Virtual scene interactive heart rehabilitation evaluation and training system

Abstract

The invention relates to the technical field of information technology and medical rehabilitation, and discloses a virtual scene interactive heart rehabilitation evaluation and training system which comprises a virtual scene online collection module, a virtual scene online analysis module and a virtual scene online processing module. The virtual scene online collection module is divided into a virtual reality helmet unit, a motion capture unit, a physiological monitoring unit and a training progress tracking unit, and the virtual scene online analysis module comprises a virtual environment simulator analysis unit, a motion analysis unit and a user interaction unit. A comfort level optimization index X1, a motion abnormity index value Ri and a training progress index Tv are calculated in a virtual scene online analysis module, and a virtual scene online processing module carries out parameter adjustment on a helmet wearing mode according to the comfort level optimization index X1 and adjusts the positions of a camera and a sensor according to the motion abnormity index value Ri and the training progress index Tv. By means of the operation, the helmet has the advantages that the helmet is comfortable to wear, and all fine motion deviations can be captured.

Description

Technical Field

[0001] The present invention relates to the fields of information technology and medical rehabilitation technology, and in particular to a virtual scenario interactive cardiac rehabilitation assessment and training system. Background Art

[0002] The virtual scenario interactive cardiac rehabilitation assessment and training system is an interdisciplinary comprehensive technology application that integrates the cutting-edge progress of information technology and the professional needs of medical rehabilitation, aiming to provide more efficient and personalized rehabilitation services for patients with cardiovascular diseases. This not only represents the development direction of modern rehabilitation medicine, but also reflects the huge potential of technological innovation in improving human health.

[0003] Although this technology brings many benefits to life, the current model of helmet wearing is uncomfortable, and motion capture technology cannot perfectly capture all subtle movement deviations. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the shortcomings of the existing technology, the present invention provides a virtual scenario interactive cardiac rehabilitation assessment and training system, which has the advantages of being comfortable to wear and able to capture all subtle movement deviations, solving the problems of uncomfortable helmet wearing and inability to capture all subtle movement deviations.

[0006] (II) Technical solution

[0007] To achieve the above-mentioned object, the present invention provides the following technical solutions: a virtual scenario interactive cardiac rehabilitation assessment and training system, comprising a virtual scenario online collection module, a virtual scenario online analysis module and a virtual scenario online processing module;

[0008] The virtual scenario online collection module includes a virtual reality helmet unit, a motion capture unit, a physiological monitoring unit and a training progress tracking unit. The virtual reality helmet unit collects virtual reality helmet data through a built-in inertial measurement part, a camera and a sensor, and after numbering, is connected to the virtual scenario online analysis module through a network. The motion capture unit collects motion capture data through an optical camera, an inertial measurement unit and a sensor device, and after numbering, is connected to the virtual scenario online analysis module through a network. The physiological monitoring unit collects physiological monitoring data through a multi-dimensional sensor and a detection device, and after numbering, is connected to the virtual scenario online analysis module through a network. The training progress tracking unit collects training progress tracking data through a camera and an online registration form, and numbers them. The virtual scenario online collection module is connected to the virtual scenario online analysis module through a network.

[0009] The virtual scenario online analysis module includes a virtual environment simulator analysis unit, a motion analysis unit and a user interaction unit. The virtual environment simulator analysis unit calculates a comfort optimization index Xl according to virtual reality helmet data, motion capture data and physiological monitoring data. The motion analysis unit calculates a motion abnormality index Ri according to the motion capture data and physiological monitoring data. The user interaction unit calculates a training progress index Tv according to the training progress tracking data. The virtual scenario online analysis module and the virtual scenario online processing module are connected via a network.

[0010] The virtual scenario online processing module adjusts the parameters of the helmet wearing mode according to the comfort optimization index Xl, and adjusts the positions of the camera and the sensor according to the abnormal movement index Ri and the training progress index Tv.

