A wearable dynamic bone metabolism monitoring system and risk assessment method based on quantum precision measurement and multi-modal perception

Abstract

The application discloses a wearable dynamic bone metabolism monitoring system and risk assessment method based on quantum precision measurement and multi-modal perception, relates to the technical field of biomedical sensing and health monitoring, and comprises a wearable probe, wherein the wearable probe comprises a solid quantum sensor chip, is attached to the surface of a to-be-measured bone part, a multi-modal auxiliary sensor array, a biological electrical impedance analysis module and an ultrasonic transducer, a miniature exciter, applies a standardized micro-force pulse to the to-be-measured bone, a signal processing and calculation unit, and extracts micro-vibration characteristics. The application introduces quantum precision measurement into bone metabolism monitoring for the first time by adopting a diamond nitrogen-vacancy color center quantum chip, realizes ultrahigh-sensitivity detection of micro-vibration, synchronously collects three types of data, namely, vibration characteristics, muscle group states and bone cortex structures, constructs a multi-dimensional information matrix, breaks through the limitation of a single index, cooperates in a double mode of active excitation and passive monitoring, eliminates daily interference, and ensures data comparability.

Description

Technical Field

[0001] This invention relates to the field of biomedical sensing and health monitoring technology, specifically to a wearable dynamic bone metabolism monitoring system and risk assessment method based on quantum precision measurement and multimodal sensing. Background Technology

[0002] Bone metabolism is a dynamic balance between osteoblast-mediated bone formation and osteoclast-mediated bone resorption. Traditional assessment methods have significant limitations: dual-energy X-ray absorptiometry (DEXA) only provides a static "snapshot" of bone mineral density and cannot reflect short-term metabolic changes; the detection of biochemical markers of bone metabolism requires blood sampling and is affected by various factors, cannot be localized and is not real-time; bone biopsy is invasive and not repeatable. Quantum sensing technology, especially diamond nitrogen-vacancy color centers, has extremely high sensitivity to extremely weak magnetic fields and stress changes at room temperature. This invention is the first to creatively propose its application to the skeletal system, aiming to solve the following problems: It enables non-invasive, real-time, and localized dynamic monitoring of bone metabolic activity, capturing short-term (hours to days) biological changes that traditional technologies cannot detect; Establish a quantitative correlation model between the micro- and nano-mechanical vibration characteristics of bone and the metabolic activities of underlying cells; This provides a tool for high-frequency screening and dynamic evaluation of treatment efficacy that can be used in families and communities, filling a gap in existing technologies.

[0003] The shortcomings of existing wearable dynamic bone metabolism monitoring are: 1. Patent document CN121075564A discloses a method and system for dynamic tracking and intervention of bone health based on exercise and nutrition. The document states, "This invention discloses a method and system for dynamic tracking and intervention of bone health based on exercise and nutrition. The method includes: collecting gait information, bone metabolite information, and bone status measurement parameters of a target user using a wearable device; processing the gait information, bone metabolite information, and bone status measurement parameters based on an artificial intelligence (AI) model to obtain bone status prediction parameters; assessing the target user's bone health status based on the bone status prediction parameters and the target user's health data to obtain health status prediction parameters; the health status prediction parameters include: real-time bone health status assessment indicators and dynamic trend data of bone health; generating targeted exercise planning and dietary planning based on the health status prediction parameters; wherein, the targeted exercise planning is used to guide the target user's exercise; and the dietary planning is used to guide the target user's diet." However, the monitoring methods described in the above document have limited sensitivity, making it difficult to capture micro-vibration signals with frequencies ranging from 1kHz to 10MHz and extremely small amplitudes, easily missing early or subtle signs of abnormal bone metabolism. Summary of the Invention

[0004] The purpose of this invention is to provide a wearable dynamic bone metabolism monitoring system and risk assessment method based on quantum precision measurement and multimodal sensing, so as to solve the technical problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a wearable dynamic bone metabolism monitoring system based on quantum precision measurement and multimodal sensing, comprising a wearable probe, wherein the wearable probe includes: A solid-state quantum sensor chip, based on diamond nitrogen-vacancy color centers, is used to attach to the surface of the bone site to be tested. It senses the micro-vibration and outputs the original vibration signal by detecting the spin state changes of the diamond nitrogen-vacancy color centers caused by micro-nano-scale mechanical vibrations excited by bone metabolism and external stimulation. The frequency range of the micro-vibration is 1kHz-10MHz, and the amplitude is ≤500nm. A multimodal auxiliary sensor array, including a bioelectrical impedance analysis module for monitoring periosseous muscle groups and an ultrasound transducer for assessing cortical bone structures; The miniature exciter applies standardized micro-force pulses to the bone under test in response to active test commands to induce forced vibrations; The signal processing and computing unit, connected to the probe, is configured to receive raw signals and multimodal data, extract micro-vibration features, input them into a pre-trained bone metabolism activity assessment model, and output quantitative bone metabolism activity parameters.

[0006] Preferably, the signal processing and computing unit is further configured to collect the inherent skeletal vibrations excited by the load of daily human activities as the original vibration signal in passive monitoring mode, or to collect the forced vibration response as the original vibration signal in active testing mode.

[0007] Preferably, the signal processing and computing unit is further configured to compare and analyze the micro-vibration features extracted in real time with the pre-established individual baseline spectrum, calculate the relative change, and output the bone metabolism activity parameter based on the relative change. The individual baseline spectrum is obtained by measuring the wearable probe under standard resting conditions during the first use or periodic calibration, and is associated with and registered with the bone structure parameters obtained from the medical imaging examination at the same time to establish a multi-dimensional initial baseline profile of personal bone health.

[0008] Preferably, the micro-vibration characteristics include at least the characteristic frequency peak position, the energy proportion of a specific frequency band, the vibration damping coefficient, and the signal nonlinearity, and the bone metabolic activity parameters include at least the bone formation activity index, the bone resorption activity index, and the net metabolic balance index, wherein the net metabolic balance index is the difference between the bone formation activity index and the bone resorption activity index.

