Multi-mode dynamic acousto-optic-electric AI robot and control method thereof

Abstract

The invention discloses a multi-mode dynamic acousto-optic-electric AI robot, and relates to the technical field of rehabilitation physiotherapy robots. The AI robot comprises a DMS sound field system for generating dynamic sound waves; the human body fluctuation system generates mechanical vibration; the light wave system generates a dynamic light graphic pattern; the high-definition touch integration module is used for displaying graphs and texts; the camera system comprises a 4K camera; the microphone array system comprises at least two microphone arrays; the electromagnetic sensor collects mechanical motion information; the biosensor collects human body biological information; the network interface interacts and exchanges data with external equipment; the edge AI calculation and control system processes input information, operates an AI algorithm and synchronously outputs a control signal; and the cloud AI system and the server are used for protecting personal privacy, generating health condition evaluation and providing a reference optimal health care scheme. By integrating sound wave stimulation, mechanical vibration stimulation and light wave stimulation and combining edge AI calculation, real-time biological feedback driven personalized movement and health care activities are achieved.

Description

Technical Field

[0001] This invention belongs to the field of rehabilitation and physiotherapy robot technology, and more specifically, it relates to a multimodal dynamic acoustic-optical-electric AI robot and its control method. Background Technology

[0002] There are many rehabilitation and physiotherapy devices and methods. Among them, mechanical vibration therapy has been shown to enhance microcirculation and reduce spasticity. For example, whole-body vibration (WBV) increases skin blood flow and promotes nitric oxide release in the 20-50Hz range, which helps with vasodilation and lymphatic circulation. Studies have shown that WBV can relieve spasticity in neurological diseases such as cerebral palsy and stroke, and improve motor function and gait. A recent study in 2025 showed that WBV improved postural control and motor performance in children with cerebral palsy, improving walking speed and time-limited "stand-up and walk" test performance. Another study showed that when WBV is combined with resistance training, vibration transmission from the robotic motor unit to the body promotes muscle activation and cellular regeneration. The mechanism of WBV includes the skeletal muscle pump effect, enhancing blood flow and releasing endogenous analgesic substances such as endorphins. Clinical trials have shown that 12 weeks of WBV training can improve balance in older adults and reduce the risk of falls by 20%. However, these methods are often static and lack AI integration for personalization, limiting the optimization of exercise and rehabilitation effects.

[0003] Phototherapy, particularly at a wavelength of 670 nm, supports vasodilation through the release of nitric oxide stored in the endothelium, independent of nitric oxide synthase, and enhances circulatory and mitochondrial function. A 2025 study showed that 670 nm photobiological modulation (PBM) inhibits retinal degeneration, increases ATP production, and reduces inflammation in ophthalmic therapy. Immunomodulatory studies show that PBM modulation reduces reactive oxygen species (ROS) production and releases nitric oxide, regulating immune function. A cancer treatment review indicates that PBM promotes cell repair at the molecular level but lacks multimodal integration. Cellular-level effects of PBM include cytochrome c oxidase (CCO) activation, enhancing electron transport chain efficiency. Clinical applications extend to wound healing, with 670 nm light accelerating epithelial regeneration by 30%. However, existing phototherapy devices operate in isolation, not combined with vibration or sound, failing to achieve synergistic benefits such as enhanced nitric oxide diffusion.

[0004] Sound and multisensory stimulation at 20Hz-40Hz have been used for Alzheimer's Disease (AD) prevention, promoting glycolymphatic clearance of amyloid plaques and enhancing cognition through gamma wave induction. A 2025 study confirmed that 40Hz multisensory stimulation improves CA3–CA1 coordination, enhances neuronal function, and reduces AD pathology. Studies integrating dopamine sensing and 40Hz stimulation have shown improved cognition and modulated neurotransmitters in AD models. The mechanisms of sound therapy include brainwave entrainment; 40Hz sound induces gamma rhythms, improving memory consolidation. Clinical trials have shown that daily 40Hz sound exposure reduces anxiety in AD patients by 25%. However, while these modalities are promising, their lack of integration with artificial intelligence (AI) for adaptive therapy limits personalization.

[0005] Internationally, AI in healthcare is rapidly advancing, with various multimodal AI robotic systems emerging for personalized diagnosis and treatment. Patents such as US11923088B2 describe AI for digital therapeutic environments, but do not integrate physical modalities such as vibration and light into posture-adaptive robots. Similarly, multimodal AI assistants process data, but lack robotic hardware for movement, rehabilitation, and therapy delivery. Trends to 2025 indicate that multimodal AI robots will be used for personalized therapies in rehabilitation and movement, and assistive medical robots, but edge AI closed-loop feedback and multi-posture adaptation have not yet been achieved. Currently, AI applications are expanding to predictive models, using neural networks to process biological signals, but existing systems suffer from high computational latency, hindering real-time feedback control and program adjustments.

[0006] While existing technologies are inspiring, they fail to provide a unified system. This system combines acoustic, optical, and mechanical wave stimulation with edge AI for real-time personalization of user movement, training, and rehabilitation.

[0007] Existing vibration devices, such as the body vibration machine disclosed in US20070239088A1 (2007), use a single, independent vibration plate, which cannot meet the needs of horizontal, zoned movement, leading to imbalances such as insufficient lower limb movement or excessive head movement. Furthermore, they lack AI feedback, electromagnetic sensors, and the integration of physiological information. Their single drive system cannot achieve closed-loop feedback control for safety, and they lack AI technology for personalized health and wellness programs. The absence of photoelectric sensors to sense movement results in single-frequency waveforms that cannot output specific waveforms adapted to the health and movement needs of different parts of the body, such as smooth trapezoidal waves for horizontal applications, relatively vigorous sawtooth waves required for trunk movement, and various complex multi-frequency composite waveforms used in medical rehabilitation and prevention. The multimodal interactive robot disclosed in CN106985137B (2019) focuses on emotions but is not applied to health therapy. The health promotion device disclosed in US4055170A (1977) uses sound vibration but lacks dynamic adjustment and multimodal capabilities. US5113852A (1992) discloses a body vibration device that uses vibration elements but does not employ multimodal technology. Existing patents feature posture adaptation, such as a supine position for paralyzed patients. This invention innovatively integrates an AI closed loop to achieve non-obvious synergy.

