Device adaptive control system based on multimodal neural perception and user feedback

Abstract

The present invention discloses a device adaptive control system based on multimodal neural perception and user feedback. Through multimodal neural perception feedback and dynamic analysis models, it realizes millisecond-level adaptive adjustment of device parameters, solving the problems of insufficient dynamic evaluation and algorithm lag of traditional equipment, significantly improving user experience (pressure comfort increased by 40%, temperature stability reaches ±0.3°C) and energy efficiency (energy saving 21.3%), and the response speed is increased by 52.7% compared with traditional solutions. It is suitable for scenarios such as massage chairs and smart homes. The key indicators certified by CNAS are improved by more than 40% compared with the existing technology. It has significant technological advancement and market value, and solves the problems of existing massage equipment control systems, which, on the one hand, lack dynamic evaluation of the real-time status of the user's muscle tension and fatigue level, and on the other hand, are difficult to cope with the nonlinear characteristics of changing physiological signals.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence and intelligent control, and in particular to a device adaptive control system based on user pain feedback and multimodal neural perception feedback. Background Art

[0002] Traditional massage equipment mainly relies on preset programs for operation, which has the following defects:

[0003] Insufficient personalization: Lack of dynamic assessment of the user's real-time muscle tension and fatigue level;

[0004] Algorithm hysteresis: Existing devices mostly use fixed rule control, which is difficult to cope with the nonlinear characteristics of changing physiological signals;

[0005] For example, existing patents have proposed massage machines based on pressure feedback, but they fail to integrate multimodal data. Others have proposed using electromyographic signals as a reference, but similarly, they lack a dynamic feedback mechanism. Therefore, there is an urgent need for an adaptive control system that integrates modal perception and intelligent algorithms. Summary of the Invention

[0006] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0007] In view of the above-mentioned problems existing in the existing massage equipment control system, the present invention is proposed.

[0008] Therefore, the technical problem solved by the present invention is to solve the problem that the existing massage equipment control system lacks dynamic evaluation of the real-time status of the user's muscle tension and fatigue level on the one hand, and is difficult to cope with the nonlinear characteristics of changing physiological signals on the other hand.

[0009] To solve the above technical problems, the present invention provides the following technical solutions: an adaptive control system for equipment based on multimodal neural perception and user feedback, comprising the following components: a data signal acquisition module, which collects signal data during the massage process based on a configured sensor array; a data processing module, which is wirelessly connected to the data signal acquisition module to obtain each signal data; a data analysis module, which is data-connected to the data processing module, establishes an analysis model based on the processed signal data, inputs each signal data, outputs an analysis value, and transmits the analysis value to an execution module; and an execution module, which performs adaptive adjustment of relevant parameters based on the analysis value output by the data analysis module.

[0010] As a preferred solution of the device adaptive control system based on multimodal neural perception and user feedback described in the present invention, the signal data collected by the data signal acquisition module specifically includes: pressure sensing data: the pressure sensor is distributedly installed on the surface of the massage head, with a sampling frequency of ≥100Hz, for detecting muscle contact pressure and obtaining the pressure sensing data; temperature sensing data: the temperature sensor is embedded in the massage pad, and monitors the skin surface temperature in real time with an accuracy of ±0.1°C to obtain the temperature sensing data; electromyographic signal sensing data: the electromyographic signal sensor collects the electromyographic signal (EMG) of the target muscle group through an electrode sheet, with a bandwidth of 5Hz-500Hz, to obtain the electromyographic signal sensing data; wherein, each muscle group is equipped with a set of pressure sensors, temperature sensors and electromyographic signal sensors.

[0011] As a preferred solution of the device adaptive control system based on multimodal neural perception and user feedback described in the present invention, the data processing module specifically includes: after collecting the electromyographic signal sensing data, high-pass filtering, high-magnification and low-pass filtering conditioning steps are required.

[0012] As a preferred solution of the device adaptive control system based on multimodal neural perception and user feedback described in the present invention, wherein: the data analysis module establishes the analysis model based on the processed signal data, and outputs the analysis value specifically including the following steps: S1: within the detection time period of the electromyographic signal sensor, the electromyographic signal voltage value δ is collected at uniform intervals; S2: constructing a two-dimensional coordinate system, inputting the collected electromyographic signal voltage value δ, obtaining each reference point, and obtaining a time-voltage fluctuation curve based on each reference point; S3: establishing the analysis model, and extracting the fluctuation characteristic value η based on the time-voltage fluctuation curve; S4: defining the fluctuation characteristic value η as the analysis value.

[0013] As a preferred solution of the device adaptive control system based on multimodal neural perception and user feedback described in the present invention, when collecting the electromyographic signal voltage value δ, it is acquired at intervals of 1 ms; when establishing the time-voltage fluctuation curve, discrete points are depicted with time as the horizontal axis and voltage as the vertical axis, and each reference point is connected in sequence with a continuous and uninterrupted smooth curve to form the time-voltage fluctuation curve.

[0014] As a preferred solution of the device adaptive control system based on multimodal neural perception and user feedback described in the present invention, the established analysis model is specifically:

[0015]

[0016] Wherein, η is the analytical value; k i is the transformation slope at each reference point i; n is the number of reference points; δi is the ordinate of each reference point i; 1.02 is the robust adjustment constant.

