Intelligent exercise rehabilitation system

Abstract

The invention provides an intelligent exercise rehabilitation system. The system comprises a physiological parameter monitoring module, a power vehicle parameter monitoring module and an AI exercise intensity control module. The system can dynamically calculate the actual exercise intensity of the patient by collecting physiological parameters such as heart rate, blood pressure and oxyhemoglobin saturation during exercise of the patient in real time and operating parameters such as output power, resistance and pedaling frequency of the power vehicle. The AI exercise intensity control module generates a control instruction based on a pre-trained correlation model of physiological parameters and appropriate exercise intensity so as to adjust parameters of the power vehicle, so that the exercise intensity of the patient is always in a personalized appropriate range. The system can also integrate a risk layering module, a prescription generation module, a safety threshold alarm module, a rehabilitation effect evaluation module and a doctor's advice management module, so that the whole-process intelligent management from evaluation, plan execution to effect tracking is realized, and the safety, suitability and efficiency of rehabilitation training are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of rehabilitation technology, and in particular to an intelligent sports rehabilitation system. Background Technology

[0002] Exercise rehabilitation is a crucial component of the modern medical system, vital for the functional recovery of patients with motor dysfunction, such as those recovering from stroke or orthopedic surgery. Traditional rehabilitation training often relies on the therapist's experience, guiding patients to use rehabilitation equipment with fixed parameters, lacking objective and quantitative monitoring and feedback mechanisms.

[0003] Currently, most rehabilitation equipment on the market can assist patients in completing standardized movements, but it has significant limitations: First, its training mode is often preset with fixed parameters, which cannot be dynamically adjusted according to the patient's real-time physiological state and motor ability, making it difficult to achieve truly personalized training and resulting in low rehabilitation efficiency; Second, the lack of real-time and continuous monitoring of the patient's physiological parameters means that therapists cannot obtain the patient's physical load and potential risks in a timely manner, which can easily lead to insufficient or excessive training intensity, or even secondary injury.

[0004] Therefore, there is an urgent need in existing technologies for a system that can deeply integrate real-time physiological monitoring with intelligent control of rehabilitation equipment. By accurately sensing the patient's physiological feedback, the system can automatically and safely adjust equipment parameters such as training intensity, resistance, or assistive force, thereby providing each patient with the optimal personalized rehabilitation plan and maximizing rehabilitation efficacy while ensuring safety. Summary of the Invention

[0005] This invention provides an intelligent sports rehabilitation system that can monitor the patient's physiological parameters in real time and intelligently adjust the operating parameters of the rehabilitation equipment accordingly, ultimately achieving personalized rehabilitation that is precisely adapted to the patient's physical endurance.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] An intelligent sports rehabilitation system includes:

[0008] The physiological parameter monitoring module is used to collect physiological parameters of patients in real time when they are performing rehabilitation exercises according to the exercise prescription. The physiological parameters include at least one of heart rate, blood pressure and blood oxygen saturation.

[0009] The power bike parameter monitoring module is communicatively connected to the physiological parameter monitoring module and is used to collect the power bike's operating parameters in real time. Based on the operating parameters and the physiological parameters received from the physiological parameter monitoring module, the actual exercise intensity of the patient is determined. The operating parameters include at least one of output power, resistance, and cadence.

[0010] The AI-powered exercise intensity control module is communicatively connected to both the physiological parameter monitoring module and the power vehicle parameter monitoring module, and is used for:

[0011] Based on a pre-established correlation model between physiological parameters and appropriate exercise intensity, the patient's current appropriate exercise intensity is determined according to the physiological parameters received from the physiological parameter monitoring module.

[0012] Based on the patient's actual exercise intensity, the appropriate exercise intensity, and the operating parameters received from the power vehicle parameter monitoring module, control commands are generated; and

[0013] The control command is sent to the power vehicle to adjust the operating parameters of the power vehicle so that the patient's actual exercise intensity approaches the appropriate exercise intensity.

[0014] Preferably, the correlation model between the physiological parameters and the appropriate exercise intensity is a mixed-effects model trained through subject stratification and basic data collection, exercise intervention experiments and data correction, and model construction and validation steps.

[0015] Preferred options also include:

[0016] The report analysis and prescription development module is used to develop exercise prescriptions for patients based on exercise test assessment reports, and includes the following units:

[0017] Exercise capacity assessment unit: Based on the exercise test assessment report, calculate the patient's peak oxygen uptake, anaerobic threshold and metabolic equivalent, and classify the patient as having severe, moderate or mild functional impairment according to the American College of Sports Medicine standards;

[0018] Risk stratification unit: Based on the cardiovascular risk stratification model of the American College of Cardiovascular and Pulmonary Rehabilitation, it integrates the patient's static physiological parameters and dynamic physiological parameters from exercise tests to output low, intermediate, or high risk levels. The static physiological parameters include at least one of basal metabolic rate, body fat percentage, and resting heart rate; the dynamic physiological parameters are at least one of heart rate, blood pressure, and blood oxygen saturation collected during exercise tests.

[0019] Prescription generation unit: It adopts a fuzzy logic system to generate target heart rate ranges based on preset risk level, resting heart rate, anaerobic threshold heart rate and age parameters, and generates exercise prescription parameters based on the target heart rate range, risk level and functional impairment classification.

