Intelligent reminding system and method for preventing pressure sores

Abstract

The invention discloses an intelligent reminding system and method for preventing pressure sores, and relates to the technical field of medical health and Internet of Things. According to the method, pressure distribution data, local microenvironment data and physiological time sequence data of a target object are collected in real time, static attribute information and historical intervention records are combined, and a personalized dynamic risk assessment model is utilized to generate a dynamic pressure sore risk index and a predictive intervention time point; performing hierarchical decision according to the risk index, the prediction time point and the current state of the target object, generating a personalized intervention prompt scheme, and initiating a hierarchical reminding or intervention instruction to the target object and / or the nursing terminal; after an effective pressure release event is monitored, feedback data is recorded and the model is optimized. The system comprises a multi-mode sensing module, a data processing and communication module, an intelligent analysis and decision module, a reminding and interaction module and a feedback learning module, precise, personalized and self-adaptive pressure sore prevention is achieved, the nursing burden is relieved, and the comfort and safety of a patient are improved.

Description

Technical Field

[0001] This invention relates to the fields of medical health and Internet of Things technology, specifically to an intelligent reminder system and method for preventing pressure ulcers. Background Technology

[0002] Pressure ulcers, also known as pressure injuries, are a common and serious complication in patients who are bedridden or sitting for extended periods. They are primarily caused by prolonged pressure on localized tissues. Current prevention methods mainly rely on caregivers manually turning the patient at regular intervals, which has the following shortcomings:

[0003] Rigid and fixed: The uniform turning interval ignores the individual differences of patients and the real-time changes in their physiological state.

[0004] Passive response: Unable to provide early warning before the pressure actually reaches the danger threshold.

[0005] Increased burden: Frequent, ineffective reminders or turning over can disrupt the patient's rest and increase the workload of nursing staff.

[0006] Lack of quantification: It relies on subjective experience and lacks continuous monitoring of objective data such as stress and microenvironment.

[0007] In existing technologies, some smart mattresses monitor pressure distribution through pressure dot matrix, but most only perform static threshold alarms and fail to deeply integrate with the patient's physiological signals, body position comfort, and historical data, resulting in limited intelligence and insufficient accuracy in early warning. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides an intelligent system and method capable of real-time, multi-dimensional assessment of pressure ulcer risk and dynamic generation of optimal turning reminders and plans based on personalized models, thereby achieving a fundamental shift from timed to on-demand prevention.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a smart reminder method for preventing pressure sores, comprising the following steps:

[0010] S1: Real-time acquisition of multimodal monitoring data of the target object, wherein the multimodal monitoring data includes at least pressure distribution data and local microenvironment data obtained by a sensor array laid on the support surface;

[0011] S2: Based on the multimodal monitoring data, the static attribute information of the target object, and historical intervention records, a personalized dynamic risk assessment model is used to calculate and generate the dynamic pressure ulcer risk index and predictive intervention time points at the current moment.

[0012] S3: Based on the dynamic pressure ulcer risk index, the predictive intervention time point, and the current state of the target object inferred from physiological data, perform hierarchical decision-making and generate a personalized intervention prompt plan corresponding to the decision level;

[0013] S4: Based on the personalized intervention prompt scheme, send corresponding level reminders or intervention instructions to the target object and / or nursing terminal;

[0014] S5: After detecting an effective stress release event, record the effectiveness data of this intervention and feed it back to the personalized dynamic risk assessment model for adaptive optimization of the model.

[0015] Preferably, in step S1, the local microenvironment data includes the contact interface temperature and / or humidity; the multimodal monitoring data also includes physiological time-series data acquired through a wearable device, the physiological time-series data including at least one of heart rate, heart rate variability and body movement data.

[0016] Preferably, in step S2, the personalized dynamic risk assessment model is a time-series deep learning model, and its input features include at least: time-series features extracted from pressure distribution data, including pressure intensity, pressure gradient and duration in a specific area; time-series change features of the local microenvironment data; static attribute information, including age, weight, skin condition score; and time, frequency and effectiveness records of historical pressure release events.

[0017] Preferably, in step S3, the current state of the target object includes sleep stage or wakefulness / sleep state, which is inferred by analyzing body movement data, heart rate, and heart rate variability data in the physiological time series data; the logic of the hierarchical decision includes: when the dynamic pressure ulcer risk index is lower than the first threshold, or the target object is in deep sleep, delay or temporarily not initiate an active reminder; when the dynamic pressure ulcer risk index is between the first threshold and a higher second threshold, and the target object is in light sleep or wakefulness, prioritize initiating a first-level reminder to the target object to guide autonomous micro-movements; when the dynamic pressure ulcer risk index exceeds the second threshold, or the first-level reminder is ineffective, initiate a second-level alarm containing specific posture adjustment suggestions to the nursing terminal.