[0017] As a preferred solution of the device adaptive control system based on multimodal neural perception and user feedback described in the present invention, the execution module adaptively adjusts the relevant parameters according to the analysis value, specifically including: regulating the applied pressure according to the analysis value, and the magnitude of the applied pressure is sensed by the pressure sensor; regulating the applied temperature according to the analysis value, and the magnitude of the applied temperature is sensed by the temperature sensor.

[0018] As a preferred solution of the device adaptive control system based on multimodal neural perception and user feedback of the present invention, the control of the applied pressure according to the analysis value is specifically based on the following model:

[0019]

[0020] Wherein, a' is the applied pressure after adjustment, N; a is the applied pressure before adjustment, N; η is the analytical value.

[0021] As a preferred solution of the device adaptive control system based on multimodal neural perception and user feedback of the present invention, the control of the applied temperature according to the analysis value is specifically based on the following model:

[0022]

[0023] Wherein, b' is the applied temperature after adjustment, °C; b is the applied temperature before adjustment, °C; η is the analysis value; and t is the current ambient temperature, °C.

[0024] Beneficial effects of the present invention: The present invention provides an equipment adaptive control system based on multimodal neural perception and user feedback. Through multimodal neural perception feedback and dynamic analysis models, millisecond-level adaptive adjustment of equipment parameters is achieved, which solves the problems of insufficient dynamic evaluation and algorithm lag of traditional equipment, significantly improves user experience (pressure comfort is improved by 40%, temperature stability reaches ±0.3°C) and energy efficiency (energy saving is 21.3%), and the response speed is increased by 52.7% compared with traditional solutions. It is suitable for scenarios such as massage chairs and smart homes. The key indicators certified by CNAS are improved by more than 40% compared with the existing technology. It has significant technological advancement and market value, and solves the problems of existing massage equipment control systems, on the one hand, lacking dynamic evaluation of the real-time status of user muscle tension and fatigue level, and on the other hand, being difficult to cope with the nonlinear characteristics of changing physiological signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0026] Figure 1 This is a system module diagram of the device adaptive control system based on multimodal neural perception and user feedback provided by the present invention.

[0027] Figure 2 The data analysis module provided by the present invention establishes an analysis model based on the processed signal data and outputs an analysis value. DETAILED DESCRIPTION

[0028] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0029] The existing massage equipment control system has the problem that on the one hand, it lacks dynamic assessment of the real-time status of the user's muscle tension and fatigue level, and on the other hand, it is difficult to cope with the nonlinear characteristics of changing physiological signals.

[0030] Specifically, the defects of the existing equipment control system (with experimental data):

[0031] (1) Parameter solidification problem:

[0032] Comparative Experiment 1: Traditional massage chair fixed program group in a test of 10 subjects:

[0033] Pressure distribution matching: 62.3%±8.7%;

[0034] User-initiated adjustments: manual intervention is required every 8.2 minutes on average.

[0035] Energy consumption fluctuation range: 35-72W (fluctuation rate 103%);

[0036] (2) Defects in the control algorithm:

[0037] Comparative Experiment 3: Traditional PID Control in Dynamic Load Test:

[0038] Overshoot: 28.6% (industry standard ≤15%);

[0039] Adjustment time: 1.8 seconds (target value 0.3 seconds);

[0040] Anti-interference ability: fails when the load changes by ±10%.

[0041] Therefore, the present invention provides an adaptive control system for devices based on multimodal neural perception and user feedback, specifically an adaptive control system that integrates bioelectric signal analysis and physical constraint optimization, which is suitable for scenarios such as massage chairs (Example 1), smart homes (Example 2), and industrial robots (Example 3).

[0042] See Figure 1 The device adaptive control system based on multimodal neural perception and user feedback includes the following components:

[0043] The data signal acquisition module 100 collects signal data during the massage process based on the configured sensor array;

[0044] The data processing module 200 is connected to the data signal acquisition module 100 via wireless signals to obtain the data of each signal;

[0045] The data analysis module 300 is connected to the data processing module 200, establishes an analysis model based on the processed signal data, inputs the signal data, outputs the analysis value, and transmits the analysis value to the execution module;

[0046] The execution module 400 performs adaptive adjustment of relevant parameters according to the analysis value output by the data analysis module 300 .

[0047] Specifically, the signal data collected by the data signal acquisition module 100 specifically includes:

[0048] Pressure sensing data: Pressure sensors are distributedly installed on the surface of the massage head with a sampling frequency of ≥100Hz to detect muscle contact pressure and obtain pressure sensing data;

[0049] Temperature sensing data: The temperature sensor is embedded in the massage pad to monitor the skin surface temperature in real time with an accuracy of ±0.1°C and obtain temperature sensing data;

[0050] Myoelectric signal sensing data: The myoelectric signal sensor collects the electromyographic signal (EMG) of the target muscle group through electrodes with a bandwidth of 5Hz-500Hz to obtain myoelectric signal sensing data;

[0051] Each muscle group is equipped with a set of pressure sensors, temperature sensors and electromyographic signal sensors.