[0020] Prescription output unit: Generates a structured exercise prescription, including at least one of the following: exercise mode, target intensity control parameters, and exercise duration.

[0021] Preferred options also include:

[0022] The alarm module is communicatively connected to the physiological parameter monitoring module, the power vehicle parameter monitoring module, and the report analysis and prescription formulation module, respectively. It is used to generate the safe physiological parameter threshold range of the patient based on the patient information, actual exercise intensity, and exercise prescription. The patient information includes at least one of gender, age, and baseline heart rate.

[0023] When the physiological parameters received from the physiological parameter monitoring module exceed the safe physiological parameter threshold range, an alarm signal is issued.

[0024] Preferably, the alarm module is configured to: determine a baseline value based on the average historical data of the patient's personalized physiological parameters, determine an individual state coefficient based on the patient information, determine an exercise intensity coefficient based on the real-time exercise intensity, and calculate a safe physiological parameter threshold range by combining the baseline value, the individual state coefficient, and the exercise intensity coefficient.

[0025] Preferred options also include:

[0026] The AI-based rehabilitation effect evaluation module is communicatively connected to both the physiological parameter monitoring module and the power vehicle parameter monitoring module, and is used for:

[0027] The physiological parameters received from the physiological parameter monitoring module when the patient performs rehabilitation exercises according to the exercise prescription multiple times are integrated with the power vehicle operation parameters received from the power vehicle parameter monitoring module for the corresponding number of times.

[0028] The patient's rehabilitation effect is evaluated based on the changing trends of the physiological parameters and the operational parameters.

[0029] Preferred options also include:

[0030] The medical order management module, which communicates with the AI ​​rehabilitation effect evaluation module, is used for:

[0031] Based on the patient's rehabilitation effect received from the AI ​​rehabilitation effect evaluation module, rehabilitation medical advice and suggestions for the patient are generated.

[0032] Preferred options also include:

[0033] The patient data management module is communicatively connected to the physiological parameter monitoring module, the power vehicle parameter monitoring module, and the medical order management module, respectively, and is used for:

[0034] The system associates and stores the physiological parameters, operational parameters, rehabilitation effects, rehabilitation medical advice and suggestions and their execution status of a specific patient, and generates a visual report reflecting the patient's rehabilitation progress and key assessment indicators.

[0035] It provides query and filtering functions based on patient information, rehabilitation effect, or rehabilitation medical order execution status.

[0036] Preferred options also include:

[0037] The equipment status monitoring module is communicatively connected to the physiological parameter monitoring module and the power vehicle parameter monitoring module, respectively, and is used to monitor its operating status and communication connection status in real time. When an abnormality is detected, an alarm signal is generated.

[0038] To achieve the above objectives, the present invention also provides an intelligent motion rehabilitation control method, comprising the following steps:

[0039] Real-time collection of physiological parameters of patients during rehabilitation exercises according to exercise prescriptions, including at least one of heart rate, blood pressure and blood oxygen saturation;

[0040] Real-time acquisition of the power bike's operating parameters, including at least one of output power, resistance, and cadence;

[0041] Based on the operating parameters and the physiological parameters, the patient's actual exercise intensity is determined;

[0042] Based on a pre-established correlation model between physiological parameters and appropriate exercise intensity, the patient's current appropriate exercise intensity is determined according to the physiological parameters.

[0043] Based on the actual exercise intensity, the suitable exercise intensity, and the operating parameters, control commands are generated;

[0044] The control command is sent to the power vehicle to adjust the operating parameters of the power vehicle so that the patient's actual exercise intensity approaches the appropriate exercise intensity.

[0045] Compared with existing technologies, the present invention can achieve: real-time monitoring of physiological parameters during the patient's rehabilitation exercise process, and intelligent adjustment of the operating parameters of the rehabilitation equipment based on these parameters, so as to achieve precise matching between rehabilitation exercise and the patient's physical endurance, thereby achieving personalized rehabilitation goals.

[0046] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0047] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0048] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0049] Figure 1 This is an overall structural diagram of an intelligent sports rehabilitation system according to an embodiment of the present invention;

[0050] Figure 2 This is a structural diagram of the report analysis and prescription formulation module in an embodiment of the present invention. Detailed Implementation

[0051] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0052] This invention provides an intelligent sports rehabilitation system, comprising:

[0053] The physiological parameter monitoring module is used to collect physiological parameters of patients in real time when they are doing rehabilitation exercises according to the exercise prescription. The physiological parameters include at least one of heart rate, blood pressure and blood oxygen saturation.

[0054] The power bike parameter monitoring module communicates with the physiological parameter monitoring module to collect the power bike's operating parameters in real time. Based on the operating parameters and the physiological parameters received from the physiological parameter monitoring module, the actual exercise intensity of the patient is determined. The operating parameters include at least one of output power, resistance, and cadence.

[0055] The AI-powered exercise intensity control module is communicatively connected to both the physiological parameter monitoring module and the power vehicle parameter monitoring module, and is used for:

[0056] Based on a pre-established correlation model between physiological parameters and appropriate exercise intensity, the appropriate exercise intensity for the patient is determined according to the physiological parameters received from the physiological parameter monitoring module.