[0009] Preferably, it also includes a cloud platform, which is communicatively connected to the signal processing and computing unit, and is configured as follows: The system receives and aggregates bone metabolism activity parameters and raw feature data from multiple users, continuously optimizes the bone metabolism activity assessment model using the aggregated data, integrates the analysis results with other health data of users, constructs and updates the user's skeletal digital twin, and sends early warning information to the user or the linked medical care terminal when a high-risk metabolic imbalance trend is identified.

[0010] Preferably, the working steps of this wearable dynamic bone metabolism device based on quantum precision measurement and multimodal sensing are as follows: S1. Signal acquisition: The wearable probe, which is attached to the surface of the bone to be tested, acquires the original signal of micro-nano-level mechanical vibration excited by internal metabolic activities and external excitation through a solid-state quantum sensor chip. At the same time, the bioelectrical impedance analysis module acquires the status data of the muscle groups around the bone, and the ultrasonic transducer acquires the bone cortex structure data. The frequency range of the micro-nano-level mechanical vibration is 1kHz-10MHz, and the amplitude is no more than 500nm. S2. Feature extraction: The original vibration signal is processed to extract micro-vibration features used to characterize bone metabolism. S3. Metabolic activity mapping: The extracted micro-vibration features, the data on the state of the surrounding muscle groups and the data on the cortical bone structure are input into a pre-trained bone metabolic activity assessment model, and the model outputs at least one quantitative bone metabolic activity parameter.

[0011] Preferably, the micro-nano-scale mechanical vibrations in S1 include: in passive monitoring mode, the inherent vibrations of bones excited by the load of daily human activities, or in active testing mode, the forced vibration response of bones excited by standardized micro-force pulses applied by the micro exciter built into the wearable probe.

[0012] Preferably, it also includes individual baseline calibration S0, which is performed upon first use or periodic calibration, including: S01: The subject is measured using the wearable probe under standard resting conditions to obtain the individual baseline microvibration spectrum; S02: Obtain bone structure parameters obtained from medical imaging examinations of the subject's skeletal sites during the same period; S03: Associate and register the individual baseline microvibration spectrum with the bone structure parameters to establish a personal multidimensional initial benchmark profile; In step S3, the bone metabolism activity parameters are output after the real-time extracted micro-vibration features are compared and analyzed with the individual baseline micro-vibration spectrum.

[0013] Preferably, cloud-based collaboration S4 is also included: S41: Encrypt and upload the bone metabolism activity parameters and original characteristic data to the cloud platform; S42: The cloud platform aggregates multi-user data and uses a federated learning mechanism to continuously optimize the bone metabolism activity assessment model; S43: The cloud platform will integrate the analysis results with the user's activity data and nutrition records to update the user's skeletal digital twin; S44: When a high-risk metabolic imbalance trend is identified, the cloud platform pushes early warning information to the user terminal or the bound medical care terminal.

