Tactile information feedback processing device and method for lower limb rehabilitation training

Abstract

The invention discloses a tactile information feedback processing device and method for lower limb rehabilitation training. The device comprises a display module, a lower limb action acquisition module, a training evaluation module, a feedback processing module, a lower limb rehabilitation training module and a muscle information acquisition module. The lower limb action acquisition module is used for acquiring a key point spatial position matrix set of a user in the process of performing human body lower limb muscle strength training by using the lower limb rehabilitation training module; the training evaluation module is used for performing joint evaluation processing on the strength value sequence and the key point spatial position matrix set to obtain a training effect evaluation value sequence; the lower limb rehabilitation training module is used for performing rehabilitation training on the lower limbs of the user; the feedback processing module is used for performing feedback calculation processing to obtain a feedback output value; the muscle information acquisition module is used for acquiring a muscle information set of a user.

Description

Technical Field

[0001] The present invention relates to the field of exoskeleton robots and intelligent control, and in particular to a tactile information feedback processing device and method for lower limb rehabilitation training. Background Art

[0002] As the aging of our society intensifies, the number of patients with lower limb motor dysfunction caused by various diseases including stroke is increasing. In addition, the number of patients with nerve or limb damage due to work-related injuries, traffic accidents, diseases, etc. has also increased significantly.

[0003] For the recovery of patients with lower limb movement disorders, rehabilitation training is crucial because they cannot walk. The traditional rehabilitation process based on rehabilitation physicians has the defects of insufficient personnel, high cost, long rehabilitation cycle, boring process, poor initiative, inability to accurately evaluate the rehabilitation status, and inability to provide feedback.

[0004] Currently, various types of lower limb rehabilitation training equipment mainly provide strength training for the user's lower limbs, lack the exploration and utilization of other somatosensory characteristics, and have the problem of a single training scenario that cannot be expanded, resulting in limited training effects and efficiency. Summary of the invention

[0005] The present invention mainly solves the problem that various types of lower limb rehabilitation training equipment mainly perform strength training on the user's lower limbs, lack the exploration and utilization of other somatosensory characteristics, and the training scenarios are single and cannot be expanded, resulting in limited training effects and efficiency. The present invention discloses a tactile information feedback processing device and method for lower limb rehabilitation training.

[0006] In a first aspect of the embodiment of the present application, a tactile information feedback processing device for lower limb rehabilitation training is disclosed, comprising: a display module, a lower limb motion acquisition module, a training evaluation module, a feedback processing module, a lower limb rehabilitation training module, and a muscle information acquisition module;

[0007] The display module is used to display training scene information;

[0008] The lower limb motion acquisition module is connected to the training evaluation module and is used to acquire a key point spatial position matrix set of the user during the lower limb muscle strength training of the human body using the lower limb rehabilitation training module; the key point spatial position matrix set includes a key point spatial position matrix;

[0009] The training evaluation module is used to perform joint evaluation processing on the force value sequence and the key point spatial position matrix set to obtain a training effect evaluation value sequence;

[0010] 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 ​​applied by the user during the rehabilitation training;

[0011] The feedback processing module is connected to the training evaluation module and the muscle information acquisition module, and is used to perform feedback calculation processing on the strength value sequence, the training effect evaluation value sequence, and the muscle information set to obtain a feedback output value; the feedback output value is used to adjust the tactile stimulation amount of the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training;

[0012] The muscle information collection module is used to collect the user's muscle information set; the muscle information set includes body fat percentage, total body water value, total protein value, total inorganic salt value, right lower muscle weight, left lower muscle weight and extracellular water ratio value.

[0013] The lower limb motion acquisition module is used to acquire real-time images of training scenes, identify key points of the human body in the real-time images of training scenes, and obtain a set of key point spatial position matrices; the set of key point spatial position matrices includes the key point spatial position matrix at each data acquisition moment; the row vectors of the key point spatial position matrix are the position coordinates of each key point of the lower limbs of the human body.