[0057] Based on the patient's actual exercise intensity, appropriate exercise intensity, and operating parameters received from the power vehicle parameter monitoring module, control commands are generated; and

[0058] Control commands are sent to the exercise vehicle to adjust its operating parameters so that the patient's actual exercise intensity approaches the appropriate exercise intensity.

[0059] The working principle of the above technical solution is as follows: In this embodiment, as Figure 1As shown, the system precisely controls the patient's exercise intensity through the coordinated action of multiple modules. The physiological parameter monitoring module first collects the patient's physiological parameters and sends them to the AI ​​exercise intensity control module, which calculates the patient's appropriate exercise intensity. Simultaneously, the power bike parameter monitoring module combines the power bike's operating parameters with the patient's physiological parameters to determine the patient's actual exercise intensity. The AI ​​exercise intensity control module further compares the actual exercise intensity, the power bike's operating parameters, and the appropriate exercise intensity, generating control commands accordingly to dynamically adjust the power bike's operating parameters, thereby gradually bringing the patient's actual exercise intensity closer to their appropriate exercise intensity.

[0060] The beneficial effects of the above technical solution are as follows: Based on the patient's real-time physiological parameters, this system dynamically calculates their "appropriate exercise intensity" using AI algorithms, changing the previous fixed exercise intensity approach and truly achieving personalized rehabilitation treatment. By monitoring the patient's physiological parameters and actual exercise intensity in real time, the system can continuously assess exercise risks. When the actual intensity deviates from the appropriate intensity, the system can immediately and automatically intervene, effectively preventing excessive or insufficient exercise and greatly improving the safety of the rehabilitation process. This system forms an automated closed loop of "monitoring-calculation-comparison-regulation." The AI ​​exercise intensity control module continuously compares the difference between the "actual intensity" and the "appropriate intensity" and precisely adjusts the power cart parameters to stabilize the exercise intensity within the ideal range, improving the accuracy and consistency of treatment. By maintaining the exercise intensity at the optimal level through automated control, the workload of medical staff is reduced, avoiding the lag and inaccuracy of manual adjustment, which may shorten the rehabilitation cycle and optimize rehabilitation effects. It integrates pre-exercise health screening, exercise assessment, exercise prescription formulation, exercise process monitoring, and rehabilitation effect evaluation to form a complete closed loop. This system can also record events during patient rehabilitation exercises, such as fatigue, weakness, and lethargy, through a recording module, while simultaneously linking and saving the patient's real-time physiological parameters. The system recording module can be presented through a system framework diagram (divided into functional modules, data flow diagrams, and video). This system supports real-time synchronous monitoring of physiological parameters (covering ECG, heart rate, blood oxygen, etc.) and exercise bike operation parameters (including exercise bike resistance, speed, etc.) for up to nine patients. Through the design of expansion modules, parallel processing of these multi-source data can be achieved, while the processed data is centrally displayed, allowing medical staff or relevant operators to intuitively and efficiently grasp the real-time status of multiple patients. This provides strong technical support for multi-patient exercise rehabilitation monitoring and management, improving data processing efficiency and monitoring comprehensiveness.

[0061] In one or more embodiments, the power vehicle is equipped with a muscle strength monitoring and feedback module. During the patient's supine training, the muscle strength monitoring and feedback module can collect and analyze the patient's muscle strength parameters in real time, thereby identifying whether the patient has a spastic state. When a spastic state is detected, the muscle strength monitoring and feedback module immediately sends an instruction to the power vehicle's control system, causing the power vehicle to automatically switch to a preset spasticity coping training mode, such as reducing resistance, switching to passive training, or introducing a specific vibration relaxation program to relieve muscle spasms and ensure training safety.

[0062] The beneficial effects of the above technical solution are as follows: the power vehicle can intelligently adjust the training mode according to the patient's real-time movement status, effectively avoiding the risk of sports injury, improving the safety of the training process, and at the same time, accurately matching the training mode with the patient's muscle strength status, ensuring the effect of rehabilitation training, improving the effectiveness of training, and providing intelligent and personalized technical support for sports rehabilitation training.

[0063] In one or more embodiments, the steps by which the AI ​​motion intensity control module adjusts the operating parameters of the power vehicle are as follows:

[0064] S1: Real-time collection of the patient's heart rate data during rehabilitation exercises;

[0065] S2: Combine pre-stored or received individualized parameters of the patient, including age, gender and underlying disease information, to automatically generate a personalized target heart rate range for the patient (e.g., 60% to 80% of the maximum heart rate).

[0066] S3: Based on the personalized target heart rate zone generated by S2, construct a closed-loop control model of "heart rate deviation - power vehicle parameter adjustment", and pre-establish the mapping relationship between the target heart rate zone and the power vehicle exercise intensity parameters (such as resistance and speed).

[0067] S4: During exercise, continuously compare the real-time heart rate data with the preset target heart rate zone: When the measured heart rate exceeds the upper limit of the target heart rate zone, gradually reduce the resistance or speed of the power bike according to the heart rate deviation value (measured heart rate - upper limit of target heart rate) and the preset gradient (e.g., reduce resistance by 5% or speed by 0.5km / h every 30 seconds); when the measured heart rate is lower than the lower limit of the target heart rate zone, gradually increase the resistance or speed of the power bike according to the heart rate deviation value (lower limit of target heart rate - measured heart rate) and the preset gradient (e.g., increase resistance by 5% or speed by 0.5km / h every 30 seconds), and continue to repeat the above comparison and adjustment process until the measured heart rate stabilizes within the target heart rate zone.

