Method and system for detecting pain induced by knee joint movement

Abstract

The invention relates to a knee joint movement induced pain detection method and system, the detection system is provided with a control terminal, the control terminal is in electric signal connection with an angle sensing device, a heart rate acquisition device, a plantar pressure detection unit and a camera, and a plurality of parameters of knee joint movement can be detected and analyzed in the use process. The detection system can accurately record the angle change in the knee joint movement process, can accurately identify the pain-induced critical angle, and provides an important basis for clinical diagnosis, rehabilitation evaluation and curative effect tracking; through a detection system integrating a plurality of detection modules, synchronous recording of pain occurrence time points and angle positions in the active or passive movement process of the knee joint is achieved, the limitation of a traditional pain assessment method with subjective description as a main part is overcome, and the objectivity and repeatability of detection are improved.

Description

Technical Field

[0001] This invention relates to the field of knee joint detection technology, specifically, to a method and system for detecting knee joint movement-induced pain. Background Technology

[0002] Currently, chronic knee joint diseases such as knee osteoarthritis (KOA) are widespread among the elderly and athletes. Patients often experience movement-evoked pain (MEP) during activities, but clinical assessment of this type of pain mainly relies on subjective patient scores, such as the visual analog scale (VAS) or numeric score (NRS), lacking objective, standardized, and quantifiable assessment tools.

[0003] Although some wearable devices can monitor data such as knee joint angle and pressure, there is still no dedicated assessment device for exercise-induced pain, nor is there a data analysis method that combines kinematics and pain response.

[0004] More specifically, existing technologies have the following problems:

[0005] (1) Lack of objective assessment methods for exercise-induced pain: Current clinical assessment of exercise-induced pain (MEP) of the knee joint mainly relies on subjective reports from patients, such as visual analog scale (VAS) or numerical score (NRS). The results are easily affected by fluctuations in subjective perception, making it difficult to objectively and quantitatively assess the degree of pain and the timing of its occurrence.

[0006] (2) Existing assessment equipment cannot capture pain and movement data simultaneously: Most knee joint monitoring devices on the market focus on kinematic parameters (such as angles and gait) or electromyographic signals, lacking a data acquisition and analysis system that is linked to pain response, and cannot effectively reflect the dynamics of pain induction during exercise.

[0007] (3) Lack of standardized and repeatable evoked actions and analysis procedures: The standardization of actions in clinical assessment of MEP is not high, which can easily lead to deviations in assessment results, and a unified pain recognition algorithm or assessment index has not been established.

[0008] Therefore, developing a device that can quantify and standardize the assessment of exercise-induced pain has significant clinical value. Summary of the Invention

[0009] The purpose of this invention is to provide a method and system for detecting knee joint movement-induced pain, in order to quantify and standardize the assessment of the mechanism of knee joint movement-induced pain.

[0010] The objective of this invention is achieved as follows: a method for detecting knee joint movement-induced pain, comprising the following steps:

[0011] The multimodal data synchronous acquisition step consists of several detection modules coupled with knee joint movement to form a detection system. During knee joint movement, the detection system is used to detect knee joint movement. The multimodal data obtained includes at least knee joint movement angle changes, plantar pressure changes, heart rate changes, and facial expression changes.

[0012] The pain event labeling and capture process involves the detection system automatically marking the time intervals in which knee pain occurs, and extracting all multimodal data from key time periods before and after the pain for quantitative analysis.

[0013] Furthermore, it also includes a data analysis step following the pain event labeling and capture step. In this step, a mapping relationship between knee joint motion parameters and pain is established, and the correspondence between the range of knee joint motion angles and pain points is analyzed to present data on the range of angles from which knee joint pain occurs.

[0014] Furthermore, in the data analysis step, based on the mapping relationship between knee joint motion parameters and pain, abnormal changes in plantar pressure, heart rate, and facial expression are comprehensively analyzed to serve as a basis for judging the intensity level of knee joint pain.

