A fusion feedback processing device and method for lower limb rehabilitation training

Abstract

The present invention discloses a fusion feedback processing device and method for lower limb rehabilitation training. The device includes: an augmented reality module, a motion trajectory acquisition module, a training evaluation module, a feedback processing module, a lower limb rehabilitation training module, and a physiological information acquisition module. The motion trajectory acquisition module is used to collect a set of lower limb motion trajectory information when a user performs lower limb rehabilitation training using the lower limb rehabilitation training module. The training evaluation module is used to perform evaluation and discrimination processing to obtain an evaluation analysis result. The lower limb rehabilitation training module is used to collect a sequence of strength values. The feedback processing module is used to perform feedback calculation processing to obtain a set of fusion feedback output values. The present invention mainly solves the problem that current lower limb rehabilitation training equipment mainly focuses on strength training, lacks the utilization and exploration of human perception information, and results in limited training effect and efficiency.

Description

Technical Field

[0001] The present invention relates to the fields of exoskeleton robots and intelligent control, and particularly relates to a fusion feedback processing device and method for lower limb rehabilitation training. Background Art

[0002] For the recovery of patients with lower limb movement disorders, rehabilitation training is crucial. In traditional rehabilitation treatments, rehabilitation therapists usually conduct one-on-one rehabilitation treatments for patients in a manual manner. The treatment means, experience differences, subjective awareness, and fatigue level of rehabilitation therapists will directly affect the treatment effect. Traditional rehabilitation training has defects such as a shortage of rehabilitation physicians, single-function and high-cost rehabilitation equipment, long rehabilitation cycles, boring processes, poor initiative, and inability to conduct accurate rehabilitation status evaluations.

[0003] Among various types of currently applied lower limb rehabilitation training devices, they mainly focus on strength training and lack the utilization and exploration of human perception information, resulting in limited training effects and efficiency. Summary of the Invention

[0004] The present invention mainly solves the problem that current lower limb rehabilitation training devices mainly focus on strength training and lack the utilization and exploration of human perception information, resulting in limited training effects and efficiency. The present invention discloses a fusion feedback processing device and method for lower limb rehabilitation training.

[0005] In the first aspect of the embodiments of the present application, a fusion feedback processing device for lower limb rehabilitation training is disclosed, including: an augmented reality module, a motion trajectory acquisition module, a training evaluation module, a feedback processing module, a lower limb rehabilitation training module, and a physiological information acquisition module;

[0006] The augmented reality module is used to display training scenario information to the user through augmented reality means;

[0007] The motion trajectory acquisition module is connected to the training evaluation module and is used to collect a set of lower limb motion trajectory information of the user when performing lower limb rehabilitation training using the lower limb rehabilitation training module;

[0008] The training evaluation module is connected to the motion trajectory acquisition module and is used to perform evaluation and discrimination processing on the set of lower limb motion trajectory information and standard motion trajectory information to obtain an evaluation and analysis result;

[0009] The lower limb rehabilitation training module is connected to the training evaluation module and is used to perform rehabilitation training on the user's lower limbs and collect a sequence of force values exerted by the user during the rehabilitation training;

[0010] The feedback processing module, connected to the training and evaluation module and the physiological information acquisition module, is configured to perform feedback calculation processing on the force value sequence, the evaluation and analysis result, and the physiological information set to obtain a fused feedback output value set; the fused feedback output value set includes a force feedback value for adjusting the force exerted on the user by the lower limb rehabilitation training module during the user's lower limb rehabilitation training and a tactile feedback value for adjusting the amount of tactile stimulation on the user by the lower limb rehabilitation training module during the user's lower limb rehabilitation training.

[0011] The physiological information acquisition module is configured to acquire a set of physiological parameters of the user during the lower limb rehabilitation training; the set of physiological parameters includes a heart rate information sequence, a blood pressure information sequence, a blood oxygen saturation information sequence, and a body temperature information sequence.

[0012] The motion trajectory acquisition module is implemented by using an image acquisition and analysis sub-module or an accelerometer sensor disposed on the user's lower limb.

[0013] The lower limb motion trajectory information set includes a plurality of lower limb motion trajectory information sequences.

[0014] The accelerometer sensor is configured to acquire the lower limb motion trajectory of the user during the lower limb rehabilitation training using the lower limb rehabilitation training module, and construct a lower limb motion trajectory information set by using all the acquired lower limb motion trajectories.

[0015] The image acquisition and analysis sub-module includes an image acquisition unit and an image analysis unit; the image acquisition unit is configured to acquire an image of the user during the lower limb rehabilitation training using the lower limb rehabilitation training module; the image analysis unit is configured to extract the lower limb part from the image of the user during the lower limb rehabilitation training using the lower limb rehabilitation training module to obtain the lower limb motion trajectory, and construct a lower limb motion trajectory information set by using all the acquired lower limb motion trajectories.