[0011] Preferably, the virtual reality helmet unit numbers the headband tightness comfort, weight comfort, heat comfort, humidity comfort, headband tightness discomfort, weight discomfort, heat discomfort and humidity discomfort when the helmet is worn during the virtual scenario according to the virtual reality helmet data characteristics, and the headband tightness comfort, weight comfort, heat comfort, humidity comfort, headband tightness discomfort, weight discomfort, heat discomfort and humidity discomfort when the helmet is worn during the virtual scenario are numbered E1, E2, E3, E4, z1, z2, z3, z4.

[0012] Preferably, the motion capture unit numbers the number of limb confusions, number of limb stability, acceleration data, gyroscope data and optical tracking capture data during the virtual scene according to the characteristics of the motion capture data. The limb confusion data, limb stability data, acceleration data, gyroscope data and optical tracking capture data during the virtual scene are numbered as v1, S1, S2, S3 and S4.

[0013] Preferably, the physiological monitoring unit numbers the number of stable heart rate, stable breathing, muscle relaxation, heart rate tension, rapid breathing and muscle tension during the virtual scenario according to the characteristics of the physiological monitoring data, and the number of stable heart rate, stable breathing, muscle relaxation, heart rate tension, rapid breathing and muscle tension during the virtual scenario are numbered U1, U2, U3, i1, i2, i3.

[0014] Preferably, the virtual environment simulator analysis unit calculates the comfort optimization index X1 according to the virtual reality helmet data, motion capture data and physiological monitoring data, and the calculation formula is:

[0015]

[0016] In the formula, Xl represents the comfort optimization index, E1, E2, E3, E4, z1, z2, z3, z4 represent the tightness comfort of the headband, weight comfort, heat comfort, humidity comfort, headband tightness discomfort, weight discomfort, heat discomfort and humidity discomfort when the helmet is worn during the virtual scenario, respectively; U1, U2, U3, i1, i2, i3 represent the number of stable heart rate, stable breathing, muscle relaxation, heart rate tension, rapid breathing and muscle tension during the virtual scenario, respectively; v1 and S1 represent the limb confusion data and limb stability data during the virtual scenario, respectively; a, b, c represent the weights of virtual reality helmet data, motion capture data and physiological monitoring data in the comfort optimization index, respectively.

[0017] Preferably, the motion analysis unit calculates the motion abnormality index value Ri according to the motion capture data and the physiological monitoring data, and the calculation formula is:

[0018]

[0019] In the formula, Ri represents the abnormal movement index value, U1, U2, U3, i1, i2, i3 represent the number of stable heart rate, stable breathing, muscle relaxation, heart rate tension, rapid breathing and muscle tension during the virtual scenario, respectively, and v1, S1, S2, S3, S4 represent the limb confusion data, limb stability data, acceleration data, gyroscope data and optical tracking capture data during the virtual scenario.

[0020] Preferably, the training progress tracking unit numbers the user's current training performance data and the user's performance data at the beginning of training according to the training progress tracking data characteristics, and the user's current training performance data is numbered as Y1, Y2, Y3, ... Y n , the performance data of the user at the beginning of training are numbered F1, F2, F3, ... F n .

[0021] Preferably, the user interaction unit calculates the training progress index Tv according to the training progress tracking data, and the calculation formula is:

[0022]

[0023] In the formula, Tv represents the training progress index, Y1, Y2, Y3, ...Y n Indicates the user's current training performance data, F1, F2, F3, ...F n Represents the user's performance data at the beginning of training.

[0024] Preferably, the virtual scenario online processing module adjusts parameters of the helmet wearing mode according to the comfort optimization index Xl. The helmet wearing mode is divided into a vent mode, a occluded mode and a semi-permeable vent mode according to its function. When the comfort optimization index Xl<0.85, the virtual scenario online processing module automatically switches the helmet mode to the vent mode. When the comfort optimization index Xl<0.95, the helmet mode is automatically switched to the semi-permeable vent mode. When the comfort optimization index Xl=1, the helmet mode is automatically switched to the vent mode.