[0014] Preferably, the bone metabolism activity assessment model is obtained in advance through the following steps: A1: Collect sample datasets, which include bone micro-vibration feature data measured by the solid-state quantum sensor chip, and concurrent bone metabolism status labels obtained by the clinical gold standard method; A2: Preprocess and feature-engineer the sample dataset to construct training and validation sets; A3: Use machine learning algorithms to train the training set to establish a predictive model from bone micro-vibration features to bone metabolic state labels; A4: The trained model is validated and optimized using the validation set to obtain the bone metabolism activity assessment model; The clinical gold standard method includes one or more of the following: serum bone turnover marker detection, high-resolution quantitative CT of peripheral bone, or tetracycline double-labeled bone biopsy histomorphometric analysis.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention introduces quantum precision measurement into the field for the first time by using a diamond nitrogen-vacancy color center quantum chip, achieving ultra-high sensitivity to micro-vibrations with frequencies of 1kHz-10MHz and amplitudes of ≤500nm. It simultaneously collects three types of data: vibration characteristics, muscle group status, and bone cortex structure, constructing a multi-dimensional information matrix. This breaks through the limitations of single-index evaluation. Through the synergy of active excitation and passive monitoring modes, it eliminates daily interference, ensures data comparability, and accurately captures subtle metabolic changes. Finally, the pre-trained model interprets the multimodal features and outputs quantitative metabolic parameters, thereby realizing a closed loop from physical perception to physiological assessment. This transforms bone metabolism monitoring from a complex examination in medical institutions into a health management tool that can be used at home, frequently, and dynamically tracked. 2. In passive monitoring mode, the system automatically collects the inherent vibrations of bones excited by the load of daily human activities. It can continuously acquire the natural response of bones in real physiological environment without the user's awareness, providing data support with high ecological validity for establishing personal daily baselines. In active testing mode, the system applies standardized micro-force pulses through micro exciters and collects determined forced vibration responses. Controllable and repeatable external excitation eliminates interference caused by differences in daily activities, making the measurement data at different time points highly consistent and comparable. The two modes complement each other, thereby ensuring the continuity and compliance of data collection and ensuring the standardization and accuracy of evaluation results. 3. This invention, through an individual baseline calibration mechanism, collects the individual baseline micro-vibration spectrum under standard resting conditions during initial use or periodic calibration, and associates and registers it with bone structure parameters obtained from concurrent medical imaging examinations. This establishes a multidimensional initial baseline profile of individual bone health, including functional characteristics and structural parameters. During daily monitoring, the system compares and analyzes the micro-vibration features extracted in real time with the individual baseline spectrum, calculates the relative changes, and outputs bone metabolic activity parameters based on this. This effectively eliminates the interference caused by natural differences in bone structure between different individuals and fluctuations in measurement conditions at different time points, making each user their own reference system. This allows for the sensitive capture of subtle metabolic changes caused by disease progression or treatment interventions, ensuring the accuracy, comparability, and clinical reference value of long-term dynamic monitoring data. 4. This invention comprehensively characterizes the micromechanical state of bones by extracting multidimensional vibration features such as characteristic frequency peak positions, specific frequency band energy proportions, vibration damping coefficients, and signal nonlinearity. These features are then mapped to bone formation activity index, bone resorption activity index, and the net metabolic balance index of the difference between the two. This transforms physical signals into physiologically meaningful quantitative parameters. The net metabolic balance index intuitively reflects the dynamic balance direction of bone formation and resorption, making the metabolic-dominant trend readily apparent. Through the combination of multidimensional features and multi-parameter outputs, the system accurately quantifies the degree of bone metabolic activity and balance, providing rich and intuitive dynamic data support for early warning, efficacy evaluation, and personalized intervention. 5. This invention aggregates multi-user data through a cloud platform and continuously optimizes the bone metabolism activity assessment model using large-scale group data. This allows the model's accuracy to improve as the user base grows. Simultaneously, it integrates bone metabolism analysis results with multi-source health data such as user activity levels and nutritional records to construct and dynamically update a digital twin of bones for each user. This enables a multi-dimensional and predictable virtual mapping of individual bone health. When a high-risk metabolic imbalance trend is identified, a tiered warning is automatically sent to the user or healthcare provider. This allows the assessment model to have self-evolution capabilities and upgrades bone metabolism monitoring into an intelligent service system integrated into overall health management, providing strong support for early risk intervention. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the core hardware and data of the system of the present invention; Figure 2 This is a schematic diagram of the dual-mode measurement workflow of the present invention; Figure 3 This is a schematic diagram of individual baseline calibration and relative change analysis according to the present invention; Figure 4 This is a schematic diagram of feature extraction and metabolic activity parameter mapping of the present invention; Figure 5 This is a schematic diagram of the cloud-based collaboration and skeletal digital twin architecture of the present invention; Figure 6 This is a schematic diagram of the working steps of the method of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 This invention provides an embodiment of a wearable dynamic bone metabolism monitoring system based on quantum precision measurement and multimodal sensing, comprising a wearable probe, which includes: a solid-state quantum sensor chip based on diamond nitrogen-vacancy color centers, used to adhere to the surface of the bone site to be tested, sensing micro-vibrations and outputting raw vibration signals by detecting changes in the spin state of the diamond nitrogen-vacancy color centers caused by micro-nano-scale mechanical vibrations excited by bone metabolic activity and external stimulation, with the frequency range of the micro-vibrations being 1kHz-10MHz and the amplitude ≤500nm; a multimodal auxiliary sensor array, including a bioelectrical impedance analysis module for monitoring periosseous muscle groups and an ultrasonic transducer for evaluating cortical bone structure; a micro exciter that applies standardized micro-force pulses to the bone to be tested in response to active test commands to excite forced vibrations; and a signal processing and computing unit connected to the probe, configured to receive raw signals and multimodal data, extract micro-vibration features, input them into a pre-trained bone metabolism activity assessment model, and output quantitative bone metabolism activity parameters. Furthermore, by attaching a wearable probe containing a diamond nitrogen-vacancy center chip to the surface of the bone site to be tested, the system utilizes the ultra-high sensitivity of quantum sensors to micro-nano-scale mechanical vibrations with frequencies ranging from 1kHz to 10MHz and amplitudes ≤500nm. This allows for direct detection of spin state changes in diamond nitrogen-vacancy centers caused by lattice strain resulting from bone metabolic activity and external stimuli, outputting the raw vibration signal in real time. This marks the first time quantum precision measurement technology has been introduced into bone metabolism monitoring, enabling non-invasive and direct sensing of bone micromechanical activity. This provides raw data accuracy unattainable by traditional sensors for subsequent assessment. Simultaneously, while the quantum sensor acquires micro-vibrations, a multimodal auxiliary sensor array operates in parallel. A bioelectrical impedance analysis module monitors the mass and state of periosseous muscle groups, and an ultrasonic transducer assesses cortical bone thickness and density. These three components simultaneously acquire three types of data: metabolic vibration characteristics, muscle mechanical environment, and bone structural parameters, forming a multidimensional information matrix. This provides a more comprehensive physiological background for subsequent fusion analysis, overcoming the limitation of single indicators accurately reflecting the complexity of bone metabolism. Furthermore, upon initiating an active test command, a micro-exciter applies standard... Micro-force pulses are used to excite a defined forced vibration response, which complements the natural bone vibrations under load collected in the daily passive monitoring mode. The active mode eliminates interference from differences in daily activities through controllable excitation, making measurement data at different time points highly comparable. It can accurately capture subtle changes in metabolic activity over hours to days. The signal processing and computing unit receives the raw vibration signal and multimodal data, extracts micro-vibration features, and inputs them into a pre-trained bone metabolic activity assessment model. This model is trained based on sample clinical data and can intelligently interpret the complex mapping relationship between multimodal features and bone metabolic status. Finally, it outputs quantified bone metabolic activity parameters, which intuitively reflect the metabolic activity level of local bones. Through the above collaborative work, the system realizes a complete closed loop from physical perception to physiological assessment. It can obtain quantified bone metabolic activity parameters and their changing trends at any time. Based on this, the system provides data support for early warning, efficacy tracking, and personalized health advice. It transforms bone metabolic information that could only be obtained through complex examinations in medical institutions into a health management tool that can be used at home, frequently, and dynamically tracked.

