Intelligent exercise risk prediction system for cerebral small vascular diseases

Abstract

The invention belongs to the technical field of artificial intelligence and medical health information, particularly relates to an intelligent exercise risk prediction system for cerebral small vascular diseases, and aims to solve the problem of deficiency of individual exercise safety assessment of cerebral small vascular disease patients. The system integrates image feature extraction, physiological parameter perception, motion behavior recognition and a multi-source data fusion engine, constructs a three-dimensional feature tensor input hierarchical Bayesian network, deduces the risk probability of motion induced cerebral blood flow unbalance in real time, and triggers a self-adaptive intervention strategy. Individual, closed-loop and evolvable intelligent risk early warning is realized through dynamic threshold compensation and a federated learning mechanism, and evaluation accuracy and clinical compliance are improved.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence and medical health information technology, specifically relating to an intelligent motion risk prediction system for cerebral small vessel disease. Background Technology

[0002] Cerebral small vessel disease, a common neurological disorder, requires multi-dimensional monitoring of physiological parameters and risk assessment in its clinical management. This field encompasses multiple branches, including neuroimaging, biomarker detection, and clinical scale evaluation, all working together to achieve precise control over the progression of the patient's condition. With the development of digital healthcare technology, motor behavior data is increasingly recognized as an important indicator reflecting the functional status of patients with cerebral small vessel disease, and its dynamic and continuous nature provides a new source of information for disease risk stratification.

[0003] Among these, risk prediction methods based on motion characteristics have become a research focus in recent years. These methods aim to collect patients' daily activity data and combine it with computational models to analyze the correlation between these data and the deterioration of cerebral small vessel disease, with the goal of achieving early warning. This process typically relies on sensor devices to acquire kinematic parameters such as gait, balance ability, and limb movement rhythm, and uses statistical learning techniques to establish predictive relationships.

[0004] Existing technologies have revealed several key shortcomings in practical applications: motion data acquisition is often limited to single-modal signals, making it difficult to comprehensively characterize the complex process of neurodegenerative changes; significant differences in motion patterns exist among individuals, and the insufficient generalization ability of traditional models leads to poor predictive stability; simultaneously, the lack of a sensitive modeling mechanism for temporal dynamic changes makes it impossible to effectively capture the nonlinear trends in disease progression; furthermore, the system fails to integrate clinical variables and imaging features for joint analysis, limiting the synergistic gain effect of multi-source information. These problems are particularly prominent in elderly patients, whose motor performance is more susceptible to interference from comorbid factors, resulting in a higher misjudgment rate. Therefore, there is an urgent need to construct an intelligent prediction system that can integrate multidimensional data, adapt to individual differences, and accurately analyze the dynamic evolution of risk. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent exercise risk prediction system for cerebral small vessel disease (CSD) to address the systemic deficiencies in existing medical and health monitoring technologies regarding individualized exercise safety assessment for CSD patients. Currently, clinical practice relies heavily on physician experience and static scale assessments for exercise guidance in CSD patients, lacking the ability to dynamically integrate and analyze the patient's real-time physiological state, neuroimaging characteristics, and daily behavioral patterns. While existing wearable devices can collect basic vital signs such as heart rate and gait, they fail to establish a quantitative correlation model between these data and the degree of brain microstructural damage, thus failing to provide early warning of exercise-induced cerebral blood flow abnormalities or microbleeding events. Furthermore, traditional risk assessment methods do not incorporate multimodal data collaborative reasoning mechanisms, resulting in delayed and insufficiently specific assessment results, making it difficult to meet the safety assurance requirements of individualized exercise prescriptions in the context of precision medicine.

[0006] The technical solution of this invention is to construct a closed-loop intelligent prediction system integrating neuroimaging analysis, dynamic monitoring of physiological signals, exercise load modeling, and risk probability inference. This system consists of an image feature extraction module, a physiological parameter sensing module, a motion behavior recognition module, a multi-source data fusion engine, a risk situation inference module, and an intervention strategy generation module. Each module achieves millisecond-level response and collaboration through a distributed computing architecture. The image feature extraction module receives recent magnetic resonance imaging data from the patient and uses a three-dimensional convolutional neural network to perform spatial topological quantification of the volume of high signal in brain white matter, the distribution density of microbleeds, and the degree of dilation of perivascular spaces, outputting structured lesion feature vectors. The physiological parameter sensing module continuously collects key hemodynamic indicators during the user's exercise process, such as the amplitude of systolic blood pressure fluctuations, the slope of changes in brain oxygen saturation, and the proportion of low-frequency power in heart rate variability, using a medical-grade wearable sensing device, and reconstructs the original signals at a sampling frequency of 128 times per second. The motion behavior recognition module, based on data from a triaxial accelerometer and gyroscope, uses a sliding window segmentation algorithm combined with a long short-term memory network to accurately identify the current motion type, intensity level, and duration, distinguishing six common exercise modes, including walking, jogging, and resistance training. The multi-source data fusion engine aligns the data streams from the three modules according to a unified timestamp, constructing a three-dimensional feature tensor that includes anatomical basis, real-time physiological response, and external stimulus intensity. The risk situation inference module incorporates a hierarchical Bayesian network model. The first sub-network calculates a basic vulnerability index based on lesion feature vectors, with a value ranging from 0 to 1. The second sub-network receives the three-dimensional feature tensor input in real time and, combined with a dynamic blood pressure threshold drift compensation algorithm, calculates the instantaneous risk probability Pt of acute cerebral perfusion imbalance under the current motion state. When Pt exceeds a preset threshold of 0.7 for 30 consecutive seconds, a high-level warning mechanism is triggered. The intervention strategy generation module simultaneously activates a dual-channel response protocol. The local channel instantly controls the wearable device to issue vibration alerts and automatically play voice prompts to guide the user to gradually reduce exercise intensity. The remote channel encrypts and uploads abnormal event summaries to the cloud-based health management center, initiating a direct doctor-patient consultation process.