[0025] Preferably, the virtual scenario online processing module adjusts or increases the positions of the camera and sensors according to the abnormal movement index value Ri and the training progress index Tv. When the abnormal movement index value Ri≥0.8, the virtual scenario online processing module automatically adjusts the camera's camera point at the limb movement position and adds 3 sensors. When the training progress index Tv≥0.95, the current rehabilitation training is maintained. When the training progress index Tv<0.95, one rehabilitation training is added on the original basis.

[0026] Compared with the prior art, the present invention provides a virtual scenario interactive cardiac rehabilitation assessment and training system, which has the following beneficial effects:

[0027] 1. The comfort optimization index X1 of the present invention evaluates the overall comfort of the user when using the virtual reality system by comprehensively analyzing the virtual reality helmet data (comfort parameters such as the tightness, weight, heat and humidity of the headband), motion capture data (number of limb confusions, number of stable times, etc.) and physiological monitoring data (heart rate, breathing, muscle status, etc.). The formula not only takes into account the physical properties of the helmet, but also takes into account the user's physiological response and motion capture data. These three characteristics change with the changes in the virtual environment and user needs. The comfort optimization index can adjust the use mode of the helmet in real time to maximize the comfort and experience of the user.

[0028] 2. The present invention calculates the abnormal motion index value Ri, which helps to monitor the physiological data of heart rate, breathing and muscle status in real time. Combined with the motion data, the formula can detect the abnormal physical state of the user in time, so that the motion analysis unit can automatically trigger the alarm mode and suspend training. At the same time, the virtual scene online processing module can automatically adjust the camera's camera point at the limb movement position according to the range of the abnormal motion index value Ri, thereby improving the motion capture degree while ensuring the safety of the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the structure of the present invention; DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] See also Figure 1 , a virtual scenario interactive cardiac rehabilitation assessment and training system, including a virtual scenario online collection module, a virtual scenario online analysis module and a virtual scenario online processing module;

[0032] The virtual scenario online collection module includes a virtual reality helmet unit, a motion capture unit, a physiological monitoring unit and a training progress tracking unit. The virtual reality helmet unit collects virtual reality helmet data through a built-in inertial measurement part, a camera and a sensor, and after numbering, it is connected to the virtual scenario online analysis module through a network. The motion capture unit collects motion capture data through an optical camera, an inertial measurement unit and a sensor device, and after numbering, it is connected to the virtual scenario online analysis module through a network. The physiological monitoring unit collects physiological monitoring data through multi-dimensional sensors and detection equipment, and after numbering, it is connected to the virtual scenario online analysis module through a network. The training progress tracking unit collects training progress tracking data through a camera and an online registration form, and numbers it. The virtual scenario online collection module is connected to the virtual scenario online analysis module through a network.

[0033] The virtual scenario online analysis module includes a virtual environment simulator analysis unit, a motion analysis unit and a user interaction unit. The virtual environment simulator analysis unit calculates the comfort optimization index Xl according to the virtual reality helmet data, the motion capture data and the physiological monitoring data. The motion analysis unit calculates the motion abnormality index value Ri according to the motion capture data and the physiological monitoring data. The user interaction unit calculates the training progress index Tv according to the training progress tracking data. The virtual scenario online analysis module and the virtual scenario online processing module are connected through a network.

[0034] The virtual scenario online processing module adjusts the parameters of the helmet wearing mode according to the comfort optimization index Xl, and adjusts the position of the camera and sensor according to the action abnormality index Ri and the training progress index Tv.

[0035] The virtual reality helmet unit numbers the headband tightness comfort, weight comfort, heat comfort, humidity comfort, headband inappropriate tightness, weight discomfort, heat discomfort and humidity discomfort when the helmet is worn during a virtual scenario according to the virtual reality helmet data characteristics. The headband tightness comfort, weight comfort, heat comfort, humidity comfort, headband inappropriate tightness, weight discomfort, heat discomfort and humidity discomfort when the helmet is worn during a virtual scenario are numbered E1, E2, E3, E4, z1, z2, z3, z4.