[0019] Please see Figure 2 The present invention provides an embodiment of a wearable dynamic bone metabolism monitoring system based on quantum precision measurement and multimodal sensing. The signal processing and computing unit is further configured to collect the inherent bone vibrations excited by the daily activity load of the human body as the original vibration signal in passive monitoring mode, or to collect the forced vibration response as the original vibration signal in active testing mode. Furthermore, when users wear the wearable probe normally during daily activities, the system automatically enters passive monitoring mode. In this mode, the signal processing and computing unit collects the inherent vibrations of the bones excited by the load of daily human activities in real time as the raw vibration signal. This mode requires no active operation and can continuously acquire the vibration response of the bones in real physiological environments in a completely imperceptible state, reflecting the natural mechanical characteristics of the bones under daily mechanical loads. This provides ecologically valid data support for establishing an individual's daily baseline. When standardized assessment is required, an active testing mode can be triggered through the interactive interface. The system responds to the command and applies standardized micro-force pulses to the bone under test through a micro exciter, exciting the bones to produce a definite forced vibration response. The signal processing and computing unit simultaneously collects this response as the raw vibration signal, which can then be used for controlled, Repeatable external stimuli eliminate interference from factors such as differences in daily activities, walking speed, and load conditions, ensuring high consistency and comparability of measurement data across different time points and users. The system then uses the skeletal intrinsic vibration data collected in passive monitoring mode for long-term trend analysis and daily anomaly warning, reflecting metabolic fluctuations in bones under natural conditions. The forced vibration response data collected in active testing mode is used for standardized comparison and refined assessment, accurately capturing minute metabolic changes over hours to days. The data from the two modes corroborate and complement each other in time series. The passive mode provides high-frequency, low-intervention continuous monitoring, ensuring data collection compliance and ecological validity, while the active mode provides highly standardized, highly comparable, and accurate measurements, ensuring the reliability and scientific rigor of clinical assessments. The signal processing and computing unit fuses and analyzes the vibration signals acquired in both modes. Using active test data as a standardized reference point, it calibrates the fluctuation range of passive monitoring data and uses passive monitoring data as a continuous background to verify the representativeness of the active test results. Through this dual-mode collaborative data acquisition strategy, the system can eliminate measurement errors caused by individual differences in daily activities, ensure the longitudinal comparability of long-term monitoring data, accurately quantify the dynamic trends of bone metabolic activity, and provide a reliable data foundation for early identification of metabolic imbalances. Furthermore, through the organic combination of passive monitoring and active testing modes, this system ensures both the convenience and compliance of data acquisition and the standardization and comparability of measurement results. It allows for the acquisition of dynamic bone metabolic data with both ecological authenticity and clinical accuracy during daily wear, without the need for frequent visits to medical institutions. This provides a revolutionary technological means for early warning of osteoporosis, tracking of treatment effects, and personalized health management.

[0020] Please see Figure 3The present invention provides an embodiment of a wearable dynamic bone metabolism monitoring system based on quantum precision measurement and multimodal sensing. The signal processing and computing unit is further configured to compare and analyze the micro-vibration features extracted in real time with the pre-established individual baseline spectrum, calculate the relative change, and output bone metabolism activity parameters based on the relative change. The individual baseline spectrum is obtained by measuring the wearable probe under standard resting conditions during the first use or periodic calibration, and is associated with and registered with bone structure parameters obtained from the same medical imaging examination to establish a multi-dimensional initial baseline profile of personal bone health. Furthermore, by wearing a wearable probe in a standard resting state during the initial system use or periodic calibration, the micro-vibration signals of the bone site under test are collected by a solid-state quantum sensor chip to obtain an individual baseline micro-vibration spectrum. This is done under completely standardized physiological conditions, eliminating interference from factors such as daily activities and emotional fluctuations, and establishing a stable and repeatable personalized reference zero point for all subsequent dynamic monitoring. At the same time as calibration, medical imaging examinations of the bone site under test are conducted at a medical institution to obtain bone structure parameters, which are used to introduce clinically recognized structural gold standards into the calibration process. This facilitates the establishment of a direct correlation between subsequent vibration monitoring data and authoritative bone structure indicators, solving the problem that pure vibration measurement is difficult to trace to absolute physical meaning. Then, the signal processing and computing unit associates and registers the obtained individual baseline micro-vibration spectrum with the obtained medical imaging bone structure parameters to jointly establish a multi-dimensional initial baseline profile of personal bone health. This profile can simultaneously include functional and structural dimensions, thus forming dual-mode data of personal bone health. This facilitates the deep integration of dynamic functional data measured by quantum sensors with static structural data measured by traditional imaging, providing a dual calibration basis for each subsequent monitoring. In daily use after the initial baseline profile is established, the system continuously or periodically collects the user's skeletal micro-vibration signals and extracts micro-vibration features in real time. Each measurement is performed at the same physiological location and with the same sensor configuration, ensuring longitudinal consistency of the data. The signal processing and calculation unit compares the extracted micro-vibration features with the established individual baseline micro-vibration spectrum item by item, calculating the relative change of each feature relative to the baseline, thereby eliminating individual differences: different individuals have natural differences in bone size, thickness, and density, and directly comparing absolute values ​​is meaningless. By comparing with their own baseline, each user can become their own control, making the assessment results truly reflect their own physiological changes. It also eliminates differences in measurement conditions: even with standardized operations, measurements at different time points may still have slight differences in conditions. The calculation of relative changes can effectively offset these systematic errors. Furthermore, it sensitively captures subtle changes: based on the comparison of a precise baseline, it can amplify the signals of real physiological changes, enabling the system to capture data from hours to days that are imperceptible by traditional methods. The system monitors fluctuations in bone metabolic activity. Based on calculated relative changes, it uses a pre-set algorithm or evaluation model to output quantified bone metabolic activity parameters. These parameters are no longer absolute values ​​without reference, but rather metabolic activity indicators with clear physiological significance relative to an individual's initial state. For example, a 30% increase in energy in a certain frequency band relative to the baseline indicates enhanced bone metabolic activity in that area; a 15% decrease in vibration damping coefficient relative to the baseline indicates early changes in bone microstructure. By establishing a complete technical path—integrating an individual baseline spectrum with a gold standard for imaging—and comparing and calculating relative changes, this system successfully solves the measurement challenges commonly faced by wearable medical devices. Users do not need to understand complex physical parameters to obtain dynamic metabolic indicators that are accurately compared with their historical state. Clinicians do not need to worry about the difficulty of interpretation caused by individual differences and can make early warnings and efficacy judgments based on relative change trends. This mechanism makes home-based, high-frequency dynamic monitoring of bone metabolism truly clinically valuable, laying a methodological foundation for the precise prevention and control of osteoporosis.