[0008] Existing sound therapy and vibration therapy devices, such as traditional "dynamic fitness machines" or sound-driven vibration machines (e.g., patents CN116251327B (2024) and US5113852A (1992)), often employ static vibration modes and lack real-time monitoring. This leads to uncontrolled vibration, excessive movement, and a lack of contingency plans for user falls or other accidents, increasing the risk of injury. Furthermore, due to the limited and simple control methods, these devices ignore individual differences, resulting in rigid fitness programs and poor exercise effects. Users may experience acceleration exceeding their individual tolerance thresholds, potentially causing side effects such as cardiovascular problems, nausea, dizziness, nerve damage, or headaches. Existing vibration devices, such as the body vibration machine in US20070239088A1 (2007), use a single, independent vibration unit, which cannot meet the needs of supine zoned exercise, leading to insufficient lower limb movement or excessive head movement, resulting in imbalances. They also lack AI feedback, integration of electromagnetic sensors, and physiological information. Its single drive system cannot achieve closed-loop feedback control for safety, lacks AI technology for personalized health and wellness programs, and lacks photoelectric sensors to sense movement, resulting in a single-frequency waveform that cannot output specific waveforms adapted to the health and movement needs of different parts of the body. For example, it cannot output the smooth trapezoidal waveform for the head in supine applications, the relatively vigorous sawtooth waveform required for trunk movements, or the various complex multi-frequency composite waveforms used in medical rehabilitation and prevention. While the vibration transmission device disclosed in patent DE102007003361A1 can transmit stimulation, it lacks a feedback mechanism, and long-term use may exacerbate joint or bone damage, especially harmful to pregnant women, joint replacement recipients, and cardiovascular patients. These devices, lacking intelligent management functions, feedback detection devices, and real-time monitoring systems, commonly suffer from malfunctions such as unstable power, mechanical wear, noise interference, and even uncontrolled vibration, further limiting their applicability and creating health risks.

[0009] The challenges of integrating AI with multimodal health and exercise lie in data scarcity and privacy protection: cross-modal fusion of biological signals (such as heart rate and EEG) with vibration and photoelectric stimulation requires processing massive amounts of heterogeneous data, but existing systems face bias and interpretability problems, resulting in poor model generalization. Real-time computation is highly complex, and edge AI needs to balance power consumption and accuracy, while clinical validation is difficult, and ethical issues such as amplified bias (e.g., insufficient data for minority groups) hinder its widespread adoption. Multimodal AI in health and exercise also needs to address cross-scale integration (such as macroscopic posture and microscopic cellular responses) and trust generation to ensure the safety and harmlessness of daily exercise and health care. Summary of the Invention

[0010] The technical problem this invention aims to solve is to provide a multimodal dynamic acoustic-optical-electric AI robot and its control method. By integrating sound waves, mechanical vibrations, and light wave stimulation, combined with edge AI computing, it enables real-time biofeedback-driven personalized movement and wellness activities, applicable to fields such as Alzheimer's disease prevention, blood pressure and circulation improvement, stress relief, sleep regulation, and neurorehabilitation. This technology integrates an acoustic engineering DMS system to generate an immersive sound field; mechanical engineering electromagnetic levitation vibration for frictionless stimulation; optical engineering LED light sources for multi-wavelength photobiological modulation (PBM); and an AI deep learning model for data fusion and optimization.

[0011] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A multimodal dynamic acoustic-optical-electric AI robot, comprising: The DMS sound field system has at least two independent channels for generating dynamic sound waves, supports real-time sound field rendering, and achieves dual dynamic audio of sound field dynamics and content dynamics, with a frequency range of 20Hz-20kHz. The human body wave system consists of several electromagnetically driven broadband modal suspension vibration systems, with each electromagnetically driven broadband modal suspension vibration system constituting a vibration unit. The vibration unit generates mechanical vibration with an amplitude of 0.1-50mm and a fundamental oscillation frequency range of 2-500Hz. The light wave system uses LED light sources to generate dynamic light patterns synchronized with sound waves and mechanical waves, supporting visible light to near-infrared light; A high-definition touch integrated module is used to display graphics and text and supports user interaction; The camera system includes several 4K cameras, supports multi-camera synchronization, virtual UHF video and image acquisition, stereo graphics and video reconstruction, spectral analysis, AI-controlled exposure adjustment, and supports desensitization processing and privacy protection. The microphone array system, comprising at least two microphone arrays, can automatically identify, track, and locate sound sources, perform DSP noise reduction, decoupling of multiple sound sources, intelligent sensitivity and dynamic range adaptation, and support desensitization processing and privacy protection. Electromagnetic sensors and waveform generation systems are used to directly or indirectly collect mechanical motion information, realize closed-loop control, and the output signal can be adjusted in real time, and realize other health and fitness-specific waveforms including standard sine waves and pre-distortion correction. Biosensors and parameter acquisition systems are used to directly or indirectly collect human biological information, including heart rate, respiration, electrocardiogram, electroencephalogram, and blood oxygen saturation. The network interface module is used to interact with and exchange data with external devices, including but not limited to 4G / 5G / 6G, WiFi, Bluetooth and Type-C ports. It supports AES-256 encryption, is compatible with external sensing and monitoring devices, and enables remote monitoring. The edge AI computing and control system connects to the DMS sound field system, human body wave system, light wave system, camera system, microphone array system, electromagnetic sensors, biosensors, and network interface. It processes sensor input information including ECG, EEG, respiration, SpO2, PPG, IMU, electromagnetic displacement and current, array microphone signals, and camera images (with desensitized features such as posture, expression, and skin perfusion). It runs AI algorithms and, based on biofeedback, synchronously outputs control signals to adaptively adjust system parameters, achieving target scenarios such as sleep aid, sedation, and alertness / rehabilitation. The system's computing and control response time is less than 50 milliseconds. The self-test, maintenance, and upgrade system includes a self-test system that runs regularly for comprehensive diagnostics, checking the operating status of hardware and software; a maintenance system that performs remote updates and local cleaning of equipment; and an upgrade system that regularly introduces new algorithms and health data models. The cloud-based AI system and server are used to input anonymized user data, perform AI health analysis on the anonymized data, generate detailed health status assessments, and provide users with optimal health and wellness plans based on the analysis results, as well as predict potential health risks and preventative measures. Data encryption technology is applied throughout the entire transmission and storage process to protect user information security and personal privacy.

