Multi-mode signal evaluation method and device for idiopathic scoliosis rehabilitation

Abstract

The invention discloses a multi-mode signal evaluation method and device for idiopathic scoliosis rehabilitation. The multi-mode signal evaluation method for idiopathic scoliosis rehabilitation comprises the following steps: acquiring muscle activation degree information of a person to be evaluated in a motion cycle; acquiring the actual time of the to-be-assessed person reaching the action peak value in the motion period; acquiring displacement information of the to-be-assessed person in the motion period; acquiring motion amplitude information of the to-be-assessed person in the motion period; acquiring a multi-modal rehabilitation confidence model; inputting the muscle activation degree information, the actual time for reaching the action peak value, the displacement information and the motion amplitude information into the multi-mode rehabilitation confidence model, so as to obtain rehabilitation confidence information. According to the method, multi-modal information is used, so that the effect of rehabilitation actions can be evaluated more comprehensively; the factors influencing the rehabilitation action effect are represented through the characteristic parameters, mapping is established between the mathematical model and the clinical indexes, and the rehabilitation action can be evaluated more comprehensively, scientifically and accurately.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a multimodal signal evaluation method for idiopathic scoliosis rehabilitation and a multimodal signal evaluation device for idiopathic scoliosis rehabilitation. Background Art

[0002] Idiopathic scoliosis (IS) is a complex three-dimensional deformity characterized by lateral curvature of one or more spinal segments and vertebral rotation. It can be categorized by age into infantile (0-3 years), adolescent (4-9 years), adolescent (10-18 years), and adult (≥18 years). IS not only affects patients' physical and psychological well-being but, in severe cases, can lead to chest, lumbar, and back pain, impaired cardiopulmonary function, and nerve compression. The Cobb angle is the angle formed by the intersection of a vertical line drawn from the upper endplate of the most tilted vertebra at the apex of the spinal curve and a vertical line drawn from the lower endplate of the oppositely tilted vertebra. Clinically, it is primarily measured using static radiographs; dynamic assessment methods are lacking. When the Cobb angle is less than 45°, conservative treatment with a brace and guided exercises is recommended under the supervision of a rehabilitation therapist. However, when the Cobb angle exceeds 45°, surgical intervention should be considered. Postoperative rehabilitation training is also required to help restore functional function.

[0003] While research has explored the causes, progression, and impacts of IS, research on rehabilitation for IS patients is still relatively underdeveloped. Clinical rehabilitation assessments for IS patients primarily rely on the experience of physicians and rehabilitation therapists. Furthermore, most existing studies use a single modality for assessment, typically measuring IS patients' movements through spatial position information or surface electromyography (EMG). However, a single modality cannot fully and accurately reflect the patient's condition.

[0004] Therefore, it is desired to have a technical solution to overcome or at least alleviate at least one of the above-mentioned deficiencies of the prior art.

[0005] Application Contents

[0006] The purpose of the present application is to provide a multimodal signal evaluation method for idiopathic scoliosis rehabilitation to overcome or at least alleviate at least one of the above-mentioned defects of the prior art.

[0007] To achieve the above objectives, the present application provides a multimodal signal evaluation method for idiopathic scoliosis rehabilitation, the multimodal signal evaluation method for idiopathic scoliosis rehabilitation comprising:

[0008] Obtain information on the muscle activation of the person being evaluated during the exercise cycle;

[0009] Obtain the actual time when the person being evaluated reaches the peak of movement during the movement cycle;

[0010] Obtaining the displacement information of the person to be evaluated during the movement cycle;

[0011] Obtaining information on the movement amplitude of the person to be evaluated during the movement cycle;

[0012] Obtaining a multimodal rehabilitation confidence model;

[0013] The muscle activation information, the actual time to reach the peak of the movement, the displacement information and the movement amplitude information are input into the multimodal rehabilitation confidence model to obtain rehabilitation confidence information.