[0021] Please see Figure 4 The present invention provides an embodiment of a wearable dynamic bone metabolism monitoring system based on quantum precision measurement and multimodal sensing. The micro-vibration characteristics include at least the characteristic frequency peak position, the energy ratio of a specific frequency band, the vibration damping coefficient and the signal nonlinearity. The bone metabolism activity parameters include at least the bone formation activity index, the bone resorption activity index and the net metabolic balance index, wherein the net metabolic balance index is the difference between the bone formation activity index and the bone resorption activity index. Furthermore, the signal processing and computing unit performs in-depth analysis on the raw micro-vibration signals acquired by the quantum sensor, extracting micro-vibration features in at least four dimensions: Characteristic frequency peak position: Identify the location of characteristic frequencies where energy is concentrated in the vibration spectrum, reflecting the stiffness characteristics of the skeletal structure; Energy percentage in a specific frequency band: Calculate the proportion of energy in different frequency ranges to the total energy, and quantify the contribution of different metabolic processes; Vibration damping coefficient: measures the attenuation rate of vibration signals, reflects the ability of bone tissue to absorb vibration, and is closely related to the integrity of bone microstructure; Signal nonlinearity: assesses the nonlinear characteristics of vibration signals, indicating the presence of microscopic damage or metabolically active regions in the bone matrix; The original vibration signal is then transformed into a feature vector with clear physical meaning, providing a quantitative basis for subsequent bioanalysis. Unlike traditional methods that only focus on a single indicator, this method can jointly characterize the mechanical state of the bone through multi-dimensional features, like drawing a vibration fingerprint map of the bone. This makes subsequent assessments of metabolic activity more comprehensive and accurate. The extracted multi-dimensional micro-vibration features are then input into a pre-trained bone metabolic activity assessment model. The model establishes a feature-physiological mapping relationship based on large-sample clinical data and outputs at least three core quantitative parameters: Bone formation activity index: quantitatively reflects the activity level of osteoblast-mediated bone formation. This index is highly correlated with features such as the energy proportion of a specific frequency band and the shift of characteristic frequency peaks. When bone formation is enhanced, this index shows an upward trend. Bone resorption activity index: quantitatively reflects the activity level of osteoclast-mediated bone resorption process. This index is mainly associated with characteristics such as broadband energy and signal nonlinearity in the high-frequency band. When bone resorption occurs, this index increases significantly. Net metabolic balance index: defined as the difference between the bone formation activity index and the bone resorption activity index, which directly reflects the net balance of bone metabolism; Furthermore, for the first time, purely physical vibration characteristics are transformed into metabolic activity indicators with clear physiological significance, enabling bone metabolism information that could only be obtained through blood tests or bone biopsies to be presented in real time and quantitatively through non-invasive vibration measurement. The system specifically calculates the net metabolic balance index, which is the bone formation activity index minus the bone resorption activity index: Net metabolic balance index > 0: Bone formation activity is greater than bone resorption activity, and the skeleton is in a net formation state, suggesting that bone mass may increase or remain stable. Net metabolic balance index ≈ 0: Bone formation and bone resorption are in dynamic equilibrium, and bone metabolism is stable; A net metabolic balance index < 0 indicates that bone resorption activity is greater than bone formation activity, and bone is in a state of net loss, suggesting an increased risk of osteoporosis or disease progression. Furthermore, the complex dual-indicator assessment is simplified into an intuitive directional indicator, enabling users and clinicians to clearly judge the current dominant trend of bone metabolism without having to deeply understand the specific numerical meaning of the two indices. For example, if the net metabolic balance index is monitored to gradually rise from a negative value to zero or even a positive value after osteoporosis patients receive anti-bone resorption drug treatment, it can be intuitively judged that the treatment is effective. The system combines three parameters—bone formation activity index, bone resorption activity index, and net metabolic balance index—to provide a comprehensive assessment of bone metabolic status. High formation + high absorption + balance ≈ 0: This indicates an increased bone turnover rate that remains in balance, which is common during the growth and development period or in the early stages of certain drug treatments. Low formation + high absorption + balance < 0: This indicates low-turnover bone loss, commonly seen in osteoporosis in the elderly; High formation + low absorption + balance > 0: This indicates that anabolism is dominant, which is the ideal response mode for bone formation-promoting drug therapy; Low formation + low absorption + balance ≈ 0: This indicates that bone metabolism is in a state of low activity, which may be seen in prolonged bed rest or certain endocrine diseases. By interpreting the combination of these three parameters, we can not only determine the level of bone metabolism activity, but also the direction of bone metabolism balance, providing much richer dynamic information for clinical decision-making than a single bone mineral density measurement. The system plots trend curves for the three activity indices obtained from previous measurements, focusing on tracking the evolution of the net metabolic balance index: Short-term trends: capturing early responses to drug treatment and immediate effects of rehabilitation exercises; Long-term trends: reflecting the natural progression of osteoporosis or the cumulative effect of long-term interventions; Inflection point identification: When the net metabolic balance index changes from a stable state to a continuous downward trend, the system automatically issues an early warning, indicating that it may be entering a period of accelerated bone loss; Through the above steps, a complete transformation chain from physical vibration characteristics to metabolic activity index is achieved. The peak position of the characteristic frequency reflects changes in structural stiffness, indicating possible fluctuations in bone density. The energy proportion of specific frequency bands distinguishes the contribution of osteogenic and osteoclast formation, revealing the dominant factor in the metabolic process. The vibration damping coefficient indicates the integrity of microstructure and provides early warning of bone quality decline. The signal nonlinearity captures microscopic damage signals and identifies metabolic abnormalities in the early stages. The final output of the bone formation activity index, bone resorption activity index, and net metabolic balance index together constitutes quantitative data on bone metabolic status. Users can intuitively grasp their own bone metabolic dynamics without understanding complex vibration physics terminology. Clinicians can make early diagnoses, evaluate treatment efficacy, and adjust treatment plans based on quantitative indicators without relying on invasive examinations.