[0012] Preferably, the robot has three postures: standing, upright, and lying down. The standing posture uses one to four vibration units, the sitting posture uses one to two vibration units, and the lying posture uses three to eight vibration units to accommodate different movement postures in different areas.

[0013] Preferably, the light wave system outputs dynamic light graphic patterns synchronized with sound waves and mechanical waves, and achieves a synergistic effect through edge AI computing and control system regulation, and is placed above in a horizontal position to cover the user's entire body.

[0014] Preferably, the camera system, microphone array system, biosensor, infrared sensor, and electromagnetic sensor constitute an AI perception system, which is controlled by an edge AI computing and control system, supports real-time data fusion, and uses sensors at appropriate locations under different postures.

[0015] Preferably, the edge AI computing and control system embeds health big data, supports cross-modal fusion algorithms, and achieves multimodal collaboration.

[0016] Preferably, the light wave system uses multi-color RGB LEDs to visualize emotions and synchronize with biological rhythms. A control method for a multimodal dynamic acoustic-optical-electric AI robot includes the following steps: Step 1: Data collection. The AI ​​perception system acquires user biometric data, including heart rate, ECG, EEG, respiration, SpO2, PPG, and IMU; it also includes standing, sitting, and lying posture data and recognition. Step 2: Generate control signals. Feed user biometric data to the edge AI computing and control system, and use machine learning models, including but not limited to CNN, LSTM, Transformer and their combined architectures, to calculate personalized parameters and generate control signals. Step 3: Output adjustment. Use control signals to adjust the DMS sound field system to output sound waves, music and light fields, control the electromagnetic drive broadband mode suspension vibration system to generate mechanical vibration, and control the light wave system to generate dynamic light patterns. Step 4: Output transmission, in which sound waves stimulate neural pathways, mechanical vibrations enhance cell movement and circulation, increase NO release, and light patterns activate the visual and brain nervous systems; Step 5: Feedback loop, integrating electromagnetic sensors, fusing mechanical motion data with biological data and optimizing the output in real time until the best effect and dynamic balance are achieved, supporting safety threshold monitoring.

[0017] Step Six: Effectiveness Evaluation. The overall effectiveness is evaluated using built-in models such as a time-series Bayesian evaluator or a causal inference scorer based on multi-indicator weighted scoring (HRV↑, breathing regularity↑, expected change in EEGα / θ, subjective scale, sleep stage proxy, etc.).

[0018] Preferably, the AI ​​algorithm includes an emotion quantification model, micro-expression feedback, empathy model, multimodal information fusion, and semantic AI model, for precise personalization, integrating convolutional neural networks to process biological data, and adapting to different postures.

[0019] The beneficial effects of adopting the above technical solution are as follows: Compared with existing fitness or rehabilitation devices that only support single vibration or light stimulation and lack biofeedback capabilities, the present invention has the following advantages: (1) Achieve multimodal synergistic stimulation and real-time closed-loop control to improve safety and individual adaptability.

[0020] This invention is the first to combine three physical stimuli—sound waves, mechanical vibrations, and light waves—with edge AI control to form a unified system. By integrating multiple physiological signals such as heart rate, EEG, respiration, facial expressions, and postures to establish a closed-loop feedback channel, the control response time is less than 50 milliseconds. When the user experiences fatigue, dizziness, or abnormal reactions, the stimulation intensity can be dynamically reduced or the device can be automatically shut down, thereby avoiding the risk of excessive vibration or injury caused by the lack of feedback mechanisms in traditional devices.

[0021] (2) Provide personalized health interventions to overcome the limitations of existing fixed-procedure equipment in the fields of rehabilitation and neuromodulation.

[0022] This invention uses a multimodal AI model to identify users' health status, psychological stress, and motor responses in real time, generating personalized stimulation combinations, including 20–40Hz gamma wave sound for neurocognitive activation, 2–500Hz mechanical waves for improving microcirculation, and 670–850nm light waves for promoting NO release and cell repair. This significantly improves the effectiveness in scenarios such as sleep aid, stress regulation, Alzheimer's disease prevention, and paralysis rehabilitation, overcoming the problems of fixed parameters and inability to be tailored to individual needs in traditional devices.

[0023] (3) Supports multi-posture zone output, suitable for bedridden, sitting and standing users, expanding the scope of clinical and home use.

[0024] This invention uses multiple vibration units distributed in the head, chest, back, lumbosacral region and lower limbs to achieve zoned stimulation control in different postures. In particular, it meets the rehabilitation needs of long-term bedridden patients or people with nerve damage in the supine mode, avoiding the problem of excessive head vibration or insufficient lower limb vibration caused by uneven stimulation in traditional whole-plate vibration devices, and significantly improving the coverage of applicable populations and the compatibility of rehabilitation scenarios.

[0025] (4) It combines health monitoring and health care intervention functions to build a joint platform for sports and medical rehabilitation.

[0026] This invention can not only output multimodal stimulation, but also collect physiological parameters such as ECG, EEG, SpO2 and hemodynamics, support health data recording and cloud analysis, and can be used for active conditioning (such as improving sleep and exercise recovery), as well as for early screening of chronic diseases or neurobehavioral monitoring, filling the technological gap of existing fitness equipment that does not have medical-grade sensing and rehabilitation capabilities.

[0027] (5) It has the ability to expand and upgrade IoT models, and supports remote monitoring and continuous learning optimization.

[0028] The device connects to a cloud-based health platform via a network interface, enabling incremental model training and therapy upgrades based on historical user data. This allows for long-term multi-user management and remote healthcare services, overcoming the limitations of traditional closed-system devices that cannot be maintained or upgraded. It has broad application value in fields such as digital health, home rehabilitation, and AI-driven healthcare. (See attached diagram.) Figure 1 This is a schematic diagram of the hardware components of an AI robot; Figure 2 This is the architecture diagram of the DMS sound field system; Figure 3 This is a flowchart of the control method for AI robots; Figure 4 This is a flowchart of the Search Enhancement Generation (RAG) process; Figure 5 This is a structural block diagram of RAG; Figure 6 This is a flowchart of the method for adjusting output (sound, vibration, light). Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0030] like Figure 1 As shown, this AI robot includes a DMS sound field system, a human body wave system, a light wave system, a high-definition touch integrated module, a camera system, a microphone array system, electromagnetic sensors, biosensors, a network interface, an edge AI computing and control system, as well as a self-testing, maintenance and upgrade system, and a cloud AI system and server.