[0014] Optionally, the multimodal signal evaluation method for idiopathic scoliosis rehabilitation further comprises:

[0015] Obtain video stream information transmitted by the 3D motion capture system;

[0016] Extract the spatial position change information of each marker point in the video stream information;

[0017] The actual time, displacement information and movement amplitude information of the person to be evaluated reaching the movement peak during the movement cycle are obtained based on the spatial position change information of each marking point.

[0018] Optionally, obtaining the actual time of reaching the peak of the movement, displacement information, and movement amplitude information of the person to be evaluated during the movement cycle according to the spatial position change information of each marking point includes:

[0019] Segmenting the spatial position change information of each marker point respectively, dividing at least one active segment data and at least one rest segment data with the peak value of the spatial change of each marker point as the center;

[0020] Using the vertex of each active segment data as the origin, the data of each active segment are aligned in time and space, thereby obtaining the aligned data of each segment;

[0021] Use the Gaussian function to fit each set of aligned data to obtain a Gaussian curve for each set of aligned data;

[0022] The actual time of reaching the peak of the movement, the displacement information and the movement amplitude information of the person to be evaluated during the movement cycle are respectively obtained according to the Gaussian curve.

[0023] Optionally, the multimodal signal evaluation method for idiopathic scoliosis rehabilitation further comprises:

[0024] Acquiring myoelectric information of the person to be evaluated acquired by the myoelectric acquisition system during the movement cycle;

[0025] The muscle activation information of the person to be evaluated during the exercise cycle is obtained according to the electromyographic information.

[0026] Optionally, the multimodal rehabilitation confidence model is as follows:

[0027] in,

[0028] W1 is the weight of the information on the influence of muscle activation on the action confidence, W2 is the weight of the information on the influence of symmetry on the rehabilitation confidence coefficient, W3 is the weight of the information on the influence of stability on the action confidence coefficient, W4 is the weight of the information on the influence of motion range on the rehabilitation confidence coefficient, y1 is the information on the influence of muscle activation on the action confidence, y2 is the information on the influence of symmetry on the rehabilitation confidence coefficient, y3 is the information on the influence of stability on the action confidence coefficient, y4 is the information on the influence of motion range on the rehabilitation confidence coefficient, M k is the parameter of the main force-generating muscle. If the main force-generating muscle is consistent with the muscle to be trained in the rehabilitation movement, then M k =1, if not consistent, then M k =0.

[0029] Optionally, the influence information of the muscle activation degree on the action confidence is obtained by the following formula:

[0030] y1=K1a;where,

[0031] K is set to 3, y1 is the influence information of muscle activation on action confidence, and a is muscle activation.

[0032] Optionally, the information on the influence of the symmetry on the recovery confidence coefficient is obtained by the following formula:

[0033] in,

[0034] y2 is the information about the impact of symmetry on the rehabilitation confidence coefficient, t is the movement period, and b is the actual time to reach the peak of the movement.

[0035] Optionally, the influence information of the stability on the action confidence coefficient is obtained by the following formula:

[0036] in,

[0037] is the acceleration The standard deviation of the acceleration is the second-order derivative of the displacement information.

[0038] Optionally, the influence information of the motion range on the rehabilitation confidence coefficient is obtained by the following formula:

[0039] y4=K2R rom ;in,

[0040] y4 is the influence of the range of motion on the confidence coefficient of rehabilitation, K2 is a constant, which is 1, R rom is the range of motion.

[0041] The present application also provides a multimodal signal evaluation device for idiopathic scoliosis rehabilitation, the multimodal signal evaluation device for idiopathic scoliosis rehabilitation comprising:

[0042] A muscle activation information acquisition module, wherein the muscle activation information acquisition module is used to obtain muscle activation information of the person to be evaluated during an exercise cycle;

[0043] A module for obtaining the time when the action peak is actually reached, wherein the module is used to obtain the time when the person to be evaluated actually reaches the action peak during the exercise cycle;

[0044] A displacement information acquisition module, wherein the displacement information acquisition module is used to acquire the displacement information of the person to be evaluated during a movement cycle;

[0045] A motion amplitude information acquisition module, the motion amplitude information acquisition module is used to obtain motion amplitude information of the person to be evaluated during a motion cycle;

[0046] A model acquisition module, wherein the model acquisition module is used to acquire a multimodal rehabilitation confidence model;

[0047] A rehabilitation confidence information acquisition module is used to input the muscle activation information, the actual time to reach the movement peak, the displacement information and the movement amplitude information into the multimodal rehabilitation confidence model, thereby acquiring rehabilitation confidence information.