[0016] The lower limb rehabilitation training module includes a fixed seat, a foot pedal, and a rotating connecting arm; the rotating connecting arm is configured to connect the foot pedal to the fixed seat; a mechanical sensor is disposed on the foot pedal, and the mechanical sensor is configured to measure the force value sequence exerted by the user during the lower limb rehabilitation training using the lower limb rehabilitation training module; a plurality of soft needle-like structures for generating tactile stimulation are disposed on the surface of the foot pedal; a motor for controlling the force exerted on the user by the foot pedal is disposed on the rotating connecting arm.

[0017] In a second aspect of the embodiments of the present invention, a fused feedback processing method for lower limb rehabilitation training is disclosed, which is implemented by using the fused feedback processing device for lower limb rehabilitation training, and includes:

[0018] S1. Use the augmented reality module to display training scenario information to the user; use the lower limb rehabilitation training module to perform rehabilitation training on the user's lower limbs, and collect a sequence of force values exerted by the user during the rehabilitation training.

[0019] S2. Use the motion trajectory acquisition module to collect a set of lower limb motion trajectory information of the user during the lower limb rehabilitation training using the lower limb rehabilitation training module; use the physiological information acquisition module to collect a set of physiological parameters of the user during the lower limb rehabilitation training.

[0020] S3. Use the training evaluation module to perform evaluation and discrimination processing on the set of lower limb motion trajectory information and the standard motion trajectory information to obtain an evaluation and analysis result.

[0021] S4. Use the feedback processing module to perform feedback calculation processing on the sequence of force values, the evaluation and analysis result, and the set of physiological information to obtain a set of fusion feedback output values; the set of fusion feedback output values includes a force feedback value for adjusting the force exerted by the lower limb rehabilitation training module on the user during the user's lower limb rehabilitation training and a tactile feedback value for adjusting the tactile stimulation amount of the lower limb rehabilitation training module on the user during the user's lower limb rehabilitation training.

[0022] The performing evaluation and discrimination processing on the set of lower limb motion trajectory information and the standard motion trajectory information to obtain an evaluation and analysis result includes:

[0023] S31. Use the set of lower limb motion trajectory information to construct a lower limb motion matrix; the row vectors of the lower limb motion matrix are the lower limb motion trajectory information.

[0024] S32. Using the standard motion trajectory information as row vectors, copy the row vectors in the column direction to obtain a standard motion matrix; the dimension of the standard motion matrix is the same as that of the lower limb motion matrix.

[0025] S33. Subtract the lower limb motion matrix from the standard motion matrix to obtain a difference matrix.

[0026] S34. Perform eigenvalue decomposition processing on the difference matrix to obtain an eigenvalue sequence.

[0027] S35. Perform fitting calculation processing on the eigenvalue sequence to obtain a weight factor vector.

[0028] S36. Perform evaluation calculation processing on the eigenvalue sequence, the weight factor vector, the lower limb motion matrix, and the standard motion matrix to obtain an evaluation and analysis result.

[0029] The expression of the evaluation calculation processing is:

[0030]

[0031]

[0032] Among them, λ i is the i-th eigenvalue in the eigenvalue sequence and also the eigenvalue corresponding to the i-th row vector of the difference matrix. A ij and B ij are the elements of the i-th row and j-th column of the lower limb motion matrix and the standard motion matrix respectively. M and N are the row dimension and column dimension of the lower limb motion matrix respectively. v is the evaluation and analysis result. h i is the i-th evaluation sub-result value, and k i is the i-th element of the weight factor vector.

[0033] Performing feedback calculation and processing on the force value sequence, evaluation and analysis result, and physiological information set to obtain a set of fusion feedback output values, including:

[0034] Performing force feedback calculation and processing on the force value sequence and evaluation and analysis result to obtain a force feedback value for adjusting the force exerted on the user by the lower limb rehabilitation training module when the user is performing lower limb rehabilitation training;

[0035] Performing tactile feedback calculation and processing on the evaluation and analysis result and physiological information set to obtain a tactile feedback value for adjusting the tactile stimulation amount on the user by the lower limb rehabilitation training module when the user is performing lower limb rehabilitation training.

[0036] Performing force feedback calculation and processing on the force value sequence and evaluation and analysis result to obtain a force feedback value for adjusting the force exerted on the user by the lower limb rehabilitation training module when the user is performing lower limb rehabilitation training, including:

[0037] Obtaining a standard force value;

[0038] Performing force feedback calculation and processing on the standard force value, force value sequence, and evaluation and analysis result to obtain a force feedback value for adjusting the force exerted on the user by the lower limb rehabilitation training module when the user is performing lower limb rehabilitation training;

[0039] The expression of the force feedback calculation and processing is:

[0040]

[0041] Among them, L2() and L3() are the second-order Legendre polynomial and the third-order Legendre polynomial respectively. ρ is a preset multiplication factor. f i is the i-th element of the force value sequence. NF is the length of the force value sequence. f0 is the standard force value. np is the force feedback value. v is the evaluation and analysis result.

