Fusion feedback processing device and method for upper limb rehabilitation training

Abstract

The invention discloses a fusion feedback processing device and method for upper limb rehabilitation training. The device comprises a virtual reality display module, a motion trail collection module, an evaluation analysis module, a feedback processing module, an upper limb rehabilitation training module and a human body information collection module. The virtual reality display module is used for displaying scene information of upper limb rehabilitation training to a user; the motion trail acquisition module is used for acquiring an upper limb motion trail information set when a user performs upper limb rehabilitation training by using the upper limb rehabilitation training module; the evaluation and analysis module is used for evaluating and judging the upper limb movement track information set and the standard movement track information to obtain an evaluation and analysis result; the feedback processing module is used for performing fusion feedback calculation processing to obtain a fusion feedback output value set; the human body information acquisition module is used for acquiring a human body parameter information set.

Description

Technical Field

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

[0002] In order to solve the muscle strength training and rehabilitation problems of users with upper limb motor disorders, a variety of upper limb rehabilitation training equipment has been developed. For typical users with upper limb motor disorders, including users with upper limb dysfunction caused by common diseases such as spinal cord injury, stroke, brain trauma, fracture surgery, etc. The current upper limb rehabilitation training equipment mainly rehabilitates users from the aspect of strength training, lacks comprehensive rehabilitation of users' multiple types of senses, and lacks feedback based on rehabilitation effects during the rehabilitation process, resulting in the influence of training efficiency and effect. Summary of the invention

[0003] The present invention mainly solves the problem that the current upper limb rehabilitation training equipment mainly rehabilitates users from the aspect of strength training, lacks comprehensive rehabilitation of users' multiple types of senses, and lacks feedback based on rehabilitation effects during the rehabilitation process, resulting in affected training efficiency and effects. The present invention discloses a fusion feedback processing device and method for upper limb rehabilitation training.

[0004] In a first aspect of the embodiment of the present application, a fusion feedback processing device for upper limb rehabilitation training is disclosed, comprising: a virtual reality display module, a motion trajectory acquisition module, an evaluation and analysis module, a feedback processing module, an upper limb rehabilitation training module, and a human body information acquisition module;

[0005] The virtual reality display module is used to display scene information of upper limb rehabilitation training to the user;

[0006] The motion trajectory acquisition module is used to acquire a set of upper limb motion trajectory information of the user when performing upper limb rehabilitation training using the upper limb rehabilitation training module;

[0007] The evaluation and analysis module is connected to the motion trajectory acquisition module and is used to evaluate and distinguish the upper limb motion trajectory information set and the standard motion trajectory information to obtain an evaluation and analysis result;

[0008] The feedback processing module is connected to the human body information acquisition module, the evaluation and analysis module and the upper limb rehabilitation training module, and is used to perform fusion feedback calculation processing on the force value sequence, the human body parameter information set and the evaluation and analysis result to obtain a fusion feedback output value set; the fusion feedback output value set includes a force feedback value for adjusting the force applied by the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training and a tactile feedback value for adjusting the tactile stimulation amount of the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training; the force value sequence is acquired by using the upper limb rehabilitation training module;

[0009] The human body information collection module is used to collect a set of human body parameter information of the user when performing upper limb rehabilitation training using the upper limb rehabilitation training module.

[0010] The motion trajectory acquisition module is implemented by an image acquisition and analysis submodule or an accelerometer sensor disposed on the upper limb of the user;

[0011] The upper limb motion trajectory information set includes a plurality of upper limb motion trajectory information sequences;

[0012] The accelerometer sensor is used to collect the upper limb motion trajectory of the user when performing upper limb rehabilitation training using the upper limb rehabilitation training module, and construct an upper limb motion trajectory information set using all collected upper limb motion trajectories;

[0013] The image acquisition and analysis submodule includes an image acquisition unit and an image analysis unit; the image acquisition unit is used to acquire images of a user when performing upper limb rehabilitation training using the upper limb rehabilitation training module; the image analysis unit is used to extract upper limb parts from the images of the user when performing upper limb rehabilitation training using the upper limb rehabilitation training module to obtain upper limb motion trajectories, and to construct an upper limb motion trajectory information set using all acquired upper limb motion trajectories.