[0031] The DMS sound field system generates an immersive sound field and supports dynamic multi-dimensional audio. The detailed working principle of the DMS sound field system is as follows: Figure 2 As shown: Based on system feedback signals, the DSP processing and AI adaptive control modules retrieve music from the content library. The AI ​​algorithm adjusts the sound amplitude and 4-gamma-wave induced wave frequency (around 40Hz) based on EEG feedback to promote cognitive function. The music + gamma-wave signal is sent to the sound signal processing module to adjust delay, phase, amplitude, rhythm, etc. The adjusted sound signal is then sent to the power amplifier and channel crossover module to drive the sound played by the multi-channel speakers, creating a dynamic sound field. Extended principle: When N=8 channels, a spatial sound field is created, supporting dynamic audio animation modes and real-time rendering modes for audio content.

[0032] The human body vibration system consists of several electromagnetically driven broadband modal levitation vibration systems. These systems utilize electromagnetic levitation to achieve precise, frictionless vibration synchronized with the user's vital signs, with a frequency accuracy of ±0.1Hz. The output waveform supports smooth trapezoidal waves, enhanced spike waves, and complex waveforms such as multi-frequency harmonics. Detailed description: The electromagnetically driven broadband modal levitation vibration system transmits vibration through a platform, supporting multiple postures, such as standing postures to enhance lower limb circulation. Further details: The electromagnetically driven broadband modal levitation vibration system includes an electromagnetic drive unit and multiple sensors to detect position, velocity, acceleration, and waveform, with a waveform control accuracy of 0.1Hz.

[0033] The light wave system uses an LED array to generate adaptive patterns. For example, 670nm pulses can promote the release of nitric oxide in the body, increasing ATP levels. The mechanism of the light wave system involves edge AI technology and a control system that regulates the wavelength intensity generated by the LED light source array and modulates the synchronous acoustic vibration output of the DMS sound field system to stimulate mitochondria and neural responses. Further, the LED array supports 16:9 aspect ratio and 4K high-definition resolution, and pattern generation uses a GAN model to create dynamic visual stimuli.

[0034] The edge AI computing and control system uses an emotion AI model to process data from biosensors, achieving EQ quantification and multimodal fusion. Hardware expansion includes a Broadcom AI chip as the processor, supporting encrypted data storage. Model training utilizes transfer learning and supports offline mode.

[0035] The high-definition touchscreen integrated module enables user interaction, displays real-time feedback, supports multiple languages, and voice commands. Further, the software is based on Linux or Android, and the UI allows for customization of therapy duration and intensity.

[0036] The audio-visual system consists of a camera system and a microphone array system, as well as an infrared sensor. The camera system includes several 4K cameras that capture micro-expressions and physiological data. The microphone array system comprises eight microphone arrays that collect sound from all directions. The infrared sensor monitors body temperature and supports gesture recognition. Additional features: 4K camera frame rate 60-240fps, microphone sensitivity -40dB, infrared temperature measurement range 0-50°C.

[0037] Biosensors are used to directly or indirectly collect human biological information, including but not limited to heart rate, respiration, electrocardiogram (ECG), electroencephalogram (EEG), and blood oxygen saturation. Further examples include optical heart rate sensors and magnetic field-type EEG modules suitable for horizontal positioning, with data sampling rates exceeding 100Hz.

[0038] The network interface is used to interact with and exchange data with external devices, including but not limited to 4G / 5G / 6G, WiFi, Bluetooth and Type-C ports, supporting AES-256 encryption, compatible with external sensing and monitoring devices, and enabling remote monitoring; network interface expansion: adaptive transmission rate, supporting blockchain data sharing.

[0039] System Interaction Expansion: Components communicate via a high-speed bus, with the AI ​​core coordinating data flow. For example, physiological data is input into the AI, output signals drive modalities, and the feedback loop iterates at high speed, ensuring critical response times are <1 millisecond. Power Consumption Optimization: Edge computing reduces cloud dependence, making it suitable for home use. Expansion: The system features redundant design, with a downtime failure rate of <0.01%, and is compatible with expansion modules such as wearable detection systems and VR headsets.

[0040] The AI ​​technology of this invention integrates multiple algorithms specifically designed for processing multi-biosensor data (including photoelectric sensors, such as audio-visual-electrical signals) to achieve advanced control over sound field synthesis, composite vibration, and adjusted light waves. Specifically, for acoustic processing, the edge AI computing and control system uses a combined network structure of Short-Time Fast Fourier Transform (FFT) and Long Short-Time Memory Network (LSTM) and Convolutional Neural Network (CNN), while also incorporating knowledge-based models and data-driven models. This hybrid approach can analyze the sound field in real time and fuse it with multimodal physiological detection signals (such as heart rate, respiration, and movement). The resulting AI-controlled sound field can produce effects that are impossible without AI algorithms. For example, audio therapy dynamically adjusts based on the user's physiological state, rationally adjusting vibration modes and parameters according to the user's height, weight, body type, and bone type to achieve the optimal effect required by the user.

[0041] The AI ​​algorithm of this invention features a functionally combined network architecture inspired by the modular organization of the human brain. Different regions handle specialized tasks, and theoretical knowledge and experimental data are processed in different brain regions and fused together in the network. This structure integrates different network types for different functions: CNN for extracting spatial features from sensor data, LSTM for time-series modeling of physiological signals, and Short-Time FFT for frequency domain analysis of acoustic inputs. These components are integrated into a highly efficient system for processing multimodal data.

[0042] The implementation of the AI ​​algorithm of this invention begins with a first-layer functional decision network. This layer evaluates the input task and data, as well as prior theories and knowledge, to determine the required network function, and then assigns the computational task to the appropriate sub-network (e.g., CNN for spatial data, LSTM for temporal data). The computation results are then fed to a reinforcement learning (RL) network, which optimizes the output based on feedback loops. The RL network improves the assignment by refining the decision network's allocation, making task assignment and computation more accurate. This iterative process continues until the RL network reaches predefined parameters, and then the results are passed to the output network. The output network generates specified signals to control system operation and facilitate user interaction, ensuring real-time adaptability and accuracy.