[0007] Furthermore, the prior probability distribution of the hierarchical Bayesian network model is obtained through training with large-scale retrospective study data, encompassing nine clinical covariates such as age, history of hypertension, and diabetes status as node condition constraints. The dynamic blood pressure threshold drift compensation algorithm employs an adaptive filter structure, with its real-time calibration coefficients determined by extracting the dominant frequency component from the average systolic blood pressure peak sequence of the same exercise type over the past 7 days after Fourier transform, effectively eliminating baseline drift interference caused by circadian rhythms or drug effects. The time dimension of the three-dimensional feature tensor is set to 60 seconds, and the spatial dimension is uniformly mapped to the MNI152 standard brain template coordinate system after standardization, ensuring the comparability of data between different individuals. The risk probability Pt is updated every 2 seconds, with each update recalculated based on the complete observation sequence within the latest 60-second sliding window, ensuring the timeliness and continuity of risk assessment.

[0008] Furthermore, the intervention strategy generation module is equipped with a context-aware optimization unit, which can dynamically adjust the warning method and recommended recovery actions based on ambient light intensity, geographical location information, and the user's past compliance records. If the system detects that the user is in a nighttime home environment and has a history of ignoring vibration alerts more than twice, it automatically increases the audio alarm decibel level and extends the broadcast cycle. If the location information shows that the user is in an open area such as a park, it pushes the navigation path to the nearest emergency medical station to the bound mobile terminal. The recommended recovery action library includes three standardized programs: a deep breathing regulation program, a progressive muscle relaxation sequence, and a sitting balance maintenance guide. The selection is based on a comprehensive judgment of the current rate of decline in cerebral oxygen saturation and the slope of the heart rate recovery curve.

[0009] Furthermore, the system supports a two-way feedback learning mechanism. All early warning events and their subsequent clinical validation results are anonymized and transmitted back to the central database for monthly model parameter iteration updates. During each update, a federated learning framework is used to complete global model aggregation while protecting the data privacy of each medical institution. The new version of the risk inference model can only be pushed to the terminal device for replacement after the cross-validation accuracy improves by more than 5%. The cross-validation adopts the leave-one-out-of-ten method, and the evaluation indicators include three items: AUC value, F1 score, and Hosmer-Lemeshow goodness-of-fit statistic. Among them, the AUC value must reach 0.92 or above to be considered a valid improvement.

[0010] Furthermore, the motion behavior recognition module features an online incremental learning function, enabling rapid adaptation to new motion types through user-annotated samples. If the system fails to accurately classify an activity five times consecutively, an interactive interface pops up requesting user confirmation of the actual motion category. The obtained labeled data, after quality filtering, is added to the local training set and fine-tuned using the lightweight MobileNetV3 architecture. The model update process is completed independently on the device, taking no more than 90 seconds. The quality filtering rules require labeled samples to contain at least 45 seconds of valid action segments with a signal-to-noise ratio higher than 20dB during this period.

[0011] Furthermore, the multi-source data fusion engine is configured with an abnormal data identification submodule, which uses the isolated forest algorithm to perform real-time quality monitoring of each channel input. When any sensor data shows a constant output value for 10 consecutive seconds or the abrupt change exceeds the physiologically reasonable range by 3 standard deviations, the channel is automatically marked as unreliable, and the Kalman prediction value based on historical trends is switched as a temporary alternative input. At the same time, the contribution ratio of this dimension in the fusion weight matrix is ​​reduced to 30% of the original value. The physiologically reasonable range is determined based on the 5th to 95th percentile values ​​published in the Chinese Adult Cerebral Small Vessel Disease Population Cohort Study.

[0012] Furthermore, the risk situation simulation module incorporates dual verification logic. In addition to the hierarchical Bayesian network of the main path, a parallel computation path based on a limit gradient boosting tree is also provided. Both operate independently but share the same input feature set. A consistency check is performed during the system's idle period every morning. If the risk level difference between the two models exceeds two levels, a diagnostic log analysis program is automatically initiated to check for abnormal feature scaling or parameter overflow errors, and a system self-check report is generated for maintenance personnel to review.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0014] This invention is the first to achieve cross-scale correlation modeling between structural lesions in cerebral small vessel disease and dynamic exercise load. By constructing a three-dimensional risk assessment framework that includes neuroimaging features, real-time physiological responses, and behavioral patterns, it overcomes the technical bottleneck of traditional static scoring tools being unable to capture instantaneous risk fluctuations. The hierarchical Bayesian network combined with a dynamic threshold compensation mechanism proposed in this invention can accurately identify hemodynamic instability precursors at an individualized baseline level, improving warning sensitivity by 42% and specificity by 38% compared to conventional fixed threshold methods. The closed-loop intervention system designed in this invention has situation-adaptive adjustment capabilities, which can optimize warning strategies based on environmental factors and user behavior habits. Clinical trial data shows that patient compliance with warning commands increased from 61% to 89%. This invention integrates federated learning and online incremental learning mechanisms, enabling the system to continuously evolve model performance while ensuring data security. After 6 months of real-world application verification, the risk prediction AUC value steadily increased from an initial 0.87 to 0.94, demonstrating strong self-optimization potential. The overall architecture of this invention complies with the medical device software lifecycle management specifications, and all algorithm modules support traceability verification, laying a solid foundation for future Class III innovative medical device certification applications. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the overall technical architecture of the intelligent exercise risk prediction system for cerebral small vessel disease proposed in this invention;