[0052] It should be noted that the pressure sensor, temperature sensor and electromyographic signal sensor involved in the present invention are all direct applications of existing conventional electronic sensors, and no unnecessary details are given here.

[0053] Furthermore, the data processing module 200 specifically includes the following steps: after collecting the electromyographic signal sensing data, it is necessary to perform high-pass filtering, high-magnification amplification and low-pass filtering conditioning steps.

[0054] It should be noted that surface electromyography (EMG) is the electrical signal accompanying muscle contraction and is an important method for noninvasively detecting muscle activity on the body surface. Its application background is mainly concentrated in the fields of rehabilitation medicine and sports science.

[0055] With the advancement of detection technology and signal processing techniques, using SEMG to replace needle-electrode EMG for comprehensive clinical non-invasive diagnosis has become a hot topic in biomedical and medical research. Human SEMG is weak, susceptible to interference, and difficult to measure. Therefore, effectively acquiring and extracting SEMG has become a key technology in SEMG applications.

[0056] Most of the SEMG acquisition devices used transmit the collected SEMG via wires to a data acquisition card or specific medical instrument for analysis and processing. The sampling electrodes are attached to the skin surface, and the power supply for the electrodes is indirectly derived from the mains. This method has the following significant shortcomings: (1) It requires a dedicated instrument to complete the signal acquisition, which limits its application, such as being unable to collect SEMG from the limbs of athletes during sprinting; (2) Because of the wire connection, when the limbs move, it is easy to cause relative movement between the electrodes and the skin, affecting the acquisition results; (3) The power supply is obtained after being processed from the mains, which introduces a large amount of power frequency interference, increasing the difficulty of data processing; (4) If the wire is long, it is very easy to introduce other interference from the environment, such as electromagnetic interference.

[0057] After reviewing the prior art, the present invention also provides a miniature, wireless, battery-powered SEMG acquisition device with data storage function, which effectively solves the common problems of current SEMG acquisition devices.

[0058] Hardware circuit design

[0059] According to neurophysiology, a muscle action potential generates a potential difference between -90mV and 30mV. Because the human body is a poor conductor of electricity (with an internal resistance on the order of 1MΩ), only a peak value of approximately 1mV can be detected from surface electrodes. Literature indicates that SEMG often contains a mixture of very low-frequency (near DC) and high-frequency interference signals, while the effective EMG signal spectrum is distributed between 10-500Hz. Therefore, the signals detected from the patch electrodes require signal conditioning, including high-pass filtering (DC isolation), high-magnification, and low-pass filtering (to remove high-frequency interference).

[0060] The SEMG acquisition device uses Ag / AgCl surface electrodes, which have a low polarization voltage and can quickly acquire stable myoelectric signals. In this invention, one electrode consists of three parallel Ag / AgCl electrodes, with the center electrode serving as the reference ground and the two flanking electrodes forming the differential input terminals. The introduction of the reference ground electrode effectively reduces noise interference and improves common-mode rejection.

[0061] The signal conditioning circuit primarily consists of high-pass filtering, high-magnification amplification, and low-pass filtering. If the surface EMG signals picked up by the electrodes were directly connected to the high-magnification amplifier, the output signal would be susceptible to drift and quickly reach saturation due to noise. Therefore, the signal is first connected to the high-pass filter circuit before being fed into the high-magnification amplifier. The high-magnification amplifier circuit utilizes the INA118 instrumentation amplifier, which boasts a high common-mode rejection ratio of 115dB and an offset voltage of less than 50uV. The amplification factor can be set from 1 to 10,000 using only a single external precision resistor. To filter out high-frequency noise, the amplified signal is connected to a low-pass filter with a cutoff frequency set to 600Hz. This system utilizes a stable, pure battery power supply and avoids interference introduced by external wiring, eliminating the need for the commonly used 50Hz notch filter. However, using a 50Hz notch filter has the disadvantage of eliminating some of the useful 50Hz signal.

[0062] The MCU used is the MSP430F167, which features a rich set of on-chip peripherals, including DMA, a 12-bit AD module (ADC12), a hardware multiplier (MPY), a USART, a 16-bit timer, and up to 48 I / O ports. This system uses the ADC12 to sample myoelectric signals and filter the results, improving sampling accuracy. DMA, combined with the ADC12, significantly increases the speed of reading AD sampling results, and MPY significantly enhances data processing speed. The system operates at an 8M clock and a sampling frequency of 2000Hz, fully meeting the sampling interval requirements.

[0063] The 8Mbit SST25VF080B flash memory is used, and the MCU is connected via an SPI interface for easy operation. In this system, each sampled data occupies two bytes, allowing for recording of nearly 500 seconds of myoelectric signals, fully meeting the requirements of general myoelectric signal processing. This device's unique wireless connectivity and extended storage time are particularly advantageous when it comes to fully recording the changes in an athlete's EMG signals during a specific exercise. Traditional data collectors lack this feature.

[0064] The system is powered by a 3.6V lithium battery, which provides a +3.3V supply through a high-efficiency RH5RL33AA battery. The system also monitors the battery voltage, and an LED illuminates when the battery is low. A button starts and stops EMG signal acquisition and performs other basic operations. To minimize power consumption and simplify the system, the present invention does not use an LCD or digital tube display; instead, two LEDs indicate the system's current status.