[0014] 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 mechanical sensor is provided on the foot pedal, and the mechanical sensor is used to measure the force value sequence applied by the user when performing lower limb rehabilitation training using the lower limb rehabilitation training module; the surface of the foot pedal is provided with a plurality of soft needle-like structures for generating tactile stimulation.

[0015] In a second aspect of the embodiment of the present application, a tactile information feedback processing method for lower limb rehabilitation training is disclosed, which is implemented by using the tactile information feedback processing device for lower limb rehabilitation training, including:

[0016] S1, using the display module to display training scene information; using the lower limb rehabilitation training module to perform rehabilitation training on the user's lower limbs, and collecting a sequence of force values ​​applied by the user during the rehabilitation training;

[0017] S2, using the lower limb motion acquisition module to acquire a set of key point spatial position matrices of the user during the lower limb rehabilitation training process using the lower limb rehabilitation training module; using the muscle information acquisition module to acquire a set of muscle information of the user;

[0018] S3, using the training evaluation module to jointly evaluate the force value sequence and the key point spatial position matrix set to obtain a training effect evaluation value sequence;

[0019] S4, using the feedback processing module, performing feedback calculation processing on the strength value sequence, the training effect evaluation value sequence, and the muscle information set to obtain a feedback output value.

[0020] The joint evaluation process of the force value sequence and the key point spatial position matrix set to obtain the training effect evaluation value sequence includes:

[0021] S31, performing time alignment processing on the force value sequence and the key point spatial position matrix set to obtain an aligned force value sequence and key point spatial position matrix set;

[0022] S32, obtaining a key point standard position vector and a standard force value f; taking the key point standard position vector as a row vector, and copying it in the column direction to obtain a key point standard position matrix having the same dimension as the key point spatial position matrix;

[0023] S33, for each data collection moment, extracting the force value f and the key point spatial position matrix at the data collection moment from the aligned force value sequence and key point spatial position matrix set;

[0024] S34, performing difference calculation processing on the key point spatial position matrix and the key point standard position matrix at the time of data collection to obtain a position evaluation sub-value;

[0025] S35, performing joint difference calculation processing on the strength value and the position evaluation sub-value at the data collection moment to obtain a training effect evaluation value at the data collection moment;

[0026] The expression for the joint difference calculation process is:

[0027]

[0028] Wherein, H2 is the training effect evaluation value at the time of data collection, and exp represents the exponential operation of the constant e;

[0029] S36, constructing a training effect evaluation value sequence using the training effect evaluation values ​​at all data collection moments.

[0030] The expression for the difference calculation process is:

[0031]

[0032] Among them, M and N are the row dimension and column dimension of the key point spatial position matrix, respectively, and δ jis the evaluation item value of the jth column, A ij and B ij are the elements of the i-th row and j-th column of the key point spatial position matrix and the key point standard position matrix respectively, H1 is the position evaluation subvalue, ρ and τ are preset constant values.

[0033] The feedback calculation process is performed on the strength value sequence, the training effect evaluation value sequence, and the muscle information set to obtain a feedback output value, including:

[0034] Performing a first feedback calculation process on the strength value sequence and the training effect evaluation value sequence to obtain an intermediate feedback amount;

[0035] A second feedback calculation process is performed on the intermediate feedback amount, the training effect evaluation value sequence and the muscle information set to obtain a feedback output value.

[0036] The expression of the first feedback calculation process is:

[0037]

[0038] Among them, H3 is the intermediate feedback amount, N0 is the length of the training effect evaluation value sequence, H 2,i is the i-th element of the training effect evaluation value sequence, H 2,max is the maximum value element of the training effect evaluation value sequence, f i is the i-th element of the strength value sequence.

