A wearable pain relief device and remote monitoring system therefor

Abstract

The application discloses a wearable pain relief device and a remote monitoring system thereof, which comprises a multi-modal 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. The multi-modal sensor array collects pain-related physiological parameters such as electromyography, skin conductance, body surface temperature, heart rate and brain oxygen, the data fusion module generates a pain estimate value, the control module dynamically adjusts the stimulation intensity based on the PID+derivative compensation algorithm, and the transcutaneous electrical stimulation module outputs an electric current to intervene in pain. The communication module uploads data to the remote monitoring system, and medical staff can monitor and remotely adjust parameters in real time through the cloud platform. The application realizes real-time monitoring, intelligent control and remote intervention of pain, has the advantages of personalized treatment, data security and scalability, and is suitable for postoperative pain management, chronic neuropathic pain and the like.

Description

Technical Field

[0001] This invention relates to a medical assistive device, and more particularly to a wearable pain relief device and its remote monitoring system. Background Technology

[0002] Pain is a common symptom in clinical medicine. Traditional pain management methods mainly rely on drug therapy (such as opioids) or physical therapy (such as heat application and massage). However, drug therapy has problems such as addiction, tolerance, and side effects, while traditional physical therapy often lacks the ability to monitor in real time and personalize adjustments. While existing transcutaneous electrical stimulation (TES) devices can relieve pain to some extent, they have the following limitations: 1. They cannot monitor pain-related physiological indicators in real time, making it difficult to dynamically adjust stimulation parameters; 2. They lack multimodal sensor fusion, making it impossible to comprehensively assess the pain state; 3. They lack remote monitoring capabilities, requiring patients to frequently travel to 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 capable of real-time monitoring, intelligent control, and remote intervention to meet the needs of modern pain management. Summary of the Invention

[0003] The purpose of this invention is to provide a wearable pain relief device and its remote monitoring system.

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

[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 from the user. The data fusion processing module is used to perform weighted fusion of the physiological parameters to generate a pain estimate for assessing the user's pain state. The control module is used to generate a stimulation output control signal based on the error between the pain estimate 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. The communication module is used to upload sensor data, pain estimate, and stimulation control parameters to the remote monitoring system.

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

[0007] The remote monitoring system for the wearable pain relief device of this invention includes a real-time operating system, a user interface, and a remote control module. The real-time operating system manages task scheduling and adjusts stimulation parameters based on sensor data, pain estimates, and stimulation control parameters. The user interface displays the device status, real-time pain relief effect, and physiological data curves in real time, allowing medical personnel to view the status, data fluctuations, and abnormalities of the wearable pain relief device. The remote control module allows users to manually adjust personalized stimulation parameters. When the system detects abnormal physiological indicators or device malfunctions, it immediately notifies relevant personnel for intervention.

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

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

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

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

[0012] S4: 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 this invention are:

[0015] This invention relates to a wearable pain relief device and its remote monitoring system. Compared with existing technologies, this 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 oxygenation, 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, the intensity of stimulation is dynamically adjusted to achieve precise intervention for pain relief;

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

[0019] Personalized treatment: It allows users to manually adjust stimulation parameters and optimize treatment plans through long-term trend analysis to meet individual needs. Attached Figure Description

[0020] Figure 1 This is a diagram of the device system architecture of the present invention. Detailed Implementation

[0021] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions herein 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. The multimodal physiological sensor array is used to collect physiological parameters related to pain from the user. The data fusion processing module is used to perform weighted fusion of the physiological parameters to generate a pain estimate for assessing the user's pain state. The control module is used to generate a stimulation output control signal based on the error between the pain estimate 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. The communication module is used to upload sensor data, pain estimates, and stimulation control parameters to the remote monitoring system.

[0023] The multimodal physiological sensor array includes an electromyography (EMG) sensor, a skin temperature sensor, a skin impedance / conductivity sensor, and a near-infrared sensor. It collects pain-related physiological parameters including EMG signals, skin conductance, body surface temperature, heart rate, and near-infrared brain oxygenation signals. The data fusion processing module uses a weighted fusion formula to perform the fusion of these physiological parameters.

[0024]

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

[0026] The control module generates the stimulus output control signal based on 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 system error signal at time t, E(t) > 0 indicates that the current pain index is higher than the target and more stimulation is needed; E(t) < 0 indicates the opposite.

[0030] Stimulus intensity calculation:

[0031] PID control with derivative compensation is used.

[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 intensity directly affected by 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] Determining the final stimulus amplitude using nonlinear activation compensation:

[0035]

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

[0037] Exponential smoothing of the pain index:

[0038]

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

[0040] The remote monitoring system for the wearable pain relief device of this invention includes a real-time operating system, a user interface, and a remote control module. The real-time operating system manages task scheduling and adjusts stimulation parameters based on sensor data, pain estimates, and stimulation control parameters. The user interface displays the device status, real-time pain relief effect, and physiological data curves in real time, allowing medical personnel to view the status, data fluctuations, and abnormalities of the wearable pain relief device. The remote control module allows users to manually adjust personalized stimulation parameters. When the system detects abnormal physiological indicators or device malfunctions, it immediately notifies relevant personnel for intervention.

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

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

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

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

[0045] S4: 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 neuropathic pain after surgery (such as after spinal surgery, diabetic neuropathy).

[0049] Target functions: Real-time monitoring of physiological pain 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 shown in the table below:

[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 Pain levels can be viewed via a webpage, alarms can be received, and stimulation programs can be remotely set. User App Android / iOS, features include manual start / stop, viewing trend graphs, and pain self-assessment.

