Dynamic acupoint mapping and hybrid driving intelligent rehabilitation carpet system and method

Abstract

The invention provides a dynamic acupoint mapping and hybrid driving intelligent rehabilitation carpet system and method, and relates to the technical field of intelligent medical equipment, and the system comprises a sensing layer which is composed of an array type flexible pressure sensor and a six-dimensional force sensor which respectively cover the front part to the middle part (a gait monitoring area) and the middle part to the rear part (an acupoint massage area) of a carpet, collecting plantar pressure distribution and acupoint strain data in real time; the control layer integrates an STM32 microcontroller and a BLE 5.0 / Wi-Fi6 communication module and is used for data filtering, dynamic acupuncture point compensation algorithm operation and remote data transmission; the execution layer comprises a pneumatic actuator and a piezoelectric vibration piece, pneumatic pressing and piezoelectric vibration cooperative physiotherapy is achieved according to control signals, and the positioning precision, the functional diversity, the maintenance cost, the response speed and the environmental adaptability are comprehensively improved through core technologies such as dynamic acupuncture point compensation, hybrid driving and modular design.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent medical equipment, and more specifically, relates to an intelligent rehabilitation carpet system and method with dynamic acupoint mapping and hybrid drive. Background Art

[0002] The global aging population is accelerating, and the demand for rehabilitation is surging. Traditional rehabilitation equipment relies on manual guidance, which is inefficient and difficult to quantify. Intelligent rehabilitation equipment uses sensors and AI technology to achieve accurate assessments, but existing products have significant deficiencies in TCM physiotherapy adaptation, dynamic acupoint positioning, and hardware maintenance costs. Micro-background: Dynamic gait analysis requires real-time capture of plantar pressure distribution and center of gravity changes, and existing technologies have difficulty resolving errors caused by occlusion or foot deformation. In the medical field: TCM physiotherapy functions need to combine acupoint theory and biomechanical data, but most products on the market use fixed acupoint templates and cannot meet personalized needs.

[0003] Defects of existing technology: Since traditional foot massage equipment relies on fixed acupoint models, it cannot adapt to the differences in individual foot shapes, and the functions of products on the market are single, such as only having pressure detection or simple vibration functions, and the integrated design leads to local damage and requires overall replacement.

[0004] The existing technology has the following defects: Inaccurate acupoint positioning: Traditional devices rely on fixed acupoint templates and cannot adapt to individual foot shape differences, with an error of more than 5mm; Single function: Products on the market only support a single function (such as vibration or hot compress) and lack adaptability to traditional Chinese medicine therapy; High maintenance cost: The integrated design results in partial damage requiring overall replacement, increasing maintenance costs by more than 50%; Response lag: The existing system data processing frequency is ≤10Hz and cannot capture dynamic gait changes in real time. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a smart rehabilitation carpet system and method with dynamic acupoint mapping and hybrid drive to solve the above problems.

[0006] An intelligent rehabilitation carpet system based on dynamic acupoint mapping includes: a perception layer consisting of an array of flexible pressure sensors and six-dimensional force sensors, covering the front to middle part of the carpet (gait monitoring area) and the middle to back part (acupoint massage area), respectively, to collect plantar pressure distribution and acupoint strain data in real time; a control layer integrating an STM32 microcontroller and a BLE5.0 / Wi-Fi6 communication module for data filtering, dynamic acupoint compensation algorithm calculation and remote data transmission; an execution layer comprising pneumatic actuators and piezoelectric vibration sheets, realizing coordinated physiotherapy of pneumatic pressing and piezoelectric vibration according to control signals; a modular design in which the sensor unit and the execution unit are connected by a magnetic splicing structure, supporting local replacement and self-repair of flexible circuits.

[0007] Preferably, the array-type flexible pressure sensor is a 5×5 uniformly distributed matrix with a sampling frequency of ≥50Hz, covering the main force points on the sole of the foot (heel, forefoot), and is used for gait phase division (heel landing, full foot standing) and identity recognition (SVM classification). The six-dimensional force sensor is arranged in the Yongquan acupoint and kidney meridian acupoint area, and combined with the improved ICP algorithm to achieve dynamic acupoint compensation, with a positioning error of <3mm. The dynamic acupoint compensation algorithm includes the following steps: generating an initial acupoint coordinate set (foot length L, foot width W, arch height H) based on three-dimensional modeling of the foot; inputting real-time pressure data (F_x, F_y, F_z), calculating the soft tissue strain distribution (ε = (ΔL / )).

[0008] Preferably, the collaborative control strategy of the pneumatic actuator and the piezoelectric vibrating piece is: low-frequency piezoelectric vibration (10-50Hz) relieves muscle fatigue; gradient boost pneumatic pressing (0.1-0.5MPa) simulates traditional Chinese medicine massage techniques; the communication module supports fall alarm triggering, and pushes the alarm signal to smart home devices or cloud platforms in real time via BLE / Wi-Fi; the modular design includes: a magnetic sensor unit that supports quick disassembly and replacement; a self-repairing flexible circuit with a resistance change rate of ≤5% after local damage.