[0039] The performing a second feedback calculation process on the intermediate feedback amount, the training effect evaluation value sequence and the muscle information set to obtain a feedback output value includes:

[0040] Obtaining a standard muscle information set; the standard muscle information set includes a standard value of each muscle index;

[0041] Performing difference evaluation processing on the muscle information set and the standard muscle information set to obtain a muscle evaluation result value je;

[0042] Feedback calculation processing is performed on the intermediate feedback amount, the training effect evaluation value sequence and the muscle evaluation result value to obtain a feedback output value ne.

[0043] The expression of the feedback calculation process is:

[0044]

[0045] Among them, L1(), L2(), L3() represent the first-order Laguerre polynomial, the second-order Laguerre polynomial, and the third-order Laguerre polynomial respectively, and κ1 and κ2 represent the preset first weighting coefficient and the second weighting coefficient respectively.

[0046] The beneficial effects of the present invention are:

[0047] The present invention solves the problem that various types of current lower limb rehabilitation training equipment mainly perform strength training on the user's lower limbs, lack the exploration and utilization of other somatosensory characteristics, and the training scenarios are single and cannot be expanded, resulting in limited training effects and efficiency.

[0048] The present invention aims at the problem of accurate calculation of the amount of tactile information feedback in lower limb rehabilitation training. By integrating force value sequence, training effect evaluation value sequence, muscle information, etc., the present invention achieves comprehensive coverage of user training information and ensures the comprehensiveness and accuracy of the feedback results.

[0049] In the process of calculating the tactile information feedback amount of lower limb rehabilitation training, the present invention performs a first feedback calculation process on the force value sequence and the training effect evaluation value sequence to obtain an intermediate feedback amount; and performs a second feedback calculation process on the intermediate feedback amount, the training effect evaluation value sequence and the muscle information set to obtain a feedback output value. Through a two-step calculation method, the stability and convergence of the feedback result are ensured, and the divergence of the feedback result is avoided.

[0050] When evaluating the training effect, the present invention obtains a training effect evaluation value sequence by jointly evaluating the force value sequence and the key point spatial position matrix set, thereby ensuring the comprehensiveness of the training evaluation result. The key point spatial position matrix and the key point standard position matrix at the time of data collection are subjected to difference calculation processing to obtain position evaluation subvalues, and a model for joint difference calculation processing is established to ensure the real-time and accuracy of the training effect evaluation value. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a composition diagram of the device of the present invention;

[0052] Figure 2 It is a flow chart for implementing the method of the present invention. DETAILED DESCRIPTION

[0053] In order to better understand the content of the present invention, an embodiment is given here.

[0054] Figure 1 It is a composition diagram of the device of the present invention; Figure 2 It is a flow chart for implementing the method of the present invention.

[0055] In view of the problem that various types of current lower limb rehabilitation training equipment mainly perform strength training on the user's lower limbs, lack the exploration and utilization of other somatosensory characteristics, and the training scenarios are single and cannot be expanded, resulting in limited training effects and efficiency, the present invention discloses a tactile information feedback processing device and method for lower limb rehabilitation training.

[0056] In a first aspect of the embodiment of the present application, a tactile information feedback processing device for lower limb rehabilitation training is disclosed, comprising: a display module, a lower limb motion acquisition module, a training evaluation module, a feedback processing module, a lower limb rehabilitation training module, and a muscle information acquisition module;

[0057] The display module is used to display training scene information;

[0058] The lower limb motion acquisition module is connected to the training evaluation module and is used to acquire a key point spatial position matrix set of the user during the lower limb muscle strength training of the human body using the lower limb rehabilitation training module; the key point spatial position matrix set includes a key point spatial position matrix;

[0059] The training evaluation module is used to perform joint evaluation processing on the force value sequence and the key point spatial position matrix set to obtain a training effect evaluation value sequence;

[0060] 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 ​​applied by the user during the rehabilitation training;

[0061] The feedback processing module is connected to the training evaluation module and the muscle information acquisition module, and is used to perform feedback calculation processing on the strength value sequence, the training effect evaluation value sequence, and the muscle information set to obtain a feedback output value; the feedback output value is used to adjust the tactile stimulation amount of the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training;