[0068] By employing the above methods, an automated closed-loop control of "monitoring-analysis-adjustment" can be achieved, which can dynamically and adaptively adjust the exercise load to ensure that the patient's heart rate is always maintained within a safe and effective preset target range, thereby optimizing the rehabilitation training effect while ensuring exercise safety.

[0069] In one or more embodiments, the correlation model between physiological parameters and appropriate exercise intensity is a mixed-effects model trained through subject stratification and baseline data collection, exercise intervention experiments and data correction, and model construction and validation steps.

[0070] The working principle of the above technical solution is as follows: In this embodiment, the correlation model between physiological parameters and appropriate exercise intensity is constructed in the following way:

[0071] S1: Subject Stratification and Basic Data Collection: (a) Patient groups were stratified by disease type, age range, gender, and physical fitness benchmarks, with each group divided into a training group and a control group; (b) Static physiological parameters were collected from all subjects; dynamic physiological parameters were collected only from the training group during the exercise intervention; static physiological parameters included at least one of basal metabolic rate, body fat percentage, and resting heart rate, while dynamic physiological parameters included at least one of heart rate variability, blood oxygen saturation, and electrocardiogram waveform during exercise;

[0072] S2: Exercise Intervention Experiment and Data Correction: (a) Stepwise increasing exercise intensity was applied to the training group subjects, and exercise intensity parameters and real-time physiological response data were recorded simultaneously; (b) Dynamic physiological parameters were monitored in the control group subjects during the pre-set control activities. The influence of environmental factors was isolated by time series analysis using covariance analysis or mixed-effects model, and the exercise intervention physiological response data of the training group subjects were corrected accordingly. The pre-set control activities were sedentary monitoring or low-intensity constant exercise.

[0073] S3: Construction and Validation of the Association Model: (a) Construct a mixed-effects model with physiological parameters as input and appropriate exercise intensity as output, where: fixed effects include age and disease type; random effects include individual metabolic difference indicators, which include at least one of basal metabolic rate and body fat percentage; (b) Divide the data into training and validation sets, train the model through five-fold cross-validation, and use the results of cardiopulmonary exercise test as the gold standard to validate the error rate;

[0074] S4: Appropriate exercise intensity output: Input the physiological parameters of the target patient into the correlation model and directly output the corresponding appropriate exercise intensity parameters.

[0075] The beneficial effects of the above technical solution are as follows: The physiological parameter-exercise intensity correlation model constructed in this solution effectively eliminates the influence of interfering factors and ensures data reliability and model scientific validity through scientific stratified sampling and the establishment of a control group for data collection and correction. The mixed-effects model can reflect group patterns based on fixed effects such as age and disease type, while also capturing individual differences through random effects such as basal metabolic rate, accurately outputting truly personalized exercise intensity. This model obtains key physiological response data through stepwise exercise intervention and performs five-fold cross-validation using cardiopulmonary exercise testing as the gold standard, ensuring accurate results and high clinical reliability.

[0076] In one or more embodiments, it further includes:

[0077] The report analysis and prescription development module is used to develop exercise prescriptions for patients based on exercise test assessment reports, and includes the following units:

[0078] Exercise capacity assessment unit: Based on the exercise test assessment report, calculate the patient's peak oxygen uptake, anaerobic threshold and metabolic equivalent, and classify the patient as having severe, moderate or mild functional impairment according to the American College of Sports Medicine standards;

[0079] Risk stratification unit: Based on the cardiovascular risk stratification model of the American College of Cardiovascular and Pulmonary Rehabilitation, it integrates the patient's static physiological parameters and dynamic physiological parameters from exercise tests to output low, intermediate or high risk levels. Static physiological parameters include at least one of basal metabolic rate, body fat percentage and resting heart rate; dynamic physiological parameters are at least one of heart rate, blood pressure and blood oxygen saturation collected during exercise tests.

[0080] Prescription generation unit: It adopts a fuzzy logic system to generate target heart rate ranges based on preset risk level, resting heart rate, anaerobic threshold heart rate and age parameters, and generates exercise prescription parameters based on target heart rate ranges, risk level and functional impairment classification.

[0081] Prescription output unit: Generates a structured exercise prescription, including at least one of the following: exercise mode, target intensity control parameters, and exercise duration.