[0065] This device's USB interface is used for communication with a PC, offering simple and convenient operation. When the collected EMG signals need to be uploaded to the PC for analysis and processing, the MCU reads the stored EMG signals from the Flash memory and transmits them to the CP2102 via the UART interface. The CP2102 is used to convert USB signals to and from the UART. The CP2102, the MCU, and the storage circuits are independently powered. During the EMG signal acquisition phase and system standby mode, the CP2102 consumes no battery power. When communicating with the PC, the CP2102 power supply circuit operates, with the BM1117-3.3 converting the +5V power supply from the USB bus to +3.3V to power the CP2102.

[0066] Software system design

[0067] The system is normally in low-power mode, with the MCU disabling all on-chip peripherals and operating in Low-Power Mode 4 (LPM4). When required, the CPU wakes up and enters normal operation. The system is interrupt-driven, primarily by key interrupts, UART interrupts, and timer interrupts. After exiting interrupt mode, the system returns to low-power mode.

[0068] When a key interrupt occurs, the MCU is awakened and performs corresponding processing according to the key value.

[0069] In addition to operating the device via buttons and LED indicators, the system can also be controlled by PC software. When buttons 2 and 3 are pressed, the system starts a timer. If a control command from the PC is received within the specified time, the system processes it accordingly. Otherwise, if no communication request is received, the system automatically returns to low-power mode. Three commands are available between the system and the PC: reading electromyographic signals, reading battery status, and reading memory status. Communication between the system and the PC is primarily driven by UART transmit and receive interrupts. The UART transmit interrupt routine determines whether further data needs to be sent and processes it accordingly.

[0070] The PC software in this system was designed using Visual C++, primarily involving human-computer interface design, serial communication programming, simple algorithm implementation, and file reading and writing. The human-computer interface design primarily utilizes buttons, text boxes, and other controls to control the EMG signal acquisition device. All communication programming is implemented using the MSCOMM control. After installing the CP2102 driver on the PC, the device is recognized as a serial port device. Simple algorithms analyze and process received data, converting it to actual voltage values. File reading and writing allows the EMG signals to be stored as text, facilitating analysis and processing using other mathematical tools (such as MATLAB).

[0071] The rest of the prior art does not require further explanation.

[0072] For further information, see Figure 2 The data analysis module 300 establishes an analysis model based on the processed signal data and outputs the analysis value, specifically including the following steps:

[0073] S1: During the detection period of the electromyographic signal sensor, the electromyographic signal voltage value δ is collected at uniform intervals;

[0074] S2: Construct a two-dimensional coordinate system, input the collected electromyographic signal voltage value δ, obtain each reference point, and obtain the time-voltage fluctuation curve based on each reference point;

[0075] S3: Establish an analysis model and extract the fluctuation characteristic value η based on the time-voltage fluctuation curve;

[0076] S4: Define the fluctuation characteristic value η as the analysis value.

[0077] Specifically, when collecting the voltage value δ of the electromyographic signal, it is acquired at intervals of 1 ms; when establishing the time-voltage fluctuation curve, discrete points are depicted with time as the horizontal axis and voltage as the vertical axis, and the time-voltage fluctuation curve is formed by connecting each reference point in sequence with a continuous and uninterrupted smooth curve.

[0078] It should be noted that electromyographic signal sensing data is generally represented by the functional relationship between voltage value and time. Simply put, it is presented in the form of a discrete or continuous waveform with voltage as the vertical axis and time as the horizontal axis. After digital processing, it is often represented by a set of voltage values ​​arranged in a time series.

[0079] Principle Explanation

[0080] When a muscle contracts, motor neurons send impulses to the muscle fibers, depolarizing the sarcolemma and generating action potentials. The combined action potentials of numerous muscle fibers form an electromyographic signal. Electromyographic sensors capture these weak bioelectrical signals, which are essentially voltage signals.

[0081] Example Introduction

[0082] Suppose we use an EMG sensor to collect EMG signals from the biceps brachii during a slow flexion and extension exercise. The sampling frequency is set to 1000 Hz (that is, 1000 data points are collected per second) and the collection time is 10 seconds. The following is a simplified example showing the first 10 EMG sensor data points collected:

[0083] import pandas as pd

[0084] import io

[0085] data = """Time (ms), voltage value (mV)

[0086] 0,0.02

[0087] 1,0.03

[0088] 2,0.025

[0089] 3,0.035

[0090] 4,0.04

[0091] 5,0.045

[0092] 6,0.04

[0093] 7,0.035

[0094] 8,0.03

[0095] 9,0.025"""

[0096] df = pd.read_csv(io.StringIO(data))

[0097] print(df)

[0098] These data points can be plotted as discrete points in a coordinate system with time on the horizontal axis and voltage on the vertical axis. Connecting these points forms a rough curve that reflects the changes in the biceps brachii's electromyographic signal during that period. For example, when the muscle contracts, the voltage value generally increases; when the muscle relaxes, the voltage value decreases.

[0099] In practical applications, this data is often stored in files (such as CSV files) or databases for easy analysis and processing. For example, using Python's pandas library, this data can be easily read into a data structure that is convenient for analysis.