[0014] The upper limb rehabilitation training module includes a training platform and a pointing component for patients to hold, the training platform includes a base and a bracket; the pointing component is movably connected to the bracket; the pointing component is provided with a mechanical sensor and a force control submodule, the mechanical sensor is used to measure the force value sequence applied by the user when using the upper limb rehabilitation training module for upper limb rehabilitation training; the surface of the pointing component is provided with a plurality of soft needle-like structures for generating tactile stimulation; the force control submodule is used to control the mechanical value applied to the user by the pointing component during the training process.

[0015] The human body parameter information set includes body fat percentage, total body water value, total protein value, total inorganic salt value, right upper muscle weight, left upper muscle weight, right lower muscle weight, left lower muscle weight and extracellular water ratio value.

[0016] In a second aspect of an embodiment of the present invention, a fusion feedback processing method for upper limb rehabilitation training is disclosed, which is implemented by using the fusion feedback processing device for upper limb rehabilitation training, and includes:

[0017] Using the virtual reality display module, displaying scene information of upper limb rehabilitation training to the user;

[0018] Using the motion trajectory acquisition module, a set of upper limb motion trajectory information of the user is acquired when the user performs upper limb rehabilitation training using the upper limb rehabilitation training module;

[0019] Using the evaluation and analysis module, the upper limb motion trajectory information set and the standard motion trajectory information are evaluated and processed to obtain an evaluation and analysis result;

[0020] Using the human body information collection module, a human body parameter information set of the user is collected when the user performs upper limb rehabilitation training using the upper limb rehabilitation training module;

[0021] The feedback processing module is used to perform fusion feedback calculation processing on the strength value sequence, the human body parameter information set and the evaluation and analysis result to obtain a fusion feedback output value set.

[0022] The evaluating and distinguishing processing of the upper limb motion trajectory information set and the standard motion trajectory information to obtain the evaluation and analysis results includes:

[0023] Using the upper limb motion trajectory information set, a motion trajectory matrix is ​​constructed; the row vector of the motion trajectory matrix is ​​an upper limb motion trajectory information sequence;

[0024] The standard motion trajectory information is used as a row vector, and the row vector is copied in a column direction to obtain a standard motion matrix; the dimension of the standard motion matrix is ​​the same as the dimension of the motion trajectory matrix;

[0025] Subtracting the motion trajectory matrix from the standard motion matrix to obtain a difference matrix;

[0026] Performing evaluation and calculation processing on the difference matrix to obtain an evaluation and analysis result;

[0027] The expression of the evaluation calculation process is:

[0028]

[0029] Among them, h is the evaluation analysis result value, A ij is the element of the i-th row and j-th column of the standard motion matrix, B ij is the element of the i-th row and j-th column of the motion trajectory matrix, is the mean of the i-th row of the standard motion matrix, M and N are the row and column dimensions of the motion trajectory matrix, respectively.

[0030] The fusion feedback calculation process is performed on the strength value sequence, the human body parameter information set and the evaluation analysis result to obtain a fusion feedback output value set, including:

[0031] Performing a first feedback calculation process on the strength value sequence and the evaluation analysis result to obtain first feedback result information;

[0032] Performing a second feedback calculation process on the first feedback result information, the human body parameter information set and the evaluation and analysis result to obtain a force feedback value for adjusting the force applied by the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training and a tactile feedback value for adjusting the tactile stimulation amount of the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training;

[0033] The force feedback value and the tactile feedback value are used to construct a fusion feedback output value set.

[0034] The performing a first feedback calculation process on the strength value sequence and the evaluation analysis result to obtain first feedback result information includes:

[0035] The feedback processing module obtains the standard force value f0;

[0036] Performing a first feedback calculation process on the standard force value f0, the force value sequence and the evaluation analysis result h to obtain first feedback result information p1;

[0037] The expression of the first feedback calculation process is:

[0038]

[0039] Among them, F is the number of elements contained in the force value sequence, f i is the i-th element of the strength value sequence, is the average value of all elements of the force magnitude sequence, and π is the circumference constant.

