A real-time visualization and dynamic calibration platform for standardizing massage techniques

Abstract

This invention discloses a real-time visualization and dynamic calibration platform for standardizing massage techniques, comprising a wearable sensing device, a processing device, and a feedback device. The wearable sensing device is a smart massage glove integrating an optical detection unit, a pressure detection unit, and a motion sensing unit, used to simultaneously collect data on pressure points, pressure depth, and hand movements. The processing device, based on multi-sensor data fusion technology, identifies massage technique types through a machine learning model and compares real-time parameters with a standard technique database. The feedback device provides multimodal real-time calibration feedback based on the comparison results through vibration, display, and audio units. This invention overcomes the technical shortcomings of existing technologies, such as inaccurate pressure assessment due to differences in human tissue stiffness, single parameter measurement, and lack of real-time feedback. It achieves quantitative assessment, visual monitoring, and dynamic calibration of massage techniques, significantly improving the standardization of massage teaching and the safety of clinical operations.

Description

Technical Field

[0001] This invention belongs to the field of healthcare informatics technology, specifically a real-time visualization and dynamic calibration platform for the standardization of massage techniques. Background Technology

[0002] As an important component of external treatment methods in Traditional Chinese Medicine, the efficacy of massage largely depends on the accuracy and standardization of the practitioner's techniques. However, traditional massage techniques have long faced the following technical challenges in their inheritance and practice: First, the quantitative assessment of massage techniques presents significant challenges. While existing technologies have attempted to measure pressure using pressure sensors, the significant differences in tissue hardness across different parts of the body mean that relying solely on pressure parameters cannot accurately assess the actual effect of the massage. For example, applying the same pressure to the muscular buttocks versus the superficial anterior tibial region produces drastically different tissue deformations and stimulation intensities. This difference in tissue characteristics makes it difficult for existing technologies to accurately determine whether the pressure is "appropriate," severely impacting the accuracy of technique assessment.

[0003] Secondly, existing technologies have significant shortcomings in terms of the comprehensiveness of parameter measurement. Single-function measurement devices on the market, such as pressure testing gloves, can only acquire information about the pressure band and cannot simultaneously detect key parameters such as pressure depth and motion trajectory. While high-precision motion capture systems can record kinematic parameters, they are complex, expensive, and cannot detect biomechanical effects, making them difficult to promote and use in clinical settings.

[0004] Third, existing technologies lack effective real-time feedback mechanisms. During massage teaching and clinical practice, practitioners often struggle to obtain accurate feedback in a timely manner. While some studies have attempted to provide feedback through video analysis or simple pressure cues, these methods suffer from significant delays and limited information dimensionality, failing to achieve truly real-time dynamic calibration.

[0005] Fourth, the problem of low standardization in massage techniques has not been effectively resolved. Due to the lack of objective and unified evaluation standards, there are significant differences in techniques among different operators, affecting the repeatability and comparability of the results. Currently, there is no systematic solution in the technology that can simultaneously address multi-parameter measurement, real-time feedback, and standardized evaluation.

[0006] Furthermore, in the field of Tuina scientific research, the lack of reliable data collection and analysis tools makes it difficult to conduct in-depth research on key scientific issues such as the mechanism of action of massage techniques and dose-effect relationships, which restricts the modernization of Tuina discipline.

[0007] In summary, existing technologies suffer from shortcomings such as incomplete parameter measurement, insufficient evaluation accuracy, imperfect feedback mechanisms, and low standardization. There is an urgent need for an integrated system that can achieve simultaneous measurement of multiple parameters, intelligent evaluation, and real-time calibration to promote the standardization and scientific development of massage techniques. Summary of the Invention

[0008] The purpose of this invention is to provide a real-time visualization and dynamic calibration platform for the standardization of massage techniques. This invention overcomes the technical defects of existing technologies, such as inaccurate pressure assessment due to differences in the hardness of human tissues, single parameter measurement, and lack of real-time feedback. It realizes the quantitative assessment, visual monitoring, and dynamic calibration of massage techniques, significantly improving the standardization of massage teaching and the safety of clinical operations.