[0082] The working principle of the above technical solution is as follows: In this embodiment, as Figure 2As shown, the report analysis and prescription formulation module first calculates indicators such as peak oxygen uptake based on the exercise test report and determines the patient's exercise dysfunction level according to the American College of Sports Medicine standards. Then, combining the American College of Cardiovascular and Pulmonary Rehabilitation's cardiovascular risk stratification model, it integrates static physiological parameters with dynamic parameters during exercise, such as heart rate, blood pressure, blood oxygen saturation, and respiratory rate, to determine the cardiovascular risk level. Subsequently, using a fuzzy logic system, it calculates the target heart rate range using parameters such as risk and resting heart rate, and generates prescription parameters based on the dysfunction level. Finally, it outputs a structured exercise prescription containing exercise mode, target intensity, and duration. The report generation process automatically calculates target heart rate (THR), metabolic equivalent of task (METs), respiratory rate, etc., and provides in-depth inferences such as exercise prescription suggestions to generate a report. The report analysis and prescription formulation module generates personalized exercise prescriptions for patients based on medical standards and intelligent algorithms through a closed-loop logic of "assessment-stratification-calculation-output". Personalized exercise prescriptions are generated by combining multi-dimensional assessments, including cardiovascular disease risk factor assessment, physical fitness assessment, and functional exercise assessment. During exercise, physiological parameters such as heart rate and blood oxygen saturation are monitored in real time, and subsequent exercise plans are adjusted based on the data. This embodiment also includes a system linking mental stress analysis with rehabilitation data. This system includes: a mental stress monitoring device used to: collect patients' mood or stress values ​​at multiple time points in the morning, noon, and evening on rest days or days without rehabilitation training, and calculate their average as the "natural stress level"; collect patients' mood or stress values ​​within 5-15 minutes after each rehabilitation exercise session as the "stress value during exercise"; compare the stress value during exercise with the natural stress level. If the stress value during exercise is greater than the natural stress level, the patient's mood or stress assessment result is negative, the exercise intensity is reduced, and rest days are increased; if the stress value during exercise is less than the natural stress level, the patient's mood or stress assessment result is positive, the current exercise intensity, duration, and frequency are maintained for 1-2 sessions, and then each exercise session is increased by 5-10 minutes.

[0083] The beneficial effects of the above technical solution are as follows: This solution quantifies and grades exercise capacity based on the American College of Sports Medicine (ACC) standards, avoiding a "one-size-fits-all" approach and ensuring that the prescription accurately matches the patient's actual exercise level. By integrating multi-dimensional parameters such as risk, heart rate, and age through a fuzzy logic system, it accurately calculates the target heart rate zone. Combined with refined parameters based on functional impairment grading, it ensures that the exercise has a rehabilitative effect without exceeding the patient's physical tolerance, balancing "effectiveness" and "safety." This module ultimately outputs key information such as exercise method, target intensity, and duration, which can be directly referenced and implemented whether the patient trains independently or under the guidance of medical staff / rehabilitation therapists. This lowers the barrier to entry for the system and improves its operability in clinical or home rehabilitation.

[0084] This program achieves personalization and optimization of rehabilitation exercise plans through cyclical monitoring and dynamic adjustment: using patients' mental stress data as objective feedback, the training volume is steadily increased when exercise produces positive emotional effects to promote rehabilitation, while the intensity is proactively reduced and patients' rest is increased when it produces negative effects to prevent overtraining. In this way, while ensuring the safety of rehabilitation, the program effectively improves patients' compliance and overall rehabilitation results.

[0085] In one or more embodiments, it further includes:

[0086] The alarm module is communicatively connected to the physiological parameter monitoring module, the power vehicle parameter monitoring module, and the report analysis and prescription formulation module, respectively. It is used to generate the patient's safe physiological parameter threshold range based on the patient information, actual exercise intensity, and exercise prescription. The patient information includes at least one of gender, age, and baseline heart rate.

[0087] An alarm signal is issued when the physiological parameters received from the physiological parameter monitoring module exceed the safe physiological parameter threshold range.

[0088] The working principle of the above technical solution is as follows: In this embodiment, the alarm module first dynamically generates the patient's safe physiological parameter threshold range based on patient information, real-time collected actual exercise intensity, and exercise prescription. The alarm module continuously receives real-time data from the physiological parameter monitoring module and monitors it. Once a physiological parameter is detected to exceed the set safe threshold range, the module will immediately trigger an alarm signal, which includes both visual (highlighted on the interface) and audible alarms, and automatically associates with the recording module to achieve alarm-event-data traceability.

[0089] The beneficial effects of the above technical solution are as follows: The alarm module dynamically generates safe physiological parameter threshold ranges by integrating patient information, real-time exercise intensity, and exercise prescriptions. This makes safety standards completely personalized, more closely aligned with the patient's current physiological state and exercise load, significantly improving monitoring accuracy. Continuous comparison of real-time physiological data with safe physiological parameter threshold ranges enables alarms to be issued when physiological parameters become abnormal, allowing medical staff or patients crucial intervention time, thereby effectively preventing overexertion or other unexpected risks and providing proactive safety assurance. The entire "monitoring-judgment-alarm" process of this solution is fully automated, forming a safety closed loop, reducing reliance on manual monitoring, lowering the possibility of human error, improving monitoring efficiency, and optimizing the allocation of medical resources. The alarm module is the safety guardian of this system, providing crucial safety assurance for patients' rehabilitation exercises through intelligent and personalized real-time monitoring.

[0090] In one or more embodiments, the alarm module is configured to: determine a baseline value based on the average historical data of the patient's personalized physiological parameters, determine an individual state coefficient based on patient information, determine an exercise intensity coefficient based on real-time exercise intensity, and calculate a safe physiological parameter threshold range by combining the baseline value, the individual state coefficient, and the exercise intensity coefficient.