[0040] The second feedback calculation processing is performed on the first feedback result information, the human body parameter information set and the evaluation and analysis result to obtain a force feedback value for adjusting the force applied by the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training and a tactile feedback value for adjusting the tactile stimulation amount of the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training, including:

[0041] Obtaining a standard information set of artificial parameters; the standard information set of artificial parameters includes a standard value of body fat percentage, a standard value of total body water, a standard value of total protein, a standard value of total inorganic salts, a standard value of right upper muscle weight, a standard value of left upper muscle weight, a standard value of right lower muscle weight, a standard value of left lower muscle weight, and a standard value of extracellular water ratio;

[0042] The human body parameter information set and the artificial parameter standard information set are represented as a first parameter sequence and a second parameter sequence respectively;

[0043] The first parameter sequence and the second parameter sequence are respectively subjected to high-order self-cumulants calculation to obtain a high-order self-cumulants set; the high-order self-cumulants set includes a first high-order self-cumulants and a second high-order self-cumulants; the first high-order self-cumulants include a first parameter fourth-order cumulant RX4, a first parameter sixth-order cumulant RX6 and a first parameter eighth-order cumulant RX8; the second high-order self-cumulants include a second parameter third-order cumulant RY3, a second parameter fifth-order cumulant RY5 and a second parameter seventh-order cumulant RY7; the calculation process of the high-order self-cumulants includes:

[0044]

[0045] Wherein, FFT represents Fourier transform, the elements in the first parameter sequence and the second parameter sequence are represented as x(n) and y(n), respectively, n represents the number of numerical values ​​in the sequence, n=1, 2, ..., N, and N represents the number of numerical values ​​contained in the parameter sequence;

[0046] The first parameter sequence and the second parameter sequence are respectively subjected to high-order cross-cumulant calculation to obtain a high-order cross-cumulant set; the high-order cross-cumulant set includes a first cross-cumulant RXY 34 , the second mutual accumulation amount RXY 56 and the third cross-cumulant RXY 78 ; The calculation process of the high-order cross-cumulant is expressed as:

[0047]

[0048]

[0049] Performing fusion calculation processing on the high-order mutual cumulative quantity set and the high-order self-cumulative quantity set to obtain a human body parameter evaluation value a1;

[0050] Performing force feedback calculation processing on the human body parameter evaluation value a1, the first feedback result information p1 and the evaluation analysis result h to obtain a force feedback value for adjusting the force exerted by the lower limb rehabilitation training module on the user when the user performs lower limb rehabilitation training;

[0051] The human body parameter evaluation value a1, the first feedback result information p1 and the evaluation analysis result h are processed by tactile feedback calculation 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 performs lower limb rehabilitation training.

[0052] The expression of the force feedback calculation process is:

[0053]

[0054] Wherein, L1 is the force feedback value, β1 and β2 are respectively the preset first weighting factor and the second weighting factor;

[0055] The expression for the tactile feedback calculation process is:

[0056]

[0057] Wherein, L2 is the tactile feedback amount, and exp represents the power function of the constant e.

[0058] The beneficial effects of the present invention are:

[0059] The present invention solves the problem that current upper limb rehabilitation training equipment mainly rehabilitates users from the aspect of strength training, lacks comprehensive rehabilitation of users' multiple types of senses, and lacks feedback based on rehabilitation effects during the rehabilitation process, resulting in affected training efficiency and effects.

[0060] In the upper limb rehabilitation training equipment of the present invention, based on the training effect and the user's body parameter information set, integrated feedback is performed from two aspects of strength and touch, and two feedback quantities are obtained, thereby improving the user's upper limb rehabilitation training effect.

[0061] Before performing feedback processing, the present invention evaluates and discriminates the upper limb motion trajectory information set and the standard motion trajectory information to obtain an evaluation and analysis result, thereby ensuring the accuracy and comprehensiveness of the feedback result. During the evaluation and analysis, an evaluation calculation processing model is established to accurately extract and evaluate the abnormal amount of the user's motion trajectory, thereby improving the accuracy of the evaluation.

[0062] When performing feedback processing, the present invention adopts a two-step processing architecture in order to improve the stability and convergence of the feedback processing. First, a first feedback calculation processing is performed on the force value sequence and the evaluation and analysis results to obtain first feedback result information; then a second feedback calculation processing is performed on the first feedback result information, the human body parameter information set and the evaluation and analysis results to obtain a force feedback value for adjusting the force applied to the user by the lower limb rehabilitation training module when the user performs lower limb rehabilitation training and a tactile feedback value for adjusting the tactile stimulation amount of the user by the lower limb rehabilitation training module when the user performs lower limb rehabilitation training; in this way, the stability and convergence of the feedback processing are ensured.