[0009] The technical solution adopted in this invention is as follows: A real-time visualization and dynamic calibration platform for standardizing massage techniques, comprising: Wearable sensing devices, including: An optical detection unit is used to emit detection light toward the pressing area and receive reflected light signals; Pressure detection unit for real-time monitoring of the pressing pressure channel; Motion sensing unit, used to collect hand movement data; The processing device, communicatively connected to the wearable sensing device, is used for: The parameters of massage techniques are evaluated based on reflected light signals, pressure points, and hand movement data. The parameters of the evaluated massage techniques were compared with a standard technique database; Provide calibration feedback based on the comparison results; The massage technique parameters include at least one of the following: pressure depth, pressure intensity, pressure frequency, and movement trajectory.

[0010] Preferably, the wearable sensing device is a smart massage glove, the optical detection unit includes multiple miniature light sources and photodetectors disposed near the fingertips and / or palm of the glove, the pressure detection unit includes multiple flexible pressure sensors distributed in the contact area of ​​the glove, and the motion sensing unit includes an inertial measurement unit.

[0011] Preferably, the miniature light source includes at least two different wavelengths of LED light source, the at least two different wavelengths being selected from the visible light band and the near-infrared light band; the photodetector is arranged in pairs with the miniature light source to form a distributed optical detection array; the pressure detection range of the flexible pressure sensor is 0-10N, and the accuracy is ±0.1N; the inertial measurement unit is a 9-axis IMU with a sampling rate of not less than 100Hz.

[0012] Preferably, the processing device calculates the pressing depth d based on the reflected light signal from the optical detection unit, using the following formula: ; Where I is the received light intensity denoted as , where is the emitted light intensity, and μ is the tissue's absorption coefficient of the detection light.

[0013] Preferably, the processing device includes a mobile terminal and a cloud platform. The mobile terminal is used to display a heat map of pressure depth, a pressure intensity distribution map, and a hand movement trajectory in real time. The cloud platform stores the standard technique database and a machine learning model. The standard technique database includes standard parameter ranges for eight basic techniques: pressing, rubbing, pushing, grasping, kneading, pinching, acupressure, and plucking. The machine learning model identifies massage technique types and evaluates technique quality based on multi-sensor data.

[0014] Preferably, the system further includes a feedback device for providing the calibration feedback, the feedback device including at least one of a vibration unit, a display unit, and an audio unit, when the pressure p deviates from the target force range [p min ,p max When [the vibration is detected], vibration feedback is triggered. The vibration intensity V is related to the degree of deviation and is calculated as follows: ; Where k is the proportionality coefficient. The target pressure value.

[0015] Preferably, the processing device calculates a comprehensive score S based on the extracted massage technique parameters, using the following formula: ; in These are the weighting coefficients. The score for the i-th massage technique parameter, i=1,2,…,n, where n is the number of massage technique parameters, Σ =1.

[0016] Preferably, the wearable sensing device further includes a data processing unit and a wireless communication unit. The data processing unit is used to preprocess and fuse optical signals, pressure signals, and motion data. The wireless communication unit is used to transmit the processed data to the processing device. The data processing unit uses a Kalman filter algorithm for data fusion, and the state vector x is represented as: ; Where d is the pressing depth, v is the velocity, a is the acceleration, and p is the pressure.

[0017] A standardized calibration method for massage techniques includes the following steps: Step S1: Acquire the reflected light signal, pressure signal, and hand movement signal of the pressing area through the wearable sensing device; Step S2: Calculate the pressing depth based on the reflected light signal; Step S3: Determine the massage technique parameters based on the pressure signal, hand movement signal, and calculated pressure depth; Step S4: Compare the determined massage technique parameters with the standard parameters in the standard technique database; Step S5: Provide massage technique calibration feedback based on the comparison results.

[0018] A non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the massage technique standardization calibration method, and a massage technique calibration device, comprising a memory and a processor, wherein the memory stores the computer program and the processor, when executing the program, implements the method.

[0019] Compared with the prior art, the present invention has the following significant advantages: First, this invention, through multimodal sensor fusion technology, achieves for the first time the synchronous and precise quantification of core parameters of massage techniques. Addressing the deficiency in existing technologies that rely solely on pressure sensors to accurately assess the effect of pressure, this invention innovatively introduces an optical detection unit to calculate the subcutaneous pressure depth by measuring the attenuation rate of reflected light intensity. This technological breakthrough enables the system to distinguish the actual depth of action under different tissue characteristics, thereby accurately determining whether the pressure is adequate. For example, the system can identify that greater pressure is needed to reach the predetermined depth in harder tissues, while pressure needs to be appropriately reduced in softer tissues. This characteristic significantly improves the accuracy of technique assessment.