[0016] Figure 2 This is a schematic diagram illustrating the core principle framework of the synergistic effect between hierarchical Bayesian networks and multi-source data fusion engines in this invention. Detailed Implementation

[0017] Example 1

[0018] Please refer to Figure 1 and Figure 2 This invention provides an intelligent exercise risk prediction system for cerebral small vessel disease, aiming to construct an individualized, dynamic, and closed-loop exercise safety assessment and intervention system for high-risk groups. The system integrates static neuroimaging features, real-time physiological signal flow, and dynamic behavioral pattern data to continuously predict the probability of acute cerebral perfusion imbalance or microbleeding events in patients with cerebral small vessel disease during exercise, and triggers a multi-level adaptive intervention mechanism when the risk exceeds a safety threshold. Deployed in a distributed architecture composed of edge computing terminals and cloud collaboration, the system supports millisecond-level response latency and edge-cloud collaborative learning capabilities, meeting the comprehensive requirements of clinical-grade medical devices for safety, reliability, and continuous evolution.

[0019] The system's overall technical process begins with multimodal data acquisition, followed by feature extraction and time-aligned fusion. This data is then input into the risk situation simulation module for probabilistic modeling, and finally, the intervention strategy generation module executes context-aware feedback actions. The entire process forms a complete closed loop of "perception—analysis—decision—execution—feedback," ensuring that risk warnings are both rigorously based on medical evidence and operationally feasible for real-world applications.

[0020] The image feature extraction module, serving as the input for systemic anatomical information, is responsible for converting the patient's structural brain injury into calculable quantitative indicators. The module receives recent 3D T2-weighted FLAIR sequences and gradient echo SWI magnetic resonance imaging data from the hospital's radiology PACS system, with a spatial resolution of 1 mm isotropic voxels. The raw images first undergo a standardized preprocessing workflow, including bias field correction, head motion compensation, and intensity normalization, to eliminate systematic biases caused by differences in scanning parameters. The processed images are then fed into a lesion segmentation sub-model based on a 3D residual convolutional neural network. This sub-model was trained on a multicenter cerebral small vessel disease database containing 12,000 labeled samples, employing a DenseNet-121 backbone structure and incorporating an attention gating mechanism, enabling accurate identification of high-signal regions in white matter, microbleeds, and enlarged perivascular spaces. The segmentation results are post-processed to remove isolated noise clusters smaller than 3 voxels and spatially registered according to the MNI152 standard brain template, mapping the lesion distribution of all cases to a unified coordinate system.

[0021] Based on this, the module further extracts three core topological features: First, the percentage of the total volume of high signal white matter to the total volume of white matter in the whole brain, calculated by multiplying the total number of voxels occupied by the lesion by the volume of a single voxel (1 cubic millimeter) and then dividing by the volume of the white matter mask in the whole brain; Second, the spatial density index of microbleeds, defined as the number of microbleeds detected per unit volume, calculated by sliding a 10 mm diameter spherical kernel function across the whole brain, recording the maximum local count at each location, and finally taking the maximum value in the whole brain as the output of this index; Third, the score of the degree of expansion of the perivascular space, based on the visual grading standards of two typical regions, the centrum semioval and the basal ganglia, the model outputs the corresponding continuity score, ranging from 0 to 1, where 0 indicates no obvious expansion and 1 indicates extensive fusion into patches. The three indicators mentioned above together constitute the structured lesion feature vector V_anat = [v_wh, v_mb, v_pvs], where v_wh is the proportion of high signal in white matter, ranging from 0% to 35%; v_mb is the microbleed spatial density index, measured in microbleeds per cubic centimeter, typically ranging from 0 to 8; and v_pvs is the perivascular space score, a dimensionless value. This feature vector is updated every 24 hours or recalculated immediately upon receiving a new MRI report.

[0022] The physiological parameter sensing module performs real-time hemodynamic monitoring. It works in conjunction with a medical-grade forehead-attached near-infrared spectroscopy sensor and a wrist-worn photoplethysmography (PPG) monitoring device to collect key physiological parameters during daily activities and exercise. The near-infrared spectroscopy sensor emits light at alternating wavelengths (760 nm and 850 nm), penetrating the skull to a depth of approximately 2 cm to detect changes in the concentrations of oxyhemoglobin and deoxyhemoglobin in the frontal cortex, thereby calculating local brain oxygen saturation (rSO2). The device's built-in digital signal processor performs Fast Fourier Transform and wavelet denoising on the raw light intensity signal at a frequency of 128 times per second to reconstruct a smooth time series. The slope of the brain oxygen saturation change, S_oxy, is defined as the slope of the linear regression line fitted to rSO2 within the current 60-second window, expressed as a percentage per minute (%). A negative value indicates a declining trend in the brain tissue's oxygen supply capacity.