[0063] When estimating the mechanical feedback amount and the tactile feedback amount, the present invention establishes corresponding feedback processing models respectively, ensures accurate matching and analysis of respective signal characteristics, and improves the accuracy and real-time performance of feedback calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a composition diagram of the device of the present invention;

[0065] Figure 2 It is a flow chart for implementing the method of the present invention. DETAILED DESCRIPTION

[0066] In order to better understand the content of the present invention, an embodiment is given here.

[0067] 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.

[0068] With regard to the current upper limb rehabilitation training equipment, which mainly rehabilitates users from the aspect of strength training, lacks comprehensive rehabilitation of users' multiple types of senses, and lacks feedback based on rehabilitation effects during the rehabilitation process, resulting in problems such as affected training efficiency and effects. The present invention discloses a fusion feedback processing device and method for upper limb rehabilitation training.

[0069] In a first aspect of the embodiment of the present application, a fusion feedback processing device for upper limb rehabilitation training is disclosed, comprising: a virtual reality display module, a motion trajectory acquisition module, an evaluation and analysis module, a feedback processing module, an upper limb rehabilitation training module, and a human body information acquisition module;

[0070] The virtual reality display module is used to display scene information of upper limb rehabilitation training to the user;

[0071] The motion trajectory acquisition module is used to acquire a set of upper limb motion trajectory information of the user when performing upper limb rehabilitation training using the upper limb rehabilitation training module;

[0072] The evaluation and analysis module is connected to the motion trajectory acquisition module and is used to evaluate and distinguish the upper limb motion trajectory information set and the standard motion trajectory information to obtain an evaluation and analysis result;

[0073] The feedback processing module is connected to the human body information acquisition module, the evaluation and analysis module and the upper limb rehabilitation training module, and is used to perform fusion feedback calculation processing on the force value sequence, the human body parameter information set and the evaluation and analysis result to obtain a fusion feedback output value set; the fusion feedback output value set includes a force feedback value for adjusting the force applied by the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training and a tactile feedback value for adjusting the tactile stimulation amount of the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training;

[0074] The human body information collection module is used to collect a set of human body parameter information when the user performs upper limb rehabilitation training using the upper limb rehabilitation training module;

[0075] The motion trajectory acquisition module is implemented by an image acquisition and analysis submodule or an accelerometer sensor disposed on the upper limb of the user;

[0076] The upper limb motion trajectory information set includes a plurality of upper limb motion trajectory information sequences;

[0077] The accelerometer sensor is used to collect the upper limb motion trajectory of the user when performing upper limb rehabilitation training using the upper limb rehabilitation training module, and construct an upper limb motion trajectory information set using all collected upper limb motion trajectories;

[0078] The image acquisition and analysis submodule includes an image acquisition unit and an image analysis unit; the image acquisition unit is used to acquire images of the user when performing upper limb rehabilitation training using the upper limb rehabilitation training module; the image analysis unit is used to extract upper limb parts from the images of the user when performing upper limb rehabilitation training using the upper limb rehabilitation training module to obtain upper limb motion trajectories, and construct an upper limb motion trajectory information set using all acquired upper limb motion trajectories;

[0079] The upper limb rehabilitation training module includes a training platform and a pointing component for the patient to hold, the training platform includes a base and a bracket; the pointing component is movably connected to the bracket. The pointing component is provided with a mechanical sensor and a force control submodule, the mechanical sensor is used to measure the force value sequence applied by the user when performing upper limb rehabilitation training using the upper limb rehabilitation training module; the surface of the pointing component is provided with a plurality of soft needle-like structures for generating tactile stimulation. The force control submodule is used to control the mechanical value applied to the user by the pointing component during the training process;

[0080] The soft needle-like structure can be realized by using a long strip of rubber material.