[0042] Performing tactile feedback calculation and processing on the evaluation analysis result and the set of physiological information to obtain a tactile feedback value for adjusting the amount of tactile stimulation on the user during lower limb rehabilitation training by the lower limb rehabilitation training module, including:

[0043] Obtaining standard physiological information; the standard physiological information includes a heart rate standard value, a blood pressure standard value, a blood oxygen saturation standard value, and a body temperature standard value;

[0044] Representing the standard physiological information as a standard physiological vector; the first to fourth elements of the standard physiological vector are the heart rate standard value, the blood pressure standard value, the blood oxygen saturation standard value, and the body temperature standard value respectively;

[0045] Representing the set of physiological information as a physiological information matrix; the first to fourth row vectors of the physiological information matrix are the heart rate information sequence, the blood pressure information sequence, the blood oxygen saturation information sequence, and the body temperature information sequence respectively;

[0046] Performing tactile feedback calculation and processing on the evaluation analysis result, the standard physiological vector, and the physiological information matrix to obtain a tactile feedback value for adjusting the amount of tactile stimulation on the user during lower limb rehabilitation training by the lower limb rehabilitation training module.

[0047] The expression of the tactile feedback calculation and processing is:

[0048]

[0049] where pe is the tactile feedback value, v is the evaluation analysis result, T i () represents the i-th order polynomial of the first kind of Chebyshev polynomial, p i is the i-th feedback sub-item, A ij represents the element in the i-th row and j-th column of the physiological information matrix, z i represents the i-th element of the standard physiological vector, exp represents the exponential operation of the constant e, and n is the length of the standard physiological vector.

[0050] The beneficial effects of the present invention are:

[0051] The present invention mainly solves the problem that the current lower limb rehabilitation training equipment mainly focuses on strength training, lacks the utilization and exploration of human perception information, and results in limited training effects and efficiency.

[0052] Based on the training effect of the user during lower limb rehabilitation training and the collected physiological parameters, the present invention performs tactile feedback calculation and processing on the evaluation analysis results and the set of physiological information to obtain a tactile feedback value for adjusting the amount of tactile stimulation of the lower limb rehabilitation training module on the user during lower limb rehabilitation training. At the same time, the feedback of two types of training variables is realized, improving the training efficiency.

[0053] When calculating the force feedback value, the present invention performs force feedback calculation and processing on the standard force value, the force value sequence, and the evaluation analysis results to obtain a force feedback value for adjusting the force exerted by the lower limb rehabilitation training module on the user during lower limb rehabilitation training. A model for force feedback calculation and processing is specifically established, improving the accuracy and effectiveness of force feedback calculation.

[0054] When calculating the tactile feedback value, the present invention performs tactile feedback calculation and processing on the evaluation analysis results, the standard physiological vector, and the physiological information matrix to obtain a tactile feedback value for adjusting the amount of tactile stimulation of the lower limb rehabilitation training module on the user during lower limb rehabilitation training. By comprehensively considering the evaluation analysis results and the standard physiological vector, the calculation accuracy of the tactile feedback value is improved. Description of the Drawings

[0055] Figure 1 It is a composition diagram of the device of the present invention;

[0056] Figure 2 It is an implementation flowchart of the method of the present invention. Detailed Embodiments

[0057] To better understand the content of the present invention, an embodiment is given here.

[0058] Figure 1 It is a composition diagram of the device of the present invention; Figure 2 It is an implementation flowchart of the method of the present invention.

[0059] Aiming at the problem that the current lower limb rehabilitation training equipment mainly focuses on strength training, lacks the utilization and exploration of human perception information, and results in limited training effects and efficiency, the present invention discloses a fusion feedback processing device and method for lower limb rehabilitation training.

[0060] In the first aspect of the embodiments of the present application, a fusion feedback processing device for lower limb rehabilitation training is disclosed, including: an augmented reality module, a motion trajectory acquisition module, a training evaluation module, a feedback processing module, a lower limb rehabilitation training module, and a physiological information acquisition module;

[0061] The augmented reality module is used to display training scenario information to the user through augmented reality means;

[0062] The motion trajectory acquisition module is connected to the training and evaluation module and is used to collect a set of lower limb motion trajectory information of the user during lower limb rehabilitation training using the lower limb rehabilitation training module;

[0063] The training and evaluation module is connected to the motion trajectory acquisition module and is used to perform evaluation and discrimination processing on the set of lower limb motion trajectory information and the standard motion trajectory information to obtain an evaluation and analysis result;