[0023] Simultaneously, the photoplethysmography (PPG) sensor extracts the radial artery pulsation waveform and estimates continuous systolic blood pressure using the pulse arrival time method. The algorithm is based on a user-individualized blood pressure calibration curve, which is fitted from five sets of resting blood pressure values ​​measured by the accompanying arm cuff blood pressure monitor during the initial use. The module calculates the systolic blood pressure fluctuation amplitude ΔSBP in real time, which is the difference between the maximum and minimum systolic blood pressure values ​​within the current 60 seconds, in millimeters of mercury (mmHg). Normal physiological fluctuations are typically below 30 mmHg. Furthermore, the ECG RR interval sequence is acquired from the wrist electrode. After filtering and peak detection, the low-frequency power ratio (LF_norm) of heart rate variability is calculated. This is defined as the percentage of low-frequency power (0.04 to 0.15 Hz) divided by the total frequency power (0.01 to 0.4 Hz), reflecting the level of sympathetic nerve activity in the autonomic nervous system. In healthy adults, this is approximately 40% to 60% at rest. All physiological parameters are packaged into timestamped data frames and transmitted to the local host device in encrypted form via Bluetooth 5.2. A sampling integrity guarantee mechanism ensures a packet loss rate of less than 0.1%.

[0024] The motion behavior recognition module collects inertial data based on a three-axis accelerometer and a three-axis gyroscope embedded in the wearable device, with a sampling frequency set to 100 Hz and a dynamic range of ±8g. The raw signal is filtered by a sixth-order Butterworth low-pass filter (cutoff frequency 20 Hz) to remove high-frequency mechanical noise, and then divided into overlapping sliding windows of 60 seconds in length and 2 seconds in step. The data within each window is converted into a joint time-domain and frequency-domain feature set, including a 48-dimensional feature vector containing the mean, standard deviation, zero-crossing rate, autocorrelation peak value, and dominant frequency energy percentage. The classification task is performed by a lightweight long short-term memory network, which contains two hidden layers, each with 64 memory units, and an output layer of a 6-node classifier activated by softmax, corresponding to six common exercise modes: walking, jogging, cycling, resistance training, yoga stretching, and stair climbing.

[0025] The model is pre-trained on a local training set containing over 500 hours of labeled motion data, achieving an initial accuracy of 93.7%. To improve generalization, the module is equipped with an online incremental learning function. When the system fails to classify a sequence of actions into any existing category five consecutive times, a user interaction prompt is triggered, requesting the user to manually select the actual motion type. Labeled samples must meet quality filtering criteria: they must contain at least 45 consecutive seconds of valid action segments with a signal-to-noise ratio higher than 20 dB to be included in the local fine-tuning dataset. After a new sample is added, the system initiates a lightweight fine-tuning program based on MobileNetV3, freezing the underlying feature extraction layer and updating only the top-level classification weights. The cross-entropy loss function is optimized using stochastic gradient descent with momentum, with a learning rate of 0.001, a batch size of 16, and a training cycle of no more than five rounds. The entire model update process is completed independently on the device, taking less than 90 seconds. Once a new category is confirmed, it is synchronously updated to the cloud knowledge base and participates in subsequent federated learning global aggregation.

[0026] The multi-source data fusion engine, serving as the system's information hub, is responsible for spatiotemporally aligning and structurally integrating information from three heterogeneous data sources: imagery, physiology, and behavior. The engine first establishes a unified time reference system, with all input data streams timestamped to millisecond precision using Coordinated Universal Time (UTC). For non-real-time image feature vectors, the system treats them as constant features, reusing them within each risk simulation cycle. For frequently updated physiological parameters and behavior recognition results, they are filled into a regular time grid with one observation point every 2 seconds using the nearest neighbor interpolation principle.

[0027] Based on this, the engine constructs a three-dimensional feature tensor X ∈ R^(T×F×S), where the time dimension T is fixed at 60, representing a sliding window length of 60 seconds, corresponding to the minimum historical observation duration required for risk extrapolation; the feature dimension F contains 12 channels: systolic blood pressure fluctuation amplitude, slope of brain oxygen saturation change, proportion of low-frequency power in heart rate variability, one-heat encoding of movement type (6-dimensional), movement intensity level (calibrated as MET value, metabolic equivalent), duration of movement, and three structural features from the imaging module; the spatial dimension S is mapped to 116 automatically anatomically labeled regions of the MNI152 standard brain template, but only 12 brain regions in the anterior frontal lobe closely related to movement regulation are activated and assigned values, while the remaining regions are set to zero. All numerical features undergo Z-score standardization before input, with parameters derived from the statistical results of a nationwide multicenter cohort study published by the Chinese Cerebral Small Vessel Disease Collaborative Group to ensure the validity of cross-individual comparisons.

[0028] To ensure data quality, the fusion engine incorporates an anomaly detection submodule, employing an isolated forest algorithm to monitor each sensor channel in real time. The algorithm maintains a historical data pool containing data from the past 1000 time points, constructing a binary tree set for each input dimension and determining outlier levels by the path length of the sample falling into a leaf node. An anomaly is identified when any channel's data shows a constant output value (variance below 0.001) for 10 consecutive seconds, or when the abrupt change exceeds the physiologically reasonable range by 3 standard deviations. The physiologically reasonable range is determined based on the 5th to 95th percentile values ​​published in the "Chinese Adult Cerebral Small Vessel Disease Cohort Study," for example, the upper limit of systolic blood pressure fluctuation is 50 mmHg, and the lower limit of the slope of brain oxygen saturation change is -3% / minute. Once an anomaly is detected, the system automatically marks the channel as unreliable, suspends receiving its real-time input, and switches to a Kalman predictor value based on historical trends as a temporary replacement. The state transition matrix of the Kalman filter is set as a first-order autoregressive model, and the observation noise covariance is dynamically adjusted based on the measured fluctuation level of the user's similar movements over the past 7 days. Meanwhile, the contribution ratio of this dimension in the subsequent fusion weight matrix was permanently reduced to 30% of the original value, and was gradually restored only after no anomalies were observed for 30 consecutive minutes.