[0081] The human body parameter information set includes body fat percentage, total body water value, total protein value, total inorganic salt value, right upper muscle weight, left upper muscle weight, right lower muscle weight, left lower muscle weight and extracellular water ratio value;

[0082] The evaluating and distinguishing processing of the upper limb motion trajectory information set and the standard motion trajectory information to obtain the evaluation and analysis results includes:

[0083] Using the upper limb motion trajectory information set, a motion trajectory matrix is ​​constructed; the row vector of the motion trajectory matrix is ​​an upper limb motion trajectory information sequence;

[0084] The standard motion trajectory information is used as a row vector, and the row vector is copied in a column direction to obtain a standard motion matrix; the dimension of the standard motion matrix is ​​the same as the dimension of the motion trajectory matrix;

[0085] Subtracting the motion trajectory matrix from the standard motion matrix to obtain a difference matrix;

[0086] Performing evaluation and calculation processing on the difference matrix to obtain an evaluation and analysis result;

[0087] The expression of the evaluation calculation process is:

[0088]

[0089] Among them, h is the evaluation analysis result value, A ij is the element of the i-th row and j-th column of the standard motion matrix, B ij is the element of the i-th row and j-th column of the motion trajectory matrix, is the mean of the i-th row of the standard motion matrix, M and N are the row and column dimensions of the motion trajectory matrix respectively;

[0090] The feedback processing module performs fusion feedback calculation processing on the strength value sequence, the human body parameter information set and the evaluation analysis result to obtain a fusion feedback output value set, including:

[0091] The feedback processing module performs a first feedback calculation process on the strength value sequence and the evaluation analysis result to obtain first feedback result information;

[0092] Performing a second feedback calculation process on the first feedback result information, the human body parameter information set and the evaluation and analysis result to obtain a force feedback value for adjusting the force applied by the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training and a tactile feedback value for adjusting the tactile stimulation amount of the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training;

[0093] Using the force feedback value and the tactile feedback amount, a fusion feedback output value set is constructed;

[0094] The feedback processing module performs a first feedback calculation process on the strength value sequence and the evaluation analysis result to obtain first feedback result information, including:

[0095] The feedback processing module obtains the standard force value f0;

[0096] Performing a first feedback calculation process on the standard force value f0, the force value sequence and the evaluation analysis result h to obtain first feedback result information p1;

[0097] The expression of the first feedback calculation process is:

[0098]

[0099] Among them, F is the number of elements contained in the force value sequence, f i is the i-th element of the force value sequence, f is the average value of all elements of the force value sequence, and pi is the pi constant.

[0100] The feedback processing module performs a second feedback calculation process on the first feedback result information, the human body parameter information set and the evaluation and analysis result to obtain a force feedback value for adjusting the force applied by the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training and a tactile feedback value for adjusting the tactile stimulation amount of the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training, including:

[0101] Obtaining a standard information set of artificial parameters; the standard information set of artificial parameters includes a standard value of body fat percentage, a standard value of total body water, a standard value of total protein, a standard value of total inorganic salts, a standard value of right upper muscle weight, a standard value of left upper muscle weight, a standard value of right lower muscle weight, a standard value of left lower muscle weight, and a standard value of extracellular water ratio;

[0102] The human body parameter information set and the artificial parameter standard information set are represented as a first parameter sequence and a second parameter sequence respectively;

[0103] The first parameter sequence and the second parameter sequence are respectively subjected to high-order self-cumulants calculation to obtain a high-order self-cumulants set; the high-order self-cumulants set includes a first high-order self-cumulants and a second high-order self-cumulants; the first high-order self-cumulants include a first parameter fourth-order cumulant RX4, a first parameter sixth-order cumulant RX6 and a first parameter eighth-order cumulant RX8; the second high-order self-cumulants include a second parameter third-order cumulant RY3, a second parameter fifth-order cumulant RY5 and a second parameter seventh-order cumulant RY7; the calculation process of the high-order self-cumulants includes:

[0104]

[0105] Wherein, FFT represents Fourier transform, the elements in the first parameter sequence and the second parameter sequence are represented as x(n) and y(n), respectively, n represents the number of numerical values ​​in the sequence, n=1, 2, ..., N, and N represents the number of numerical values ​​contained in the parameter sequence;