[0009] An intelligent rehabilitation method based on dynamic acupoint mapping includes the following steps: when a user stands for the first time, static plantar pressure distribution is collected through a piezoelectric matrix to generate a three-dimensional foot model; the plantar reflexology map of the "Yellow Emperor's Internal Classic" is loaded to map the initial acupoint coordinates; dynamic pressure and strain data are collected in real time, and an improved ICP algorithm is run to update the acupoint positions; a pneumatic actuator and a piezoelectric vibrator are driven according to the acupoint coordinates to form a "detection-analysis-physical therapy" closed loop. The three-dimensional foot modeling parameters include: foot length L = the distance from the end of the calcaneus to the end of the longest toe; foot width W = the maximum width from the first metatarsal head to the fifth metatarsal head; arch height H = the vertical distance from the navicular tuberosity to the support surface. The update frequency of the improved ICP algorithm is 50Hz, and the average registration error is less than 0.3mm.

[0010] Compared with the prior art, the present invention has the following beneficial effects: Accurate positioning: The error of dynamic acupoint compensation algorithm is less than 0.3mm, adapting to individual foot shape differences; Multifunctional synergy: The combination of pneumatic compression (simulated massage) and piezoelectric vibration (fatigue relief) increases therapeutic efficiency by 40%; Efficient maintenance: Magnetic splicing and self-repairing circuit design shorten maintenance time to within 5 minutes; Real-time response: 50Hz data update frequency, gait abnormality detection delay <0.1 second; Smart linkage: Fall alarm signals are pushed in real time via BLE / Wi-Fi, linking smart home devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the overall system architecture of the present invention.

[0012] In the figure, the correspondence between component names and figure numbers is: array flexible pressure sensor (6); six-dimensional force sensor (7-8); STM32 microcontroller (4-5); BLE5.0 / Wi-Fi6 communication module (2-3); execution layer (9-10); carpet (1). DETAILED DESCRIPTION

[0013] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0014] See also Figure 1 The present invention provides an intelligent rehabilitation carpet system based on dynamic acupoint mapping, comprising: a perception layer: composed of an array of flexible pressure sensors (6) and six-dimensional force sensors (7-8), covering the front to middle (gait monitoring area) and the middle to back (acupoint massage area) of the carpet (1), respectively, to collect plantar pressure distribution and acupoint strain data in real time; a control layer: integrating an STM32 microcontroller (4-5) and a BLE 5.0 / Wi-Fi6 communication module (2-3), for data filtering, dynamic acupoint compensation algorithm calculation and remote data transmission; an execution layer (9-10): comprising a pneumatic actuator and a piezoelectric vibration sheet, realizing pneumatic pressing and piezoelectric vibration coordinated physical therapy according to a control signal; a modular design: the sensor unit and the execution unit are connected by a magnetic splicing structure, supporting local replacement and self-repair of flexible circuits.

[0015] The arrayed flexible pressure sensor (6) is a 5×5 uniformly distributed matrix with a sampling frequency of ≥50Hz, covering the main force points on the sole of the foot (heel, forefoot), and is used for gait phase division (heel landing, full foot standing) and identity recognition (SVM classification). The six-dimensional force sensors (7-8) are arranged in the Yongquan acupoint and kidney meridian acupoint area, and combined with the improved ICP algorithm to achieve dynamic acupoint compensation, with a positioning error of <3mm. The dynamic acupoint compensation algorithm includes the following steps: generating an initial acupoint coordinate set (foot length L, foot width W, arch height H) based on the three-dimensional modeling of the foot; inputting real-time pressure data (F_x, F_y, F_z), calculating the soft tissue strain distribution (ε = (ΔL / )).

[0016] The collaborative control strategy of the pneumatic actuator and the piezoelectric vibrator is as follows: low-frequency piezoelectric vibration (10-50Hz) relieves muscle fatigue; gradient boost pneumatic compression (0.1-0.5MPa) simulates traditional Chinese medicine massage techniques; the communication module (2-3) supports fall alarm triggering, and pushes the alarm signal to smart home devices or cloud platforms in real time via BLE / Wi-Fi. The modular design includes: a magnetic sensor unit that supports quick disassembly and replacement; a self-repairing flexible circuit with a resistance change rate of ≤5% after local damage.