[0064] The lower limb rehabilitation training module is connected to the training and evaluation module and is used to perform rehabilitation training on the user's lower limbs and collect a sequence of force values exerted by the user during the rehabilitation training;

[0065] The feedback processing module is connected to the training and evaluation module and the physiological information acquisition module and is used to perform feedback calculation processing on the sequence of force values, the evaluation and analysis result, and the set of physiological information to obtain a set of fusion feedback output values; the set of fusion feedback output values includes a force feedback value for adjusting the force exerted on the user by the lower limb rehabilitation training module during the user's lower limb rehabilitation training and a tactile feedback value for adjusting the tactile stimulation amount of the lower limb rehabilitation training module on the user during the user's lower limb rehabilitation training;

[0066] The physiological information acquisition module is used to collect a set of physiological parameters of the user during lower limb rehabilitation training; the set of physiological parameters includes a heart rate information sequence, a blood pressure information sequence, a blood oxygen saturation information sequence, and a body temperature information sequence;

[0067] The augmented reality module can be implemented through AR glasses or an AR helmet.

[0068] The motion trajectory acquisition module is implemented by using an image acquisition and analysis sub-module or an accelerometer sensor disposed on the user's lower limbs;

[0069] The set of lower limb motion trajectory information includes a number of lower limb motion trajectory information sequences;

[0070] The accelerometer sensor is used to collect the lower limb motion trajectory of the user during lower limb rehabilitation training using the lower limb rehabilitation training module, and a set of lower limb motion trajectory information is constructed by using all the collected lower limb motion trajectories;

[0071] The image acquisition and analysis sub-module includes an image acquisition unit and an image analysis unit; the image acquisition unit is used to collect an image of the user during lower limb rehabilitation training using the lower limb rehabilitation training module; the image analysis unit is used to extract the lower limb part from the image of the user during lower limb rehabilitation training using the lower limb rehabilitation training module to obtain the lower limb motion trajectory, and a set of lower limb motion trajectory information is constructed by using all the collected lower limb motion trajectories;

[0072] The lower limb rehabilitation training module includes a fixed seat, a foot pedal, and a rotating connecting arm; the rotating connecting arm is used to connect the foot pedal to the fixed seat; a force sensor is provided on the foot pedal, and the force sensor is used to measure the sequence of force values applied by the user during lower limb rehabilitation training using the lower limb rehabilitation training module; on the surface of the foot pedal, a plurality of soft needle-like structures for generating tactile stimulation are provided; a motor for applying a force value to the user by the foot pedal is provided on the rotating connecting arm.

[0073] The training evaluation module performs evaluation and discrimination processing on the lower limb movement trajectory information set and the standard movement trajectory information to obtain an evaluation and analysis result, including:

[0074] Using the lower limb movement trajectory information set, a lower limb movement matrix is constructed; the row vectors of the lower limb movement matrix are the lower limb movement trajectory information;

[0075] Taking the standard movement trajectory information as the row vector, the row vector is copied in the column direction to obtain a standard movement matrix; the dimension of the standard movement matrix is the same as the dimension of the lower limb movement matrix;

[0076] Subtraction processing is performed on the lower limb movement matrix and the standard movement matrix to obtain a difference matrix;

[0077] Eigenvalue decomposition processing is performed on the difference matrix to obtain an eigenvalue sequence;

[0078] Fitting calculation processing is performed on the eigenvalue sequence to obtain a weight factor vector;

[0079] Evaluation calculation processing is performed on the eigenvalue sequence, the weight factor vector, the lower limb movement matrix, and the standard movement matrix to obtain an evaluation and analysis result;

[0080] The expression of the evaluation calculation processing is:

[0081]

[0082] where λ i is the i-th eigenvalue in the eigenvalue sequence and is also the eigenvalue corresponding to the i-th row vector of the difference matrix, A ij and B ij are the elements of the i-th row and j-th column of the lower limb movement matrix and the standard movement matrix respectively, M and N are the row dimension and column dimension of the lower limb movement matrix respectively, v is the evaluation and analysis result, h i is the i-th evaluation sub-result value, and k i is the i-th element of the weight factor vector;

[0083] The feedback processing module performs feedback calculation processing on the force value sequence, evaluation and analysis results, and physiological information set to obtain a set of fusion feedback output values, including:

[0084] Performs force feedback calculation processing on the force value sequence and evaluation and analysis results to obtain a force feedback value used to adjust the force exerted on the user by the lower limb rehabilitation training module when the user performs lower limb rehabilitation training;

[0085] Performs tactile feedback calculation processing on the evaluation and analysis results and physiological information set to obtain a tactile feedback value used to adjust the tactile stimulation amount on the user by the lower limb rehabilitation training module when the user performs lower limb rehabilitation training;