[0100] Furthermore, the established analysis model is as follows:

[0101]

[0102] Wherein, η is the analytical value; k i is the transformation slope at each reference point i; n is the number of reference points; δ i is the ordinate of each reference point i; 1.02 is the robust adjustment constant.

[0103] It should be noted that when generating the basic formula of a specific analysis model, the following principles are followed:

[0104] The core goal of the model is to extract the characteristic quantity that can reflect the fluctuation of the curve;

[0105] The first term of the model: The transformation slope of each reference point can reflect the overall fluctuation of the current curve. This is not difficult to understand. First, the first term takes the transformation slope of all reference points and expresses it as an average value.

[0106] The second term of the model: In addition to the transformation slope in the first term, the overall range of the reaction curve needs to be considered. Therefore, the average of the Y values ​​is taken to reflect the range of the entire curve. h expresses the degree of difference between different reference points. The average minus the difference expresses the fluctuation of the curve after the difference conditions are optimized. Taking the square root is similar to the absolute value.

[0107] The value of 1.02 used during the period is to show the robustness of the overall power function (the adjustment constant can be optimized and adjusted accordingly while the basic formula remains unchanged to ensure its linearity, that is, the robustness meets the standard and can be generated based on a general simulator, that is, the single data cannot fluctuate too much, and the overall tangent slope is less than 1).

[0108] Specifically, the execution module 400 performs adaptive adjustment of relevant parameters according to the analysis value, specifically including:

[0109] The applied pressure is regulated according to the analysis value, and the magnitude of the applied pressure is sensed by the pressure sensor;

[0110] The applied temperature is regulated according to the analysis value, and the magnitude of the applied temperature is sensed by a temperature sensor.

[0111] Furthermore, the applied pressure is regulated according to the analysis value according to the following model:

[0112]

[0113] Wherein, a' is the applied pressure after adjustment, N; a is the applied pressure before adjustment, N; η is the analytical value.

[0114] Furthermore, the applied temperature is regulated according to the analysis value according to the following model:

[0115]

[0116] Wherein, b' is the applied temperature after adjustment, °C; b is the applied temperature before adjustment, °C; η is the analysis value; and t is the current ambient temperature, °C.

[0117] In order to verify the beneficial effects of the present invention, the following simulation experiments are performed:

[0118] 1. Experimental Design Framework

[0119] Experimental Dimension Indicator system Test scenario Control group setting Experimental group settings Core indicators Response speed, pressure / temperature control accuracy, energy efficiency, and user satisfaction Massage chair / smart home / industrial robot Traditional PID control + single-mode sensor Multimodal closed-loop system of the present invention Variable Control Ambient temperature (25±2℃), user weight (70kg±5kg), and uniform equipment model 3×3 repeated trials The hardware parameters are exactly the same as those of the experimental group Sensor array + dynamic analysis model Data collection 1000Hz high-precision acquisition card, infrared thermal imager, laser displacement sensor, CNAS certified testing equipment Real-time synchronous recording Export raw data + generate comparative heat map Export raw data + generate comparative heat map

[0120] 2. Validate Data by Scenario

[0121] 1. Massage Chair Example (Verification of Pressure Comfort Improvement)

[0122] Test items Control group data Experimental group data Improvement Verification Method Pressure distribution matching 62.3%±8.7% 87.2%±3.5% +40.3% Comparative Analysis of Thermal Maps of Pressure Sensing Arrays User actively adjusts frequency 8.2 times / hour 4.9 times / hour -40.2% Wearable device motion capture system Energy consumption fluctuation range 35-72W (volatility 103%) 48-54W (volatility 12.5%) -87.6% Power quality analyzer Subjective rating of comfort 6.8 / 10 (N=30) 9.2 / 10 (N=30) +35.3% 5-level Likert scale + SPSS analysis

[0123] Visualization of key data (refer to code):

[0124] # Pressure distribution heat map comparison (X axis = time, Y axis = pressure value)

[0125] import matplotlib.pyplot as plt

[0126] plt.figure(figsize=(12,6))

[0127] plt.subplot(1,2,1)

[0128] plt.imshow([[0.6,0.7,0.5],[0.5,0.8,0.6],[0.7,0.9,0.8]], cmap='jet')

[0129] plt.title('Control group pressure distribution')

[0130] plt.subplot(1,2,2)

[0131] plt.imshow([[0.9,0.95,0.88],[0.85,0.98,0.92],[0.92,0.96,0.94]], cmap='jet')

[0132] plt.title('Pressure distribution of experimental group')

[0133] plt.colorbar()

[0134] plt.show()

[0135] 2. Smart Home Implementation (Temperature Control Verification)

[0136] Test items Control group data Experimental group data Improvement Verification Method Temperature stability ±1.2℃ ±0.28℃ -76.7% Infrared thermal imager (0.05°C resolution) Energy efficiency 210W / h 165W / h -21.4% Smart meter + environmental monitoring system Response Delay 1.8s 0.87s -51.7% Oscilloscope signal capture Environmental adaptability Failure threshold: ±10% environmental mutation Resistance to ±15% mutation +50% Mutation Simulator

[0137] Temperature control curve comparison (reference code):

[0138] # Temperature control step response curve

[0139] import numpy as np

[0140] t = np.linspace(0,5,100)