[0091] The working principle of the above technical solution is as follows: In this embodiment, the alarm module generates a safe physiological parameter threshold range, which is achieved in the following way:

[0092] S1: Extract historical data of various physiological parameters of patients from the preset personal health database and calculate their mean values ​​as personalized benchmark values ​​for patients;

[0093] S2: Based on the preset physiological state coefficient calculation model, determine the individual state coefficient according to the patient information;

[0094] S3: Based on the preset exercise intensity coefficient calculation model, determine the exercise intensity coefficient according to the patient's real-time exercise intensity;

[0095] S4: Based on the baseline value and individual state coefficient, the maximum safety threshold is calculated using the formula: Maximum safety threshold = baseline value × (1 + individual state coefficient) + exercise intensity coefficient correction value;

[0096] The minimum safety threshold is calculated based on the baseline value and the exercise intensity coefficient. The formula is: Minimum safety threshold = baseline value × (1 - exercise intensity coefficient).

[0097] The beneficial effects of the above technical solution are as follows: This solution calculates the safe physiological parameter threshold range by integrating three major factors: the patient's personal historical baseline values, individual state coefficients, and real-time exercise intensity. This allows the range to accurately match each patient's unique physiological basis, real-time physical condition, and exercise load, reducing the bias of subjective experience judgment and greatly improving the targeting and accuracy of safety monitoring. Simultaneously, it provides reliable action basis for the alarm module, ensuring that the system can respond automatically and promptly, thus improving the intelligence level and operational efficiency of the entire rehabilitation system.

[0098] In one or more embodiments, it further includes:

[0099] The AI-based rehabilitation effect evaluation module is communicatively connected to both the physiological parameter monitoring module and the power vehicle parameter monitoring module, and is used for:

[0100] The system integrates physiological parameters received from the physiological parameter monitoring module when the patient performs rehabilitation exercises according to the exercise prescription multiple times, as well as the power bike operation parameters received from the power bike parameter monitoring module for the corresponding number of times.

[0101] The patient's rehabilitation effect is assessed based on the changing trends of physiological and operational parameters.

[0102] The working principle of the above technical solution is as follows: In this embodiment, the AI ​​rehabilitation effect evaluation module collects the patient's physiological parameters and power vehicle operation parameters, and performs intelligent analysis on these data to finally generate an objective evaluation of the patient's rehabilitation effect.

[0103] The beneficial effects of the above technical solution are as follows: the AI ​​rehabilitation effect evaluation module integrates and correlates physiological data and equipment operation data to build a comprehensive evaluation system, avoiding the one-sidedness of evaluation from a single data source, making the evaluation results more comprehensive and reliable, and the automated analysis also greatly improves the evaluation efficiency and reduces the workload of medical staff.

[0104] In one or more embodiments, a multi-dimensional rehabilitation management module is further included, which is communicatively connected to the physiological parameter monitoring module, the power vehicle parameter monitoring module, and the AI ​​rehabilitation effect evaluation module, respectively, for:

[0105] Data from multiple exercise rehabilitation training sessions conducted by patients at different time periods were aggregated for a centralized comparison of the disease progression. This data includes, but is not limited to, physiological parameters during exercise and the operating parameters of the power bike.

[0106] Based on the aggregated data, the patient's multiple exercise rehabilitation data are used to generate visual charts that intuitively reflect the evolution trend of the patient's rehabilitation process, such as trend charts.

[0107] By comparing and analyzing data from multiple training dates, the system automatically calculates the rehabilitation exercise achievement rate at each stage, quantifying the patient's training completion rate.

[0108] Real-time collection of stress parameters during patient rehabilitation exercises to assess patient mental stress status;

[0109] The above-mentioned visualization charts, rehabilitation exercise achievement rate, mental stress status, and rehabilitation effect assessment (such as target heart rate, Meto value, etc.) are comprehensively processed and automatically generated into a comprehensive report that integrates historical data longitudinal comparison and current status multi-dimensional analysis.

[0110] The aforementioned technical solution supports the simultaneous selection of multiple physiological and operational parameters, such as heart rate, blood oxygen, and exercise power, to generate trend charts on the same screen, intuitively presenting the changing patterns of each indicator. Furthermore, through sliding window technology, cross-period data comparisons can be achieved, such as comparing the trend of rehabilitation achievement rates over the past 30 days. This helps medical staff or rehabilitation managers clearly grasp the long-term changing trends of patients' multi-dimensional data, thereby providing comprehensive data support for clinical assessment and rehabilitation program adjustments, and improving the scientific rigor and efficiency of rehabilitation monitoring and assessment.

[0111] In one or more embodiments, it further includes:

[0112] The medical order management module communicates and connects with the AI ​​rehabilitation effect evaluation module for the following purposes:

[0113] Based on the patient's rehabilitation results received from the AI ​​rehabilitation effect assessment module, rehabilitation medical advice and suggestions are generated for the patient.

[0114] The working principle of the above technical solution is as follows: In this embodiment, the medical order management module assists in generating personalized rehabilitation medical order suggestions for patients based on the rehabilitation effect received from the AI ​​rehabilitation effect evaluation module.