[0015] Furthermore, in the data analysis step, when analyzing plantar pressure, we analyze abnormal situations such as shift of the plantar center of gravity or rapid unloading of force when the knee joint experiences pain, as a basis for judging the stability and weight-bearing pattern of the standing posture.

[0016] Furthermore, in the data analysis step, when analyzing heart rate changes, abnormal heart rate acceleration parameters will be used as pain stress signals.

[0017] Furthermore, in the data analysis step, when analyzing changes in facial expressions, several facial features are extracted based on image algorithms to quantify the degree of pain.

[0018] Furthermore, in the multimodal data synchronous acquisition step, a camera device is used to capture videos of facial expression changes.

[0019] Furthermore, it also includes a step for generating and presenting analysis results after the data analysis step. This step includes: outputting a comparison chart of subjective and objective pain indicators, and identifying the critical angle for pain induction based on the angle changes during knee joint movement and the patient's self-reported pain intensity.

[0020] As another aspect of the present invention, a knee joint motion-induced pain detection system for the above-described method is proposed, wherein the detection module used in the multimodal data synchronous acquisition step includes:

[0021] Angle sensing devices are used to collect knee flexion and extension angles in real time.

[0022] Heart rate monitoring equipment is used to record dynamic heart rate during knee joint movements;

[0023] The plantar pressure detection unit is used to detect changes in plantar pressure during knee joint movement;

[0024] A camera for capturing video and / or images is positioned directly in front of the subject or mounted on the subject's head-mounted display to capture facial expressions and changes when experiencing knee pain.

[0025] Flexible pressure sensors are used to monitor changes in knee joint load or ground reaction force;

[0026] The control terminal is electrically connected to the angle sensing device, heart rate acquisition device, plantar pressure detection unit and camera. It has a display interface and a voice prompt device. The display interface is a touch screen that can be operated by touch. The display interface is used to display all detection data and analysis results, and to display the video and / or images captured by the camera.

[0027] The beneficial effects of this invention are as follows:

[0028] Improve the objectivity and repeatability of detection: By integrating several detection modules, the detection system can simultaneously record the time and angle of pain occurrence during active or passive knee joint movement, overcoming the limitations of traditional pain assessment methods that are mainly based on subjective description, and improving the objectivity and repeatability of detection.

[0029] Precise identification of pain trigger points: This detection system can accurately record the angle changes of the knee joint during movement and accurately identify the critical angle for pain induction, providing an important basis for clinical diagnosis, rehabilitation assessment and efficacy tracking;

[0030] Improve the efficiency of clinical operation and data management: The various parts of the testing system adopt a modular design, which is convenient for flexible combination and is suitable for clinical rehabilitation rooms and research environments. The test data can be transmitted to the control terminal in real time and automatically generate charts for subsequent analysis and archiving, thereby improving data processing efficiency.

[0031] Supports multimodal information fusion assessment: It can provide multi-angle data support for studying the central and peripheral mechanisms of exercise-induced pain;

[0032] Adaptable to various application scenarios: This device is suitable for pain assessment in patients with various knee joint-related diseases such as osteoarthritis, postoperative rehabilitation, and sports injuries, and has broad application prospects in scientific research, rehabilitation, and clinical assessment.

[0033] Saves manpower and testing time: By automatically collecting and judging pain-inducing point information, it avoids errors caused by manual recording and interference with the assessment process, significantly saving testing time and reducing operator training costs. Attached Figure Description

[0034] Figure 1 This is a system layout diagram of the present invention.

[0035] Figure 2 This is a schematic diagram of the core steps of the present invention. Detailed Implementation

[0036] The following will refer to the appendices in the embodiments of the present invention. Figure 1-2 The technical solutions in the embodiments of the present invention are clearly and completely described herein. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0037] like Figure 1 As shown, a knee joint motion-induced pain detection system is proposed, which mainly includes the following modules:

[0038] Angle sensing devices, such as gyroscopes / inertial navigation modules (IMUs), are used to collect knee flexion and extension angles in real time. More specifically, they measure the relative angle between the thigh and the lower leg in real time and calculate the knee flexion and extension angles. Pain often occurs within a specific angle range. By conducting several tests, the range of knee flexion and extension angles at the time of pain can be statistically analyzed and used for clinical localization.