[0063] Operating scenario:

[0064] 1. The user puts on the device and begins daily use;

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

[0066] P(t)=Σα i (t)·φ(m i (t))

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

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

[0069]

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

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

[0072] 6. If a decline in long-term efficacy is detected, the remote monitoring system will suggest new stimulation frequencies or electrode placement patterns through optimized algorithms.

[0073] Experimental data:

[0074] Changes in pain index: 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): decreased from an average of 7.2 to 3.1, with a pain relief rate of 85% (compared to approximately 60% for traditional transcutaneous electrical stimulation devices).

[0077] Response time: The average response time from the detection of a pain signal to the output of a stimulus is 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 80Hz and the pulse width is adjusted to 400μs (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 results:

[0083] Medical staff intervention: Through a remote monitoring system, doctors discovered that the pain index of two patients was abnormally high within 3 hours. They adjusted the stimulation parameters in time, and the pain index returned to normal within 1 hour after the adjustment.

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

[0085] Compared to traditional methods:

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

[0087] The device of this 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 reduced the average pain index (NRS) from 7.2 to 3.1 in patients with postoperative chronic neuralgia, achieving an effectiveness rate of 85%, significantly better than traditional transcutaneous electrical stimulation devices (effective rate 60%). This invention significantly improves pain relief by using multimodal sensors to monitor pain-related physiological signals in real time and dynamically adjusting stimulation parameters using intelligent control algorithms. The introduction of a remote monitoring system allows 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 estimation range: 0 to 10 (compliant with common pain rating scales, such as the Visual Analogue Scale (VAS)). Stimulus intensity range: 0 to 80 mA (refer to hardware parameters; maximum output current is 80 mA). Relationship model: Stimulus intensity increases with increasing pain estimation value, but there is a saturation effect (i.e., once the pain estimation value reaches a certain level, the stimulus intensity no longer increases significantly).

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

[0093]

[0094] Where: I(t): stimulus intensity (unit: mA); I max : Maximum stimulus intensity (80mA); P(t): Pain estimate (range: 0 to 10); P0: Midpoint of the curve (assumed to be 5, indicating that the stimulus intensity reaches half of its maximum value when the pain estimate is 5); k: Steepness of the curve (assumed to be 1, indicating the rate of ascent of the curve).

[0095] Curve characteristics:

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

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

[0098] High pain estimate range (7 to 10): Stimulus intensity tends to saturate, approaching its maximum value. For example, when the pain estimate is 9, the stimulus intensity is approximately 75 mA; when the pain estimate is 10, the stimulus intensity is close to 80 mA.

[0099] Dynamic adjustment mechanism:

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

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

[0102] Saturation protection: When the pain estimate is extremely high (e.g., 9 to 10) and the stimulus intensity is close to the maximum value (75 to 80 mA), it prevents overload from causing discomfort to the patient.

[0103] Simulation 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] As 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 is 7, the device automatically adjusts the stimulation intensity to 60mA for rapid pain relief. If the pain estimate remains around 5 for an extended period, the device uses a remote monitoring system to suggest adjustments to stimulation parameters (such as frequency or electrode placement) to the doctor.

[0107] The relationship between the pain estimate and stimulus intensity described above using the Sigmoid function ensures both effective pain relief and avoids the risk of overstimulation. In practical applications, the curve parameters can be further optimized based on experimental data.

[0108] The hardware described in the above embodiments is all existing hardware used in medical research. It supports the algorithms of this invention, enabling both real-time control and long-term analysis to achieve "closed-loop remote treatment" that is safe and controllable. Furthermore, it supports adding multiple sensors, upgrading the TinyML model, and pushing OTA algorithm updates, demonstrating scalability.

[0109] The technical solutions of the present invention are not limited to the specific embodiments described above. Any technical modifications made in accordance with the technical solutions of the present invention fall within the protection scope of the present invention.

Claims

1. A wearable pain relief device, characterized in that: The device integrates 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 on a flexible material. The multimodal physiological sensor array is used to collect physiological parameters related to pain from the user. The data fusion processing module is used to perform weighted fusion of the physiological parameters to generate a pain estimate for assessing the user's pain state. The control module is used to generate a stimulation output control signal based on the error between the pain estimate 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 estimates, and stimulation control parameters to the remote monitoring system. The pain-related physiological parameters include electromyography (EMG), skin conductance, body surface temperature, heart rate, and near-infrared brain oxygenation. The data fusion processing module uses a weighted fusion formula to perform the fusion of these physiological parameters as follows: in: Indicates sensor At any moment The normalized output function, Indicates sensor Dynamic weights, , Pain estimates that represent the user's pain status; The control module generates the stimulus output control signal based on the following calculation method: Set a target pain threshold Calculate the error signal: in: For the system at time The error signal, This indicates that the current pain level is higher than the target, and more stimulation is needed. Conversely; Stimulus intensity calculation: Determining the final stimulus amplitude using nonlinear activation compensation: in: The final determined stimulus amplitude; and These are the minimum and maximum permissible stimulus amplitudes, respectively. To control the steepness of the activation function; The contribution of the equilibrium error to its rate of change; The activation threshold; Approximately .

2. The wearable pain relief device according to claim 1, characterized in that: Exponential smoothing of the pain index: in: The pain index after smoothing; It acts as a smoothing factor, controlling the weighting of new and old data.

3. A remote monitoring system for the wearable pain relief device as described in claim 1, characterized in that: The system includes a real-time operating system, a user interface, and a remote control module. The real-time operating system manages task scheduling and adjusts stimulation parameters based on sensor data, pain estimates, and stimulation control parameters. The user 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 abnormalities of the wearable pain relief device. The remote control module allows users to manually adjust personalized stimulation parameters. When the system detects abnormal physiological indicators or device malfunctions, it immediately notifies relevant personnel for intervention.

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