[0062] The muscle information collection module is used to collect the user's muscle information set; the muscle information set includes body fat percentage, total body water value, total protein value, total inorganic salt value, right lower muscle weight, left lower muscle weight and extracellular water ratio value;

[0063] The lower limb action acquisition module is used to acquire a real-time image of a training scene, perform human key point recognition on the real-time image of the training scene, and obtain a key point spatial position matrix set; the key point spatial position matrix set includes a key point spatial position matrix at each acquisition moment; the row vector of the key point spatial position matrix is ​​the position coordinate of each key point of the lower limb of the human body;

[0064] 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 mechanical sensor is provided on the foot pedal, and the mechanical sensor is used to measure the force value sequence applied by the user when performing lower limb rehabilitation training using the lower limb rehabilitation training module; the surface of the foot pedal is provided with a plurality of soft needle-like structures for generating tactile stimulation.

[0065] The soft needle-like structure may be a needle-like structure made of rubber.

[0066] The training evaluation module is used to perform joint evaluation processing on the force value sequence and the key point spatial position matrix set to obtain a training effect evaluation value sequence, including:

[0067] The training evaluation module performs time alignment processing on the force value sequence and the key point spatial position matrix set to obtain an aligned force value sequence and key point spatial position matrix set;

[0068] Obtain the key point standard position vector and the standard force value f; use the key point standard position vector as a row vector and copy it in the column direction to obtain a key point standard position matrix with the same dimension as the key point spatial position matrix;

[0069] For each data collection moment, extracting the force value f and the key point spatial position matrix at the data collection moment from the aligned force value sequence and key point spatial position matrix set;

[0070] Performing difference calculation processing on the key point spatial position matrix and the key point standard position matrix at the time of data collection to obtain a position evaluation sub-value;

[0071] The expression for the difference calculation process is:

[0072]

[0073]

[0074] Among them, M and N are the row dimension and column dimension of the key point spatial position matrix, respectively, and δ j is the evaluation item value of the jth column, A ij and B ij are the elements of the i-th row and j-th column of the key point spatial position matrix and the key point standard position matrix respectively, H1 is the position evaluation subvalue, ρ and τ are preset constant values;

[0075] ρ is a preset constant value, which can be 0.8, and τ can be 0.2.

[0076] Performing joint difference calculation processing on the strength value and the position evaluation sub-value at the data collection moment to obtain the training effect evaluation value at the data collection moment;

[0077] The expression for the joint difference calculation process is:

[0078]

[0079] Wherein, H2 is the training effect evaluation value at the time of data collection, and exp represents the exponential operation of the constant e;

[0080] Using the training effect evaluation values ​​at all data collection moments, a training effect evaluation value sequence is constructed;

[0081] The feedback processing module is used to perform feedback calculation processing on the strength value sequence, the training effect evaluation value sequence, and the muscle information set to obtain a feedback output value, including:

[0082] The feedback processing module performs a first feedback calculation process on the strength value sequence and the training effect evaluation value sequence to obtain an intermediate feedback amount;

[0083] A second feedback calculation process is performed on the intermediate feedback amount, the training effect evaluation value sequence and the muscle information set to obtain a feedback output value.

[0084] The expression of the first feedback calculation process is:

[0085]

[0086] Among them, H3 is the intermediate feedback amount, N0 is the length of the training effect evaluation value sequence, H 2,i is the i-th element of the training effect evaluation value sequence, H 2,max is the maximum value element of the training effect evaluation value sequence, f i is the i-th element of the strength value sequence;

[0087] The performing a second feedback calculation process on the intermediate feedback amount, the training effect evaluation value sequence and the muscle information set to obtain a feedback output value includes:

[0088] Obtaining a standard muscle information set; the standard muscle information set includes a standard value of each muscle index;

[0089] Performing difference evaluation processing on the muscle information set and the standard muscle information set to obtain a muscle evaluation result value je;

[0090] Performing feedback calculation processing on the intermediate feedback amount, the training effect evaluation value sequence and the muscle evaluation result value to obtain a feedback output value ne;

[0091] The expression of the feedback calculation process is:

[0092]

[0093] Wherein, L1(), L2(), L3() represent a first-order Laguerre polynomial, a second-order Laguerre polynomial, and a third-order Laguerre polynomial, respectively, and κ1 and κ2 represent a preset first weighting coefficient and a second weighting coefficient, respectively;

[0094] 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.

