stimulation intensity to 60mA to quickly relieve pain. If the pain estimate remains around 5 for a long time, the device recommends that the doctor adjust the stimulation parameters (such as frequency or electrode position) through the remote monitoring system.

[0107] The relationship between the sigmoid function pain estimate and stimulus intensity ensures effective pain relief while avoiding the risk of overstimulation. In practical applications, the curve parameters can be further optimized based on experimental data.

[0108] The hardware in the above examples is already used in medical research and supports the algorithm of this invention, enabling both immediate control and long-term analysis, achieving safe and controllable "closed-loop remote treatment." Furthermore, it supports adding multiple sensors, upgrading TinyML models, and pushing OTA algorithm updates, demonstrating scalability.

[0109] The technical solution of the present invention is not limited to the above-mentioned specific embodiments. Any technical variations made according to the technical solution of the present invention fall within the protection scope of the present invention.

Claims

1. A wearable pain relief device, characterized in that: The system comprises a multimodal physiological sensor array, a data fusion processing module, a control module, a transcutaneous electrical stimulation output module, a communication module and a remote monitoring system integrated on a flexible material, wherein the multimodal physiological sensor array is used to collect physiological parameters related to pain of the user, the data fusion processing module is used to perform weighted fusion on the physiological parameters to generate a pain estimation value for evaluating the user's pain state, the control module is used to generate a stimulation output control signal based on the error between the pain estimation value and the target pain value; the transcutaneous electrical stimulation output module is used to control the current output to the user's body surface to intervene in pain relief according to the stimulation output control signal, and the communication module is used to upload sensor data, pain estimation value and stimulation control parameters to the remote monitoring system.

2. The wearable pain relief device according to claim 1, wherein: The physiological parameters related to pain include electromyographic signals, skin conductance, body surface temperature, heart rate, and near-infrared brain oxygen signals. The fusion formula for weighted fusion of the physiological parameters by the data fusion processing module is: Where: φ(m i (t)) represents the normalized output function of sensor i at time t, α i (t) represents the dynamic weight of sensor i, n ≥ 2, and P(t) represents the pain estimation value of the user's pain state.

3. The wearable pain relief device according to claim 2, wherein: The control module generates the stimulation output control signal according to the following calculation method: Set a target pain threshold P target , calculate the error signal: E(t)=P(t)-P target Where: E(t) is the error signal of the system at time t. If E(t)>0, it means that the current pain index is higher than the target and stimulation needs to be increased; if E(t)<0, the opposite is true. Stimulus intensity calculation: Using PID+differential compensation: Where: I(t) represents the stimulus intensity at time t, I0 is the basic stimulus intensity; γ is the proportional gain, which controls the strength of the direct influence of the error; λ is the differential gain, which reflects the response to the rate of change of the error; Δt is the sampling time interval; Determine the final stimulus amplitude using nonlinear activation compensation: Where: S(t) is the final stimulus amplitude; S min and S max are the minimum and maximum stimulus amplitudes allowed, respectively; η controls the steepness of the activation function; μ is used to balance the contribution of the error and its rate of change; θ is the activation threshold; Approximately 4. The wearable pain relief device according to claim 3, wherein: Perform exponential smoothing on the pain index: in: is the smoothed pain index; β is the smoothing factor, which controls the weight distribution of new and old data.

5. A remote monitoring system for a wearable pain relief device according to claim 1, characterized in that: It includes a real-time operating system, a user interaction interface, and a remote control module. The real-time operating system is used to manage task scheduling and adjust stimulation parameters based on sensor data, pain estimation values, and stimulation control parameters. The user interaction interface displays the device status, real-time pain relief effects, and physiological data curves in real time. Medical staff can view the status, data fluctuations, and abnormal conditions of the wearable pain relief device in real time. The remote control module allows users to manually adjust personalized stimulation parameters. When the system detects abnormal physiological indicators or equipment failure, it immediately notifies relevant personnel to intervene.

6. A method for controlling the wearable pain relief device according to claim 1, characterized in that: The following steps are involved: S1: Collect the user's pain-related physiological signals; S2: Normalize and map physiological signals and calculate pain estimation; S3: Calculate the output stimulation intensity based on the error between the pain estimate of the user's pain state and the target pain threshold; S4: The stimulation intensity is output to the transcutaneous electrical stimulation output module to output transcutaneous electrical stimulation to relieve pain; S5: Upload physiological signals and control data to the remote monitoring system.

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