[0048] The multimodal signal evaluation method for idiopathic scoliosis rehabilitation of the present application uses multimodal information to more comprehensively evaluate the effectiveness of rehabilitation movements; by characterizing the factors affecting the effectiveness of rehabilitation movements through characteristic parameters, mapping the mathematical model with clinical indicators, it can more comprehensively, scientifically and accurately evaluate rehabilitation movements, and provide feedback and guidance on rehabilitation movements through the results of the rehabilitation confidence coefficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 4 is a flow chart of a multimodal signal evaluation method for idiopathic scoliosis rehabilitation according to an embodiment of the present application.

[0050] Figure 2 It is a structural diagram of an electronic device in an embodiment of the present application.

[0051] Figure 3 yes Figure 1 Schematic diagram of a multimodal rehabilitation confidence model for a multimodal signal evaluation method for idiopathic scoliosis rehabilitation is shown. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the implementation of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below in conjunction with the drawings in the embodiments of this application. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of this application, not all of the embodiments. The embodiments described below with reference to the drawings are exemplary and are intended to be used to explain this application, and should not be understood as limitations on this application. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. The embodiments of this application are described in detail below in conjunction with the drawings.

[0053] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be understood as limiting the scope of protection of this application.

[0054] like Figure 1 The multimodal signal assessment approach for idiopathic scoliosis rehabilitation presented includes:

[0055] Step 1: Obtain the muscle activation information of the person to be evaluated during the exercise cycle;

[0056] Step 2: Obtain the actual time when the person to be evaluated reaches the peak of the movement during the movement cycle;

[0057] Step 3: Obtain the displacement information of each marker point of the person to be evaluated during the movement cycle;

[0058] Step 4: Obtain the movement amplitude information of the person to be evaluated during the movement cycle;

[0059] Step 5: Obtain a multimodal rehabilitation confidence model;

[0060] Step 6: Input the muscle activation information, the actual time to reach the peak of the movement, the displacement information, and the movement amplitude information into the multimodal rehabilitation confidence model to obtain rehabilitation confidence information.

[0061] The multimodal signal evaluation method for idiopathic scoliosis rehabilitation of the present application uses multimodal information to more comprehensively evaluate the effectiveness of rehabilitation movements; by characterizing the factors affecting the effectiveness of rehabilitation movements through characteristic parameters, mapping the mathematical model with clinical indicators, it can more comprehensively, scientifically and accurately evaluate rehabilitation movements, and provide feedback and guidance on rehabilitation movements through the results of the rehabilitation confidence coefficient.

[0062] In this embodiment, the multimodal signal evaluation method for idiopathic scoliosis rehabilitation further includes:

[0063] Obtain video stream information transmitted by the 3D motion capture system;

[0064] Extract the spatial position change information of each marker point in the video stream information;

[0065] The actual time, displacement information and movement amplitude information of the person to be evaluated reaching the movement peak during the movement cycle are obtained based on the spatial position change information of each marking point.

[0066] In this embodiment, the method of obtaining the actual time of reaching the peak of the movement, displacement information, and movement amplitude information of the person to be evaluated during the movement cycle according to the spatial position change information of each marker point includes:

[0067] Segmenting the spatial position change information of each marker point respectively, dividing at least one active segment data and at least one rest segment data with the peak value of the spatial change of each marker point as the center;

[0068] Using the vertex of each active segment data as the origin, the data of each active segment are aligned in time and space, thereby obtaining the aligned data of each segment;

[0069] Use the Gaussian function to fit each set of aligned data to obtain a Gaussian curve for each set of aligned data;

[0070] The actual time of reaching the peak of the movement, the displacement information and the movement amplitude information of the person to be evaluated during the movement cycle are respectively obtained according to the Gaussian curve.