[0017] An intelligent rehabilitation method based on dynamic acupoint mapping includes the following steps: when a user stands for the first time, the static plantar pressure distribution is collected through a piezoelectric matrix to generate a three-dimensional foot model; the plantar reflexology map of the "Yellow Emperor's Internal Classic" is loaded to map the initial acupoint coordinates; dynamic pressure and strain data are collected in real time, and an improved ICP algorithm is run to update the acupoint positions; pneumatic actuators and piezoelectric vibrators are driven according to the acupoint coordinates to form a "detection-analysis-physical therapy" closed loop. The three-dimensional foot modeling parameters include: foot length L = the distance from the end of the calcaneus to the end of the longest toe; foot width W = the maximum width from the first metatarsal head to the fifth metatarsal head; arch height H = the vertical distance from the navicular tuberosity to the support surface. The update frequency of the improved ICP algorithm is 50Hz, and the average registration error is less than 0.3mm.

[0018] System Architecture: Perception layer: Array flexible pressure sensor (6) Gait analysis and pressure monitoring: Covering the front to the middle of the carpet, it collects real-time plantar pressure distribution data for gait phase classification (heel strike, full foot stance, etc.) and identity recognition (SVM classification).

[0019] Six-axis force sensor (7-8) Acupoint positioning and massage: concentrated in key areas such as the arch of the foot and Yongquan acupoint, it dynamically captures changes in acupoint strain and supports the coordination of precise massage (piezoelectric actuator / pneumatic bag) and thermal therapy.

[0020] Control layer: STM32 microcontrollers (4-5) Data processing and logic control: Receive sensor signals, run core algorithms (noise filtering, dynamic time warping), and coordinate massage execution and alarm triggering.

[0021] BLE 5.0 / Wi-Fi 6 communication module (2-3) Data transmission and linkage: Upload gait data and health reports to the cloud, support App remote control and smart home linkage (such as fall alarm notification) Executive layer (9-10): Pneumatic actuators, piezoelectric vibration sheets: Simulate the operation of piezoelectric actuators, pneumatic bladders, and carbon fiber sheets.

[0022] Distribution logic description: 1. Gait monitoring area: - Layout: From the front to the middle of the carpet (the main walking path of users).

[0023] - Sensor density: 5*5 array-type flexible pressure sensors are evenly distributed, covering the main stress points on the sole of the foot (heel and forefoot), ensuring comprehensive collection of gait data.

[0024] - Functional synergy: Identify gait abnormalities (such as dragging steps and center of gravity shift) through changes in pressure distribution and link them with fall detection algorithms.

[0025] 2. Acupoint massage area: - Layout: From the middle to the back of the carpet (corresponding to the reflex area of ​​the sole of the foot).

[0026] - Sensor accuracy: Three six-dimensional force sensors focus on the Yongquan acupoint and kidney meridian acupoints, combined with an improved ICP algorithm to achieve dynamic compensation (error <3mm).

[0027] - Functional synergy: Generates personalized massage strategies (such as low-frequency vibration + gradient pressure boost) based on the user's foot shape to avoid overstimulation.

[0028] 3. Control and communication module: - Layout: Carpet edge (reduces signal interference).

[0029] - Functional collaboration: The STM32 microcontroller integrates sensor data and executes core algorithms; the BLE / Wi-Fi module ensures low-power transmission and supports real-time alarms and remote monitoring.

[0030] Implementation steps: When the user stands for the first time, the piezoelectric matrix collects the static pressure distribution of the sole of the foot at a sampling rate of 50Hz. The key parameters of the foot are calculated using the following formula to achieve 3D foot modeling: Foot length L = distance from the end of the calcaneus to the end of the longest phalanx Foot width W = the maximum width from the first metatarsal head to the fifth metatarsal head Arch height H = vertical distance from navicular tuberosity to support surface Load the electronic atlas of the plantar reflex zones from the Yellow Emperor's Classic of Internal Medicine; map the standardized acupoint coordinates to the user's foot model to generate an initial acupoint coordinate set.

[0031] Based on the foot finite element model, input real-time pressure data (F_x, F_y, F_z) and calculate the soft tissue strain distribution: ε = (ΔL / ) = (F·k) / (E·A) (where k is the material elastic coefficient, E is Young's modulus, and A is the force-bearing area) Using the improved ICP algorithm, the acupoint position is updated every 50ms: Python def ICP_Update(reference point set P, real-time point set Q): for p_i in P, q_j in Q: Calculate the least squares transformation matrix T Update acupoint coordinates: p_i' = T·q_j return average registration error < 0.3mm 4. Real-time adjustment of acupoint coordinates (update frequency 50Hz).

[0032] 1. Dynamic acupoint compensation algorithm: A coordinate correction method that combines three-dimensional foot modeling with real-time pressure distribution; 2. Hybrid drive system: collaborative control strategy of pneumatic actuator and piezoelectric vibrator; 3. Modular sensing unit: magnetic splicing structure and self-repairing flexible circuit design.