[0036] The motion capture unit numbers the number of limb confusions, limb stability, acceleration data, gyroscope data and optical tracking capture data during the virtual scene according to the characteristics of the motion capture data. The limb confusion data, limb stability data, acceleration data, gyroscope data and optical tracking capture data during the virtual scene are numbered v1, S1, S2, S3 and S4.

[0037] The physiological monitoring unit numbers the number of stable heart rate, stable breathing, muscle relaxation, heart rate tension, rapid breathing and muscle tension during the virtual scenario according to the characteristics of the physiological monitoring data. The number of stable heart rate, stable breathing, muscle relaxation, heart rate tension, rapid breathing and muscle tension during the virtual scenario are numbered U1, U2, U3, i1, i2, i3.

[0038] The virtual environment simulator analysis unit calculates the comfort optimization index Xl based on the virtual reality helmet data, motion capture data and physiological monitoring data. The calculation formula is:

[0039]

[0040] In the formula, Xl represents the comfort optimization index, E1, E2, E3, E4, z1, z2, z3, z4 represent the tightness comfort of the headband, weight comfort, heat comfort, humidity comfort, headband tightness discomfort, weight discomfort, heat discomfort and humidity discomfort when the helmet is worn during the virtual scenario, respectively; U1, U2, U3, i1, i2, i3 represent the number of stable heart rate, stable breathing, muscle relaxation, heart rate tension, rapid breathing and muscle tension during the virtual scenario, respectively; v1 and S1 represent the limb confusion data and limb stability data during the virtual scenario, respectively; a, b, c represent the weights of virtual reality helmet data, motion capture data and physiological monitoring data in the comfort optimization index, respectively.

[0041] The advantages are: The comfort optimization index Xl evaluates the user's overall comfort when using the virtual reality system by comprehensively analyzing the virtual reality helmet data (comfort parameters such as the tightness, weight, heat and humidity of the headband), motion capture data (number of limb confusions, number of stability, etc.) and physiological monitoring data (heart rate, breathing, muscle status, etc.). This formula not only takes into account the physical properties of the helmet, but also the user's physiological reactions and motion capture data. These three characteristics change with the changes in the virtual environment and user needs. The comfort optimization index can adjust the usage mode of the helmet in real time to maximize the comfort and experience of the user experience.

[0042] The motion analysis unit calculates the motion abnormality index value Ri based on the motion capture data and physiological monitoring data. The calculation formula is:

[0043]

[0044] In the formula, Ri represents the abnormal movement index value, U1, U2, U3, i1, i2, i3 represent the number of stable heart rate, stable breathing, muscle relaxation, heart rate tension, rapid breathing and muscle tension during the virtual scenario, respectively, and v1, S1, S2, S3, S4 represent the limb confusion data, limb stability data, acceleration data, gyroscope data and optical tracking capture data during the virtual scenario.

[0045] The advantages are: by calculating the abnormal motion index value Ri, it is helpful to monitor the physiological data of heart rate, breathing and muscle status in real time, and combined with the motion data, the formula can detect the abnormal physical state of the user in time, so that the motion analysis unit can automatically trigger the alarm mode and suspend training. At the same time, the virtual scenario online processing module can automatically adjust the camera's camera point at the limb movement position according to the range of the abnormal motion index value Ri, thereby improving the motion capture degree while ensuring the safety of the user.

[0046] The training progress tracking unit numbers the user's current training performance data and the user's performance data at the beginning of training according to the training progress tracking data characteristics. The user's current training performance data is numbered as Y1, Y2, Y3, ... Y n , the user's performance data at the beginning of training is numbered F1, F2, F3, ... F n .

[0047] The user interaction unit calculates the training progress index Tv according to the training progress tracking data, and the calculation formula is:

[0048]

[0049] In the formula, Tv represents the training progress index, Y1, Y2, Y3, ...Y nIndicates the user's current training performance data, F1, F2, F3, ...F n Represents the user's performance data at the beginning of training.