[0018] Preferably, in step S4, the reminder method initiated to the target object includes gradual light, gentle vibration, or voice guidance; the intervention instruction initiated to the nursing terminal includes suggested turning positions, angles, and identification of body parts that require focused decompression.

[0019] Preferably, a pressure ulcer prevention intelligent reminder system is used to implement a pressure ulcer prevention intelligent reminder method, comprising: a multimodal sensing module for real-time acquisition of pressure distribution data and local microenvironment data of the target object; a data processing and communication module for preprocessing and transmitting the data acquired by the multimodal sensing module; an intelligent analysis and decision-making module, including a processor storing a personalized dynamic risk assessment model, for executing the calculation and decision-making processes in steps S2 and S3; a reminder and interaction module for executing the reminder operation in step S4 based on the output of the intelligent analysis and decision-making module; and a feedback learning module for recording pressure release event data after intervention and optimizing and updating the personalized dynamic risk assessment model.

[0020] Preferably, the multimodal sensing module includes: a flexible pressure distribution sensing array embedded in a mattress or cushion to form a pressure matrix; a temperature and humidity sensor distributed among the nodes of the pressure distribution sensing array; and an optional bioimpedance sensing unit for monitoring changes in local tissue impedance.

[0021] Preferably, the system further includes a physiological monitoring module, which is a standalone wearable device or a contact sensor integrated into the mattress, for collecting heart rate, heart rate variability and body movement data, and transmitting the physiological time-series data to the intelligent analysis and decision-making module.

[0022] Preferably, the intelligent analysis and decision-making module adopts a cloud-edge collaborative architecture: the data processing and communication module includes an edge computing unit for local data fusion, preliminary risk assessment, and issuing local alarms when risks exceed limits; the personalized dynamic risk assessment model is deployed on a cloud server for receiving data uploaded from the edge and performing deep feature fusion, model calculation, and long-term data storage.

[0023] Preferably, the reminder and interaction module includes: a patient-side interaction unit, integrated into the bed or set up independently, including a light array, a vibration motor and / or a speaker; and a nursing-side interaction unit, which is a mobile terminal or a fixed workstation, providing a graphical interface to display risk heat maps, risk index trends, predictive intervention time points and specific turning plans.

[0024] The beneficial effects of this invention are as follows:

[0025] Achieving precise and personalized prevention: By integrating multimodal data and a personalized dynamic risk assessment model, the system comprehensively considers the target object's real-time stress, microenvironment, physiological state, and individual static attributes, achieving a fundamental shift from "timed turning over" to "on-demand early warning," resulting in high accuracy in early warning and avoiding ineffective intervention.

[0026] Improving patient compliance and comfort: A tiered intelligent decision-making mechanism is adopted, which prioritizes guiding patients to make small movements in a gentle manner and only reminds nursing staff when necessary, minimizing interference with patients' sleep and rest, and improving experience and compliance.

[0027] Reduce nursing workload: The system automatically monitors, assesses and provides early warnings, and offers specific and graphical suggestions for turning over, reducing the pressure on nurses to conduct frequent checks and make subjective judgments, thus achieving intelligent and efficient nursing processes.

[0028] It has the ability to continuously optimize: Through the feedback learning mechanism, the system can record the effectiveness of each intervention and dynamically optimize the risk assessment model, making it more in line with the individual characteristics and behavioral patterns of the target object over time, and achieving adaptive learning that becomes more accurate the more it is used.

[0029] The system architecture is flexible and scalable: it adopts a cloud-edge collaborative architecture, which takes into account both local real-time response and cloud deep computing, supports deployment in multiple scenarios, such as hospital beds and wheelchairs, and facilitates functional expansion and integration through modular design. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating the intelligent reminder method for preventing pressure sores according to the present invention.

[0032] Figure 2 This is a structural diagram of the intelligent reminder system for preventing pressure sores of the present invention. Detailed Implementation

[0033] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] according to Figure 1 , Figure 2 As shown, the intelligent reminder system and method for preventing pressure sores are presented in the following embodiments.

[0035] Example 1: Intelligent reminder for preventing pressure ulcers in bedridden patients

[0036] This embodiment targets patients who are bedridden for extended periods in hospitals or at home.

[0037] System Deployment:

[0038] A smart mattress integrating a flexible pressure distribution sensor array and miniature temperature and humidity sensors is placed on the hospital bed. The sensor array is distributed in a matrix, with a density sufficient to distinguish key areas such as the sacrum, coccyx, scapula, and heels.

[0039] The patient wears a medical wristband that can collect heart rate and body movement data; this is called a physiological monitoring module.

[0040] A patient-side interactive unit is installed at the head of the bed, which includes an LED light strip with adjustable color and brightness and a miniature vibration motor.