[0043] Additional details regarding the network architecture implementation: The functional combination network employs a hierarchical design. The initial decision layer uses a lightweight decision tree or simple neural network to classify the input data type and task, ensuring efficient resource allocation. The CNN component includes multiple convolutional and pooling layers for extracting spatial features from sensor images, while the LSTM layer contains memory units with input, forget, and output gates to model the temporal dependence of physiological signals. The short-time FFT module processes acoustic data with overlapping windows, inputting frequency domain features into the LSTM. These subnetworks are interconnected by a fusion layer that weights and combines the outputs, and is optimized by an RL network using Q-learning or policy gradient methods to enhance iterative performance.

[0044] The control method for this AI robot includes the following steps: Step 1: Collect data through camera system, microphone array system, infrared sensor, biosensor and electromagnetic sensor, support multi-source fusion, closed-loop control, output motion waveform correction, output audio waveform pre-distortion correction; Step Two: Data preprocessing utilizes DSP signal processing. The fusion model integrates multimodal inputs, such as heart rate and micro-expression data. Control processing includes closed-loop control of the vibration platform's motion trajectory, audio signal pre-distortion processing, and real-time speaker linearization. Extension: The fusion algorithm dynamically adjusts weights, combined with output motion waveform correction and output audio waveform pre-distortion correction, to output a personalized optimal motion program.

[0045] Step 3: Output motion waveform correction, output audio waveform pre-distortion correction, and output personalized optimal motion program; Step Four: AI calculates personalized control signals and optimizes them using deep learning. Specifically, targeted training is performed on a pre-trained model to adapt it to specific tasks or domains. Through RAG (Retrieval Enhanced Generation), a two-stage architecture of "retrieval + generation" is used: first, a dedicated knowledge base is searched, and then a large model generates answers based on the retrieval results. This significantly reduces illusion problems, transforming control issues into vector-based document retrieval, concatenating them into a prompt input generation model, ensuring the accuracy and traceability of answers, and more precisely serving scenarios requiring high credibility, such as this patent. Extension: Model training is based on health big data, and calculated parameters such as vibration amplitude are based on user history. Optimization utilizes a multi-objective frequency domain transformation fast genetic algorithm, ensuring that audio output and motion waveform output form a complete closed-loop control.

[0046] Step 5: Use the personalized control signal optimized by deep learning to control the DMS sound field system, human body wave system and light wave system to generate a suitable sound field, human body vibration and light field; Step Six: Return to Step One until dynamic equilibrium is reached, that is, the system dynamically adjusts to the optimal state.

[0047] Specifically, the AI-computed personalized signal employs deep learning technology, tailoring a pre-trained model to specific tasks or domains through targeted training. The core architecture combines these functional networks, including a Convolutional Neural Network (CNN) for spatial feature extraction, a Long Short-Term Memory (LSTM) network for time series modeling, and a Short-Time Fast Fourier Transform (FFT) for frequency domain analysis. An emotional intelligence model is used for personalized emotion analysis and management. Model training is based on health big data and proprietary calibration data. The emotional intelligence model generates data, specifically optimizing parameters such as vibration amplitude, waveform, and frequency, while also incorporating user historical data for personalized adjustments. Through a two-stage "retrieval + generation" architecture of Retrieval Enhanced Generation (RAG), relevant documents are first retrieved from a dedicated knowledge base, transforming the control problem into a vector retrieval task. The retrieval results are then concatenated into a prompt input generation model, ensuring the accuracy and traceability of the answer, making it suitable for high-confidence scenarios such as those described in this patent.

[0048] Improvements and Technical Architecture of Genetic Algorithm: The Multi-Objective Frequency-Domain Transform Fast Genetic Algorithm (MFT-FGA) is used to optimize the closed-loop control of audio output and motion waveform output. Improvements to the genetic algorithm include introducing frequency-domain transformation to enhance computational efficiency and employing adaptive crossover and mutation strategies to accelerate convergence. In the network structure, the genetic algorithm is integrated with a functional combinatorial network to perform multi-objective optimization on multimodal health data (such as vibration amplitude and physiological signals). Objectives include maximizing effectiveness, improving physiological health indicators, sleep, mood regulation, concentration, eye fatigue relief, memory improvement, minimizing energy consumption, and personalized fitness. The Multi-Objective Frequency-Domain Transform Fast Genetic Algorithm further enhances global search capabilities through parallel computation and an elite retention strategy, ensuring robustness of parameter optimization.

[0049] Detailed description of the model training and implementation process: The model training process first uses a pre-trained deep learning model as the foundation, and then fine-tunes it on a health big data dataset, self-calibrated data, and emotional intelligence model-generated data. The data includes the user's historical vibration amplitude, audio response, and physiological indicators. The training adopts supervised learning combined with reinforcement learning (RL), in which the RL network optimizes the output parameters of the genetic algorithm based on closed-loop feedback. The implementation process is divided into three stages: (1) The data preprocessing layer extracts features through FFT and CNN; (2) The RAG module retrieves knowledge base and generates preliminary prompts, which are input into LSTM for time series prediction; (3) The MFT-FGA algorithm iteratively optimizes vibration amplitude and audio parameters until the multi-objective convergence condition is reached. The final result generates control signals through the output network, forming a closed-loop system for user interaction, ensuring high accuracy and personalized experience.

[0050] Detailed Description of RAG Architecture: The Retrieval Enhanced Generation (RAG) architecture is a hybrid model combining retrieval and generation techniques, designed to improve the accuracy, traceability, and relevance of generated content. It is particularly suitable for scenarios requiring high credibility, such as the AI-powered personalized signal system described in this patent. The RAG architecture consists of two main phases: a retrieval phase and a generation phase. By integrating an external knowledge base with a Large Language Model (LLM), it significantly reduces illusion problems and ensures that answers are based on reliable information.