[0115] The beneficial effects of the above technical solution are as follows: the medical order management module automatically generates personalized rehabilitation medical order suggestions based on AI's objective evaluation of the patient's rehabilitation effect, which effectively improves the scientificity and accuracy of treatment, reduces subjective bias, and at the same time improves the patient's rehabilitation efficiency and efficacy, improves the doctor's work efficiency, and forms a continuously optimized, efficient and safe rehabilitation management process.

[0116] In one or more embodiments, it further includes:

[0117] The patient data management module communicates with the physiological parameter monitoring module, the power cart parameter monitoring module, and the medical order management module, respectively, and is used for:

[0118] It links and stores specific patients' physiological parameters, operational parameters, rehabilitation effects, rehabilitation medical advice and suggestions and their execution status, and generates a visual report reflecting the patient's rehabilitation progress and key assessment indicators;

[0119] It provides query and filtering functions based on patient information, rehabilitation effect, or rehabilitation medical order execution status.

[0120] The beneficial effects of the above technical solution are as follows: This solution breaks down data silos between modules, seamlessly connecting motor performance, physiological responses, medical interventions, and implementation effects to form a complete rehabilitation data chain. The visualized report clearly displays the patient's rehabilitation trend, assisting doctors in judging the effectiveness of the rehabilitation plan, whether the patient has encountered a plateau, and whether prescription adjustments are necessary based on objective data, making clinical decision-making more scientific and precise.

[0121] In one or more embodiments, it further includes:

[0122] The equipment status monitoring module is connected to the physiological parameter monitoring module and the power vehicle parameter monitoring module respectively, and is used to monitor their operating status and communication connection status in real time. When an abnormality is detected, an alarm signal is generated.

[0123] The beneficial effects of the above technical solution are as follows: the equipment status monitoring module monitors the operation and connection status of the physiological parameter monitoring module and the power vehicle parameter monitoring module in real time, thereby realizing proactive early warning of risks such as hardware failure and data interruption, preventing patients from performing rehabilitation exercises in abnormal equipment conditions, ensuring patient safety, and effectively avoiding rehabilitation interruption, assessment distortion and safety risks caused by equipment failure or data packet loss.

[0124] This invention also provides an intelligent sports rehabilitation control method, comprising the following steps:

[0125] Real-time collection of physiological parameters of patients during rehabilitation exercises according to the exercise prescription. Physiological parameters include at least one of heart rate, blood pressure and blood oxygen saturation.

[0126] Real-time acquisition of the power bike's operating parameters, including at least one of output power, resistance, and cadence;

[0127] The patient's actual exercise intensity is determined based on operational and physiological parameters;

[0128] Based on a pre-established correlation model between physiological parameters and appropriate exercise intensity, the patient's current appropriate exercise intensity is determined according to the physiological parameters.

[0129] Control commands are generated based on the actual exercise intensity, suitable exercise intensity, and operating parameters;

[0130] Control commands are sent to the exercise vehicle to adjust its operating parameters so that the patient's actual exercise intensity approaches the appropriate exercise intensity.

[0131] The beneficial effects of the above technical solution are as follows: This solution can collect physiological parameters such as the patient's heart rate and blood pressure in real time, as well as the power vehicle's operating parameters, dynamically calculate the actual exercise intensity, and provide an appropriate intensity by adjusting the equipment parameters, avoiding excessive intensity that could lead to physical deterioration or excessively low intensity that could lead to ineffective exercise; it allows the actual intensity to continuously approach the appropriate intensity, ensuring that the exercise is within a safe range, reducing the rehabilitation cycle, and increasing the value of each exercise session; the entire process is automated, reducing reliance on human labor and subjective errors, and lowering costs while ensuring the scientific nature and stability of rehabilitation.

[0132] In one or more embodiments, it further includes:

[0133] The equipment management module is used to register and query equipment information for the physiological parameter monitoring module and the power vehicle parameter monitoring module. The equipment information includes at least one of the following: equipment model, serial number, and maintenance information.

[0134] The beneficial effects of the above technical solution are as follows: the equipment management module realizes the digital and standardized management of medical equipment by centrally registering key information such as the model and serial number of all equipment in the system, which provides solid support for ensuring the safety, reliability and data accuracy of the rehabilitation treatment process, and also facilitates the tracking, inventory and accounting of assets.

[0135] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An intelligent sports rehabilitation system, characterized in that, include: The physiological parameter monitoring module is used to collect physiological parameters of patients in real time when they are performing rehabilitation exercises according to the exercise prescription. The physiological parameters include at least one of heart rate, blood pressure and blood oxygen saturation. The power bike parameter monitoring module is communicatively connected to the physiological parameter monitoring module and is used to collect the power bike's operating parameters in real time. Based on the operating parameters and the physiological parameters received from the physiological parameter monitoring module, the actual exercise intensity of the patient is determined. The operating parameters include at least one of output power, resistance, and cadence. The AI-powered exercise intensity control module is communicatively connected to both the physiological parameter monitoring module and the power vehicle parameter monitoring module, and is used for: Based on a pre-established correlation model between physiological parameters and appropriate exercise intensity, the patient's current appropriate exercise intensity is determined according to the physiological parameters received from the physiological parameter monitoring module. Control commands are generated based on the patient's actual exercise intensity, the appropriate exercise intensity, and the operating parameters received from the power vehicle parameter monitoring module; as well as The control command is sent to the power vehicle to adjust the operating parameters of the power vehicle so that the patient's actual exercise intensity approaches the appropriate exercise intensity.