[0095] The amount of tactile stimulation determines the setting density and radius of the soft needle-like structure on the surface of the foot pedal.

[0096] The muscle information collection module can be implemented by using a human body composition analyzer.

[0097] 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.

[0098] The key points of the human lower limbs include knee joints, ankle joints, feet, etc.

[0099] The step of performing difference evaluation processing on the muscle information set and the standard muscle information set to obtain a muscle evaluation result value includes:

[0100] Performing time alignment processing on the muscle information set and the standard muscle information set to obtain a time-aligned muscle information set and a time-aligned standard muscle information set;

[0101] A muscle information matrix is ​​constructed using the time-aligned muscle information set; the row vector of the muscle information is a muscle information sequence;

[0102] Taking the time-aligned standard muscle information set as a row vector, the row vector is copied in the column direction to obtain a standard muscle matrix; the dimension of the standard muscle matrix is ​​the same as the dimension of the muscle information matrix;

[0103] Subtracting the muscle information matrix from the standard muscle matrix to obtain a difference matrix;

[0104] Performing feature mean calculation on the difference matrix to obtain a mean vector;

[0105] The expression for calculating the characteristic mean is:

[0106]

[0107] Among them, b p is the pth element of the mean vector, A pq is the element in the p-th row and q-th column of the difference matrix, and n is the column dimension of the difference matrix;

[0108] Performing weight vector calculation on the difference matrix to obtain a weight vector;

[0109] The expression for calculating the weight vector is:

[0110]

[0111] Among them, v p is the pth element of the time-aligned standard muscle information set, f p is the pth element of the weight vector;

[0112] Performing weighted summation on the weight vector and the mean vector to obtain a muscle assessment result value;

[0113] The time alignment process comprises:

[0114] When the collection time interval of the muscle information set is greater than the time interval of the standard muscle information set, interpolation processing is performed on the data of the adjacent muscle information sets to obtain a time-aligned muscle information set;

[0115] When the collection time interval of the muscle information set is less than the time interval of the standard muscle information set, sampling the muscle information set to obtain data consistent with the time interval of the standard muscle information set, and using the sampled muscle information set as the time-aligned muscle information set;

[0116] In a second aspect of the embodiment of the present application, a tactile information feedback processing method for lower limb rehabilitation training is disclosed, which is implemented by using the tactile information feedback processing device for lower limb rehabilitation training, including:

[0117] S1, using the display module to display training scene information; using the lower limb rehabilitation training module to perform rehabilitation training on the user's lower limbs, and collecting a sequence of force values ​​applied by the user during the rehabilitation training;

[0118] S2, using the lower limb motion acquisition module to acquire a set of key point spatial position matrices of the user during the lower limb rehabilitation training process using the lower limb rehabilitation training module; using the muscle information acquisition module to acquire a set of muscle information of the user;

[0119] S3, using the training evaluation module to jointly evaluate the force value sequence and the key point spatial position matrix set to obtain a training effect evaluation value sequence;

[0120] S4, using the feedback processing module, performing feedback calculation processing on the strength value sequence, the training effect evaluation value sequence, and the muscle information set to obtain a feedback output value.