[0020] Secondly, this invention constructs a complete closed-loop evaluation and feedback system, achieving real-time dynamic calibration of manual techniques. The system automatically identifies the type of technique using a machine learning model and compares real-time parameters with the standardized range in a database of standard techniques. Specifically, the system can automatically adjust evaluation criteria based on the tissue characteristics of different body parts, ensuring accurate technique calibration across various anatomical locations. When a deviation is detected, the feedback device activates multimodal feedback according to a preset strategy. For example, when the system detects insufficient pressure depth at a specific location, it provides tactile feedback through a vibration unit, prompting the operator to adjust the pressure.

[0021] Third, this invention employs an edge computing collaborative architecture, balancing real-time requirements with complex computing capabilities. The mobile terminal handles real-time data visualization and low-latency feedback, while the cloud platform runs complex algorithm models and provides data storage services, ensuring the system's efficiency and scalability. Furthermore, the system can establish personalized tissue characteristic models based on individual patient differences, further improving the accuracy of manual manipulation techniques.

[0022] Fourth, this invention provides a reliable data acquisition and analysis platform for massage science research. The system can record complete operation datasets, providing objective evidence for research on the dosage-effect relationship of manipulation techniques, and powerfully promoting the development of evidence-based medicine in massage. By establishing a correlation model between tissue characteristics and manipulation parameters, the system can provide personalized manipulation plans for patients with different symptoms and physical conditions.

[0023] In summary, this invention has achieved a paradigm shift in massage techniques from experience-based inheritance to data-driven approaches through technological innovation. In particular, it has made groundbreaking progress in solving the technical problem of inaccurate pressure assessment caused by differences in the hardness of human tissues. It has broad application value and significant technological advancements in fields such as teaching and training, clinical quality control, and scientific research. Attached Figure Description

[0024] Figure 1 This is a schematic diagram from the palm view of the intelligent massage glove of the present invention; Figure 2 This is a schematic diagram showing the back of the hand as a view of the smart massage glove of the present invention; Figure 3 This is a schematic diagram of the processing device and feedback device of the present invention; Figure 4 This is a schematic diagram of the system architecture of the present invention; Figure 5 This is a schematic diagram of the method steps of the present invention.

[0025] 100 Wearable sensing device; 101 Smart massage glove; 110 Optical detection unit; 111 Miniature light source; 112 Photoelectric receiver; 120 Pressure detection unit; 121 Flexible pressure sensor; 130 Motion sensing unit; 131 Inertial measurement unit; 140 Data processing unit; 150 Wireless communication unit; 200 Processing device; 210 Mobile terminal; 220 Cloud platform; 221 Standard technique database; 222 Machine learning model; 300 Feedback device; 310 Vibration unit; 320 Display unit; 330 Audio unit; 500 Massage technique calibration device; 510 Memory; 520 Processor. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0027] See Figures 1 to 5 The present invention provides a real-time visualization and dynamic calibration platform for standardizing massage techniques, including a wearable sensing device 100, a processing device 200 and a feedback device 300.

[0028] The preferred embodiment of the wearable sensing device 100 is a flexible smart massage glove 101. Its design fully considers ergonomics, signal fidelity, and wearing comfort, and also includes: an optical detection unit 110 for emitting detection light to the pressing area and receiving reflected light signals; a pressure detection unit 120 for real-time monitoring of the pressing pressure; a motion sensing unit 130 for collecting hand movement data; and a wireless communication unit 150.

[0029] The glove features a double-layer structure. The inner layer is made of skin-friendly, breathable microfiber material for sweat absorption and increased comfort. The outer layer, the main functional layer, is made of a spandex blend fabric with excellent elasticity and abrasion resistance, ensuring a close fit to different hand sizes without restricting finger joint movement. The five fingers are separated, with the fingertips either semi-open or made of ultra-thin conductive fabric to preserve the operator's direct tactile feedback. The palm surface is the main integration area for the sensors.

[0030] The optical detection unit 110 includes a miniature light source 111 and a photodetector 112. The miniature light source 111 uses surface-mount LEDs. These are arranged in a 3x3 or 4x4 array on the fingertips of the thumb, index finger, and middle finger, as well as the thenar and hypothenar eminences of the glove 101. Each array point contains two LEDs of different wavelengths: 660nm (red light) and 850nm (near-infrared light). These two wavelengths were chosen because they have different absorption characteristics for human tissues (such as oxyhemoglobin, deoxyhemoglobin, and water), which helps improve the accuracy of depth calculation and allows for a preliminary assessment of tissue condition. The driving current of the LEDs is precisely controlled by a microprocessor through a constant current source circuit to ensure the stability of the emitted light intensity I_0.