[0086] The performing force feedback calculation processing on the force value sequence and evaluation and analysis results to obtain a force feedback value used to adjust the force exerted on the user by the lower limb rehabilitation training module when the user performs lower limb rehabilitation training includes:

[0087] Obtain a standard force value;

[0088] Performs force feedback calculation processing on the standard force value, force value sequence, and evaluation and analysis results to obtain a force feedback value used to adjust the force exerted on the user by the lower limb rehabilitation training module when the user performs lower limb rehabilitation training;

[0089] The expression of the force feedback calculation processing is:

[0090]

[0091] where L2() and L3() are the second-order Legendre polynomial and the third-order Legendre polynomial respectively, ρ is a preset multiplication factor, and its value can be 0.3, f i is the i-th element of the force value sequence, NF is the length of the force value sequence, f0 is the standard force value, np is the force feedback value, and v is the evaluation and analysis result.

[0092] The performing tactile feedback calculation processing on the evaluation and analysis results and physiological information set to obtain a tactile feedback value used to adjust the tactile stimulation amount on the user by the lower limb rehabilitation training module when the user performs lower limb rehabilitation training includes:

[0093] Obtain standard physiological information; the standard physiological information includes a heart rate standard value, a blood pressure standard value, a blood oxygen saturation standard value, and a body temperature standard value;

[0094] Represent the standard physiological information as a standard physiological vector; the first element to the fourth element of the standard physiological vector are the heart rate standard value, the blood pressure standard value, the blood oxygen saturation standard value, and the body temperature standard value respectively;

[0095] The physiological information set is represented as a physiological information matrix; the first row vector to the fourth row vector of the physiological information matrix are a heart rate information sequence, a blood pressure information sequence, a blood oxygen saturation information sequence, and a body temperature information sequence, respectively;

[0096] Performing tactile feedback calculation processing on the evaluation and analysis results, the standard physiological vector and the physiological information matrix to obtain a tactile feedback value for adjusting the tactile stimulation amount of the lower limb rehabilitation training module to the user when the user is performing lower limb rehabilitation training;

[0097] The expression for the tactile feedback calculation process is:

[0098]

[0099]

[0100] Wherein, pe is the tactile feedback value, v is the evaluation analysis result, T i () represents the i-th order polynomial of the first kind of Chebyshev polynomial, p i is the i-th feedback item, A ij represents the element of the ith row and jth column of the physiological information matrix, z i represents the i-th element of the standard physiological vector, exp represents the exponential operation of the constant e, and n is the length of the standard physiological vector.

[0101] The extraction of the lower limb parts can be achieved by using human body key point recognition.

[0102] The human body key point recognition can be implemented by using SURF feature point detection or corner point detection algorithm, or OpenPose algorithm in OpenCV.

[0103] The force exerted by the lower limb rehabilitation training module on the user when the user performs lower limb rehabilitation training may be a mechanical value applied to the user by a motor-controlled foot pedal.

[0104] The lower limb rehabilitation training module includes a fixed seat, a first rotating connecting arm, a second rotating connecting arm and a foot pedal which are rotatably connected in sequence; the second rotating connecting arm and the fixed seat are rotatably arranged at both ends of the first rotating connecting arm, and the foot pedal is rotatably connected to one end of the second rotating connecting arm away from the first rotating connecting arm; a driving component is used to drive the first rotating connecting arm and the second rotating connecting arm to rotate, and the driving component includes a motor mounted on the fixed seat, and a synchronous belt connected to the rotating shafts at both ends of the first rotating connecting arm, and one end of the first rotating connecting arm is fastened to the driving shaft of the motor; when the motor drives the first rotating connecting arm and the second rotating connecting arm to be linked, the foot pedal is in a horizontal movement state.

[0105] The amount of tactile stimulation determines the setting density and radius of the soft needle-like structures on the surface of the pedal.

[0106] Performing a fitting calculation process on the eigenvalue sequence to obtain a weight factor vector, including:

[0107] Performing a linear fitting process on the elements and element serial number values of the eigenvalue sequence to obtain an optimal uniform approximation polynomial;

[0108] Using the element serial number values of the eigenvalue sequence as input values and performing a calculation process using the optimal uniform approximation polynomial to obtain a weight factor vector.

[0109] The linear fitting process uses the element serial number value Ix of the characteristic value sequence as the known independent variable and the element value of the eigenvalue sequence as the known dependent variable, constructs an approximation curve to be approximated using the known independent variable and the known dependent variable, and performs curve fitting on the approximation curve to be approximated using the function approximation method to obtain an optimal uniform approximation polynomial f(Ix).