[0050] The advantage is that the calculation formula of the training progress index Tv helps to effectively capture subtle movement deviations by comparing the user's current training performance data with the performance data at the beginning of training, thereby helping the user to continuously optimize the training effect.

[0051] The virtual scenario online processing module adjusts the parameters of the helmet wearing mode according to the comfort optimization index Xl. The helmet wearing mode is divided into vent mode, occlusive mode and semi-permeable hole mode according to its function. When the comfort optimization index Xl<0.85, the virtual scenario online processing module automatically switches the helmet mode to the vent mode. When the comfort optimization index Xl<0.95, the helmet mode is automatically switched to the semi-permeable hole mode. When the comfort optimization index Xl=1, the helmet mode is automatically switched to the vent mode.

[0052] The virtual scenario online processing module adjusts or increases the position of the camera and sensors according to the abnormal movement index value Ri and the training progress index Tv. When the abnormal movement index value Ri≥0.8, the virtual scenario online processing module automatically adjusts the camera's camera point at the limb movement position and adds 3 sensors. When the training progress index Tv≥0.95, the current rehabilitation training is maintained. When the training progress index Tv<0.95, one rehabilitation training is added on the original basis.

[0053] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Virtual scenario interactive cardiac rehabilitation assessment and training system, characterized by: It includes a virtual scenario online collection module, a virtual scenario online analysis module and a virtual scenario online processing module; The virtual scenario online collection module includes a virtual reality helmet unit, a motion capture unit, a physiological monitoring unit and a training progress tracking unit. The virtual reality helmet unit collects virtual reality helmet data through a built-in inertial measurement part, a camera and a sensor, and after numbering, is connected to the virtual scenario online analysis module through a network. The motion capture unit collects motion capture data through an optical camera, an inertial measurement unit and a sensor device, and after numbering, is connected to the virtual scenario online analysis module through a network. The physiological monitoring unit collects physiological monitoring data through a multi-dimensional sensor and a detection device, and after numbering, is connected to the virtual scenario online analysis module through a network. The training progress tracking unit collects training progress tracking data through a camera and an online registration form, and numbers them. The virtual scenario online collection module is connected to the virtual scenario online analysis module through a network. The virtual scenario online analysis module includes a virtual environment simulator analysis unit, a motion analysis unit and a user interaction unit. The virtual environment simulator analysis unit calculates a comfort optimization index Xl according to virtual reality helmet data, motion capture data and physiological monitoring data. The motion analysis unit calculates a motion abnormality index Ri according to the motion capture data and physiological monitoring data. The user interaction unit calculates a training progress index Tv according to the training progress tracking data. The virtual scenario online analysis module and the virtual scenario online processing module are connected via a network. The virtual scenario online processing module adjusts the parameters of the helmet wearing mode according to the comfort optimization index Xl, and adjusts the positions of the camera and the sensor according to the abnormal movement index Ri and the training progress index Tv.

2. The virtual scenario interactive cardiac rehabilitation assessment and training system according to claim 1, characterized in that: The virtual reality helmet unit numbers the tightness of the headband, the weight comfort, the heat comfort, the humidity comfort, the inappropriate tightness of the headband, the weight discomfort, the heat discomfort and the humidity discomfort when the helmet is worn during a virtual scenario according to the virtual reality helmet data characteristics. The tightness of the headband, the weight comfort, the heat comfort, the humidity comfort, the inappropriate tightness of the headband, the weight discomfort, the heat discomfort and the humidity discomfort when the helmet is worn during the virtual scenario are numbered E1, E2, E3, E4, z1, z2, z3, z4.

3. The virtual scenario interactive cardiac rehabilitation assessment and training system according to claim 1, characterized in that: The motion capture unit numbers the number of limb confusions, limb stability, acceleration data, gyroscope data and optical tracking capture data during the virtual scene according to the characteristics of the motion capture data. The limb confusion data, limb stability data, acceleration data, gyroscope data and optical tracking capture data during the virtual scene are numbered as v1, S1, S2, S3 and S4.