[0031] The photodetector 112 employs a high-sensitivity, low-dark-current silicon photodiode. Each photodiode is paired with a set of two-wavelength LED light sources, positioned approximately 3-5 mm from the center of the light source to optimize the signal-to-noise ratio of the received signal. A narrow-band filter can be installed in front of the photodiode to reduce interference from ambient light.

[0032] The pressure detection unit 120 employs a flexible piezoresistive pressure sensor 121. Its core is a force-sensitive resistor material, whose resistance decreases as applied pressure increases. The sensors are also distributed in an array across the fingertips, fingertips, and the thenar and hypothenar eminences, staggered or overlapping with the optical detection unit 110 to achieve complementary spatial measurements. Its measurement range is 0~10N (Newtons), covering the force range required for most massage techniques, from gentle stroking to deep pressing. Accuracy is ±0.1N or ±1% of full scale (whichever is greater), ensuring the capture of minute force changes. Response time is <10ms. Durability is >1 million press cycles. The sensor is connected to a Wheatstone bridge circuit, which is then connected to an instrumentation amplifier to amplify the weak voltage changes before converting them into digital signals via an ADC (analog-to-digital converter).

[0033] The motion sensing unit 130 employs a 9-axis inertial measurement unit 131, which typically integrates a three-axis MEMS accelerometer, a three-axis MEMS gyroscope, and a three-axis magnetometer. The IMU is securely packaged and fixed to the center of the back of the glove, which can be approximated as the center of hand motion. The accelerometer range is ±16g, the gyroscope range is ±2000dps, the magnetometer range is ±8Gauss, and the sampling rate is set to 200Hz to meet high-fidelity sampling of rapid hand movements.

[0034] The data processing unit 140 is implemented using a low-power, high-performance microcontroller (MCU). Its core tasks include: generating PWM signals to control LED switching; reading ADC values ​​to acquire pressure and optical signals; and reading raw IMU data via I2C or SPI interfaces. It applies a low-pass digital filter (such as a Butterworth filter) with a cutoff frequency of 20Hz to the pressure and optical signals to suppress high-frequency noise. It performs moving average filtering on the IMU's acceleration and angular velocity data. It performs sensor calibration upon system power-on or periodically. For example, the pressure sensor readings under zero load are used as zero-point offset compensation. The IMU requires gyroscope bias calibration and magnetometer ellipsoidal fitting calibration.

[0035] Data fusion is the core algorithm for local processing. This invention constructs a state vector. , representing the pressure depth, hand velocity in the pressing direction, acceleration, and pressure, respectively. Kalman filtering involves two steps: Based on the state estimate from the previous moment and the acceleration 'a' measured by the IMU, predict the current state 'x'. pred Covariance P pred .

[0036] The depth d calculated by the optical unit optical The pressure p measured by the pressure sensor sensor and the depth d obtained by IMU displacement integration imuThe observed values ​​are weighted and fused with the predicted values ​​to obtain the optimal state estimate x. opt Kalman filtering can effectively smooth data, suppress sensor noise and transient interference, and output stable and reliable method parameters.

[0037] The wireless communication unit 150 uses a Bluetooth 5.0 module. The MCU packages the fused data (including timestamp, sensor ID, fused depth, pressure, attitude angle, motion trajectory points, etc.) and sends it to the processing device 200 at a frequency of 100Hz.

[0038] The processing device 200 adopts an edge-cloud collaborative computing architecture to balance real-time requirements and computational complexity, including a mobile terminal 210, a cloud platform 220, a standard method database 221, and a machine learning model 222.

[0039] The mobile terminal 210 (edge ​​side) can be a high-performance tablet or smartphone, equipped with a multi-core CPU, GPU, and high-speed Wi-Fi / 5G connectivity. Data is received from the glove via Bluetooth, and various parameters are extracted.

[0040] On the virtual hand model on the screen, a heatmap of color changes is dynamically rendered using color mapping (e.g., light blue -> dark blue -> green -> yellow -> red, representing a gradual increase in depth) based on real-time depth data from each sensor point. This allows the operator to visually see the distribution of pressure on their hand. Real-time force values ​​at key pressure points are displayed next to the virtual hand model in the form of numerical labels or dynamic bar charts. Using Unity3D or a similar engine, a virtual hand model is drawn in real-time in 3D space, its movement completely synchronized with the real hand. Simultaneously, the movement trajectory over the past few seconds is plotted, with colors used to differentiate speeds (e.g., blue for slow, red for fast). The application presets a basic safety threshold (e.g., a maximum allowable pressure of 12N). If the pressure at any sensor point exceeds this threshold, a high-priority alarm is immediately triggered to ensure safety.