[0110] The eigenvalue sequence is denoted as I a , I a = [λ1, λ2, …, λ N1 , where N1 is the number of elements included in the eigenvalue sequence; the curve fitting of the approximation curve to be approximated using the function approximation method can adopt the optimal uniform linear approximation method. The optimal uniform approximation polynomial f(Ix) has the following expression:

[0111] f(Ix) = α P1 (Ix) P1 + α P1-1 (Ix) P1-1 + … + α2(Ix) 2 + α1(Ix) + α0,

[0112] where P1 is the order of the optimal uniform approximation polynomial f(Ix), and α0, α1, α2, …, α P1 are the coefficients of the optimal uniform approximation polynomial f(Ix);

[0113] In the second aspect of the embodiments of the present invention, a fusion feedback processing method for lower limb rehabilitation training is disclosed, which is implemented by using the fusion feedback processing device for lower limb rehabilitation training, including:

[0114] S1. Using the augmented reality module to display training scenario information to the user; using the lower limb rehabilitation training module to perform rehabilitation training on the user's lower limbs, and collecting a sequence of force values exerted by the user during the rehabilitation training;

[0115] S2. Using the motion trajectory acquisition module, collect the lower limb motion trajectory information set of the user during lower limb rehabilitation training using the lower limb rehabilitation training module; using the physiological information acquisition module, collect the physiological parameter set of the user during lower limb rehabilitation training.

[0116] S3. Using the training evaluation module, perform evaluation and discrimination processing on the lower limb motion trajectory information set and the standard motion trajectory information to obtain an evaluation and analysis result.

[0117] S4. Using the feedback processing module, perform feedback calculation processing on the force value sequence, evaluation and analysis result, and physiological information set to obtain a fusion feedback output value set; the fusion feedback output value set includes a force feedback value for adjusting the force exerted on the user by the lower limb rehabilitation training module during the user's lower limb rehabilitation training and a tactile feedback value for adjusting the tactile stimulation amount on the user by the lower limb rehabilitation training module during the user's lower limb rehabilitation training.

[0118] The performing evaluation and discrimination processing on the lower limb motion trajectory information set and the standard motion trajectory information to obtain an evaluation and analysis result includes:

[0119] Using the lower limb motion trajectory information set, construct a lower limb motion matrix; the row vectors of the lower limb motion matrix are the lower limb motion trajectory information.

[0120] Using the standard motion trajectory information as row vectors, copy the row vectors in the column direction to obtain a standard motion matrix; the dimension of the standard motion matrix is the same as that of the lower limb motion matrix.

[0121] Perform subtraction processing on the lower limb motion matrix and the standard motion matrix to obtain a difference matrix.

[0122] Perform eigenvalue decomposition processing on the difference matrix to obtain an eigenvalue sequence.

[0123] Perform fitting calculation processing on the eigenvalue sequence to obtain a weight factor vector.

[0124] Perform evaluation calculation processing on the eigenvalue sequence, weight factor vector, lower limb motion matrix, and standard motion matrix to obtain an evaluation and analysis result.

[0125] The expression of the evaluation calculation processing is:

[0126]

[0127]

[0128] where λ iis the i-th eigenvalue in the eigenvalue sequence and also the eigenvalue corresponding to the i-th row vector of the difference matrix, A ij and B ij are the elements of the i-th row and j-th column of the lower limb movement matrix and the standard movement matrix respectively, M and N are the row dimension and column dimension of the lower limb movement matrix respectively, ν is the evaluation analysis result, h i is the i-th evaluation sub-result value, k i is the i-th element of the weight factor vector;

[0129] Performing feedback calculation processing on the force value sequence, evaluation analysis result, and physiological information set to obtain a set of fusion feedback output values, including:

[0130] Performing force feedback calculation processing on the force value sequence and evaluation analysis result to obtain a force feedback value used to adjust the force exerted on the user by the lower limb rehabilitation training module when the user performs lower limb rehabilitation training;

[0131] Performing tactile feedback calculation processing on the evaluation analysis result and physiological information set to obtain a tactile feedback value used to adjust the tactile stimulation amount on the user by the lower limb rehabilitation training module when the user performs lower limb rehabilitation training;

[0132] Performing force feedback calculation processing on the force value sequence and evaluation analysis result to obtain a force feedback value used to adjust the force exerted on the user by the lower limb rehabilitation training module when the user performs lower limb rehabilitation training, including:

[0133] Obtaining a standard force value;

[0134] Performing force feedback calculation processing on the standard force value, force value sequence, and evaluation analysis result to obtain a force feedback value used to adjust the force exerted on the user by the lower limb rehabilitation training module when the user performs lower limb rehabilitation training;

[0135] The expression of the force feedback calculation processing is:

[0136]

[0137] where L2() and L3() are the second-order Legendre polynomial and the third-order Legendre polynomial respectively, ρ is a preset multiplication factor, and its value can be 0.3, f i is the i-th element of the force value sequence, NF is the length of the force value sequence, f0 is the standard force value, np is the force feedback value, and ν is the evaluation analysis result.