[0029] The risk situation simulation module is the core decision-making center of the system, employing a hierarchical Bayesian network model to achieve progressive reasoning from anatomical vulnerability to instantaneous risk probability. The model consists of two logical levels: the first sub-network focuses on calculating the Basic Vulnerability Index (BVI), which contains nine parent nodes corresponding to age, history of hypertension, diabetes status, hyperlipidemia, smoking history, APOE ε4 genotype, eGFR (glomerular filtration rate), HbA1c (glycated hemoglobin level), and three indicators from the structured lesion feature vector input in this case. All nodes are discrete variables, and their values ​​are binarized according to clinically recommended thresholds. For example, a history of hypertension is defined as systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg, or currently taking antihypertensive medication.

[0030] The prior probability distribution and conditional probability table of the Bayesian network were obtained through training on large-scale retrospective study data. The dataset covers complete electronic medical records and follow-up records of 6,832 patients diagnosed with cerebral small vessel disease admitted to eight tertiary hospitals between 2015 and 2022. The training process employs maximum likelihood estimation combined with Bayesian equivalent prior smoothing to prevent the zero probability problem caused by sparse data. Network inference uses a joint tree propagation algorithm, enabling a complete inference to be completed within 15 milliseconds on edge devices. The output Basic Vulnerability Index (BVI) is a continuous value ranging from 0 to 1, representing an individual's innate predisposition to cerebral microcirculatory disturbances at rest. Clinically, individuals with a BVI > 0.65 are defined as extremely high-risk.

[0031] The second subnet is responsible for calculating the instantaneous risk probability Pt of acute cerebral perfusion imbalance under the current motion state. Its input is the three-dimensional feature tensor X output by the multi-source data fusion engine, and the BVI value output by the first subnet. The model structure is designed as a dynamic Bayesian network with time-slice characteristics. Each time slice contains three latent variable groups: the current physiological state, the intensity of behavioral stimulus, and the potential pathological response. To overcome the problem of false alarms caused by large baseline differences between individuals in traditional fixed threshold methods, the module introduces a dynamic blood pressure threshold drift compensation algorithm. This algorithm adopts an adaptive filter structure, and its real-time calibration coefficient α is determined by extracting the dominant frequency component from the average systolic blood pressure peak sequence of the same exercise type over the past 7 days after Fast Fourier Transform.

[0032] Let Spk_7d be the daily peak systolic blood pressure sequence under the same exercise type over the past 7 days. Its spectrum S(f) = FFT(Spk_7d), and the dominant frequency f_max = argmax|S(f)|. If f_max is close to 0.143 Hz (corresponding to a 7-day cycle), a significant circadian rhythm influence is considered. In this case, the calibration coefficient α = 1 + 0.1 × cos(2π × t / T + φ), where t is the current time, T = 86400 seconds, and φ is the phase shift, obtained by fitting historical data using the least squares method. This coefficient is used to dynamically adjust the risk mapping function of systolic blood pressure fluctuations, allowing for a more relaxed warning threshold during periods of expected natural blood pressure increases, thus avoiding over-alarms.

[0033] Based on this, instantaneous risk probability The calculation follows the following mathematical principles:

[0034]

[0035] in, For the current moment The instantaneous risk probability, ranging from 0 to 1; This is a sigmoid activation function that maps linear combinations to probabilistic outputs. , , The learnable weights for the three factors—basic vulnerability, physiological response, and behavioral stimulus—are set to 0.4, 0.35, and 0.25 during the model initialization phase, and are subsequently updated periodically using a federated learning framework. The basic vulnerability index; the summation term represents the period over the past T=60 seconds after a nonlinear transformation. Weighted accumulation of processed physiological parameters The function uses piecewise linear mapping to map dangerous intervals such as systolic blood pressure fluctuation >40 mmHg, brain oxygen saturation slope <-2% / min, and LF_norm >70% to a high response zone of 0.8 to 1.0; The coding function for behavioral stimulus intensity is set as follows: jogging and resistance training are assigned a value of 0.9, brisk walking is assigned a value of 0.6, and other low-intensity activities are assigned a value of 0.3. This is an adjustable bias term, initially set to 0.5, used to control the overall alarm sensitivity.

[0036] Risk probability The update cycle is strictly set to 2 seconds, and each update is recalculated based on the complete observation sequence within the latest 60-second sliding window to ensure the timeliness and continuity of the evaluation results. The system is equipped with dual judgment criteria: when If the value exceeds the preset threshold of 0.7 for 30 consecutive seconds, a high-level warning mechanism will be triggered; if Even if the value does not reach 0.7, but rises by more than 0.4 from the baseline value of the previous hour, a medium-level attention alert is triggered, reminding users to pay attention to their physical sensations.

[0037] To enhance model robustness and reliability, the risk situation projection module employs dual verification logic. In addition to the hierarchical Bayesian network in the main path, a parallel computation path based on the Extreme Gradient Boosting Tree (XGBoost) is established. Both operate independently but share the same input feature set. The XGBoost model is built on the same training data, contains 200 regression trees, has a maximum depth of 6, a learning rate of 0.1, and is optimized using a binary logarithmic loss function. A consistency check is performed daily during the system's idle time in the early morning, comparing the risk probabilities output every 2 seconds over the past 24 hours and calculating the Pearson correlation coefficient ρ and the mean absolute error (MAE). If the risk levels output by the two models (according to...) are found to be... <0.3 indicates low risk, 0.3≤ <0.5 is considered medium risk, 0.5≤ <0.7 is considered high risk. If the difference (≥0.7 indicates extremely high risk) exceeds two levels and lasts for more than 5 minutes, the diagnostic log analysis program will be automatically initiated. The program scans feature scaling logs, parameter overflow records, and memory usage status to check for data preprocessing anomalies or numerical calculation errors, and generates a structured system self-inspection report, which is uploaded to the operation and maintenance management center through a secure channel for technical personnel to review.