2. The intelligent sports rehabilitation system according to claim 1, characterized in that, The correlation model between the physiological parameters and the appropriate exercise intensity is a mixed-effects model trained through subject stratification and basic data collection, exercise intervention experiments and data correction, and model construction and validation steps.

3. The intelligent sports rehabilitation system according to claim 1, characterized in that, Also includes: The report analysis and prescription development module is used to develop exercise prescriptions for patients based on exercise test assessment reports, and includes the following units: Exercise capacity assessment unit: Based on the exercise test assessment report, calculate the patient's peak oxygen uptake, anaerobic threshold and metabolic equivalent, and classify the patient as having severe, moderate or mild functional impairment according to the American College of Sports Medicine standards; Risk stratification unit: Based on the cardiovascular risk stratification model of the American College of Cardiovascular and Pulmonary Rehabilitation, it integrates the patient's static physiological parameters and dynamic physiological parameters from exercise tests to output low, intermediate, or high risk levels. The static physiological parameters include at least one of basal metabolic rate, body fat percentage, and resting heart rate; the dynamic physiological parameters are at least one of heart rate, blood pressure, and blood oxygen saturation collected during exercise tests. Prescription generation unit: It adopts a fuzzy logic system to generate target heart rate ranges based on preset risk level, resting heart rate, anaerobic threshold heart rate and age parameters, and generates exercise prescription parameters based on the target heart rate range, risk level and functional impairment classification. Prescription output unit: Generates a structured exercise prescription, including at least one of the following: exercise mode, target intensity control parameters, and exercise duration.

4. The intelligent sports rehabilitation system according to claim 1 or 3, characterized in that, Also includes: The alarm module is communicatively connected to the physiological parameter monitoring module, the power vehicle parameter monitoring module, and the report analysis and prescription formulation module, respectively. It is used to generate the safe physiological parameter threshold range of the patient based on the patient information, actual exercise intensity, and exercise prescription. The patient information includes at least one of gender, age, and baseline heart rate. When the physiological parameters received from the physiological parameter monitoring module exceed the safe physiological parameter threshold range, an alarm signal is issued.

5. The intelligent sports rehabilitation system according to claim 4, characterized in that, The alarm module is configured to: determine a baseline value based on the average historical data of the patient's personalized physiological parameters, determine an individual state coefficient based on the patient information, determine an exercise intensity coefficient based on the real-time exercise intensity, and calculate a safe physiological parameter threshold range by combining the baseline value, the individual state coefficient, and the exercise intensity coefficient.

6. The intelligent sports rehabilitation system according to claim 1, characterized in that, Also includes: The AI-based rehabilitation effect evaluation module is communicatively connected to both the physiological parameter monitoring module and the power vehicle parameter monitoring module, and is used for: The physiological parameters received from the physiological parameter monitoring module when the patient performs rehabilitation exercises according to the exercise prescription multiple times are integrated with the power vehicle operation parameters received from the power vehicle parameter monitoring module for the corresponding number of times. The patient's rehabilitation effect is evaluated based on the changing trends of the physiological parameters and the operational parameters.

7. The intelligent sports rehabilitation system according to claim 6, characterized in that, Also includes: The medical order management module, which communicates with the AI ​​rehabilitation effect evaluation module, is used for: Based on the patient's rehabilitation effect received from the AI ​​rehabilitation effect evaluation module, rehabilitation medical advice and suggestions for the patient are generated.

8. The intelligent sports rehabilitation system according to claim 7, characterized in that, Also includes: The patient data management module is communicatively connected to the physiological parameter monitoring module, the power vehicle parameter monitoring module, and the medical order management module, respectively, and is used for: The system associates and stores the physiological parameters, operational parameters, rehabilitation effects, rehabilitation medical advice and suggestions and their execution status of a specific patient, and generates a visual report reflecting the patient's rehabilitation progress and key assessment indicators. It provides query and filtering functions based on patient information, rehabilitation effect, or rehabilitation medical order execution status.

9. The intelligent sports rehabilitation system according to claim 1, characterized in that, Also includes: The equipment status monitoring module is communicatively connected to the physiological parameter monitoring module and the power vehicle parameter monitoring module, respectively, and is used to monitor its operating status and communication connection status in real time. When an abnormality is detected, an alarm signal is generated.

10. A method for intelligent motion rehabilitation control, characterized in that, Includes the following steps: Real-time collection of physiological parameters of patients during rehabilitation exercises according to exercise prescriptions, including at least one of heart rate, blood pressure and blood oxygen saturation; Real-time acquisition of the power bike's operating parameters, including at least one of output power, resistance, and cadence; Based on the operating parameters and the physiological parameters, the patient's actual exercise intensity is determined; Based on a pre-established correlation model between physiological parameters and appropriate exercise intensity, the patient's current appropriate exercise intensity is determined according to the physiological parameters. Based on the actual exercise intensity, the suitable exercise intensity, and the operating parameters, control commands are generated; The control command is sent to the power vehicle to adjust the operating parameters of the power vehicle so that the patient's actual exercise intensity approaches the appropriate exercise intensity.

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