[0138] Performing tactile feedback calculation processing on the evaluation analysis result and physiological information set to obtain a tactile feedback value used to adjust the tactile stimulation amount on the user by the lower limb rehabilitation training module when the user performs lower limb rehabilitation training, including:

[0139] Obtain standard physiological information; the standard physiological information includes standard heart rate value, standard blood pressure value, standard blood oxygen saturation value, and standard body temperature value;

[0140] Represent the standard physiological information as a standard physiological vector; the first to fourth elements of the standard physiological vector are the standard heart rate value, standard blood pressure value, standard blood oxygen saturation value, and standard body temperature value respectively;

[0141] Represent the set of physiological information as a physiological information matrix; the first to fourth row vectors of the physiological information matrix are the heart rate information sequence, blood pressure information sequence, blood oxygen saturation information sequence, and body temperature information sequence respectively;

[0142] Perform tactile feedback calculation processing on the evaluation analysis result, standard physiological vector, and physiological information matrix to obtain a tactile feedback value for adjusting the amount of tactile stimulation on the user during the user's lower limb rehabilitation training by the lower limb rehabilitation training module;

[0143] The expression of the tactile feedback calculation processing is;

[0144]

[0145]

[0146] where pe is the tactile feedback value, ν is the evaluation analysis result, T i () represents the i-th order polynomial of the first kind of Chebyshev polynomial, p i is the i-th feedback sub-item, A ij represents the element in the i-th row and j-th column of the physiological information matrix, z i represents the i-th element of the standard physiological vector, exp represents the exponential operation of the constant e, and n is the length of the standard physiological vector.

[0147] The fitting calculation processing of the eigenvalue sequence to obtain the weight factor vector includes:

[0148] Perform linear fitting processing on the elements and element serial number values of the eigenvalue sequence to obtain the best uniform approximation polynomial;

[0149] Use the element serial number values of the eigenvalue sequence as input values and perform calculation processing using the best uniform approximation polynomial to obtain the weight factor vector.

[0150] For the linear fitting process, the element serial number value Ix of the characteristic value sequence is used as the known independent variable, and the element value of the eigenvalue sequence is used as the known dependent variable. A curve to be approximated is constructed using the known independent variable and the known dependent variable, and the curve fitting of the curve to be approximated is performed using the function approximation method to obtain the best uniform approximation polynomial f(Ix).

[0151] The eigenvalue sequence is denoted as I a , I a = [λ1, λ2, …, λ N1 , where N1 is the number of elements included in the eigenvalue sequence; for the curve fitting of the curve to be approximated using the function approximation method, the best uniform linear approximation method can be adopted. The expression of the best uniform approximation polynomial f(Ix) is as follows:

[0152] f(Ix) = α P1 (Ix) P1 + α P1-1 (Ix) P1-1 + … + α2(Ix) 2 + α1(Ix) + α0,

[0153] where P1 is the order of the best uniform approximation polynomial f(Ix), and α0, α1, α2, …, α P1 are the coefficients of the best uniform approximation polynomial f(Ix).

[0154] The above are only the embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A fusion feedback processing method for lower limb rehabilitation training, characterized in that, It is implemented by using a fusion feedback processing device for lower limb rehabilitation training. The fusion feedback processing device for lower limb rehabilitation training includes: an augmented reality module, a motion trajectory acquisition module, a training evaluation module, a feedback processing module, a lower limb rehabilitation training module, and a physiological information acquisition module; the lower limb rehabilitation training module includes a fixed seat, a foot pedal, and a rotating connecting arm; the method includes: S1. Use the augmented reality module to display training scenario information to the user; use the lower limb rehabilitation training module to perform rehabilitation training on the user's lower limbs, and collect a sequence of force values exerted by the user during the rehabilitation training. S2. Use the motion trajectory acquisition module to collect a set of lower limb motion trajectory information of the user during the lower limb rehabilitation training using the lower limb rehabilitation training module; use the physiological information acquisition module to collect a set of physiological parameters of the user during the lower limb rehabilitation training. S3. Use the training evaluation module to perform an evaluation and discrimination process on the set of lower limb motion trajectory information and the standard motion trajectory information to obtain an evaluation and analysis result. S4. Use the feedback processing module to perform a feedback calculation process on the sequence of force values, the evaluation and analysis result, and the set of physiological information to obtain a set of fusion feedback output values; the set of fusion feedback output values includes a force feedback value for adjusting the force exerted on the user by the lower limb rehabilitation training module during the user's lower limb rehabilitation training and a tactile feedback value for adjusting the tactile stimulation amount on the user by the lower limb rehabilitation training module during the user's lower limb rehabilitation training; on the surface of the foot pedal, there are provided a number of soft needle-like structures for generating tactile stimulation; on the rotating connecting arm, there is a motor for controlling the force exerted on the user by the foot pedal. The evaluation and discrimination process on the set of lower limb motion trajectory information and the standard motion trajectory information to obtain an evaluation and analysis result includes: S31. Use the set of lower limb motion trajectory information to construct a lower limb motion matrix; the row vectors of the lower limb motion matrix are the lower limb motion trajectory information. S32. Use the standard motion trajectory information as row vectors and copy the row vectors in the column direction to obtain a standard motion matrix; the dimension of the standard motion matrix is the same as that of the lower limb motion matrix. S33. Subtract the lower limb motion matrix from the standard motion matrix to obtain a difference matrix. S34. Perform an eigen-decomposition process on the difference matrix to obtain a sequence of eigenvalues. S35. Perform a fitting calculation process on the sequence of eigenvalues to obtain a weight factor vector. S36. Perform an evaluation calculation process on the sequence of eigenvalues, the weight factor vector, the lower limb motion matrix, and the standard motion matrix to obtain an evaluation and analysis result. The expression of the evaluation calculation process is: Among them, λ i is the i-th eigenvalue in the eigenvalue sequence and also the eigenvalue corresponding to the i-th row vector of the difference matrix. A ij and B ij are the elements in the i-th row and j-th column of the lower limb motion matrix and the standard motion matrix respectively. M and N are the row dimension and column dimension of the lower limb motion matrix respectively. v is the evaluation and analysis result. h i is the i-th evaluation sub-result value. k i is the i-th element of the weight factor vector.