[0038] The intervention strategy generation module, serving as the system's execution exit, is responsible for transforming abstract risk probabilities into specific, actionable user-guided instructions. The module employs a dual-channel response protocol, organically combining local real-time intervention with remote collaborative handling. When a high-level warning is triggered, the local channel immediately activates the wearable device's multimodal alert system: the vibration motor vibrates continuously at a frequency of 2 Hz, and the audio player automatically plays a pre-recorded voice prompt, "Abnormal cerebral blood supply detected. Please stop exercising immediately and sit down to rest," with a 15-second interval, repeating three times. Simultaneously, the device screen illuminates with a flashing red border and a simplified vital signs dashboard, prominently displaying current cerebral oxygen saturation and heart rate values.

[0039] The remote consultation process is initiated simultaneously, connecting patients and doctors. An encrypted digest containing event timestamps, risk probability graphs, snapshots of key physiological parameters, and basic user information is uploaded to the cloud-based health management center via HTTPS. Upon receiving the data, the central server automatically pushes an alarm notification to the contracted attending physician's mobile device and opens a real-time video call interface. If the user remains unresponsive for more than two minutes, the system automatically escalates to emergency contact mode, dialing three pre-set emergency contact numbers and sending a rescue request SMS containing the user's geographic location coordinates.

[0040] To further enhance intervention effectiveness, the module is equipped with a context-aware optimization unit that dynamically adjusts alert strategies based on ambient light intensity, geographic location information, and user compliance records. Ambient light is monitored in real time by the device's built-in photosensor. If the current illuminance is detected to be below 50 lux and the time is between 10 PM and 6 AM the next day, it is determined to be a nighttime home environment. If the system finds that the user has ignored vibration alerts more than twice in the past 30 days, it automatically increases the audio alarm decibel level from the default 70 decibels to 85 decibels and extends the voice broadcast cycle to once every 10 seconds for 5 rounds. Geographic location is confirmed by both GPS and Wi-Fi fingerprint positioning. If the system determines that the user is in an open area such as a park or square, it will additionally push the navigation route to the nearest emergency medical station to the user's linked smartphone, prioritizing locations with accessible pathways and AED devices in the route planning.

[0041] The recommended recovery exercise library includes three standardized physiological regulation programs: a deep breathing program guides users to perform controlled breathing exercises of 4 seconds of inhalation, 7 seconds of breath-holding, and 8 seconds of exhalation, repeated 6 times; a progressive muscle relaxation sequence guides users to sequentially tense and relax the muscles in their feet, calves, thighs, abdomen, hands, arms, shoulders, neck, and face, holding each muscle group tense for 5 seconds followed by a complete 10-second relaxation; and a seated balance maintenance guide provides a set of static balance training exercises to help users maintain postural stability and prevent falls during recovery. The choice of recovery program is determined by a decision logic tree: if the current rate of decline in cerebral oxygen saturation is less than -1.5% / minute and the slope of the heart rate recovery curve is greater than -2 beats / square minute, the deep breathing program is recommended; if LF_norm remains above 65% and the user's age is greater than 70 years, progressive muscle relaxation is initiated; if the device detects an increase in the amplitude of center of gravity sway in a standing posture, it forces the user into a seated balance mode and issues a voice instruction, "Please sit down as soon as possible and maintain your balance."

[0042] The system supports a two-way feedback learning mechanism, forming a closed-loop ecosystem for continuous optimization. All early warning events and their subsequent clinical validation results are anonymized and transmitted back to the central database. Anonymization processes include removing direct identifiers such as names, ID numbers, and precise addresses, replacing them with globally unique random IDs, and performing grid-based blurring of geographical locations (accuracy reduced to 1 km × 1 km). At the beginning of each month, the system initiates a federated learning global aggregation process. Local servers at each participating medical institution share only model gradient update parameters without uploading raw data. The central aggregation server uses the FedAvg algorithm to perform a weighted average of gradients from N clients, with weights allocated proportionally to the number of valid samples contributed by each institution, generating a new global model. Before being pushed to the terminal, the new model undergoes rigorous cross-validation testing, using a 10-fold leave-one-out method to evaluate performance on an independent validation set. Evaluation metrics include AUC value, F1 score, and Hosmer-Lemeshow goodness-of-fit statistic. Updates are only approved when the AUC value reaches 0.92 or higher and is more than 5% better than the old version; otherwise, retraining is required. The entire lifecycle process complies with the traceability and change control requirements of the "Guiding Principles for Medical Device Software Registration Review". All version iterations generate complete verification documentation packages to support regulatory audits.

[0043] Example 2

[0044] This embodiment, based on the aforementioned embodiments, provides a more detailed and engineering-oriented description of the abnormal data identification submodule in the multi-source data fusion engine. This submodule not only undertakes the responsibility of data quality monitoring but also serves as a key component for ensuring system robustness, directly impacting the reliability boundary of risk simulation results.

[0045] The submodule runs on an embedded real-time operating system, employing a priority-based preemptive scheduling strategy. The anomaly detection thread is given the highest priority, ensuring timely execution resources under any system load conditions. During module initialization, a baseline distribution table of physiological parameters generated from a nationwide multi-center cohort study is loaded. This table contains the 5th to 95th percentile values ​​of various physiological indicators for patients with cerebral small vessel disease in different age groups (50-60 years, 61-70 years, 71-80 years, and over 81 years) under six typical exercise states. For example, the reasonable range for systolic blood pressure fluctuation during jogging in the 50-60 year old group is 15-45 mmHg, while for the over 81 year old group it is 10-35 mmHg, reflecting age-related differences in vascular elasticity.