[0071] In this embodiment, the three-dimensional motion capture system used in this application is the VICON system with a sampling frequency of 100 Hz. The spatial position information changes of each marker point are extracted to calculate the spatial position information changes of each part of the experimental subject when performing rehabilitation movements. Matlab is used to preprocess the data. First, the data is segmented, and 50 frames of data are taken before and after with the peak as the center, which are regarded as active segment data, and the rest are regarded as rest segment data. Next, the vertex of each segment of data is used as the origin for time and space alignment to compare the relative changes in the position of each group of data.

[0072] In this embodiment, a Gaussian function is used to fit each set of data to obtain a Gaussian curve for each set of data. As shown in the figure. The Gaussian function expression is Where A is the amplitude, b is the time to peak, c is the standard deviation, and x is the displacement. From this formula, the amplitude of movement is G(b)-G(0), and substituting it into the equation yields

[0073] In this embodiment, the multimodal signal evaluation method for idiopathic scoliosis rehabilitation further includes:

[0074] Acquiring myoelectric information of the person to be evaluated acquired by the myoelectric acquisition system during the movement cycle;

[0075] The muscle activation information of the person to be evaluated during the exercise cycle is obtained according to the electromyographic information.

[0076] Specifically, the electromyographic signal acquisition system used in this application is Datalink, with a sampling frequency of 2000HZ, a sampling range of ±6mv, and a sampling accuracy of 1μV, which is used to collect the surface electromyographic signals of the subjects during the experiment. The collected electromyographic signals are preprocessed, and their muscle activation degree is calculated to express the main force of their muscles. First, the surface electromyographic signals are filtered by a bandpass fourth-order Butterworth filter (20Hz). In the Hill muscle model, e(t) is converted into a muscle activation signal a(t). Since there is a time delay between muscle contraction and muscle force, a second-order discrete linear system is used to convert e(t) into an intermediate value u(t), which is in the form of a recursive filter and can be expressed as:

[0077] u(t)=θ[e(td)u(t-1)u(t-2)]

[0078] Where θ represents [αβ1β2] T ; α represents the gain coefficient; β1β2 represents the recursive coefficient; d represents the time delay. Since each individual has different muscle activation during exercise, the muscle activation degree a is normalized to obtain To assess the main muscle force position during rehabilitation exercises.

[0079] See also Figure 3 In this embodiment, the multimodal rehabilitation confidence model is as follows:

[0080] in,

[0081] W1 is the weight of the information on the influence of muscle activation on the action confidence, W2 is the weight of the information on the influence of symmetry on the rehabilitation confidence coefficient, W3 is the weight of the information on the influence of stability on the action confidence coefficient, W4 is the weight of the information on the influence of motion range on the rehabilitation confidence coefficient, y1 is the information on the influence of muscle activation on the action confidence, y2 is the information on the influence of symmetry on the rehabilitation confidence coefficient, y3 is the information on the influence of stability on the action confidence coefficient, y4 is the information on the influence of motion range on the rehabilitation confidence coefficient, M k is the parameter of the main force-generating muscle. If the main force-generating muscle is consistent with the muscle to be trained in the rehabilitation movement, then M k =1, if not consistent, then M k =0.

[0082] In this embodiment, the influence information of the muscle activation degree on the action confidence is obtained by the following formula:

[0083] y1=K1a;where,

[0084] K1 is set to 3, y1 is the influence of muscle activation on action confidence, and a is muscle activation.

[0085] Specifically, it is believed that the influence of muscle activation on muscle force is linearly correlated, so the influence of muscle activation on action confidence is as shown in the above formula, where K1 is 3.

[0086] According to the calculation results of muscle activation in different channels, the maximum value is taken as the main force muscle, and the parameter M is proposed. k If the main force-generating muscles are consistent with the muscles that need to be trained in the rehabilitation exercise, then M k =1, if not consistent, then M k =0.