[0106] The first parameter sequence and the second parameter sequence are respectively subjected to high-order cross-cumulant calculation to obtain a high-order cross-cumulant set; the high-order cross-cumulant set includes a first cross-cumulant RXY 34 , the second mutual accumulation amount RXY 56 and the third cross-cumulant RXY 78 ; The calculation process of the high-order cross-cumulant is expressed as:

[0107]

[0108] Performing fusion calculation processing on the high-order mutual cumulative quantity set and the high-order self-cumulative quantity set to obtain a human body parameter evaluation value a1;

[0109] Perform force feedback calculation processing on the human body parameter evaluation value a1, the first feedback result information p1 and the evaluation analysis result h to obtain a force feedback value;

[0110] The expression of the force feedback calculation process is:

[0111]

[0112] Wherein, L1 is the force feedback value, β1 and β2 are the preset first weighting factor and the second weighting factor, respectively, and their values ​​may be 0.3 and 0.5;

[0113] Performing tactile feedback calculation processing on the human body parameter evaluation value a1, the first feedback result information p1 and the evaluation analysis result h to obtain a tactile feedback value;

[0114] The expression for the tactile feedback calculation process is:

[0115]

[0116] Wherein, L2 is the tactile feedback value, exp represents the power function of the constant e;

[0117] The expression of the fusion calculation process is:

[0118]

[0119] Among them, α is the preset division factor, exp represents the power function of the constant e, and a1 is the human body parameter evaluation value;

[0120] The elements in the first sequence include body fat percentage, total body water value, total protein value, total inorganic salt value, right upper muscle weight, left upper muscle weight, right lower muscle weight, left lower muscle weight and extracellular water ratio value; the elements in the second sequence include standard value of body fat percentage, standard value of total body water, standard value of total protein, standard value of total inorganic salt, standard value of right upper muscle weight, standard value of left upper muscle weight, standard value of right lower muscle weight, standard value of left lower muscle weight and extracellular water ratio standard value;

[0121] The force exerted by the upper limb rehabilitation training module on the user when the user performs upper limb rehabilitation training may be a mechanical value applied to the user by the force control submodule through the pointing component during the training process.

[0122] The force control submodule can be implemented by a motor.

[0123] The upper limb part extraction from the image of the user performing upper limb rehabilitation training using the upper limb rehabilitation training module may be implemented by using a SURF feature point detection or corner point detection algorithm, or by using an OpenPose algorithm in OpenCV.

[0124] Specifically, the tactile feedback value determines the setting density and radius of the soft needle-like structure on the surface of the pointing component.

[0125] The human body information collection module can be implemented by a human body composition analyzer.

[0126] In a second aspect of an embodiment of the present invention, a fusion feedback processing method for upper limb rehabilitation training is disclosed, which is implemented by using the fusion feedback processing device for upper limb rehabilitation training, and includes:

[0127] Using the virtual reality display module, displaying scene information of upper limb rehabilitation training to the user;

[0128] Using the motion trajectory acquisition module, a set of upper limb motion trajectory information of the user is acquired when the user performs upper limb rehabilitation training using the upper limb rehabilitation training module;

[0129] Using the evaluation and analysis module, the upper limb motion trajectory information set and the standard motion trajectory information are evaluated and processed to obtain an evaluation and analysis result;

[0130] Using the human body information collection module, a human body parameter information set of the user is collected when the user performs upper limb rehabilitation training using the upper limb rehabilitation training module;

[0131] The feedback processing module is used to perform fusion feedback calculation processing on the strength value sequence, the human body parameter information set and the evaluation and analysis result to obtain a fusion feedback output value set.

[0132] The evaluating and distinguishing processing of the upper limb motion trajectory information set and the standard motion trajectory information to obtain the evaluation and analysis results includes:

[0133] Using the upper limb motion trajectory information set, a motion trajectory matrix is ​​constructed; the row vector of the motion trajectory matrix is ​​an upper limb motion trajectory information sequence;

[0134] The standard motion trajectory information is used as a row vector, and the row vector is copied in a column direction to obtain a standard motion matrix; the dimension of the standard motion matrix is ​​the same as the dimension of the motion trajectory matrix;

[0135] Subtracting the motion trajectory matrix from the standard motion matrix to obtain a difference matrix;

[0136] Performing evaluation and calculation processing on the difference matrix to obtain an evaluation and analysis result;

[0137] The expression of the evaluation calculation process is:

[0138]

[0139] Among them, h is the evaluation analysis result value, A ij is the element of the i-th row and j-th column of the standard motion matrix, B ij is the element of the i-th row and j-th column of the motion trajectory matrix, is the mean of the i-th row of the standard motion matrix, M and N are the row and column dimensions of the motion trajectory matrix, respectively.