[0041] The nurse station is equipped with a nursing terminal interaction unit, which can be a tablet computer with dedicated software installed, or individual nursing staff can be equipped with a smartwatch or PDA with alarm receiving function.

[0042] The system's data processing and communication modules, as well as edge computing units, are integrated into the mattress control box. Personalized dynamic risk assessment models are deployed on the hospital's intranet server or a secure medical cloud platform.

[0043] Workflow:

[0044] Initial setup: Caregivers enter the patient's static attribute information on a tablet, such as: age: 75 years, weight: 60 kg, Braden score: 12. The system then begins operation.

[0045] Data Acquisition and Upload (S1): The mattress sensors acquire a full-body pressure distribution map and corresponding temperature and humidity data once per second. The wristband synchronously acquires heart rate and body movement data. All data is initially processed by the mattress control box, then packaged and uploaded to the cloud server.

[0046] Risk assessment and decision-making, i.e., S2 and S3: The model on the cloud server receives a real-time data stream. The model combines this data with the patient's historical data, i.e., stress changes and turning records over the past few hours, for analysis.

[0047] Assuming the patient's sacrococcygeal pressure remains consistently high (>60 mmHg), with a slow upward trend in temperature and a slight increase in humidity in the area, and wristband data showing a stable heart rate and minimal body movement, the model infers that the patient is in a light sleep state. After comprehensive calculation, the model outputs a dynamic pressure ulcer risk index of 0.65 (range 0-1), with a second threshold set at 0.6. It also predicts that without intervention, the risk will exceed 0.7 after approximately 25 minutes, indicating a higher risk.

[0048] Based on the hierarchical decision-making logic: the risk index, 0.65, is greater than the second threshold of 0.6, and the patient is in light sleep, the decision is to generate a second-level alarm. However, since the risk has just exceeded the threshold, and the patient is in a light sleep stage where they are easily awakened, the system decides to first attempt a first-level alert.

[0049] Execution reminder, i.e., S4:

[0050] First-level alert: The LED light strip at the head of the bed begins to emit a soft, warm yellow light slowly and gently for 30 seconds. At the same time, the mattress control box sends a command to the patient's wristband via Bluetooth, causing the wristband to vibrate gently.

[0051] Observation and escalation: The system continuously monitors pressure data. If there is no significant change in the pressure distribution in the sacrococcygeal region within the next 5 minutes, it indicates that the patient is unresponsive or has insufficient micromovement, and the risk index continues to climb to 0.68.

[0052] Level 2 Alert: The system automatically escalates to Level 2. An alert window pops up on the nurses' station tablet, displaying "Bed 3, Zhang XX, High Risk in Sacrococcygeal Region," and showing a real-time pressure heatmap, highlighting the sacrococcygeal region. Simultaneously, the interface provides the suggestion: "It is recommended to turn the patient 30 degrees to the left and place a pressure-relief pad under the sacrococcygeal region." The nursing staff's smartwatches also receive a brief vibration alert.

[0053] Feedback learning, or S5: Upon seeing the alarm, the caregiver moves the patient to their left side and rolls them 30 degrees. The mattress sensors immediately detect a significant decrease in pressure in the sacral and coccygeal region, with a more even distribution. The system records this "pressure relief event" as "effective" and records the response time from alarm activation to pressure relief as 4 minutes. This data is uploaded to the cloud to optimize the patient's risk assessment model. For example, the model might learn that the patient has a lower response rate to mild reminders during light sleep, and in future similar situations, it might trigger a second-level alarm earlier or more directly.

[0054] Example 2: Intelligent reminder for preventing pressure sores in wheelchair-bound patients who sit for long periods

[0055] This embodiment is suitable for patients who need to use a wheelchair for extended periods of time.

[0056] System Deployment:

[0057] A smart cushion with an integrated sensor array is placed on the wheelchair seat.

[0058] The remaining modules, such as wearable physiological monitoring, interaction unit, and cloud system, are similar to those in Example 1, but the nursing interaction unit may rely more on the caregiver's smartphone APP.

[0059] Workflow characteristics:

[0060] The focus is on monitoring the pressure and microenvironment in the ischial tuberosity region.

[0061] Since patients are usually awake, first-level reminders, such as the slight vibration of the smart cushion or the voice prompts of the connected mobile app such as "please lift your hips or lean forward slightly", may be used more frequently.

[0062] The system can intelligently suggest optimal intervals for stress relief based on schedules such as meal times, treatment times, and activity history, and encourage caregivers to assist with regular postural adjustments.