[0022] Please see Figure 5The present invention provides an embodiment of a wearable dynamic bone metabolism risk assessment method based on quantum precision measurement and multimodal perception, which further includes a cloud platform. The cloud platform is communicatively connected to a signal processing and computing unit and is configured to: receive and aggregate bone metabolism activity parameters and raw feature data from multiple users; continuously optimize the bone metabolism activity assessment model using the aggregated data; integrate the analysis results with other health data of the user; construct and update the user's skeletal digital twin; and send early warning information to the user or the bound medical care terminal when a high-risk metabolic imbalance trend is identified. Furthermore, bone metabolism activity parameters and raw vibration characteristic data collected by wearable probes during daily use are encrypted and uploaded to the cloud platform in real time or periodically. The cloud platform receives and aggregates data from tens of thousands of users, forming a big data pool of bone metabolism covering different ages, genders, regions, lifestyles, and disease states. This helps to break down data silos from individual devices and transform scattered individual measurements into a statistically significant group knowledge base, laying a data foundation for subsequent model optimization and pattern discovery. At the same time, the cloud platform uses the aggregated multi-user big data to continuously retrain and optimize the bone metabolism activity assessment model, specifically including: discovering new features: by analyzing large-scale data, new micro-vibration characteristic patterns related to bone metabolism are identified, enriching the input dimensions of the model; Correcting biases: Using data from different populations to correct systematic biases in the model and improve the model's universality across different ethnic, age, and gender groups; Improving accuracy: As the amount of data increases, the model's prediction accuracy and generalization ability continue to improve, enabling it to interpret metabolic activity more accurately from vibrational features; This ensures that the system's model has the ability to evolve on its own. The more users there are and the longer they use the system, the higher the accuracy of the model's evaluation will be, thus forming a positive cycle where data drives model evolution and the evolved model serves more users. The cloud platform deeply integrates bone metabolism monitoring data with other user health data, including but not limited to: Activity data: Steps, activity intensity, sedentary time, etc., from wearable device IMU; Nutrition records: User-entered information on nutrient intake such as calcium and vitamin D, or synchronized via smart devices; Electronic health records include historical disease diagnoses, medication records, and laboratory test results. Lifestyle data: sleep quality, smoking and drinking habits, duration of outdoor activities, etc. By fusing multi-source data, the cloud platform constructs a comprehensive health profile for users, enabling bone metabolism assessments to be conducted in a holistic context, encompassing physiological, behavioral, and environmental factors. For instance, when the system detects an elevated bone resorption activity index, it can combine concurrent activity data and nutritional records to determine whether the increase is due to disuse bone loss caused by reduced activity or compensatory bone resorption due to insufficient calcium intake. Based on the fused multidimensional data, the cloud platform constructs a unique digital twin of the skeleton for each user. This digital twin is a dynamic virtual mapping of the user's real bones in digital space, possessing the following characteristics: Multi-dimensional: Includes multiple dimensions such as structural parameters, functional parameters, mechanical environment, and behavioral factors; Dynamics: As new monitoring data continues to flow in, the digital twin is updated in real time, truly reflecting the dynamic changes in the user's bone health; Predictable: Based on historical data and population patterns, digital twins can simulate the potential impact of different interventions on bone health, thus achieving a leap from static snapshots of regular checkups to dynamic twins that are updated in real time. This allows doctors and users to intuitively observe the evolution and future trends of bone health in a visual and interactive digital model. The cloud platform's core computing engine continuously analyzes the user's skeletal digital twin, using multiple algorithms to identify high-risk metabolic imbalance trends: Trend recognition algorithm: Detects dangerous trends such as a continuous decline in bone formation activity index, a continuous increase in bone resorption activity index, and a deepening of net metabolic balance index into negative values; Anomaly detection algorithm: Identifies significant deviations from the user's personal historical baseline, such as a sudden surge in energy in a certain frequency band or a rapid decrease in the damping coefficient; Risk prediction model: A prediction model built on big data of the population to assess the probability of a user developing osteoporosis or fracture within a specific time window in the future; When the above high-risk trends are identified, the system automatically triggers an early warning mechanism, transforming passive monitoring into proactive alerts. This allows users to take early intervention measures before significant bone loss or fractures occur. Furthermore, the system can use a tiered strategy to push early warning information to different groups based on the severity of the risk. Mild risk warning: Push personalized health advice to user terminals, such as "Bone resorption activity has been consistently high for the past two weeks, it is recommended to increase outdoor activities and supplement calcium"; Moderate risk warning: A notification will be sent to both the user and their registered family doctor or health manager, suggesting that further examinations or adjustments to the intervention plan be arranged. High-risk warning: The message will be sent to a specialist doctor or medical institution, who is advised to seek medical attention and receive clinical intervention in a timely manner; This leads to the construction of a closed-loop response mechanism involving "users, primary care physicians, and specialists," ensuring that risks at different levels receive appropriate attention and intervention, thus avoiding both over-treatment and serious misdiagnosis. Furthermore, through the complete cloud-based collaboration process described above, the system achieves a fundamental leap from isolated individual systems to collective intelligence: For individual users: they gain access to accurate assessments based on big data, continuously evolving model services, panoramic insights that integrate multi-source health information, and proactive health management driven by early warnings; For healthcare professionals: They have access to dynamically updated digital twins of the patient's skeleton, enabling them to remotely track the patient's condition, intervene in a timely manner, and assess the effectiveness of treatment. For public health: A dynamic monitoring network for bone metabolism covering a wide population has been established, providing a wealth of real-world data for epidemiological research and prevention and control policy formulation for osteoporosis; Ultimately, the cloud platform will upgrade wearable devices from personal tools into intelligent infrastructure integrated into the national health strategy through data aggregation, model evolution, multi-source fusion, digital twins, and intelligent early warning.