[0140] The fusion feedback calculation process is performed on the strength value sequence, the human body parameter information set and the evaluation analysis result to obtain a fusion feedback output value set, including:

[0141] Performing a first feedback calculation process on the strength value sequence and the evaluation analysis result to obtain first feedback result information;

[0142] Performing a second feedback calculation process on the first feedback result information, the human body parameter information set and the evaluation and analysis result to obtain a force feedback value for adjusting the force applied by the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training and a tactile feedback value for adjusting the tactile stimulation amount of the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training;

[0143] The force feedback value and the tactile feedback value are used to construct a fusion feedback output value set.

[0144] The performing a first feedback calculation process on the strength value sequence and the evaluation analysis result to obtain first feedback result information includes:

[0145] The feedback processing module obtains the standard force value f0;

[0146] Performing a first feedback calculation process on the standard force value f0, the force value sequence and the evaluation analysis result h to obtain first feedback result information p1;

[0147] The expression of the first feedback calculation process is:

[0148]

[0149] Among them, F is the number of elements contained in the force value sequence, f i is the i-th element of the force value sequence, f is the average value of all elements of the force value sequence, and π is the pi constant.

[0150] The second feedback calculation processing is performed on the first feedback result information, the human body parameter information set and the evaluation and analysis result to obtain a force feedback value for adjusting the force applied by the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training and a tactile feedback value for adjusting the tactile stimulation amount of the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training, including:

[0151] Obtaining a standard information set of artificial parameters; the standard information set of artificial parameters includes a standard value of body fat percentage, a standard value of total body water, a standard value of total protein, a standard value of total inorganic salts, a standard value of right upper muscle weight, a standard value of left upper muscle weight, a standard value of right lower muscle weight, a standard value of left lower muscle weight, and a standard value of extracellular water ratio;

[0152] The human body parameter information set and the artificial parameter standard information set are represented as a first parameter sequence and a second parameter sequence respectively;

[0153] The first parameter sequence and the second parameter sequence are respectively subjected to high-order self-cumulants calculation to obtain a high-order self-cumulants set; the high-order self-cumulants set includes a first high-order self-cumulants and a second high-order self-cumulants; the first high-order self-cumulants include a first parameter fourth-order cumulant RX4, a first parameter sixth-order cumulant RX6 and a first parameter eighth-order cumulant RX8; the second high-order self-cumulants include a second parameter third-order cumulant RY3, a second parameter fifth-order cumulant RY5 and a second parameter seventh-order cumulant RY7; the calculation process of the high-order self-cumulants includes:

[0154]

[0155] Wherein, FFT represents Fourier transform, the elements in the first parameter sequence and the second parameter sequence are represented as x(n) and y(n), respectively, n represents the number of numerical values ​​in the sequence, n=1, 2, ..., N, and N represents the number of numerical values ​​contained in the parameter sequence;

[0156] The first parameter sequence and the second parameter sequence are respectively subjected to high-order cross-cumulant calculation to obtain a high-order cross-cumulant set; the high-order cross-cumulant set includes a first cross-cumulant RXY 34 , the second mutual accumulation amount RXY 56 and the third cross-cumulant RXY 78 ; The calculation process of the high-order cross-cumulant is expressed as:

[0157]

[0158] Performing fusion calculation processing on the high-order mutual cumulative quantity set and the high-order self-cumulative quantity set to obtain a human body parameter evaluation value a1;

[0159] Performing force feedback calculation processing on the human body parameter evaluation value a1, the first feedback result information p1 and the evaluation analysis result h to obtain a force feedback value for adjusting the force exerted by the lower limb rehabilitation training module on the user when the user performs lower limb rehabilitation training;

[0160] The human body parameter evaluation value a1, the first feedback result information p1 and the evaluation analysis result h are processed by tactile feedback calculation 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 performs lower limb rehabilitation training.