[0041] The cloud platform 220 (cloud side) is built on cloud computing services (such as AWS, Azure, Alibaba Cloud) and includes web servers, application servers and database servers.

[0042] The Standard Technique Database 221 invited several renowned massage masters to perform the techniques under standard conditions while wearing gloves provided by this system. High-quality, multimodal sensor data was collected from their application of various standard techniques.

[0043] For each technique (such as "one-finger pushing method", "kneading method", "rubbing method", etc.), the database stores its standardized parameter templates, including but not limited to: Time-domain characteristics: the shape, period, and frequency of pressure-time curves and depth-time curves (e.g., the pressure method requires 120-160 times / minute).

[0044] Airspace characteristics: the contact pattern between the hand and the body surface, and the trajectory of the pressure center (such as the requirement for even force application across the entire palm in palm pressing techniques).

[0045] Dynamic characteristics: acceleration, impact force, etc.

[0046] The normal range, mean, and standard deviation for each characteristic parameter.

[0047] Machine learning model 222 employs a hybrid model combining a one-dimensional convolutional neural network and a long short-term memory network (LSTM). 1D-CNN excels at extracting features from local time series (such as the waveform of a single press), while LSTM is adept at capturing long-term temporal dependencies (such as the rhythm of a series of compound techniques).

[0048] During training, Input preprocessed and fused multi-sensor time-series data for a duration (e.g., 3 seconds), including depth, pressure, triaxial acceleration, and triaxial angular velocity.

[0049] Output 1 (Recognition): A multi-classification result that identifies the most likely type of technique (such as "pressing", "rubbing", "kneading" etc.).

[0050] Output 2 (Evaluation): A regression score for the quality of the current technique. By learning from high-quality techniques in a standard database, the model can assess how close the operator's technique is to the "gold standard" in terms of rhythm, evenness of force, and smoothness of trajectory, generating a score S_i for each sub-parameter.

[0051] The cloud platform supports online learning, and when new data from renowned experts is added, the model can be retrained to continuously improve the accuracy of recognition and evaluation.

[0052] The mobile terminal 210 uploads the received data to the cloud platform 220 in real time via Wi-Fi / 5G.

[0053] The machine learning model 222 of the cloud platform 220 performs real-time analysis of the data to complete the method recognition and quality assessment.

[0054] Based on the identified technique type, the corresponding standard parameter template is retrieved from the standard technique database 221.

[0055] Real-time data is compared with standard templates in detail to generate difference reports (e.g., "Current pressure is 15% too high", "Motion frequency is 20% too slow", "Trajectory smoothness is insufficient").

[0056] This difference report and assessment score will be sent to mobile terminal 210.

[0057] The feedback device 300 is a logical concept whose functions are implemented collaboratively by distributed hardware, providing multimodal immersive feedback, including a vibration unit 310, a display unit 320, and an audio unit 330.

[0058] Vibration unit 310 is a miniature high-response linear motor (such as an ERM or LRA motor used in smartphones) embedded inside the wrist strap of glove 101 or at the base of each finger. Vibration is triggered when the system determines that a certain parameter deviates from a target. For example, when the real-time pressure p deviates from the target range [p...] min ,p max When [the vibration intensity V is such that], the vibration intensity V is calculated by the following formula: ; in, This is the median of the target range, and k is an adjustable proportional coefficient. If the force is too high, the vibration is strong and rapid, indicating "reduce force"; if the force is too low, the vibration is weak and gentle, indicating "increase force". Vibration patterns (such as continuous, pulse) can also be used to distinguish different types of errors.

[0059] Display unit 320 is the screen of the mobile terminal (210). In addition to real-time visualization, it also provides: Text prompts, such as "Excellent!", "Please reduce the intensity", and "Pay attention to a smooth trajectory".

[0060] Graphical guidance overlays the correct motion path animation onto the virtual hand model, guiding the operator to imitate it.

[0061] The overall score is displayed in real time according to the formula. The calculated overall score, where w_i is the weight assigned to different techniques (e.g., in the "㨰 method," the weight w for scrolling frequency). frequency (higher), and Σ =1. Scores are displayed as a percentage or star rating, which is highly motivating.