[0023] Please see Figure 6 The present invention provides an embodiment of a wearable dynamic bone metabolism risk assessment method based on quantum precision measurement and multimodal sensing. The working steps of the wearable dynamic bone metabolism device based on quantum precision measurement and multimodal sensing are as follows: S1. Signal acquisition: The wearable probe, which is attached to the surface of the bone to be tested, acquires the original signals of micro-nano-level mechanical vibrations excited by internal metabolic activities and external stimuli through a solid-state quantum sensor chip. At the same time, the bioelectrical impedance analysis module acquires the status data of the muscle groups around the bone, and the ultrasonic transducer acquires the structural data of the bone cortex. The frequency range of the micro-nano-level mechanical vibration is 1kHz-10MHz, and the amplitude is no more than 500nm. S2. Feature extraction: The original vibration signal is processed to extract micro-vibration features used to characterize bone metabolism. S3, Metabolic activity mapping, inputs the extracted micro-vibration features, periosseous muscle group status data and bone cortical structure data into a pre-trained bone metabolic activity assessment model, and the model outputs at least one quantitative bone metabolic activity parameter. The micro- and nano-scale mechanical vibrations in S1 include: in passive monitoring mode, the inherent vibrations of bones excited by the load of daily human activities, or in active testing mode, the forced vibration response of bones excited by standardized micro-force pulses applied by a micro exciter built into the wearable probe. It also includes individual baseline calibration S0, which is performed upon first use or periodic calibration and includes: S01: The subject is in a standard resting state and the individual baseline microvibration spectrum is obtained by measuring with a wearable probe; S02: Obtain bone structure parameters obtained from medical imaging examinations of the subject's skeletal sites during the same period; S03: Associate and register individual baseline microvibration spectra with bone structure parameters to establish an individual multidimensional initial baseline profile; In S3, after comparing and analyzing the real-time extracted micro-vibration features with the individual baseline micro-vibration spectrum, bone metabolism activity parameters are output. It also includes cloud-based collaboration S4: S41: Encrypt and upload bone metabolism activity parameters and raw characteristic data to the cloud platform; S42: The cloud platform aggregates multi-user data and uses a federated learning mechanism to continuously optimize the bone metabolism activity assessment model. S43: The cloud platform integrates the analysis results with the user's activity data and nutrition records to update the user's skeletal digital twin; S44: When a high-risk metabolic imbalance trend is identified, the cloud platform pushes early warning information to the user terminal or the bound medical care terminal; The bone metabolism activity assessment model is obtained in advance through the following steps: A1: Collect sample datasets, which include bone micro-vibration feature data measured by solid-state quantum sensor chips, and concurrent bone metabolic status labels obtained by clinical gold standard methods; A2: Preprocess and feature-engineer the sample dataset to construct training and validation sets; A3: Use machine learning algorithms to train the training set and establish a predictive model from bone micro-vibration features to bone metabolic state labels; A4: The trained model is validated and optimized using the validation set to obtain a bone metabolism activity assessment model; The clinical gold standard method includes one or more of the following: serum bone turnover marker detection, high-resolution quantitative CT of peripheral bone, or tetracycline double-labeled bone biopsy histomorphometric analysis.

[0024] Working principle: By using a diamond nitrogen-vacancy color center quantum chip, quantum precision measurement is introduced into this field for the first time, achieving ultra-high sensitivity to micro-vibrations with frequencies of 1kHz-10MHz and amplitudes ≤500nm. It simultaneously collects three types of data: vibration characteristics, muscle group status, and bone cortical structure, constructing a multi-dimensional information matrix. This overcomes the limitations of single-index evaluation. Through the synergistic effect of active excitation and passive monitoring, daily interference is eliminated, ensuring data comparability and accurately capturing subtle metabolic changes. Finally, a pre-trained model interprets the multimodal features and outputs quantitative metabolic parameters, thus achieving a closed loop from physical perception to physiological assessment. This transforms bone metabolism monitoring from a complex examination in medical institutions into a home-based, high-frequency, dynamically trackable health management tool. Through passive monitoring... In active testing mode, the system automatically collects the inherent vibrations of bones stimulated by the load of daily human activities. It can continuously acquire the natural responses of bones in real physiological environments without the user noticing, providing highly ecologically valid data support for establishing an individual's daily baseline. In active testing mode, the system applies standardized micro-force pulses through a micro-exciter to collect determined forced vibration responses. Controllable and repeatable external excitation eliminates interference from differences in daily activities, ensuring high consistency and comparability of measurement data at different time points. The two modes complement each other, thereby ensuring the continuity and compliance of data collection and guaranteeing the standardization and accuracy of assessment results. Through an individual baseline calibration mechanism, during initial use or periodic calibration, the system operates under standard resting conditions. The system collects individual baseline microvibration spectra and associates them with bone structure parameters obtained from concurrent medical imaging examinations to establish a multidimensional initial baseline profile of individual skeletal health, including functional characteristics and structural parameters. During routine monitoring, the system compares and analyzes the extracted microvibration features with the individual's baseline spectrum, calculates relative changes, and outputs bone metabolic activity parameters based on these changes. This effectively eliminates interference from natural differences in bone structure between individuals and fluctuations in measurement conditions at different time points, making each user their own reference system. This allows for the sensitive capture of subtle metabolic changes caused by disease progression or treatment interventions, ensuring the accuracy, comparability, and clinical reference value of long-term dynamic monitoring data. This is achieved by extracting characteristic frequency peaks, specific frequency band energy proportions, and vibration... Multidimensional vibration characteristics, such as dynamic damping coefficient and signal nonlinearity, comprehensively characterize the micromechanical state of bone, and then map it into a bone formation activity index, a bone resorption activity index, and a net metabolic balance index (the difference between the two). This transforms physical signals into physiologically meaningful quantitative parameters. The net metabolic balance index intuitively reflects the dynamic balance direction of bone formation and resorption, making the metabolic-dominated trend readily apparent. Through the combination of multidimensional features and multi-parameter outputs, the system accurately quantifies the level and balance of bone metabolism, providing rich and intuitive dynamic data support for early warning, efficacy evaluation, and personalized intervention. By aggregating multi-user data through a cloud platform and continuously optimizing the bone metabolism activity assessment model using large-scale population data, the model's accuracy continuously improves with the increase in user scale.By integrating bone metabolism analysis results with multi-source health data such as user activity levels and nutritional records, a digital twin of the skeleton is built and dynamically updated for each user. This achieves a multi-dimensional and predictable virtual mapping of individual bone health. When a high-risk metabolic imbalance trend is identified, a tiered warning is automatically sent to the user or healthcare provider. This enables the assessment model to self-evolve and upgrades bone metabolism monitoring into an intelligent service system integrated into overall health management, providing strong support for early risk intervention.

[0025] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A wearable dynamic bone metabolism monitoring system based on quantum precision measurement and multimodal sensing, comprising a wearable probe, characterized in that: The wearable probe includes: A solid-state quantum sensor chip, based on diamond nitrogen-vacancy color centers, is used to attach to the surface of the bone site to be tested. It senses the micro-vibration and outputs the original vibration signal by detecting the spin state changes of the diamond nitrogen-vacancy color centers caused by micro-nano-scale mechanical vibrations excited by bone metabolism and external stimulation. The frequency range of the micro-vibration is 1kHz-10MHz, and the amplitude is ≤500nm. A multimodal auxiliary sensor array, including a bioelectrical impedance analysis module for monitoring periosseous muscle groups and an ultrasound transducer for assessing cortical bone structures; The miniature exciter applies standardized micro-force pulses to the bone under test in response to active test commands to induce forced vibrations; The signal processing and computing unit, connected to the probe, is configured to receive raw signals and multimodal data, extract micro-vibration features, input them into a pre-trained bone metabolism activity assessment model, and output quantitative bone metabolism activity parameters.