[0039] A flexible pressure sensor (optional) is used to monitor changes in knee load or ground reaction force;

[0040] Heart rate acquisition devices, such as wristband heart rate monitors, are used to record heart rate dynamics during knee joint movement. Pain stimuli generated by the knee joint can lead to sympathetic nerve activation, causing a sudden increase in heart rate or rhythm changes. The system captures potential pain responses through a heart rate mutation detection algorithm within a time window.

[0041] A camera, positioned directly in front of the subject or mounted on the subject's head-mounted display, is used to accurately capture facial expressions and changes when experiencing knee pain. After the camera captures the video stream, the system uses a facial feature point recognition algorithm to analyze the changes in expression and extracts the pain index through a facial motion coding system (such as FACS) model. This index is then matched with synchronized angle, pressure, and heart rate data to improve the accuracy of the judgment.

[0042] The plantar pressure detection unit is mainly equipped with plantar pressure sensors to detect changes in plantar pressure during knee joint movement, determine standing stability and weight-bearing patterns. When pain occurs, patients often experience reduced weight-bearing, shift in center of gravity, or cessation of movement. The plantar sensors can quantify these changes and indirectly identify pain responses.

[0043] The control terminal has a display interface and a voice prompter. The display interface is a touch screen that can be operated by touch. The display interface is used to display all detection data and analysis results, as well as video and / or images captured by the camera.

[0044] The above-mentioned components adopt a modular configuration, which facilitates batch deployment and remote monitoring. The angle sensing device, flexible pressure sensor, heart rate acquisition device, foot pressure detection unit and camera can all be worn on the human body, adapting to several scenarios such as postoperative rehabilitation, chronic pain diagnosis and geriatric bone disease management.

[0045] Based on the configuration of the above system, such as Figure 2 As shown, the specific working process is as follows.

[0046] S1. Device initialization and setup:

[0047] An inertial measurement unit (IMU) is worn on the lower thigh and upper calf to measure the angle of knee joint movement and its changes.

[0048] Integrate plantar pressure sensors into insoles or foot pedals to capture changes in the center of gravity during knee joint movement in real time.

[0049] Wearable heart rate monitoring devices, such as wristband heart rate monitors, are used to record heart rate changes during knee joint movements;

[0050] The camera is placed directly in front of the subject or on a head-mounted display to capture facial expressions during knee joint movement, especially those caused by knee pain.

[0051] After the control terminal completes the synchronization of all modules, it enters the monitoring preparation state.

[0052] S2, Guiding knee joint movement:

[0053] The system guides the subject to complete designated knee joint movements through voice prompts and / or image guidance on the control terminal. These knee joint movements include, but are not limited to, the following types:

[0054] Stand up and sit down;

[0055] Going up and down stairs;

[0056] Walk within the scheduled time;

[0057] The motion parameters (rhythm, number of repetitions, duration, etc.) can be preset or set by the doctor.

[0058] S3. Synchronous acquisition of multimodal data:

[0059] During knee joint movement, each detection module simultaneously collects the following data:

[0060] The angle of knee joint movement and its changes, especially the range of angles that cause knee pain;

[0061] Changes in plantar pressure are used to determine the stability and weight-bearing pattern of a standing posture, as well as the changes in the center of gravity of the foot during knee joint movement, especially when knee pain occurs.

[0062] Changes in heart rate (such as a sudden increase) reflect the degree of physiological stress or pain response caused by knee joint movement;

[0063] Facial expression changes are mainly monitored by video so that the system can accurately and in real time extract pain-related expressions. When the knee joint is in pain, facial expressions will be abnormal, such as frowning, opening the mouth, and eyelid changes.

[0064] In addition, the control terminal interface includes buttons for subjective pain feedback and rating, allowing operators to set these settings themselves.