[0062] The audio unit 330 utilizes the speaker of the mobile terminal 210 or a Bluetooth headset. Provided: The beeping sounds have different meanings depending on their frequency (e.g., a high frequency indicates that the parameter is too high, while a low frequency indicates that the parameter is too low).

[0063] Voice guidance: Synthesized speech directly delivers instructions, such as "Appropriate pressure, maintain rhythm."

[0064] The standardized calibration method for massage techniques includes the following steps: Step S1: The wearable sensing device 100 acquires the reflected light signal, pressure track signal and hand movement signal of the pressing part.

[0065] Step S2: In the data processing unit 140 and / or processing device 200, based on the reflected light signal, using the formula... Calculate real-time pressure depth .

[0066] Step S3: Based on the pressure signal, hand movement signal, and calculated pressure depth, determine the massage technique parameter set through data fusion (such as Kalman filtering) and feature extraction. ,For example trajectory .

[0067] Step S4: Compare the determined massage technique parameters with the standard parameter range of the corresponding technique stored in the standard technique database 221.

[0068] Step S5: Based on the comparison results, provide massage technique calibration feedback through feedback device 300. For example, calculate the vibration intensity based on the deviation. It also drives motor 311 to display prompts and scores on display screen 211. And voice instructions are played through speaker 331.

[0069] A non-volatile computer-readable storage medium (such as flash memory or SSD) storing a computer program that, when executed by a processor, implements all the steps of the above-described method flow.

[0070] A massage technique calibration device 500 includes a memory 510 and a processor 520. The memory 510 stores the computer program, and the processor 520 executes the program to implement the above-described method.

[0071] The following are some practical examples of the present invention: Case 1: The application of this invention in massage teaching and training is specifically manifested in the quantitative guidance and objective evaluation of the standardized technique acquisition process. In the embodiment described, the teaching of massage techniques is taken as an example. First, multimodal data generated during a demonstration of a standard massage technique by a renowned massage master is collected via a wearable sensing device 100. This includes pressure depth signals acquired by an optical detection unit 110, pressure intensity signals acquired by a pressure detection unit 120, and hand kinematic signals acquired by a motion sensing unit 130. The cloud platform 220 of the processing device 200 stores this data in a standard technique database 221 and trains a machine learning model 222 based on this database, thereby establishing a standard technique parameter template that includes the target force range, movement trajectory morphology characteristics, and operation frequency range. During the teaching process, students practice while wearing smart massage gloves 101. The mobile terminal 210 of the processing device 200 receives and parses the sensor data stream from the wearable sensing device 100 in real time. The machine learning model 222 identifies whether the student's current technique is a rolling technique and simultaneously calculates the real-time technique parameters. The processing device 200 compares the trainee's real-time technique parameters with the templates in the standard technique database 221 and calculates the degree of deviation. The feedback device 300 then activates a multimodal feedback mechanism: when the operation frequency is detected... Below the lower limit of the standard range When the pressure is too slow, the audio unit 330 emits a voice prompt; when the pressure detection unit 120 detects uneven pressure distribution, the vibration unit 310 generates gradient vibration in the corresponding hand area to prompt adjustment; simultaneously, the display unit 320 dynamically renders a heat map of the pressure depth and a comparison image of the motion trajectory superimposed on it. The processing device 200 also uses a formula... ; Calculate the overall score ,in For the first The weighting coefficients of each technique parameter, The scoring provides a quantitative basis for teaching assessment. This process realizes a paradigm shift in methodological teaching from experience-based transmission to data-driven approaches.

[0072] This case demonstrates that by establishing a standard technique database, real-time multi-parameter comparison, and a multimodal feedback mechanism, the present invention transforms abstract techniques into objective data, significantly improving the standardization level and teaching efficiency of massage teaching, and providing a quantifiable solution for the inheritance of techniques.