[0121] The joint evaluation process of the force value sequence and the key point spatial position matrix set to obtain a training effect evaluation value sequence includes:

[0122] Performing time alignment processing on the force value sequence and the key point spatial position matrix set to obtain an aligned force value sequence and key point spatial position matrix set;

[0123] Obtain the key point standard position vector and the standard force value f; use the key point standard position vector as a row vector and copy it in the column direction to obtain a key point standard position matrix with the same dimension as the key point spatial position matrix;

[0124] For each data collection moment, extracting the force value f and the key point spatial position matrix at the data collection moment from the aligned force value sequence and key point spatial position matrix set;

[0125] Performing difference calculation processing on the key point spatial position matrix and the key point standard position matrix at the time of data collection to obtain a position evaluation sub-value;

[0126] The expression for the difference calculation process is:

[0127]

[0128] Among them, M and N are the row dimension and column dimension of the key point spatial position matrix, respectively, and δ j is the evaluation item value of the jth column, A ij and B ij are the elements of the i-th row and j-th column of the key point spatial position matrix and the key point standard position matrix respectively, H1 is the position evaluation subvalue, ρ and τ are preset constant values;

[0129] ρ is a preset constant value, which can be 0.8, and τ can be 0.2.

[0130] Performing joint difference calculation processing on the strength value and the position evaluation sub-value at the data collection moment to obtain the training effect evaluation value at the data collection moment;

[0131] The expression for the joint difference calculation process is:

[0132]

[0133] Wherein, H2 is the training effect evaluation value at the time of data collection, and exp represents the exponential operation of the constant e;

[0134] Using the training effect evaluation values ​​at all data collection moments, a training effect evaluation value sequence is constructed;

[0135] The feedback calculation process is performed on the strength value sequence, the training effect evaluation value sequence, and the muscle information set to obtain a feedback output value, including:

[0136] Performing a first feedback calculation process on the strength value sequence and the training effect evaluation value sequence to obtain an intermediate feedback amount;

[0137] A second feedback calculation process is performed on the intermediate feedback amount, the training effect evaluation value sequence and the muscle information set to obtain a feedback output value.

[0138] The expression of the first feedback calculation process is:

[0139]

[0140] Among them, H3 is the intermediate feedback amount, N0 is the length of the training effect evaluation value sequence, H 2,i is the i-th element of the training effect evaluation value sequence, H 2,max is the maximum value element of the training effect evaluation value sequence, f i is the i-th element of the strength value sequence;

[0141] The performing a second feedback calculation process on the intermediate feedback amount, the training effect evaluation value sequence and the muscle information set to obtain a feedback output value includes:

[0142] Obtaining a standard muscle information set; the standard muscle information set includes a standard value of each muscle index;

[0143] Performing difference evaluation processing on the muscle information set and the standard muscle information set to obtain a muscle evaluation result value je;

[0144] Performing feedback calculation processing on the intermediate feedback amount, the training effect evaluation value sequence and the muscle evaluation result value to obtain a feedback output value ne;

[0145] The expression of the feedback calculation process is:

[0146]

[0147] Wherein, L1(), L2(), L3() represent a first-order Laguerre polynomial, a second-order Laguerre polynomial, and a third-order Laguerre polynomial, respectively, and κ1 and κ2 represent a preset first weighting coefficient and a second weighting coefficient, respectively;

[0148] 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.

[0149] The amount of tactile stimulation determines the setting density and radius of the soft needle-like structure on the surface of the foot pedal.

[0150] The muscle information collection module can be implemented by using a human body composition analyzer.

[0151] 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.

[0152] The key points of the human lower limbs include knee joints, ankle joints, feet, etc.