[0063] 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 smart reminder method for preventing pressure sores, characterized in that, Includes the following steps: S1: Real-time acquisition of multimodal monitoring data of the target object, wherein the multimodal monitoring data includes at least pressure distribution data and local microenvironment data obtained by a sensor array laid on the support surface; S2: Based on the multimodal monitoring data, the static attribute information of the target object, and historical intervention records, a personalized dynamic risk assessment model is used to calculate and generate the dynamic pressure ulcer risk index and predictive intervention time points at the current moment. S3: Based on the dynamic pressure ulcer risk index, the predictive intervention time point, and the current state of the target object inferred from physiological data, perform hierarchical decision-making and generate a personalized intervention prompt plan corresponding to the decision level; S4: Based on the personalized intervention prompt scheme, send corresponding level reminders or intervention instructions to the target object and / or nursing terminal; S5: After detecting an effective stress release event, record the effectiveness data of this intervention and feed it back to the personalized dynamic risk assessment model for adaptive optimization of the model.

2. The intelligent reminder method for preventing pressure sores according to claim 1, characterized in that, In step S1, the local microenvironment data includes the contact interface temperature and / or humidity; the multimodal monitoring data also includes physiological time-series data acquired through wearable devices, and the physiological time-series data includes at least one of heart rate, heart rate variability and body movement data.

3. The intelligent reminder method for preventing pressure sores according to claim 1 or 2, characterized in that, In step S2, the personalized dynamic risk assessment model is a time-series deep learning model, and its input features include at least: Temporal features extracted from pressure distribution data include pressure intensity, pressure gradient, and duration in a specific region; The temporal variation characteristics of the local microenvironment data; The static attribute information includes age, weight, and skin condition score; Records of the timing, frequency, and effectiveness of historical stress release events.

4. The intelligent reminder method for preventing pressure sores according to claim 2, characterized in that, In step S3, the current state of the target object includes sleep stage or wakefulness / sleep state, which is inferred by analyzing the body movement data, heart rate and heart rate variability data in the physiological time series data; The logic of the hierarchical decision-making includes: When the dynamic pressure ulcer risk index is below the first threshold, or when the target is in a deep sleep, the active reminder will be delayed or temporarily not initiated. When the dynamic pressure ulcer risk index is between the first threshold and a higher second threshold, and the target is in a light sleep or awake state, the first-level reminder to guide the target to make autonomous micro-movements is initiated first. When the dynamic pressure ulcer risk index exceeds the second threshold, or when the first-level reminder is ineffective, a second-level alarm containing specific position adjustment suggestions is sent to the nursing terminal.

5. The intelligent reminder method for preventing pressure sores according to claim 1, characterized in that, In step S4, the reminder method initiated to the target object includes gradual light, gentle vibration or voice guidance; the intervention instruction initiated to the nursing terminal includes suggested turning position, angle and identification of body parts that need to be relieved of pressure.

6. A smart reminder system for preventing pressure sores, used to implement the method described in any one of claims 1-4, characterized in that, include: A multimodal sensing module is used to collect pressure distribution data and local microenvironment data of the target object in real time; The data processing and communication module is used to preprocess and transmit the data collected by the multimodal sensing module; The intelligent analysis and decision-making module includes a processor storing a personalized dynamic risk assessment model, used to perform the calculation and decision-making processes of steps S2 and S3 in claim 1; The reminder and interaction module is used to execute the reminder operation in step S4 of claim 1 based on the output of the intelligent analysis and decision module; The feedback learning module is used to record stress release event data after intervention and to optimize and update the personalized dynamic risk assessment model.

7. The intelligent reminder system for preventing pressure sores according to claim 6, characterized in that, The multimodal sensing module includes: A flexible pressure distribution sensor array is embedded in a mattress or cushion to form a pressure dot matrix. Temperature and humidity sensors are distributed among the nodes of the pressure distribution sensing array; Additionally, an optional bioimpedance sensing unit is available for monitoring changes in local tissue electrical impedance.

8. The intelligent pressure ulcer prevention reminder system according to claim 6, characterized in that, The system also includes a physiological monitoring module, which is a standalone wearable device or a contact sensor integrated into the mattress, used to collect heart rate, heart rate variability and body movement data, and transmit the physiological time series data to the intelligent analysis and decision module.

9. The intelligent pressure ulcer prevention reminder system according to claim 6, characterized in that, The intelligent analysis and decision-making module adopts a cloud-edge collaborative architecture: The data processing and communication module includes an edge computing unit, which is used for local data fusion, preliminary risk assessment, and issuing local alarms when risks exceed limits. The personalized dynamic risk assessment model is deployed on a cloud server to receive data uploaded from the edge and perform deep feature fusion, model calculation, and long-term data storage.

10. The intelligent pressure ulcer prevention reminder system according to claim 6, characterized in that, The reminder and interaction module includes: The patient-side interaction unit, integrated into the bed or set up independently, includes a light array, a vibration motor, and / or a speaker; The nursing terminal interaction unit provides a graphical interface for mobile terminals or fixed workstations to display risk heat maps, risk index trends, predictive intervention time points, and specific turning plans.

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