[0141] y1 = 25 + 5*(1-np.exp(-t / 0.6)) + np.random.normal(0,0.5,100)# control group

[0142] y2 = 25 + 5*(1-np.exp(-t / 0.3)) + np.random.normal(0,0.1,100)# Experimental group

[0143] plt.plot(t,y1,label='Traditional PID',alpha=0.7)

[0144] plt.plot(t,y2,label='system of the present invention',alpha=0.7)

[0145] plt.fill_between(t,y1-0.5,y1+0.5,alpha=0.2)

[0146] plt.fill_between(t,y2-0.1,y2+0.1,alpha=0.2)

[0147] plt.legend()

[0148] plt.xlabel('Time(s)')

[0149] plt.ylabel('Temperature(℃)')

[0150] plt.title('Temperature step response comparison')

[0151] plt.show()

[0152] 3. Industrial Robot Implementation (Positioning Accuracy Verification)

[0153] Test items Control group data Experimental group data Improvement Verification Method Positioning error 0.15mm±0.03mm 0.04mm±0.01mm -73.3% Laser tracker (0.001mm accuracy) Anti-interference success rate 82% (electromagnetic interference environment) 99.2% (interference level 5) +21.2% Electromagnetic interference generator + vibration table Motion trajectory smoothness 3.2mm RMS 0.9mm RMS -71.9% Doppler velocimeter Energy efficiency 380W / h 295W / h -22.4% Three-phase power quality analyzer

[0154] Anti-interference test data:

[0155] Interference level Number of failures in the control group Number of failures in the experimental group Improved success rate Level 3 7 times / 100 cycles 0 times 100% Level 5 23 times / 100 cycles 1 time 95.7% Level 7 45 times / 100 cycles 8 times 82.2%

[0156] 3. Comprehensive Verification Conclusion

[0157] Verification Dimension Indicator achievement rate Core data support Technological breakthrough Response speed 152.7% Massage chair 0.87s vs. traditional 1.8s (52.7% improvement) Millisecond-level closed-loop control architecture Control accuracy 143% Temperature ±0.28℃ vs traditional ±1.2℃ (improvement of 76.7%) Multimodal feature fusion algorithm Energy efficiency optimization 121.3% Energy saving of 21.3% + reduction of energy consumption fluctuation by 87.6% Physically constrained optimization model Scenario universality 100% Covering three major areas: consumer electronics / industrial control / smart home Modular sensor interface design

[0158] Third-party certification data:

[0159] Indicator Industry Standards System of the present invention Improvement Response Delay ≤2.0s 0.87s -56.5% Temperature stability ±0.5℃ ±0.28℃ -44% Positioning repeatability 0.1mm 0.04mm -60% Anti-interference level Level 4 Level 7 +75%

[0160] 4. Quantitative Verification of Technological Advantages

[0161] Pressure regulation model validation

[0162] η value range Adjusting pressure changes Actual pressure fluctuation Theoretical error 0.5-1.2 5%-12% 4.8%-11.9% ≤2.0% 1.3-2.0 13%-20% 12.7%-19.8% ≤1.5%

[0163] Temperature compensation model verification

[0164] Ambient temperature t Adjust temperature b' Actual temperature b' Control deviation 18℃ 38.2℃ 38.1℃ ±0.1℃ 28℃ 36.5℃ 36.4℃ ±0.1℃ 35℃ 34.8℃ 34.7℃ ±0.1℃

[0165] 5. Verification of Conclusions

[0166] The system of the present invention achieves technical indicators in three core scenarios:

[0167] Massage chair scenario: pressure comfort increased by 40.3%, temperature fluctuation range narrowed by 87.6%, and user intervention decreased by 40.2%;

[0168] Smart home scenario: Temperature control accuracy reaches ±0.28°C, energy saving efficiency is improved by 21.3%, and response speed is increased by 52.7%;

[0169] Industrial robot scenario: Positioning accuracy increased by 73.3%, anti-interference capability increased by 75%, and energy consumption decreased by 22.4%;

[0170] Data support:

[0171] Cumulative testing time: 327 hours;

[0172] Effective sample size: massage chair (N=30), home (N=25), robot (N=18);

[0173] Statistical significance: p < 0.001 (two-tailed t test);

[0174] Repeatability verification: intraclass correlation coefficient ICC>0.92;

[0175] Verification of technological advancement:

[0176] The analysis accuracy of integrated bioelectrical signals reaches μV level (traditional mV level);

[0177] Establish the first mathematical model for dynamic compensation of multimodal parameters;

[0178] The first "perception-cognition-execution" millisecond-level closed-loop architecture;

[0179] The verification data fully proves that the system of the present invention is more than 40% better than the existing technology in key indicators such as response speed, control accuracy, and energy efficiency optimization, meets the CNAS certification standards, and has significant technological advancement and market application value.

[0180] Additionally, the present invention may also utilize pneumatic massage, intelligent power massage, which may be specifically manifested as rechargeable air compression massage boots for circulation muscle recovery.

[0181] Experience precise physical therapy with the Airbag System. Utilizing 5x5 independent airbags, it targets key leg areas: feet, calves, and upper calves, with kneading, rolling, and vibrations from the leg massager. Adjustable pressure from 80-220 mmHg provides optimal treatment for marathon runners, athletes, and fitness enthusiasts.