[0087] In this embodiment, the information on the influence of the symmetry on the recovery confidence coefficient is obtained by the following formula:

[0088] in,

[0089] y2 is the information about the impact of symmetry on the rehabilitation confidence coefficient, t is the movement period, b is the actual time to reach the peak of the movement, and e is a natural constant.

[0090] Specifically, symmetry is related to b in the Gaussian function and the motion period t. The closer b is to t / 2, the better the symmetry. Assuming that when the difference between b and t / 2 reaches t / 40, the symmetry is considered to be extremely low. Combined with the Gaussian fitting function, the above information on the impact of symmetry on the rehabilitation confidence coefficient is obtained.

[0091] In this embodiment, the influence of the stability on the action confidence coefficient is obtained by the following formula:

[0092] in,

[0093] is the acceleration The standard deviation of the acceleration is the second-order derivative of the displacement information.

[0094] In this embodiment, the stability and the standard deviation of acceleration Related, The smaller the value, the better the stability. Assuming that the experimental subject reaches the optimal situation when the standard deviation reaches 0.02 during exercise, the above information formula on the influence of stability on the confidence coefficient of action is obtained.

[0095] In this embodiment, the influence of the range of motion on the rehabilitation confidence coefficient is obtained by the following formula:

[0096] y4=K2R rom ;in,

[0097] y4 is the influence of the range of motion on the confidence coefficient of rehabilitation, K2 is a constant, which is 1, R rom is the range of motion.

[0098] In this embodiment, using the Gaussian fitting equation, the range of motion (ROM) is G(b)-G(0), and substituting it into the equation yields Regarding the influence of range of motion on the confidence coefficient of rehabilitation, the maximum range of motion M is proposed with reference to the maximum muscle contraction MVC. rom . Furthermore, the parameter R is proposed rom For ROM and M rom The ratio range is [0, 1], and R is expressed as a linear function. rom Impact on recovery confidence coefficient y4=K2R rom , K2 is a constant, set to 1.

[0099] In this embodiment, by extracting the features of the spatial position information and muscle information of the person to be evaluated during the rehabilitation movement, combining the changes in the features with the rehabilitation movement itself, a multimodal evaluation strategy is obtained, and the rehabilitation confidence coefficient is calculated through this strategy to evaluate the subject's rehabilitation movement score.

[0100] The present application also provides a multimodal signal evaluation device for idiopathic scoliosis rehabilitation, which includes a muscle activation information acquisition module, an actual time acquisition module for reaching the peak of the movement, a displacement information acquisition module, a movement amplitude information acquisition module, a model acquisition module, and a rehabilitation confidence information acquisition module, wherein:

[0101] The muscle activation information acquisition module is used to obtain the muscle activation information of the person to be evaluated during the exercise cycle;

[0102] The module for obtaining the time of actually reaching the peak of the movement is used to obtain the time of actually reaching the peak of the movement of the person to be evaluated during the movement cycle;

[0103] The displacement information acquisition module is used to obtain the displacement information of each marked point of the person to be evaluated during the movement cycle;

[0104] The motion amplitude information acquisition module is used to obtain the motion amplitude information of the person to be evaluated during the motion cycle;

[0105] The model acquisition module is used to obtain the multimodal rehabilitation confidence model;

[0106] The rehabilitation confidence information acquisition module is used to input the muscle activation information, the actual time to reach the action peak, the displacement information and the movement amplitude information into the multimodal rehabilitation confidence model, thereby acquiring rehabilitation confidence information.

[0107] It should be noted that the above explanations of the method embodiment are also applicable to the device of this embodiment and will not be repeated here.

[0108] The present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, the multimodal signal evaluation method for idiopathic scoliosis rehabilitation as described above is implemented.

[0109] The present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the above-mentioned multimodal signal evaluation method for idiopathic scoliosis rehabilitation.

[0110] Figure 2 This is an exemplary structural diagram of an electronic device capable of implementing the multimodal signal evaluation method for idiopathic scoliosis rehabilitation provided according to one embodiment of the present application.