2. The wearable dynamic bone metabolism monitoring system based on quantum precision measurement and multimodal sensing according to claim 1, characterized in that: The signal processing and computing unit is further configured to collect the inherent skeletal vibrations excited by the load of daily human activities as the original vibration signal in passive monitoring mode, or to collect the forced vibration response as the original vibration signal in active testing mode.

3. The wearable dynamic bone metabolism monitoring system based on quantum precision measurement and multimodal sensing according to claim 1, characterized in that: The signal processing and computing unit is further configured to compare and analyze the micro-vibration features extracted in real time with the pre-established individual baseline spectrum, calculate the relative change, and output the bone metabolism activity parameter based on the relative change. The individual baseline spectrum is obtained by measuring the wearable probe under standard resting conditions during the first use or periodic calibration, and is associated and registered with the bone structure parameters obtained from the medical imaging examination at the same time to establish a multi-dimensional initial baseline profile of personal bone health.

4. The wearable dynamic bone metabolism monitoring system based on quantum precision measurement and multimodal sensing according to claim 1, characterized in that: The micro-vibration characteristics include at least the characteristic frequency peak position, the energy ratio of a specific frequency band, the vibration damping coefficient, and the signal nonlinearity. The bone metabolism activity parameters include at least the bone formation activity index, the bone resorption activity index, and the net metabolic balance index, and the net metabolic balance index is the difference between the bone formation activity index and the bone resorption activity index.

5. The wearable dynamic bone metabolism monitoring system based on quantum precision measurement and multimodal sensing according to claim 1, characterized in that: It also includes a cloud platform, which is communicatively connected to the signal processing and computing unit and configured as follows: The system receives and aggregates bone metabolism activity parameters and raw feature data from multiple users, continuously optimizes the bone metabolism activity assessment model using the aggregated data, integrates the analysis results with user health data, and constructs and updates the user's skeletal digital twin.

6. A wearable dynamic bone metabolism risk assessment method based on quantum precision measurement and multimodal sensing, applicable to the wearable dynamic bone metabolism monitoring system based on quantum precision measurement and multimodal sensing as described in any one of claims 1-5, characterized in that: The working steps of this wearable dynamic bone metabolism device based on quantum precision measurement and multimodal sensing are as follows: S1. Signal acquisition: The wearable probe, which is attached to the surface of the bone to be tested, acquires the original signal of micro-nano-level mechanical vibration excited by internal metabolic activities and external excitation through a solid-state quantum sensor chip. At the same time, the bioelectrical impedance analysis module acquires the status data of the muscle groups around the bone, and the ultrasonic transducer acquires the bone cortex structure data. The frequency range of the micro-nano-level mechanical vibration is 1kHz-10MHz, and the amplitude is no more than 500nm. S2. Feature extraction: The original vibration signal is processed to extract micro-vibration features used to characterize bone metabolism. S3. Metabolic activity mapping: The extracted micro-vibration features, the data on the state of the surrounding muscle groups and the data on the cortical bone structure are input into a pre-trained bone metabolic activity assessment model, and the model outputs at least one quantitative bone metabolic activity parameter.

7. The wearable dynamic bone metabolism risk assessment method based on quantum precision measurement and multimodal sensing according to claim 6, characterized in that: The micro-nano-scale mechanical vibrations in S1 include: in passive monitoring mode, the inherent vibrations of bones excited by the load of daily human activities, or in active testing mode, the forced vibration response of bones excited by standardized micro-force pulses applied by the micro exciter built into the wearable probe.

8. The wearable dynamic bone metabolism risk assessment method based on quantum precision measurement and multimodal sensing according to claim 6, characterized in that: It also includes individual baseline calibration S0, which is performed upon first use or periodic calibration, and includes: S01: The subject is measured using the wearable probe under standard resting conditions to obtain the individual baseline microvibration spectrum; S02: Obtain bone structure parameters obtained from medical imaging examinations of the subject's skeletal sites during the same period; S03: Associate and register the individual baseline microvibration spectrum with the bone structure parameters to establish a personal multidimensional initial benchmark profile; In step S3, the bone metabolism activity parameters are output after the real-time extracted micro-vibration features are compared and analyzed with the individual baseline micro-vibration spectrum.

9. The wearable dynamic bone metabolism risk assessment method based on quantum precision measurement and multimodal sensing according to claim 6, characterized in that: It also includes cloud-based collaboration S4: S41: Encrypt and upload the bone metabolism activity parameters and original characteristic data to the cloud platform; S42: The cloud platform aggregates multi-user data and uses a federated learning mechanism to continuously optimize the bone metabolism activity assessment model; S43: The cloud platform will integrate the analysis results with the user's activity data and nutrition records to update the user's skeletal digital twin; S44: When a risk metabolic imbalance trend is identified, the cloud platform pushes early warning information to the user terminal or the bound medical care terminal.

10. A wearable dynamic bone metabolism risk assessment method based on quantum precision measurement and multimodal sensing according to claim 6, characterized in that: The bone metabolism activity assessment model is obtained in advance through the following steps: A1: Collect sample datasets, which include bone micro-vibration feature data measured by the solid-state quantum sensor chip, and concurrent bone metabolism status labels obtained by the clinical gold standard method; A2: Preprocess and feature-engineer the sample dataset to construct training and validation sets; A3: Use machine learning algorithms to train the training set to establish a predictive model from bone micro-vibration features to bone metabolic state labels; A4: The trained model is validated and optimized using the validation set to obtain the bone metabolism activity assessment model; The clinical gold standard method includes one or more of the following: serum bone turnover marker detection, peripheral bone quantitative CT, or tetracycline double-labeled bone biopsy histomorphometric analysis.

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