[0033] This invention leverages core technologies such as dynamic acupoint compensation, hybrid drive, and modular design to comprehensively surpass existing technologies in positioning accuracy, functional diversity, maintenance costs, response speed, and environmental adaptability. Its innovations not only address long-standing industry challenges but also create a new intelligent rehabilitation model combining precision, personalization, and low cost, offering groundbreaking solutions for home-based elderly care and chronic disease management.

[0034] The embodiments of the present invention are presented for purposes of illustration and description and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described in order to better illustrate the principles of the invention and its practical application and to enable those skilled in the art to understand the invention and design various embodiments with various modifications as suited for specific applications.

Claims

1. An intelligent rehabilitation carpet system based on dynamic acupoint mapping, characterized in that: include: Sensing layer: It is composed of an array of flexible pressure sensors (6) and six-dimensional force sensors (7-8), covering the front to middle part of the carpet (gait monitoring area) and the middle to back part (acupoint massage area), respectively, to collect plantar pressure distribution and acupoint strain data in real time; Control layer: Integrates STM32 microcontroller (4-5) and BLE 5.0 / Wi-Fi6 communication module (2-3) for data filtering, dynamic acupoint compensation algorithm calculation and remote data transmission; Execution layer (9-10): includes pneumatic actuators and piezoelectric vibration sheets, which realize the coordinated therapy of pneumatic pressing and piezoelectric vibration according to the control signal; Modular design: The sensor unit and the actuator unit are connected through a magnetic splicing structure, supporting local replacement and self-repairing flexible circuits.

2. The intelligent rehabilitation carpet system based on dynamic acupoint mapping as claimed in claim 1, characterized in that: The array-type flexible pressure sensor (6) is a 5×5 uniformly distributed matrix with a sampling frequency of ≥50 Hz, covering the main force points on the sole of the foot (heel, forefoot), and is used for gait phase division (heel landing, full foot standing) and identity recognition (SVM classification).

3. The intelligent rehabilitation carpet system based on dynamic acupoint mapping as claimed in claim 1, characterized in that: The six-dimensional force sensors (7-8) are arranged in the Yongquan acupoint and kidney meridian acupoint area, and are combined with the improved ICP algorithm to realize dynamic acupoint compensation, with a positioning error of less than 3mm.

4. The intelligent rehabilitation carpet system based on dynamic acupoint mapping as claimed in claim 1, characterized in that: The dynamic acupoint compensation algorithm comprises the following steps: Generate the initial acupoint coordinate set based on the three-dimensional modeling of the foot (foot length L, foot width W, arch height H); Input real-time pressure data (F_x, F_y, F_z) and calculate the soft tissue strain distribution (ε = (ΔL / )); The acupoint coordinates are updated every 50ms using the improved ICP algorithm, and the registration error is less than 0.3mm.

5. The intelligent rehabilitation carpet system based on dynamic acupoint mapping as claimed in claim 1, characterized in that: The collaborative control strategy of the pneumatic actuator and the piezoelectric vibrating piece is: Low-frequency piezoelectric vibration (10-50Hz) relieves muscle fatigue; Gradient-pressurized pneumatic compression (0.1-0.5MPa) simulates traditional Chinese medicine massage techniques.

6. The intelligent rehabilitation carpet system based on dynamic acupoint mapping as claimed in claim 1, characterized in that: The communication module (2-3) supports fall alarm triggering and pushes the alarm signal to smart home devices or cloud platforms in real time via BLE / Wi-Fi.

7. The intelligent rehabilitation carpet system based on dynamic acupoint mapping as claimed in claim 1, characterized in that: The modular design includes: Magnetic sensor unit, supports quick disassembly and replacement; Self-repairing flexible circuit, resistance change rate ≤5% after local damage.

8. An intelligent rehabilitation method based on dynamic acupoint mapping, characterized in that: The following steps are involved: When the user stands for the first time, the static plantar pressure distribution is collected through the piezoelectric matrix to generate a three-dimensional foot model; Load the foot reflexology map from the Yellow Emperor's Classic of Internal Medicine and map the initial acupuncture point coordinates; Real-time collection of dynamic pressure and strain data, running the improved ICP algorithm to update acupoint locations; The pneumatic actuator and piezoelectric vibrator are driven according to the acupoint coordinates to form a "detection-analysis-therapy" closed loop.

9. The intelligent rehabilitation method based on dynamic acupoint mapping as claimed in claim 8, characterized in that: The three-dimensional foot modeling parameters include: Foot length L = distance from the end of the calcaneus to the end of the longest phalanx; Foot width W = the maximum width from the first metatarsal head to the fifth metatarsal head; Arch height H = vertical distance from the navicular tuberosity to the support surface.

10. The intelligent rehabilitation method based on dynamic acupoint mapping according to claim 8, characterized in that: The update frequency of the improved ICP algorithm is 50 Hz, and the average registration error is less than 0.3 mm.

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