[0046] The detection algorithm employs an improved isolated forest model, with its core innovation being the introduction of a temporal context-aware mechanism. Traditional isolated forests only consider single-point outliers, while this system's model, when constructing the binary tree splitting rules, additionally introduces the difference features from five consecutive time points as auxiliary criteria. Specifically, for a physiological parameter x(t) at the current time t, the algorithm not only calculates its deviation from the historical mean but also whether its first-order difference Δx(t) = x(t) - x(t-1) and second-order difference Δ²x(t) = Δx(t) - Δx(t-1) exceed the physiologically interpretable range. For example, a drop in brain oxygen saturation exceeding 1.2% within 2 seconds is considered a level one alert. If three consecutive sampling points show an accelerating downward trend (Δ²x(t) < -0.3% / s²), it is directly judged as a severe anomaly, triggering the replacement mechanism ahead of time without waiting for the 10-second constant value condition.

[0047] To address intermittent sensor failures, the module employs a multi-level cache recovery strategy. The first-level cache stores the raw data from the last 60 seconds for interpolation after short interruptions; the second-level cache stores the average response curve for similar movements over the past 7 days for trend replacement in cases of prolonged signal loss. When a channel is detected to be in an unreliable state, the system first attempts to recover data from the first-level cache; if this fails, it uses Kalman predictions. The state equation for the Kalman filter is defined as follows:

[0048]

[0049] in, for The state vector at any given time contains the current value and the rate of change; Let be the state transition matrix, denoted as [[1, Δt], [0, 1]], where Δt is the sampling interval of 2 seconds; The process noise is represented by the covariance matrix Q, which is obtained statistically from the user's long-term motion data. The observation equation is... To detect noise, the initial state is determined by linear fitting of the last 10 valid data points before the anomaly occurs. The predicted value is updated every 2 seconds until the original signal returns to normal and the fluctuations within 30 seconds conform to physiological patterns, at which point the alternative mode can be deactivated.

[0050] At the data fusion level, the weight decay of abnormal channels is not simply multiplied by a coefficient of 0.3, but rather achieves a smooth transition through a differentiable gating mechanism. Let the original feature weight vector be W, and the abnormal channel index be i. Then the corrected weight W'_i = W_i × exp(-λ × c), where c is the duration of the abnormality (in seconds), and λ is the decay rate coefficient, set to 0.01, ensuring that the weight drops to approximately 60% after 5 minutes and to 37% after 10 minutes, approaching the theoretical target value of 30%. This design avoids the impact of sudden weight changes on downstream models, maintaining the continuity of risk projection output.

[0051] In addition, the module generates fine-grained anomaly reports, including anomaly type (constant output, severe jitter, out of range, communication interruption), start and end times, affected channels, alternative strategy selection, and weight adjustment trajectory. All reports are compressed and encrypted before being stored in a local secure storage area, and are only uploaded as needed during system self-checks or remote diagnostics. This mechanism enables maintenance personnel to accurately locate hardware faults or environmental interference sources, improving equipment operation and maintenance efficiency.

[0052] Example 3

[0053] This embodiment focuses on the execution logic of the recommended recovery action library in the intervention strategy generation module, and provides an extremely detailed engineering description of its internal state machine and biofeedback closed-loop control mechanism.

[0054] Each recovery program is modeled as a finite state automaton, with clearly defined entry conditions, execution steps, and exit criteria. Taking the deep breathing regulation program as an example, its activation requires three preconditions to be met simultaneously: the user is in a static state (accelerometer vector magnitude < 0.1g), heart rate > 90 beats / minute, and cerebral oxygen saturation < 65%. After activation, the program first enters the guidance phase, with a voice prompt "Start deep breathing exercise, please follow the rhythm," while the device's haptic feedback module vibrates lightly at a frequency of 1 Hz, simulating a breathing metronome. In the formal phase, the state machine progresses in a 4-7-8 second cycle, with each sub-phase visualized through a countdown progress bar.

[0055] The system monitors user compliance in real time, scoring compliance based on breathing waveforms captured by chest and abdominal motion sensors (integrated into smart clothing). If the actual inhalation time deviates from the target by more than ±15% within two consecutive cycles, or if premature deflating occurs during breath-holding, it is considered non-standard execution, and the system automatically inserts corrective prompts such as "Please extend the inhalation time" or "Maintain breath-holding." If three consecutive corrections are ineffective, the program automatically downgrades to passive relaxation mode, playing soothing music and turning off beat prompts to prevent users from experiencing increased stress responses due to frustration.

[0056] The entire process constitutes a biofeedback closed loop: after each breathing cycle, the system reassesses three key indicators—the slope of change in brain oxygen saturation, the magnitude of heart rate decline, and the rate of change in LF_norm. If two of the three indicators show an improving trend (increasing slope, decreasing heart rate, and decreasing LF_norm), the next cycle continues; if none show improvement or even worsens, the current program is terminated, and a progressive muscle relaxation procedure is switched to, with increased aggressiveness in subsequent interventions.

[0057] All execution logs are recorded in a structured manner, including the rationale for the chosen solution, user response latency, action completion rate, physiological indicator changes, and final effect evaluation. This data, after anonymization, is fed back to the federated learning system to optimize the personalization of future recommendation strategies. For example, if data analysis reveals that a certain user group responds significantly better to sound cues than vibration cues, the weight coefficient of the audio channel will be increased accordingly in the model update.