[0073] Case 2: In clinical massage procedures, this invention enables real-time safety monitoring and immediate risk warning of operational parameters, effectively ensuring medical safety. In a specific clinical embodiment, to address the potential risks associated with massage techniques applied to the neck area, this invention is configured as an active safety protection system. The clinician wears intelligent massage gloves 101 during the treatment. The mobile terminal 210 of the processing device 200 presets safety threshold parameters associated with neck massage techniques, such as the maximum permissible pressure. During operation, the pressure detection unit 120 continuously monitors the pressure channel at a high sampling rate. The data processing unit 140 uses a Kalman filter algorithm to fuse and filter the pressure data to obtain the optimal pressure estimate. The processing device 200 will process the optimal pressure estimate. With safety threshold Perform real-time comparisons. Once a decision is made... Upon receiving the alarm, the processing device 200 immediately generates a high-priority alarm command and sends it to the feedback device 300. The miniature linear motor 311 in the vibration unit 310 is driven to its maximum vibration intensity; the interface of the display unit 320 switches to a red warning screen and displays the over-limit information; the audio unit 330 simultaneously outputs a high-frequency alarm sound. This multimodal alarm mechanism ensures that physicians receive the danger signal at the first moment. Simultaneously, the processing device 200 records the timestamp, duration, and maximum pressure value of this over-limit event, forming a safety event log for subsequent quality analysis and traceability. This case demonstrates that by setting safety thresholds, implementing real-time data monitoring, and triggering tiered alarms, the present invention effectively reduces the medical risks of massage operations, provides reliable technical protection for clinical safety, and reflects its important value in medical quality control.

[0074] Case 3: This invention provides a technical platform for precise variable control and high-fidelity data acquisition in scientific research on massage techniques. In a study aiming to investigate the relationship between the frequency of kneading manipulation and the dose-response effect of soft tissue relaxation, this invention was used as the core experimental device. Researchers configured the processing device 200 to a specific mode to lock onto the target frequency variable. Research subjects were randomly divided into two groups; the target frequency for group A was set as follows: Group B is set as During intervention, the operator wears smart massage gloves 101. The processing device 200 analyzes in real time the operating frequency derived from data from the motion sensing unit 130 and the pressure detection unit 120. The display unit 320 of the mobile terminal 210 provides the operator with real-time frequency readings and deviation indicators from the target frequency, guiding the operator to precisely control the actual frequency within the target range. (For example Within the allowable error range. Meanwhile, the optical detection unit 110 is based on the formula... ; Calculate the pressure depth ,in The effective attenuation coefficient of the organization, In order to receive light intensity, To ensure consistent light intensity, all biomechanical parameters except frequency were maintained between the two groups. All sensor data were fully recorded and stored in the cloud platform 220's database, forming a complete and traceable manual dosage dataset. After the study, this objective dataset can be used for correlation statistical analysis with changes in the subjects' physiological indicators.

[0075] This case demonstrates that the present invention can achieve precise control of manipulation parameters and repeatable acquisition of high-quality data, providing a reliable experimental platform for the study of the mechanism of action of massage techniques and powerfully promoting the development of evidence-based medicine in the field of massage.

[0076] Case 4: This invention constructs a remote massage skills assessment and expert guidance system based on objective data streams. In this embodiment, a physician located in a remote hospital needs to undergo skills certification from a senior expert located in a central location. The applicant performs routine massage operations locally using a wearable sensing device 100 and a mobile terminal 210. The processing device 200 transmits multi-sensor data, a real-time visualization interface, and assessment parameters generated by a machine learning model 222 to a cloud platform 220 in real time via a communication network. The assessor accesses the cloud platform 220 through an authorized terminal, and can view a visualization interface that is completely synchronized with the applicant's mobile terminal 210 in real time, and can access detailed playback of historical operation data. The assessor conducts remote assessments based on the specifications of the standard techniques database 221, combined with the objective data provided by the processing device 200. The assessor can provide precise guidance suggestions based on specific data segments through the system's built-in communication module. For the certification process, the assessor can set certification thresholds, such as requiring continuous... Overall score of this operation The mean is greater than the threshold The system automatically determines whether the applicant meets the standards and generates an electronic certification report. This embodiment demonstrates that the present invention can overcome geographical limitations and achieve remote skills assessment and guidance based on unified and objective standards.

[0077] This case demonstrates that the present invention, through cloud data synchronization and remote visual guidance, breaks geographical limitations and establishes an objective and transparent skills assessment system, providing an effective way for the standardized promotion of massage techniques and the dissemination of high-quality resources.

[0078] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A real-time visualization and dynamic calibration platform for standardizing massage techniques, characterized in that, include: Wearable sensing device (100), including: An optical detection unit (110) is used to emit detection light to the pressing area and receive reflected light signals; A pressure detection unit (120) is used to monitor the pressing pressure channel in real time; A motion sensing unit (130) is used to collect hand motion data; The processing device (200), communicatively connected to the wearable sensing device (100), is used for: The parameters of massage techniques are evaluated based on reflected light signals, pressure points, and hand movement data. The parameters of the evaluated massage techniques were compared with a standard technique database; Provide calibration feedback based on the comparison results; The massage technique parameters include at least one of the following: pressure depth, pressure intensity, pressure frequency, and movement trajectory.