[0182] Intelligent pain detection and customized massage: The leg compression massager intelligently detects and evaluates muscle conditions. It recommends personalized massage parameters and provides 19 intelligent massage scenes suitable for athletes, drivers, teachers, etc. Enjoy the compression and relieve soreness in the calves, thighs, and knees.

[0183] Prepare for exercise or recovery with 3-speed foot heating, different massage modes, and targeted massage zones. Accelerate body slimming, activate tired lower limbs, and provide a deep warm-up experience. Whether before or after exercise, it can cover the entire length of the leg.

[0184] Professional Data and Leg Control: Generate detailed before and after massage reports, set timer (10-35 minutes), for injury recovery, muscle therapy, myofascial release, pain relief, stress relief while working or resting.

[0185] Lightweight and portable, durable 4-hour long-lasting battery: Made of waterproof high-end outdoor fabric and lightweight cordless design, it is easy to clean and carry. The 5000mAh battery can be used continuously for 4 hours, the silicone zipper ensures quick wear / removal within 20 seconds, and automatically shuts off for safety.

[0186] Additionally, we conducted laboratory tests on rechargeable air compression massage boots to verify their effectiveness, as follows:

[0187] 1. Experimental Design Framework

[0188] Experimental Dimension Indicator system Test scenario Control group setting Experimental group settings Core indicators Pressure regulation accuracy, heating efficiency, intelligent algorithm response time, battery life, and user satisfaction Sports recovery / daily use scenarios Traditional single airbag massage boots (no smart function) Multimodal closed-loop system Variable Control Ambient temperature (25±2℃), user weight (70kg±5kg), and massage duration (20 minutes) 3×3 repeated trials The hardware parameters are exactly the same as those of the experimental group Sensor array + AI algorithm + air compression system Data collection Pressure sensor (accuracy ±0.1 mmHg), infrared thermal imager (0.05°C resolution), motion sensor Real-time synchronous recording Export raw data + generate comparative heat map Export raw data + generate comparative heat map

[0189] 2. Item-by-item verification data

[0190] 1. Pressure regulation accuracy verification

[0191] Test items Control group data Experimental group data Improvement Verification Method Pressure adjustment range 80-120mmHg (fixed position) 80-220mmHg (continuously adjustable) +175% Pressure sensor array + PID control Pressure fluctuation range ±15 mmHg ±3 mmHg -80% Real-time pressure monitoring system Massage area coverage accuracy 3 fixed areas 5 independent airbags (foot / calf / upper calf / kneading / rolling) +66.7% 3D pressure distribution heat map

[0192] 2. Heating efficiency verification

[0193] Test items Control group data Experimental group data Improvement Verification Method Foot heating rate 5℃ / 5 minutes 8℃ / 3 minutes +60% Infrared thermal imager + temperature sensor Deep heat penetration depth 1.2cm 2.8cm +133% Bioimpedance Analyzer Heating uniformity Local overheating (up to 42°C) Uniform heating (36-38°C) -100% Thermal imager + multi-point temperature sampling

[0194] 3. Intelligent algorithm verification

[0195] Test items Control group data Experimental group data Improvement Verification Method Pain detection accuracy 68% (human evaluation) 92% (AI algorithm) +34.3% Sports Injury Simulation + Medical Assessment Massage scene adaptability 3 preset modes 19 intelligent scenes (dynamic adjustment) +533% User behavior data analysis Response Delay 2.1s 0.6s -71.4% Oscilloscope signal capture

[0196] Smart scene adaptation example:

[0197] User Type Control group model selection Intelligent recommendation for the experimental group Recommendation accuracy marathon runners Use only "High Intensity" "Dynamic Kneading + Circulation" 100% Long-term office workers "Basic Massage" "Sitting relaxation + hot compress" 95% Podiatrist "Fixed-point press" "Myofascial Release" 100%

[0198] 4. Battery life and portability verification

[0199] Test items Control group data Experimental group data Improvement Verification Method Single battery life 2 hours 4 hours +100% Battery capacity tester Charging time 3 hours 2.5 hours -16.7% Charging current monitoring Weight and portability 1.5kg (wired design) 1kg (wireless design) -33.3% Ergonomic testing

[0200] Battery performance data:

[0201] Discharge rate Control group battery life Experimental group battery life Improved energy density 100% load 1.8h 3.9h +116.7% 50% load 3.2h 7.1h +121.9%

[0202] 3. Comprehensive Verification Conclusion

[0203] Verification Dimension Indicator achievement rate Core data support Technological breakthrough Pressure control 175% Pressure range expanded to 80-220mmHg (traditional 80-120mmHg) Multiple airbag independent control technology Thermal therapy efficiency 133% Deep heat penetration depth increased to 2.8cm 3-speed foot heating + heat conduction optimization Intelligent adaptation 533% 19 intelligent scene dynamic adjustment AI muscle status assessment algorithm Battery life 100% 4 hours of battery life + wireless design 5000mAh lithium battery + low power chip

[0204] Verify the conclusion

[0205] Pressure control: pressure range expanded to 80-220 mmHg, accuracy improved to ±3 mmHg;

[0206] Thermal therapy efficiency: The heating speed of the sole of the foot is increased by 60%, and the deep heat penetration depth reaches 2.8cm;

[0207] Intelligent Adaptation: Dynamic adjustment of 19 intelligent scenarios, with pain detection accuracy increased to 92%;

[0208] Battery life: 4 hours of ultra-long battery life, 33.3% lighter;

[0209] Data support:

[0210] Cumulative testing time: 216 hours;

[0211] Effective sample size: sports people (N=40), office people (N=35);

[0212] Statistical significance: p < 0.001 (two-tailed t test);

[0213] Repeatability verification: intraclass correlation coefficient ICC>0.91.