[0051] like Figure 4 As shown, the Search Enhancement Generation (RAG) process includes a retrieval phase (1, 2, 3) and a generation phase (4, 5), with the specific process as follows: 1. Transform the input question or task into a high-dimensional vector representation. First, the system uses an embedding model (such as a Transformer-based sentence vector encoder) to convert the user query and document content from the knowledge base into vector representations in a vector space; 2. These vectors are stored in a vector database (such as FAISS or Milvus) to support efficient nearest neighbor search. In this embodiment of the invention, the vectors (dedicated knowledge base) include health big data, vibration amplitude parameters, and user historical data; 3. Input Retrieval: Using cosine similarity or Euclidean distance calculations, quickly locate the most relevant document fragments to the input query (usually the top k matches, where k is adjustable, e.g., 5-10). The innovation of this stage lies in transforming the control problem (such as vibration amplitude optimization) into a vector retrieval task, ensuring that the retrieval results are highly relevant to the specific task and providing contextual support for subsequent generation stages. 4. During the generation phase, the retrieved document fragments are concatenated into a structured prompt, which is then input into a pre-trained large-scale generative model (such as a Transformer-based language model). 5. Generating Response or Control Signals: Upon receiving a prompt, typically including the query, retrieved context, and task instructions, such as "Generate personalized vibration control signals based on the following health data," the generation model utilizes this contextual information, combined with its internal knowledge, to generate a response or control signal. In this patent, the generation process collaborates with a functional combination network (CNN, LSTM, FFT). LSTM processes time-series data to predict user responses, CNN extracts multimodal sensor features, and FFT optimizes the frequency domain audio output. RAG dynamically adjusts the prompt content to ensure consistency between the generated signal (such as audio or motion waveforms) and the retrieved data, forming a closed-loop feedback mechanism for closed-loop control, further improving accuracy.

[0052] like Figure 5 As shown, the structural block diagram of RAG is as follows: Preprocessing: Knowledge base documents are pre-embedded and indexed, and batch processing is used to improve efficiency; Dynamic retrieval: Adjust retrieval parameters based on real-time input, such as adding a context window or weighting. Generative optimization: By fine-tuning the generative model, incorporating health-specific terminology and rules, irrelevant output is reduced; Feedback loop: The generated results are compared with user feedback or sensor data to update the knowledge base index or adjust the retrieval strategy. For this patent, RAG is combined with the Multi-Target Frequency Domain Transform Fast Genetic Algorithm (MFT-FGA). The genetic algorithm optimizes the priority of retrieval results and the convergence speed of generation parameters, ensuring the system maintains high efficiency in real-time scenarios.

[0053] like Figure 6 As shown, the methods for adjusting the output (sound, vibration, light) are as follows: The optimal control signals obtained by AI calculations include sound waveforms, vibration waveforms, and optical graphic video output signals; By assigning dedicated drivers to the corresponding working systems, the multimodal outputs are coordinated and consistent, enabling personalized therapy under closed-loop control. Preferably, the adjustment logic includes a Kalman filter (for noise filtering and state estimation) that combines real-time physiological detection signals with AI algorithms and a PID controller (for precise adjustment of output parameters), with synchronization delay controlled to <50ms to ensure the system's immediate response and stability.

[0054] Furthermore, the AI-adjusted output employs advanced control systems and signal processing technologies. Through dynamic allocation based on an AI optimization model, it adapts the sound, vibration, and light modal outputs to real-time user feedback and posture changes. The core architecture integrates the aforementioned multimodal signal generation network, including a Kalman filter for physiological data noise suppression, a PID controller for waveform stabilization, and a real-time synchronization module for modal coordination, ensuring precise coordination of the output signal across combinations such as 40Hz vibration and 670 nm red light. The fusion of knowledge-driven and data-driven approaches makes the adjustment process more intelligent and efficient.

[0055] The improvements made to the Kalman filter and PID controller are as follows: The algorithm integrates a Kalman filter with a PID controller for closed-loop control of output adjustment. Improvements include introducing adaptive state estimation to enhance noise robustness and employing a multi-objective optimization strategy to accelerate convergence. In the network structure, the Kalman filter processes real-time physiological signals (e.g., heart rate deviation), the PID controller adjusts output parameters (e.g., vibration amplitude), and collaborative optimization is performed on multimodal data (e.g., sound waveforms and light intensity). Objectives include maximizing improvements in health and exercise indicators, minimizing synchronization delay, and maximizing personalized fitness. The algorithm further enhances global stability through parallel computation and feedback loops, ensuring that vibration output does not exceed a safe threshold in horizontal mode.

[0056] The detailed description of the model training and implementation process is as follows: The model training process first utilizes a pre-trained control model as a foundation, then fine-tunes it on large-scale health data and real-time physiological datasets to adapt it to multimodal output tasks. Training employs supervised learning combined with model predictive control (MPC), where the MPC network optimizes PID parameters based on closed-loop feedback.

[0057] The implementation process is divided into three stages: (1) The data input layer preprocesses physiological signals through a Kalman filter; (2) The adjustment module searches the optimization library and generates a preliminary waveform, which is then input into the PID for parameter fine-tuning; (3) The synchronization module iteratively corrects the sound, vibration, and light outputs until the multi-objective convergence condition (such as delay <50 ms) is met. The final result generates an execution signal through the driver, forming a closed-loop system for user interaction, ensuring high precision and a personalized experience.

[0058] The following is a detailed description of Kalman filters and PID architecture: The Kalman filter and PID architecture is a hybrid framework combining state estimation and feedback control, designed to improve the accuracy, stability, and robustness of output tuning, particularly suitable for the multimodal real-time control scenarios described in this patent. The architecture consists of two main components: a Kalman filter stage and a PID control stage. By integrating noise filtering with parameter tuning, it significantly reduces system errors and ensures synchronization efficiency. The detailed structure and implementation process are as follows: I. Kalman Filter Stage The core of the Kalman filter stage is to use a recursive algorithm to optimally estimate the physiological detection signal, separating noise (such as sensor jitter or environmental interference) from the real-time data. Initially, the system predicts the physiological state (such as heart rate fluctuations) at the next moment using a state transition model (such as linear dynamic system equations), and then corrects the prediction error by combining an observation model (such as camera or biosensor input). In this patent, the Kalman filter processes multimodal signals (such as video pose data and electrocardiograms), dynamically adjusting noise weights through the covariance matrix to support high-precision pose recognition (e.g., using an extension of YOLO). The innovation of this stage lies in transforming the physiological problem into a state estimation task, ensuring that the filtering result is highly correlated with the specific output (such as vibration waveforms), and providing clean input for the subsequent PID stage.

[0059] II. PID Control Stage In the PID control stage, the filtered signal is input to a proportional-integral-derivative (PID) controller to generate the optimal output waveform. The controller responds to the current error with a proportional term, eliminates accumulated deviation with an integral term, and predicts future trends with a derivative term, achieving precise adjustment of sound, vibration, and light signals. For example, for a 40Hz vibration + red light combination, the PID adjusts the amplitude to match the user's biofeedback. In this patent, the control process and functional combination network work together; LSTM processes the time series to predict deviations, and CNN extracts features to optimize the graphic output. The PID ensures stability through adaptive gain (such as parameter tuning based on a genetic algorithm), forming a closed-loop feedback mechanism for output adjustment, further improving response speed.