[0153] The step of performing difference evaluation processing on the muscle information set and the standard muscle information set to obtain a muscle evaluation result value includes:

[0154] Performing time alignment processing on the muscle information set and the standard muscle information set to obtain a time-aligned muscle information set and a time-aligned standard muscle information set;

[0155] A muscle information matrix is ​​constructed using the time-aligned muscle information set; the row vector of the muscle information is a muscle information sequence;

[0156] Taking the time-aligned standard muscle information set as a row vector, the row vector is copied in the column direction to obtain a standard muscle matrix; the dimension of the standard muscle matrix is ​​the same as the dimension of the muscle information matrix;

[0157] Subtracting the muscle information matrix from the standard muscle matrix to obtain a difference matrix;

[0158] Performing feature mean calculation on the difference matrix to obtain a mean vector;

[0159] The expression for calculating the characteristic mean is:

[0160]

[0161] Among them, b p is the pth element of the mean vector, A pq is the element in the p-th row and q-th column of the difference matrix, and n is the column dimension of the difference matrix;

[0162] Performing weight vector calculation on the difference matrix to obtain a weight vector;

[0163] The expression for calculating the weight vector is:

[0164]

[0165] Among them, v p is the pth element of the time-aligned standard muscle information set, f p is the pth element of the weight vector;

[0166] Performing weighted summation on the weight vector and the mean vector to obtain a muscle assessment result value;

[0167] The time alignment process comprises:

[0168] When the collection time interval of the muscle information set is greater than the time interval of the standard muscle information set, interpolation processing is performed on the data of adjacent muscle information sets to obtain a time-aligned muscle information set;

[0169] When the collection time interval of the muscle information set is less than the time interval of the standard muscle information set, the muscle information set is sampled to obtain data consistent with the time interval of the standard muscle information set, and the muscle information set after sampling is used as the time-aligned muscle information set.

[0170] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A tactile information feedback processing device for lower limb rehabilitation training, characterized in that: include: Display module, lower limb motion acquisition module, training evaluation module, feedback processing module, lower limb rehabilitation training module, muscle information acquisition module; The display module is used to display training scene information; The lower limb motion acquisition module is connected to the training evaluation module and is used to acquire a key point spatial position matrix set of the user during the lower limb muscle strength training of the human body using the lower limb rehabilitation training module; the key point spatial position matrix set includes a key point spatial position matrix; The training evaluation module is used to perform joint evaluation processing on the force value sequence and the key point spatial position matrix set to obtain a training effect evaluation value sequence; 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 ​​applied by the user during the rehabilitation training; The feedback processing module is connected to the training evaluation module and the muscle information acquisition module, and is used to perform feedback calculation processing on the strength value sequence, the training effect evaluation value sequence, and the muscle information set to obtain a feedback output value; The feedback output value is used to adjust the amount of tactile stimulation of the user by the lower limb rehabilitation training module when the user is performing lower limb rehabilitation training; The muscle information collection module is used to collect the user's muscle information set; the muscle information set includes body fat percentage, total body water value, total protein value, total inorganic salt value, right lower muscle weight, left lower muscle weight and extracellular water ratio value.

2. The tactile information feedback processing device for lower limb rehabilitation training according to claim 1, characterized in that: The lower limb motion acquisition module is used to acquire real-time images of training scenes, identify key points of the human body in the real-time images of training scenes, and obtain a set of key point spatial position matrices; the set of key point spatial position matrices includes the key point spatial position matrix at each data acquisition moment; the row vectors of the key point spatial position matrix are the position coordinates of each key point of the lower limbs of the human body.

3. The tactile information feedback processing device for lower limb rehabilitation training according to claim 1, characterized in that: 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 mechanical sensor is provided on the foot pedal, and the mechanical sensor is used to measure the force value sequence applied by the user when performing lower limb rehabilitation training using the lower limb rehabilitation training module; the surface of the foot pedal is provided with a plurality of soft needle-like structures for generating tactile stimulation.

4. A tactile information feedback processing method for lower limb rehabilitation training, characterized in that: The method is implemented by using a tactile information feedback processing device for lower limb rehabilitation training according to any one of claims 1 to 3, comprising: S1, using the display module to display training scene information; using the lower limb rehabilitation training module to perform rehabilitation training on the user's lower limbs, and collecting a sequence of force values ​​applied by the user during the rehabilitation training; S2, using the lower limb motion acquisition module to acquire a set of key point spatial position matrices of the user during the lower limb rehabilitation training process using the lower limb rehabilitation training module; using the muscle information acquisition module to acquire a set of muscle information of the user; S3, using the training evaluation module to jointly evaluate the force value sequence and the key point spatial position matrix set to obtain a training effect evaluation value sequence; S4, using the feedback processing module, performing feedback calculation processing on the strength value sequence, the training effect evaluation value sequence, and the muscle information set to obtain a feedback output value.