4. The virtual scenario interactive cardiac rehabilitation assessment and training system according to claim 1, characterized in that: The physiological monitoring unit numbers the number of stable heart rate, stable breathing, muscle relaxation, heart rate tension, rapid breathing and muscle tension during the virtual scenario according to the characteristics of the physiological monitoring data. The number of stable heart rate, stable breathing, muscle relaxation, heart rate tension, rapid breathing and muscle tension during the virtual scenario are numbered U1, U2, U3, i1, i2, i3.

5. The virtual scenario interactive cardiac rehabilitation assessment and training system according to claim 4, characterized in that: The virtual environment simulator analysis unit calculates the comfort optimization index X1 according to the virtual reality helmet data, motion capture data and physiological monitoring data, and the calculation formula is: In the formula, Xl represents the comfort optimization index, E1, E2, E3, E4, z1, z2, z3, z4 represent the tightness comfort of the headband, weight comfort, heat comfort, humidity comfort, headband tightness discomfort, weight discomfort, heat discomfort and humidity discomfort when the helmet is worn during the virtual scenario, respectively; U1, U2, U3, i1, i2, i3 represent the number of stable heart rate, stable breathing, muscle relaxation, heart rate tension, rapid breathing and muscle tension during the virtual scenario, respectively; v1 and S1 represent the limb confusion data and limb stability data during the virtual scenario, respectively; a, b, c represent the weights of virtual reality helmet data, motion capture data and physiological monitoring data in the comfort optimization index, respectively.

6. The virtual scenario interactive cardiac rehabilitation assessment and training system according to claim 4, characterized in that: The motion analysis unit calculates the motion abnormality index value Ri according to the motion capture data and the physiological monitoring data, and the calculation formula is: In the formula, Ri represents the abnormal movement index value, U1, U2, U3, i1, i2, i3 represent the number of stable heart rate, stable breathing, muscle relaxation, heart rate tension, rapid breathing and muscle tension during the virtual scenario, respectively, and v1, S1, S2, S3, S4 represent the limb confusion data, limb stability data, acceleration data, gyroscope data and optical tracking capture data during the virtual scenario.

7. The virtual scenario interactive cardiac rehabilitation assessment and training system according to claim 1, characterized in that: The training progress tracking unit numbers the user's current training performance data and the user's performance data at the beginning of training according to the training progress tracking data characteristics, and the user's current training performance data is numbered as Y1, Y2, Y3, ... Y n , the performance data of the user at the beginning of training are numbered F1, F2, F3, ... F n .

8. The virtual scenario interactive cardiac rehabilitation assessment and training system according to claim 7, characterized in that: The user interaction unit calculates the training progress index Tv according to the training progress tracking data, and the calculation formula is: In the formula, Tv represents the training progress index, Y1, Y2, Y3, ...Y n Indicates the user's current training performance data, F1, F2, F3, ...F n Represents the user's performance data at the beginning of training.

9. The virtual scenario interactive cardiac rehabilitation assessment and training system according to claim 5, characterized in that: The virtual scenario online processing module adjusts the parameters of the helmet wearing mode according to the comfort optimization index Xl. The helmet wearing mode is divided into a vent mode, a occluded mode and a semi-permeable vent mode according to its function. When the comfort optimization index Xl<0.85, the virtual scenario online processing module automatically switches the helmet mode to the vent mode. When the comfort optimization index Xl<0.95, the helmet mode is automatically switched to the semi-permeable vent mode. When the comfort optimization index Xl=1, the helmet mode is automatically switched to the vent mode.

10. The virtual scenario interactive cardiac rehabilitation assessment and training system according to claim 8, characterized in that: The virtual scenario online processing module adjusts or increases the position of the camera and sensors according to the abnormal movement index value Ri and the training progress index Tv. When the abnormal movement index value Ri≥0.8, the virtual scenario online processing module automatically adjusts the camera's camera point at the limb movement position and adds 3 sensors. When the training progress index Tv≥0.95, the current rehabilitation training is maintained. When the training progress index Tv<0.95, one rehabilitation training is added on the original basis.

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