[0060] The implementation details and optimizations for the PID control stage are as follows: The implementation of this architecture includes several key steps: (1) Preprocessing: Physiological data is pre-filtered and indexed, and batch processing is used to improve efficiency; (2) Dynamic adjustment: Kalman covariance or PID gain is adjusted according to real-time input, such as increasing physiological weights; (3) Control optimization: By fine-tuning the controller, specific rules in the health field are incorporated to reduce irrelevant fluctuations; (4) Feedback loop: The adjustment results are compared with user feedback, and the state model or adjustment strategy is updated to ensure that the system maintains a high-efficiency response in real-time scenarios. For this patent, the architecture is combined with the Multi-Target Frequency Domain Transform Fast Genetic Algorithm (MFT-FGA). The genetic algorithm optimizes the filtering parameters and control convergence speed to ensure that the system synchronization delay is <50 ms in multi-pose mode.

[0061] In the implementation of this invention, the therapeutic effect is achieved by transmitting stimulation, and through cyclic optimization and efficacy evaluation, the expected goals of stress reduction, sleep improvement and other physiological indicator improvement are achieved.

[0062] Preferably, stimulation is delivered to achieve therapeutic effects, such as improving brain connectivity. Further, stimulation mechanisms include nitric oxide release and gamma wave induction, with quantifiable benefits such as increased blood flow, improved sleep quality (shorter sleep onset time, increased deep sleep duration, fewer nighttime awakenings), improved alertness scores, muscle relaxation (reduced electromyographic (EMG) amplitude, decreased subjective tension scores, increased joint range of motion), and shorter muscle soreness relief time.

[0063] Furthermore, iterative optimization continues until dynamic equilibrium is reached. Details: Error calculation uses a threshold algorithm; if the deviation exceeds 5%, readjustment is performed. Extensions: The loop frequency is 1Hz, and interruption conditions such as manual user-manual stop are supported.

[0064] Furthermore, the efficacy is assessed by quantifying factors such as stress reduction, sleep improvement, and other physiological and diagnostic indicators. Extended: The assessment model uses machine learning scoring to generate reports to support future sessions and export CSV data.

[0065] The AI ​​hardware of this invention, an edge AI computing and control system, includes a processor (such as a Broadcom AI chip), 128GB of memory, 16GB of RAM, and I / O connected via a bus. Instructions enable robot control methods, supporting low-power edge computing. Expansion features include hardware support for PyTorch and TensorFlow, integrated GPU acceleration for multimodal fusion, and a bus bandwidth >10Gbps ensuring real-time processing. A security module includes a hardware encryption chip to prevent data leakage. Further expansion features include a passive cooling system, an 8-hour continuous lithium battery power supply, and USB 3.2 interface support.

[0066] Clinical application simulation of this invention: In a simulation trial, the robot was used for Alzheimer's prevention: 40Hz vibration + 670nm light, combined with AI adjustment, was superior to single therapy. Extended application: For stroke patients in a supine position, WBV combined with phototherapy improves motor function.

[0067] Application scenarios of this invention: This invention supports multiple usage postures to adapt to different user needs and health conditions.

[0068] I. Standing Mode: The user stands on the vibration platform, facing the control screen, sound field speakers, and lighting devices. This mode optimizes balance, muscle coordination, and circulation improvement, suitable for healthy individuals or patients in the early stages of rehabilitation. AI adjusts vibration intensity to enhance bone density and fat reduction. The platform is 0.6m x 0.5m elliptical in shape, with vertical vibration propagation. AI monitors center of gravity shift and outputs 40Hz sound to enhance focus. Typical therapy duration is 30-50 minutes.

[0069] II. Seated Mode: The user sits on the vibration platform, facing the control screen, sound field speakers, and light-emitting devices. This mode is suitable for extended therapy sessions, focusing on relaxation, stress relief, and bone health. Studies show it improves posture and flexibility, and the combined light and sound stimulation enhances psychological benefits. Extensions: The platform is height-adjustable, vibration is vertical, 670nm light targets the upper body, and AI integrates heart rate data to optimize relaxation cycles. The therapy can be extended to 60 minutes, supporting use in office environments.

[0070] III. Lying Down Position (Suitable for paralyzed patients and those with sleep problems): The user lies flat on the vibration platform, facing the control screen, sound field speakers, and light source (placed above). This mode is designed for those with limited mobility, reducing the overall problems associated with prolonged bed rest. AI automatically detects posture and optimizes output, such as reducing intensity to avoid discomfort. Features include: a platform mattress design, a light array covering the entire body, non-contact sensors monitoring breathing and sleep, and AI algorithms adjusting nitric oxide release stimulation for paralysis. Therapy duration is 45 minutes.

[0071] Extended scenarios: Remote mode, using 5G connection to monitor supine therapy; Combined mode, switching from standing to sitting for progressive rehabilitation; Children's mode.

[0072] Safety and ethical extensions of this invention: Detailed safety mechanisms: A threshold monitoring system checks heart rate deviations in real time; if the deviation exceeds 10%, it automatically reduces intensity or shuts down. Further details: The algorithm uses fuzzy logic to assess risk, and thresholds are user-defined. Ethical considerations: Data privacy complies with GDPR standards, and biometric authentication ensures user consent. Further details: An informed consent module is presented via touchscreen, and data is stored anonymously. Commercial applications: The home version supports a subscription model, and the hospital version integrates with EHR systems. Further details: CE / FDA certification process, and materials biocompatibility testing.

[0073] Extending AI algorithms: AI fusion algorithm extension: using CNN to process image data, RNN to sequence biological signals, and fusion layer to calculate weights.

[0074] Pseudocode extension: ``` defmultimodal_fusion(video,audio,bio): video_feat=cnn.extract(video)#CNNforvideo audio_feat=rnn.extract(audio)#RNNforaudiosequence bio_feat=mlp.extract(bio)#MLPforbiosignals fused=concatenate(video_feat,audio_feat,bio_feat) attention=softmax(dot(fused,query))#Attentionmechanism returnattention*fused defoptimize_signals(params): if params['pose'] == 'Lying down': signals['vibration']*=0.8#Reduceforsupine returnsignals ``` Robot Manufacturing and Materials: The robot's shell uses biocompatible plastic, and the platform is reinforced with carbon fiber for durability. Manufacturing Process: 3D printing prototypes, assembly, and testing. Further Details: The supply chain includes LED suppliers and magnetic levitation components; the production line is 80% automated. Material Details: The platform features an antibacterial coating, and the lighting system uses UV-protective filters. Environmental Considerations: Recyclable materials are used, and lifecycle assessments reduce the carbon footprint.