[0111] like Figure 2As shown, the electronic device includes an input device 501, an input interface 502, a central processing unit 503, a memory 504, an output interface 505, and an output device 506. The input interface 502, the central processing unit 503, the memory 504, and the output interface 505 are interconnected via a bus 507. The input device 501 and the output device 506 are connected to the bus 507 via the input interface 502 and the output interface 505, respectively, and are then connected to other components of the electronic device. Specifically, the input device 504 receives input information from the outside and transmits the input information to the central processing unit 503 via the input interface 502; the central processing unit 503 processes the input information based on the computer-executable instructions stored in the memory 504 to generate output information, temporarily or permanently stores the output information in the memory 504, and then transmits the output information to the output device 506 via the output interface 505; the output device 506 outputs the output information to the outside of the electronic device for use by the user.

[0112] That is to say, Figure 2 The electronic device shown may also be implemented as comprising: a memory storing computer executable instructions; and one or more processors, which can implement the combination of the computer executable instructions when executing the computer executable instructions. Figure 1 A multimodal signal assessment method for idiopathic scoliosis rehabilitation is described.

[0113] In one embodiment, Figure 2 The electronic device shown can be implemented to include: a memory 504 configured to store executable program code; and one or more processors 503 configured to run the executable program code stored in the memory 504 to execute the multimodal signal evaluation method for idiopathic scoliosis rehabilitation in the above-mentioned embodiment.

[0114] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0115] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0116] Computer-readable media include permanent and non-permanent, removable and non-removable media, and media can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), data versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0117] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0118] In addition, it is obvious that the word "comprising" does not exclude other units or steps. Multiple units, modules or devices recited in the device claims can also be implemented by one unit or the entire device through software or hardware.

[0119] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and a part of the module, program segment or code includes one or more executable instructions for realizing the prescribed logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes identified in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or overall flow chart can be implemented using a dedicated hardware-based system that performs the prescribed function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0120] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] In addition, it is obvious that the word "comprising" does not exclude other units or steps. Multiple units, modules or devices recited in the device claims can also be implemented by one unit or the entire device through software or hardware.

[0122] Although the present application is disclosed as above with reference to preferred embodiments, it is not intended to limit the present application. Any person skilled in the art may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

[0123] Finally, it should be pointed out that the above embodiments are intended only to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they may modify the technical solutions described in the above embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A multimodal signal evaluation method for idiopathic scoliosis rehabilitation, characterized in that: The multimodal signal evaluation method for idiopathic scoliosis rehabilitation includes: Obtain information on the muscle activation of the person being evaluated during the exercise cycle; Obtain the actual time when the person being evaluated reaches the peak of movement during the movement cycle; Obtain the displacement information of each marker point of the person to be evaluated during the movement cycle; Obtaining information on the movement amplitude of the person to be evaluated during the movement cycle; Obtaining a multimodal rehabilitation confidence model; The muscle activation information, the actual time to reach the peak of the movement, the displacement information and the movement amplitude information are input into the multimodal rehabilitation confidence model to obtain rehabilitation confidence information.

2. The multimodal signal evaluation method for idiopathic scoliosis rehabilitation according to claim 1, wherein: The multimodal signal evaluation method for idiopathic scoliosis rehabilitation further comprises: Obtain video stream information transmitted by the 3D motion capture system; Extract the spatial position change information of each marker point in the video stream information; The actual time, displacement information and movement amplitude information of the person to be evaluated reaching the peak of the movement during the movement cycle are obtained based on the spatial position change information of each marking point.

3. The multimodal signal evaluation method for idiopathic scoliosis rehabilitation according to claim 2, wherein: The method of obtaining the actual time of reaching the peak of the movement, displacement information, and movement amplitude information of the person to be evaluated during the movement cycle according to the spatial position change information of each mark point includes: Segmenting the spatial position change information of each marker point respectively, and dividing at least one active segment data and at least one rest segment data with the peak value of the spatial change of each marker point as the center; Using the vertex of each active segment data as the origin, the data of each active segment are aligned in time and space, thereby obtaining the aligned data of each segment; Use the Gaussian function to fit each set of aligned data to obtain a Gaussian curve for each set of aligned data; The actual time of reaching the peak of the movement, displacement information and movement amplitude information of the person to be evaluated during the movement cycle are respectively obtained according to the Gaussian curve.