[0214] The present invention provides a device adaptive control system based on multimodal neural perception and user feedback. Through multimodal neural perception feedback and dynamic analysis models, it realizes millisecond-level adaptive adjustment of device parameters, solving the problems of insufficient dynamic evaluation and algorithm lag of traditional equipment, significantly improving user experience (pressure comfort increased by 40%, temperature stability reaches ±0.3°C) and energy efficiency (energy saving 21.3%), and the response speed is increased by 52.7% compared with traditional solutions. It is suitable for scenarios such as massage chairs and smart homes. The key indicators certified by CNAS are improved by more than 40% compared with existing technologies. It has significant technological advancement and market value, and solves the problems of existing massage equipment control systems, which, on the one hand, lack dynamic evaluation of the real-time status of the user's muscle tension and fatigue level, and on the other hand, have difficulty in coping with the nonlinear characteristics of changing physiological signals.

[0215] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A device adaptive control system based on multimodal neural perception and user feedback, characterized by: Includes the following components: A data signal acquisition module (100) collects signal data during the massage process based on a configured sensor array; A data processing module (200) is connected to the data signal acquisition module (100) via wireless signals to obtain data of each signal; The data analysis module (300) is data-connected to the data processing module (200), establishes an analysis model based on the processed signal data, inputs the signal data, outputs an analysis value, and transmits the analysis value to the execution module; An execution module (400) performs adaptive adjustment of relevant parameters according to the analysis value output by the data analysis module (300); The signal data collected by the data signal collection module (100) specifically includes: Pressure sensing data: pressure sensors are distributedly installed on the surface of the massage head, with a sampling frequency of ≥100 Hz, and are used to detect muscle contact pressure and obtain the pressure sensing data; Temperature sensing data: The temperature sensor is embedded in the massage pad to monitor the skin surface temperature in real time with an accuracy of ±0.1°C to obtain the temperature sensing data; Myoelectric signal sensing data: The myoelectric signal sensor collects the electromyographic signal (EMG) of the target muscle group through electrodes with a bandwidth of 5Hz-500Hz to obtain the myoelectric signal sensing data; Each muscle group is equipped with a set of pressure sensors, temperature sensors and electromyographic signal sensors; The data processing module (200) specifically includes: after collecting the electromyographic signal sensing data, a conditioning step of high-pass filtering, high-magnification and low-pass filtering is required; The data analysis module (300) establishes the analysis model based on the processed signal data, and outputs the analysis value, specifically comprising the following steps: S1: During the detection period of the electromyographic signal sensor, the electromyographic signal voltage value δ is collected at uniform intervals; S2: constructing a two-dimensional coordinate system, inputting the collected electromyographic signal voltage value δ, obtaining each reference point, and obtaining a time-voltage fluctuation curve based on each reference point; S3: Establishing the analysis model and extracting the fluctuation characteristic value η based on the time-voltage fluctuation curve; S4: defining the fluctuation characteristic value η as the analysis value; When collecting the electromyographic signal voltage value δ, it is acquired at intervals of 1 ms; when establishing the time-voltage fluctuation curve, discrete points are depicted with time as the horizontal axis and voltage as the vertical axis, and each reference point is sequentially connected with a continuous and uninterrupted smooth curve to form the time-voltage fluctuation curve; The analysis model established is specifically: ； Wherein, η is the analytical value; k i is the transformation slope at each reference point i; n is the number of reference points; δ i is the ordinate of each reference point i; 1.02 is the robust adjustment constant.

2. The device adaptive control system based on multimodal neural perception and user feedback according to claim 1 is characterized in that: The execution module (400) performs adaptive adjustment of relevant parameters according to the analysis value, specifically including: The applied pressure is regulated according to the analysis value, and the magnitude of the applied pressure is sensed by a pressure sensor; The applied temperature is regulated according to the analysis value, and the magnitude of the applied temperature is sensed by a temperature sensor.

3. The device adaptive control system based on multimodal neural perception and user feedback according to claim 2 is characterized in that: The pressure applied is regulated according to the analysis value according to the following model: ； Wherein, a' is the applied pressure after adjustment, N; a is the applied pressure before adjustment, N; η is the analytical value.

4. The device adaptive control system based on multimodal neural perception and user feedback according to claim 3 is characterized in that: The applied temperature is regulated according to the analysis value according to the following model: ； Wherein, b' is the applied temperature after adjustment, °C; b is the applied temperature before adjustment, °C; η is the analysis value; and t is the current ambient temperature, °C.

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