[0075] User Interface Design: The UI supports touch and voice input. Extensions: Custom therapy wizard(), real-time chart generation using Matplotlib.

[0076] Safety protocols: The protocols include an emergency button and software redundancy. Further details: ISO 13485 compliant, Risk Management FMEA.

[0077] Manufacturing quality control: SixSigma, defect rate <0.1%. Extensions: test automation, supply chain auditing.

[0078] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A multimodal dynamic acousto-optic-electric AI robot, characterized in that, Comprise: DMS sound field system, with at least two independent channels, generating dynamic sound waves, supporting real-time sound field rendering, realizing sound field dynamic and content dynamic double dynamic audio, frequency range 20Hz-20kHz; Human body wave system, composed of several electromagnetic drive wide frequency mode suspended vibration systems, one electromagnetic drive wide frequency mode suspended vibration system is a vibration unit; The vibration unit generates mechanical vibration, the vibration amplitude is 0.1-50mm, the oscillation basic frequency range is 2-500Hz, and the composite waveform and complex waveform vibration control output are supported; Light wave system, using LED light source to generate dynamic light pattern synchronous with sound wave and mechanical wave, supporting visible light to near infrared light, wavelength 670-850nm; High-definition touch integrated module, used for displaying graphics and text, supporting user interaction; Camera system, including several 4K cameras, supporting multi-camera synchronization, supporting virtual ultra-high frequency video and image acquisition, stereo graphics and video reconstruction, spectral analysis, AI controlled exposure adjustment, supporting desensitization processing and privacy protection; Microphone array system, including at least two microphone arrays, which can automatically identify sound source, track sound source, locate sound source, DSP denoise, multi-sound source decoupling, intelligent sensitivity and dynamic range self-adaptation, supporting desensitization processing and privacy protection Electromagnetic sensor and waveform generation system, used for directly or indirectly collecting mechanical motion information, realizing closed-loop control, output signal can be adjusted in real time, and realizing other special waveforms for health care and fitness including standard sine wave and pre-distortion correction; Biological sensor and parameter acquisition system, used for directly or indirectly collecting human biological information, including heart rate, respiration, electrocardiogram, electroencephalogram and blood oxygen; Network interface, used for interacting and exchanging data with external devices, including but not limited to 4G / 5G / 6G, WiFi, Bluetooth and Type-C port, supporting AES-256 encryption, compatible with external sensing and monitoring devices, realizing remote monitoring; Edge AI computing and control system, connected with DMS sound field system, human body wave system, light wave system, camera system, microphone array system, biological sensor and parameter acquisition system, network interface, processing input information, running AI algorithm, and outputting control signal based on biological feedback synchronization, closed-loop response time less than 50ms; Self-checking, maintenance and upgrading system, the self-checking system runs regularly for comprehensive diagnosis, detecting the running state of hardware and software; The maintenance system updates the equipment remotely and cleans it locally; The upgrading system regularly introduces new algorithms and health data models; Cloud AI system and server, the cloud AI system and server protect personal privacy by entering user desensitization data, data encryption technology is applied to all transmission and storage processes, AI health analysis is carried out on desensitization data, detailed health condition evaluation is generated, the cloud AI system provides reference optimal health care scheme for users based on analysis results, and predicts potential health risks and preventive measures.

2. The multi-modal dynamic acousto-optic AI robot of claim 1, wherein, The robot is provided with three postures of standing, sitting and lying. The standing posture adopts one to four vibration units, the sitting posture adopts one to two vibration units, and the lying posture has different motion postures for different areas and adopts three to eight vibration units.

3. The multi-modal dynamic acousto-optic AI robot of claim 2, wherein, The light wave system outputs dynamic light pattern synchronized with sound waves and mechanical waves, which realizes synergistic effect through edge AI calculation and control system regulation and control, and is placed above in the lying posture to cover the whole body of the user.

4. The multi-modal dynamic acousto-optic AI robot of claim 2, wherein, The camera system, microphone array system, biological sensor, infrared sensor and electromagnetic sensor constitute the AI sensing system, which is controlled by the edge AI calculation and control system, supports real-time data fusion, and uses corresponding position sensors in different postures.

5. The multi-modal dynamic acousto-optic AI robot of claim 1, wherein, The edge AI calculation and control system is embedded with health big data, supports cross-modal fusion algorithm, and realizes multi-modal collaboration.

6. The multi-modal dynamic acousto-optic AI robot of claim 1, wherein, The light wave system adopts multi-color RGB LED to realize emotion visualization and synchronization with biological rhythm.

7. A control method of a multi-modal dynamic acousto-optic AI robot, characterized in that, It includes: Step one: data acquisition, obtaining user biological data through AI sensing system, user biological data including heart rate and electroencephalogram, ECG, EEG, respiration, SpO2, PPG, IMU, and also including standing, sitting and lying posture data and face recognition; Step two: generate control signal, feed user biological data to edge AI calculation and control system, use trained machine learning model for CNN or LSTM or Transformer or any combination of the three, calculate personalized parameters and generate control signal; Step three: output adjustment, use control signal to adjust DMS sound field system to output sound waves and music, control electromagnetic drive wide frequency mode suspension vibration system to produce mechanical vibration, and control light wave system to produce dynamic light pattern; Step four: output delivery, where sound waves stimulate neural pathways, mechanical vibration enhances cell movement and circulation, improves NO release, and light pattern activates visual and brain nervous system; Step five: feedback loop, fusion of electromagnetic sensor, fusion of mechanical movement data and biological data and real-time optimization of output until optimal effect and dynamic balance are achieved, supporting safety threshold monitoring; Step six: effect evaluation, quantifying user feedback through built-in model, adjusting corresponding control, and supporting posture-specific posture.

8. The control method of a multi-modal dynamic acousto-optic AI robot according to claim 7, wherein, The AI algorithm includes emotion quantification model, micro-expression feedback, empathy model, multi-modal information fusion and semantic AI model, which is used for precise personalization, fusion of convolutional neural network processing biological data, and self-adaptation in different postures.

Images