[0065] S4. Pain Event Labeling and Capture:

[0066] This step has both automatic and subjective feedback modes, making it quite flexible to use;

[0067] Automatic mode: The system automatically marks the time intervals in which knee pain occurs, extracts all multimodal data before and after key time periods for quantitative analysis, and shows that when knee pain occurs, there will be corresponding abnormal changes in plantar pressure, heart rate and facial expression.

[0068] Subjective feedback mode (subjective feedback mode is an optional means and can be used as an auxiliary means to automatic mode): Set up an optional subjective scoring input interface, so that the patient or user can judge the intensity of knee pain at the end of the test and / or during the test. If the subject feels pain during exercise, he / she can provide real-time feedback by clicking on interface buttons, voice annotation, etc.

[0069] In short, during the knee joint angle change, the system continuously monitors changes in heart rate, plantar pressure, and facial expression (not limited to these core variables, which can be set according to needs). If knee pain occurs, abnormal changes will occur in heart rate, plantar pressure, and facial expression. Subsequently, the system will automatically mark the range of knee joint angles where pain occurs based on these abnormal changes.

[0070] S5. Data Analysis and Fusion Processing:

[0071] Data analysis and processing projects include the following:

[0072] Establish a mapping relationship between knee joint motion parameters and pain, and analyze the correspondence between the range of knee joint motion angles and pain points to present data on the range of angles that cause knee pain.

[0073] Plantar pressure: Analyze abnormal situations such as shifting the center of gravity of the foot or rapid dissipation of force when knee pain occurs;

[0074] Heart rate variability: Abnormal heart rate acceleration parameters are used as pain stress signals;

[0075] Facial expression changes: Based on image algorithms, facial features such as frowning, mouth opening, and eyelid changes are extracted to quantify the degree of pain;

[0076] Multimodal feature fusion: The constructed pain recognition model is used to determine the intensity level of pain, and can also determine whether the pain actually occurs.

[0077] S6. Generation and presentation of analysis results:

[0078] The system automatically generates a pain analysis report, which is displayed on the control terminal screen. The report includes, but is not limited to, the following types of content:

[0079] The range of knee joint angles in which knee pain occurs;

[0080] Facial expression scoring and heart rate response;

[0081] Trends in plantar pressure;

[0082] A comparison chart of subjective and objective pain indices, combined with changes in the angle of the knee joint during movement and the patient's self-reported pain intensity, can accurately identify the critical angle at which pain is induced.

[0083] Optional training / testing suggestions.

[0084] The data analyzed and summarized by the control terminal can be exported as a PDF file or uploaded to a medical database for rehabilitation tracking or postoperative evaluation.

[0085] The above system and method have the following technical advantages:

[0086] 1. Improve the objectivity and repeatability of detection: By integrating several detection modules, the detection system can simultaneously record the time and angle of pain occurrence during active or passive movement of the knee joint, overcoming the limitations of traditional pain assessment methods that are mainly based on subjective description, and improving the objectivity and repeatability of detection.

[0087] 2. Precise identification of pain trigger points: This detection system can accurately record the angle changes during knee joint movement and accurately identify the critical angle for pain induction, providing an important basis for clinical diagnosis, rehabilitation assessment and efficacy tracking;

[0088] 3. Improve the efficiency of clinical operation and data management: The various parts of the testing system adopt a modular design, which is convenient for flexible combination and is suitable for clinical rehabilitation rooms and research environments. The test data can be transmitted to the control terminal in real time and automatically generate charts for subsequent analysis and archiving, thereby improving data processing efficiency.

[0089] 4. Supports multimodal information fusion assessment: It can provide multi-angle data support for studying the central and peripheral mechanisms of exercise-induced pain;

[0090] 5. Adaptable to multiple application scenarios: This device is suitable for pain assessment in various knee joint-related diseases such as osteoarthritis, postoperative rehabilitation, and sports injuries, and has broad application prospects in scientific research, rehabilitation, and clinical assessment.

[0091] 6. Saves manpower and testing time: By automatically collecting and judging pain-inducing point information, it avoids errors caused by manual recording and interference with the assessment process, significantly saving testing time and reducing operator training costs.