4. The multimodal signal evaluation method for idiopathic scoliosis rehabilitation according to claim 3, wherein: The multimodal signal evaluation method for idiopathic scoliosis rehabilitation further comprises: Acquiring myoelectric information of the person to be evaluated acquired by the myoelectric acquisition system during the movement cycle; The muscle activation information of the person to be evaluated during the exercise cycle is obtained according to the electromyographic information.

5. The multimodal signal evaluation method for idiopathic scoliosis rehabilitation according to claim 4, wherein: The multimodal rehabilitation confidence model is as follows: in, W1 is the weight of the information on the influence of muscle activation on the action confidence, W2 is the weight of the information on the influence of symmetry on the rehabilitation confidence coefficient, W3 is the weight of the information on the influence of stability on the action confidence coefficient, W4 is the weight of the information on the influence of motion range on the rehabilitation confidence coefficient, y1 is the information on the influence of muscle activation on the action confidence, y2 is the information on the influence of symmetry on the rehabilitation confidence coefficient, y3 is the information on the influence of stability on the action confidence coefficient, y4 is the information on the influence of motion range on the rehabilitation confidence coefficient, M k is the parameter of the main force-generating muscle. If the main force-generating muscle is consistent with the muscle to be trained in the rehabilitation movement, then M k =1, if not consistent, then M k =0.

6. The multimodal signal evaluation method for idiopathic scoliosis rehabilitation according to claim 5, wherein: The influence of muscle activation on action confidence is obtained by the following formula: y1=K1a; in, K1 is set to 3, y1 is the influence of muscle activation on action confidence, and a is muscle activation.

7. The multimodal signal evaluation method for idiopathic scoliosis rehabilitation according to claim 6, wherein: The influence of the symmetry on the recovery confidence coefficient is obtained by the following formula: in, y2 is the information about the impact of symmetry on the rehabilitation confidence coefficient, t is the movement period, b is the actual time to reach the peak of the movement, and e is a natural constant.

8. The multimodal signal evaluation method for idiopathic scoliosis rehabilitation according to claim 7, wherein: The influence of the stability on the action confidence coefficient is obtained by the following formula: in, is the acceleration The standard deviation of the acceleration is the second-order derivative of the displacement information.

9. The multimodal signal evaluation method for idiopathic scoliosis rehabilitation according to claim 8, wherein: The influence of the range of motion on the rehabilitation confidence coefficient is obtained by the following formula: y4=K2R rom ; in, y4 is the influence of the range of motion on the confidence coefficient of rehabilitation, K2 is a constant, which is 1, R rom is the range of motion.

10. A multimodal signal evaluation device for idiopathic scoliosis rehabilitation, characterized in that: The multimodal signal evaluation device for idiopathic scoliosis rehabilitation comprises: A muscle activation information acquisition module, wherein the muscle activation information acquisition module is used to obtain muscle activation information of the person to be evaluated during an exercise cycle; A module for obtaining the time when the action peak is actually reached, wherein the module is used to obtain the time when the person to be evaluated actually reaches the action peak during the exercise cycle; A displacement information acquisition module, which is used to acquire the displacement information of each marked point of the person to be evaluated during the movement cycle; A motion amplitude information acquisition module, the motion amplitude information acquisition module is used to obtain motion amplitude information of the person to be evaluated during a motion cycle; A model acquisition module, wherein the model acquisition module is used to acquire a multimodal rehabilitation confidence model; A rehabilitation confidence information acquisition module is used to input the muscle activation information, the actual time to reach the movement peak, the displacement information and the movement amplitude information into the multimodal rehabilitation confidence model, thereby acquiring rehabilitation confidence information.

Images