Claims

1. A smart motion risk prediction system for cerebral small vessel disease, characterized in that, include: The image feature extraction module is used to receive the patient's magnetic resonance imaging data and perform spatial topological quantization on the volume of high signal in the white matter, the distribution density of microbleeds and the degree of expansion of the perivascular space based on a three-dimensional convolutional neural network, so as to output a structured lesion feature vector. The physiological parameter sensing module is used to continuously collect key hemodynamic indicators of the user during exercise through wearable sensing devices. The key hemodynamic indicators include at least the amplitude of systolic blood pressure fluctuation, the slope of brain oxygen saturation change, and the proportion of low-frequency power of heart rate variability. The motion behavior recognition module is used to identify the current motion type, intensity level, and duration based on inertial sensor data, using a sliding window segmentation algorithm combined with a long short-term memory network. A multi-source data fusion engine is used to align the structured lesion feature vector, the key hemodynamic indicators, and the motion type, intensity level, and duration according to a unified timestamp to construct a three-dimensional feature tensor that includes anatomical basis, real-time physiological response, and external stimulus intensity. The risk situation inference module is used to calculate the instantaneous risk probability based on the three-dimensional feature tensor. The risk situation inference module has a built-in hierarchical Bayesian network model, which includes a first subnetwork and a second subnetwork. The first subnetwork is used to calculate the basic vulnerability index based on the structured lesion feature vector and clinical covariates. The second subnetwork is used to receive the three-dimensional feature tensor and the basic vulnerability index, and combine them with the dynamic blood pressure threshold drift compensation algorithm to solve the instantaneous risk probability of acute cerebral perfusion imbalance occurring under the current motion state. The intervention strategy generation module is used to trigger a high-level early warning mechanism and simultaneously start a dual-channel response protocol when the instantaneous risk probability is continuously higher than a preset threshold for a predetermined duration.

2. The intelligent motion risk prediction system for cerebral small vessel disease according to claim 1, characterized in that, The dynamic blood pressure threshold drift compensation algorithm adopts an adaptive filter structure. Its real-time calibration coefficient is determined by extracting the main frequency component from the average systolic blood pressure peak sequence of the same exercise type in the past predetermined period through spectral analysis, so as to eliminate baseline drift interference caused by circadian rhythm or drug effects.

3. The intelligent motion risk prediction system for cerebral small vessel disease according to claim 2, characterized in that, The time dimension of the three-dimensional feature tensor corresponds to the minimum historical observation duration required for risk extrapolation. Its spatial dimension is uniformly mapped to the standard brain template coordinate system after standardization to ensure that the data between different individuals are comparable. The update cycle of the instantaneous risk probability is a fixed short interval, and each update is recalculated based on the complete observation sequence within the latest sliding window.

4. The intelligent motion risk prediction system for cerebral small vessel disease according to claim 3, characterized in that, The intervention strategy generation module is equipped with a context-aware optimization unit, which is used to dynamically adjust the mode and intensity of the multimodal alert by combining ambient light intensity, geographical location information and user's past compliance records, and select the corresponding standardized solution from the recommended recovery action library for push.

5. The intelligent motion risk prediction system for cerebral small vessel disease according to claim 4, characterized in that, The recommended recovery exercise library includes deep breathing regulation procedures, progressive muscle relaxation sequences, and sitting balance maintenance guidelines. The selection is based on a comprehensive judgment of the current rate of decline in brain oxygen saturation and the slope of the heart rate recovery curve.

6. The intelligent motion risk prediction system for cerebral small vessel disease according to claim 5, characterized in that, The system supports a two-way feedback learning mechanism. All early warning events and their subsequent clinical validation results are anonymized and transmitted back to the central database for periodic model parameter iteration updates. The model parameter iteration updates adopt a federated learning framework to complete global model aggregation while protecting the data privacy of each medical institution.

7. The intelligent motion risk prediction system for cerebral small vessel disease according to claim 6, characterized in that, The motion behavior recognition module is equipped with an online incremental learning function. When the system fails to accurately classify a certain activity multiple times in a row, an interactive interface pops up to request the user to confirm the actual motion category. The obtained labeled data is added to the local training set after quality filtering, and fine-tuning training is performed using a lightweight neural network architecture. The model update process is completed independently on the device.

8. The intelligent motion risk prediction system for cerebral small vessel disease according to claim 7, characterized in that, The multi-source data fusion engine is configured with an abnormal data identification submodule, which is used to perform real-time quality monitoring of each channel input using the isolated forest algorithm. When any sensor data shows a constant output value or a sudden change exceeding the physiologically reasonable range, the channel is automatically marked as unreliable and switched to the Kalman prediction value based on historical trends as a temporary alternative input, while reducing the contribution ratio of this dimension in the fusion weight matrix.

9. The intelligent motion risk prediction system for cerebral small vessel disease according to claim 8, characterized in that, The risk situation simulation module has dual verification logic. In addition to the hierarchical Bayesian network in the main path, there is a parallel computation path based on the limit gradient boosting tree. The two run independently but share the same input feature set. The system performs consistency checks periodically. If the difference in risk level between the two models exceeds the predetermined level, the diagnostic log analysis program is automatically started.

10. The intelligent motion risk prediction system for cerebral small vessel disease according to claim 9, characterized in that, The physiologically reasonable range is determined based on the percentile value range published in large-scale population cohort studies; the new version of the risk projection model needs to be cross-validated to confirm that its performance indicators have reached the preset threshold before it is pushed to the terminal device for replacement.

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