[0092] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "front," and "rear," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention. In this invention, it should also be noted that the terms "installation" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, an integrally formed connection, a mechanical connection, or an indirect connection through intermediate connecting parts. The specific meaning of the terms in this utility model can be understood according to the specific circumstances.

[0093] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention, and no reference numerals in the claims should be construed as limiting the scope of the claims.

[0094] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method of detecting knee movement-induced pain, characterized by, It comprises the following steps: A multi-modal data synchronous acquisition step, a plurality of detection modules coupled with knee joint motion form a detection system, during the knee joint movement process, the detection system is used to detect the knee joint movement, and the multi-modal data obtained by detection at least includes the knee joint movement angle change, the foot bottom pressure change, the heart rate change and the facial expression change; A pain event marking and capturing step, the detection system automatically marks the time interval of the knee joint generating pain, and all multi-modal data of the key time period before and after are intercepted for quantitative analysis.

2. The method for detecting knee joint movement-induced pain according to claim 1, characterized in that, It also comprises a data analysis step after the pain event marking and capturing step, in which step, the mapping relationship between the knee joint movement parameters and the pain is established, the corresponding relationship between the knee joint activity angle range and the pain point is analyzed, and the angle range data of the knee joint generating pain is presented.

3. The method of claim 2, wherein the knee motion-induced pain is detected by measuring the change in the knee flexion angle. In the data analysis step, based on the mapping relationship between the knee joint movement parameters and the pain, the abnormal changes of the foot bottom pressure, the heart rate and the facial expression are comprehensively analyzed to serve as the basis for judging the pain intensity grade of the knee joint.

4. The method of claim 3, wherein the knee motion-induced pain is detected by the method of claim 1. In the data analysis step, when analyzing the foot bottom pressure, the abnormal situation of the foot bottom gravity center deviation or rapid unloading when the knee joint generates pain is analyzed to serve as the basis for judging the stability of the standing posture and the weight bearing mode.

5. The method of claim 3, wherein the knee motion-induced pain is detected by measuring the change in the knee joint angle. In the data analysis step, when analyzing the heart rate change, the heart rate abnormal acceleration parameter is taken as the pain stress signal.

6. The method of claim 3, wherein the knee motion-induced pain is detected by measuring the change in the knee flexion angle. In the data analysis step, when analyzing the facial expression change, a plurality of facial features are extracted based on the image algorithm to quantify the pain degree.

7. The method of claim 1, wherein the knee motion-induced pain is detected by a knee flexion test. In the multi-modal data synchronous acquisition step, a camera device is used to shoot the video of the facial expression change.

8. The method of claim 2, wherein the knee motion-induced pain is detected by a method comprising: It also comprises an analysis result generation and presentation step after the data analysis step, which comprises: outputting a subjective and objective pain index comparison chart, and identifying the critical angle of pain induction according to the angle change in the knee joint movement process combined with the pain intensity reported by the patient.

9. A knee motion-induced pain detection system for performing the method of claim 1, characterized by The detection modules used in the multi-modal data synchronous acquisition step comprise: An angle sensing device for real-time acquisition of the knee joint flexion and extension angle; A heart rate acquisition device for recording the heart rate dynamics in the knee joint movement; A foot bottom pressure detection unit for detecting the foot bottom pressure change during the knee joint movement process; A camera for shooting video and / or image, which is arranged in front of the detected person or installed on the head-mounted display device of the detected person, and is used to capture the facial expression and its change when the knee is in pain; A control terminal connected with the angle sensing device, the heart rate acquisition device, the foot bottom pressure detection unit and the camera by electrical signal, which has a display interface and a voice prompter, the display interface is a touch screen which can be operated by touch, and the display interface is used to display all detection data and analysis results, and display the video and / or image collected by the camera.

10. A knee movement induced pain detection system according to claim 9, wherein, The detection modules used in the multi-modal data synchronous acquisition step also comprise a flexible pressure sensor for monitoring the knee joint load or ground reaction force change.

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