2. The real-time visualization and dynamic calibration platform for standardizing massage techniques according to claim 1, characterized in that, The wearable sensing device (100) is a smart massage glove (101). The optical detection unit (110) includes multiple miniature light sources (111) and photodetectors (112) disposed near the fingertips and / or palm of the glove. The pressure detection unit (120) includes multiple flexible pressure sensors (121) distributed in the contact area of ​​the glove. The motion sensing unit (130) includes an inertial measurement unit (131).

3. The real-time visualization and dynamic calibration platform for standardizing massage techniques according to claim 2, characterized in that, The micro light source (111) includes at least two different wavelengths of LED light source, the at least two different wavelengths being selected from the visible light band and the near-infrared light band; the photodetector (112) is arranged in pairs with the micro light source (111) to form a distributed optical detection array; the pressure detection range of the flexible pressure sensor (121) is 0-10N, and the accuracy is ±0.1N; the inertial measurement unit (131) is a 9-axis IMU with a sampling rate of not less than 100Hz.

4. The real-time visualization and dynamic calibration platform for standardizing massage techniques according to claim 1, characterized in that, The processing device (200) calculates the pressing depth d based on the reflected light signal from the optical detection unit (110), using the following formula: ； Where I is the received light intensity denoted as , where is the emitted light intensity, and μ is the tissue's absorption coefficient of the detection light.

5. The real-time visualization and dynamic calibration platform for standardizing massage techniques according to claim 1, characterized in that, The processing device (200) includes a mobile terminal (210) and a cloud platform (220). The mobile terminal (210) is used to display the pressure depth heat map, pressure intensity distribution map and hand movement trajectory in real time. The cloud platform (220) stores the standard technique database (221) and the machine learning model (222). The standard technique database (221) includes the standard parameter range of eight basic techniques: pressing, rubbing, pushing, grasping, kneading, pinching, pointing and flicking. The machine learning model (222) identifies the type of massage technique and evaluates the quality of the technique based on multi-sensor data.

6. The real-time visualization and dynamic calibration platform for standardizing massage techniques according to claim 1, characterized in that, The system also includes a feedback device (300) for providing the calibration feedback, the feedback device (300) including at least one of a vibration unit (310), a display unit (320), and an audio unit (330), when the pressure intensity p deviates from the target force range [p min p max When [the vibration is detected], vibration feedback is triggered. The vibration intensity V is related to the degree of deviation and is calculated as follows: ； Where k is the proportionality coefficient. The target pressure value.

7. The real-time visualization and dynamic calibration platform for standardizing massage techniques according to claim 1, characterized in that, The processing device (200) calculates a comprehensive score S based on the extracted massage technique parameters, using the following formula: ； in These are the weighting coefficients. The score for the i-th massage technique parameter, i=1,2,…,n, where n is the number of massage technique parameters, Σ =1.

8. The real-time visualization and dynamic calibration platform for standardizing massage techniques according to claim 1, characterized in that, The wearable sensing device (100) further includes a data processing unit (140) and a wireless communication unit (150). The data processing unit (140) is used to preprocess and fuse optical signals, pressure signals, and motion data. The wireless communication unit (150) is used to transmit the processed data to the processing device (200). The data processing unit (140) uses a Kalman filter algorithm for data fusion. The state vector x is represented as: ； Where d is the pressing depth, v is the velocity, a is the acceleration, and p is the pressure.

9. A standardized calibration method for massage techniques, characterized in that, Includes the following steps: Step S1: Acquire the reflected light signal, pressure track signal and hand movement signal of the pressing part through the wearable sensing device (100); Step S2: Calculate the pressing depth based on the reflected light signal; Step S3: Determine the massage technique parameters based on the pressure signal, hand movement signal, and calculated pressure depth; Step S4: Compare the determined massage technique parameters with the standard parameters in the standard technique database; Step S5: Provide massage technique calibration feedback based on the comparison results.

10. A non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the massage technique standardization calibration method of claim 9, and a massage technique calibration device (500) comprising a memory (510) and a processor (520), wherein the memory (510) stores the computer program and the processor (520), when executing the program, implements the method of claim 9.

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