5. The tactile information feedback processing method for lower limb rehabilitation training according to claim 4, characterized in that: The joint evaluation process of the force value sequence and the key point spatial position matrix set to obtain the training effect evaluation value sequence includes: S31, performing time alignment processing on the force value sequence and the key point spatial position matrix set to obtain an aligned force value sequence and key point spatial position matrix set; S32, obtaining a key point standard position vector and a standard force value f; taking the key point standard position vector as a row vector, and copying it in the column direction to obtain a key point standard position matrix having the same dimension as the key point spatial position matrix; S33, for each data collection moment, extracting the force value f and the key point spatial position matrix at the data collection moment from the aligned force value sequence and key point spatial position matrix set; S34, performing difference calculation processing on the key point spatial position matrix and the key point standard position matrix at the time of data collection to obtain a position evaluation sub-value; S35, performing joint difference calculation processing on the strength value and the position evaluation sub-value at the data collection moment to obtain a training effect evaluation value at the data collection moment; The expression for the joint difference calculation process is: Wherein, H2 is the training effect evaluation value at the time of data collection, and exp represents the exponential operation of the constant e; S36, constructing a training effect evaluation value sequence using the training effect evaluation values ​​at all data collection moments.

6. The tactile information feedback processing method for lower limb rehabilitation training according to claim 5, characterized in that: The expression for the difference calculation process is: Among them, M and N are the row dimension and column dimension of the key point spatial position matrix, respectively, and δ j is the evaluation item value of the jth column, A ij and B ij are the elements of the i-th row and j-th column of the key point spatial position matrix and the key point standard position matrix respectively, H1 is the position evaluation subvalue, ρ and τ are preset constant values.

7. The tactile information feedback processing method for lower limb rehabilitation training according to claim 6, characterized in that: The feedback calculation process is performed on the strength value sequence, the training effect evaluation value sequence, and the muscle information set to obtain a feedback output value, including: Performing a first feedback calculation process on the strength value sequence and the training effect evaluation value sequence to obtain an intermediate feedback amount; A second feedback calculation process is performed on the intermediate feedback amount, the training effect evaluation value sequence and the muscle information set to obtain a feedback output value.

8. The tactile information feedback processing method for lower limb rehabilitation training according to claim 6, characterized in that: The expression of the first feedback calculation process is: Among them, H3 is the intermediate feedback amount, N0 is the length of the training effect evaluation value sequence, H 2,i is the i-th element of the training effect evaluation value sequence, H 2,max is the maximum value element of the training effect evaluation value sequence, f i is the i-th element of the strength value sequence.

9. The tactile information feedback processing method for lower limb rehabilitation training according to claim 8, characterized in that: The performing a second feedback calculation process on the intermediate feedback amount, the training effect evaluation value sequence and the muscle information set to obtain a feedback output value includes: Obtaining a standard muscle information set; the standard muscle information set includes a standard value of each muscle index; Performing difference evaluation processing on the muscle information set and the standard muscle information set to obtain a muscle evaluation result value je; Feedback calculation processing is performed on the intermediate feedback amount, the training effect evaluation value sequence and the muscle evaluation result value to obtain a feedback output value ne.

10. The tactile information feedback processing method for lower limb rehabilitation training according to claim 9, characterized in that: The expression of the feedback calculation process is: Among them, L1(), L2(), L3() represent the first-order Laguerre polynomial, the second-order Laguerre polynomial, and the third-order Laguerre polynomial respectively, and κ1 and κ2 represent the preset first weighting coefficient and the second weighting coefficient respectively.

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