2. The fusion feedback processing method for lower limb rehabilitation training according to claim 1, wherein The feedback calculation process on the sequence of force values, the evaluation and analysis result, and the set of physiological information to obtain a set of fusion feedback output values includes: Perform a force feedback calculation process on the sequence of force values and the evaluation and analysis result to obtain a force feedback value for adjusting the force exerted on the user by the lower limb rehabilitation training module during the user's lower limb rehabilitation training. Perform tactile feedback calculation and processing on the evaluation analysis result and the physiological information set to obtain a tactile feedback value for adjusting the tactile stimulation amount of the lower limb rehabilitation training module on the user during the user's lower limb rehabilitation training.

3. The fusion feedback processing method for lower limb rehabilitation training according to claim 2, characterized in that, The force feedback calculation and processing of the force value sequence and the evaluation analysis result to obtain a force feedback value for adjusting the force exerted by the lower limb rehabilitation training module on the user during the user's lower limb rehabilitation training includes: Obtain a standard force value; Perform force feedback calculation and processing on the standard force value, the force value sequence, and the evaluation analysis result to obtain a force feedback value for adjusting the force exerted by the lower limb rehabilitation training module on the user during the user's lower limb rehabilitation training; The expression of the force feedback calculation and processing is: Among them, and L3(v) are the second-order Legendre polynomial and the third-order Legendre polynomial respectively, ρ is a preset multiplication factor, f i is the i-th element of the strength value sequence, NF is the length of the strength value sequence, f0 is the standard strength value, np is the force feedback value, and v is the evaluation and analysis result.

4. The fusion feedback processing method for lower limb rehabilitation training according to claim 2, wherein, The tactile feedback calculation and processing of the evaluation analysis result and the physiological information set to obtain a tactile feedback value for adjusting the tactile stimulation amount of the lower limb rehabilitation training module on the user during the user's lower limb rehabilitation training includes: Obtain standard physiological information; the standard physiological information includes a heart rate standard value, a blood pressure standard value, a blood oxygen saturation standard value, and a body temperature standard value; Represent the standard physiological information as a standard physiological vector; the first to fourth elements of the standard physiological vector are the heart rate standard value, the blood pressure standard value, the blood oxygen saturation standard value, and the body temperature standard value, respectively; Represent the physiological information set as a physiological information matrix; the first to fourth row vectors of the physiological information matrix are the heart rate information sequence, the blood pressure information sequence, the blood oxygen saturation information sequence, and the body temperature information sequence, respectively; Perform tactile feedback calculation and processing on the evaluation analysis result, the standard physiological vector, and the physiological information matrix to obtain a tactile feedback value for adjusting the tactile stimulation amount of the lower limb rehabilitation training module on the user during the user's lower limb rehabilitation training.

5. The fusion feedback processing method for lower limb rehabilitation training according to claim 4, characterized in that, The expression of the tactile feedback calculation and processing is; Among them, pe is the tactile feedback value, v is the evaluation and analysis result, T i (p i v) represents the i-th order polynomial of the first kind of Chebyshev polynomial, p i is the i-th feedback sub-item, A ij represents the element in the i-th row and j-th column of the physiological information matrix, z i represents the i-th element of the standard physiological vector, exp represents the exponential operation of the constant e, and n is the length of the standard physiological vector.

Images