[0161] The expression of the force feedback calculation process is:

[0162]

[0163] Wherein, L1 is the force feedback value, β1 and β2 are respectively the preset first weighting factor and the second weighting factor;

[0164] The expression for the tactile feedback calculation process is:

[0165]

[0166] Wherein, L2 is the tactile feedback amount, and exp represents the power function of the constant e.

[0167] The expression of the fusion calculation process is:

[0168]

[0169] Among them, α is the preset division factor, exp represents the power function of the constant e, and a1 is the human body parameter evaluation value.

[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 fusion feedback processing device for upper limb rehabilitation training, characterized in that: include: Virtual reality display module, motion trajectory acquisition module, evaluation and analysis module, feedback processing module, upper limb rehabilitation training module, human body information acquisition module; The virtual reality display module is used to display scene information of upper limb rehabilitation training to the user; The motion trajectory acquisition module is used to acquire a set of upper limb motion trajectory information of the user when performing upper limb rehabilitation training using the upper limb rehabilitation training module; The evaluation and analysis module is connected to the motion trajectory acquisition module and is used to evaluate and distinguish the upper limb motion trajectory information set and the standard motion trajectory information to obtain an evaluation and analysis result; The feedback processing module is connected to the human body information acquisition module, the evaluation and analysis module and the upper limb rehabilitation training module, and is used to perform fusion feedback calculation processing on the strength value sequence, the human body parameter information set and the evaluation and analysis result to obtain a fusion feedback output value set; The fusion feedback output value set includes a force feedback value for adjusting the force applied by the lower limb rehabilitation training module to the user when the user is performing lower limb rehabilitation training and 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; the force value sequence is acquired by using the upper limb rehabilitation training module; The human body information acquisition module is used to acquire a set of human body parameter information of a user when performing upper limb rehabilitation training using the upper limb rehabilitation training module.

2. The fusion feedback processing device for upper limb rehabilitation training according to claim 1, characterized in that: The motion trajectory acquisition module is implemented by an image acquisition and analysis submodule or an accelerometer sensor disposed on the upper limb of the user; The upper limb motion trajectory information set includes a plurality of upper limb motion trajectory information sequences; The accelerometer sensor is used to collect the upper limb motion trajectory of the user when performing upper limb rehabilitation training using the upper limb rehabilitation training module, and construct an upper limb motion trajectory information set using all collected upper limb motion trajectories; The image acquisition and analysis submodule includes an image acquisition unit and an image analysis unit; the image acquisition unit is used to acquire images of a user when performing upper limb rehabilitation training using the upper limb rehabilitation training module; the image analysis unit is used to extract upper limb parts from the images of the user when performing upper limb rehabilitation training using the upper limb rehabilitation training module to obtain upper limb motion trajectories, and to construct an upper limb motion trajectory information set using all acquired upper limb motion trajectories.

3. The fusion feedback processing device for upper limb rehabilitation training according to claim 1, characterized in that: The upper limb rehabilitation training module includes a training platform and a pointing component for patients to hold, the training platform includes a base and a bracket; the pointing component is movably connected to the bracket; the pointing component is provided with a mechanical sensor and a force control submodule, the mechanical sensor is used to measure the force value sequence applied by the user when using the upper limb rehabilitation training module for upper limb rehabilitation training; the surface of the pointing component is provided with a plurality of soft needle-like structures for generating tactile stimulation; the force control submodule is used to control the mechanical value applied to the user by the pointing component during the training process.

4. The fusion feedback processing device for upper limb rehabilitation training according to claim 1, characterized in that: The human body parameter information set includes body fat percentage, total body water value, total protein value, total inorganic salt value, right upper muscle weight, left upper muscle weight, right lower muscle weight, left lower muscle weight and extracellular water ratio value.

5. A fusion feedback processing method for upper limb rehabilitation training, characterized in that: The method is implemented by using a fusion feedback processing device for upper limb rehabilitation training according to any one of claims 1 to 4, comprising: Using the virtual reality display module, displaying scene information of upper limb rehabilitation training to the user; Using the motion trajectory acquisition module, a set of upper limb motion trajectory information of the user is acquired when the user performs upper limb rehabilitation training using the upper limb rehabilitation training module; Using the evaluation and analysis module, the upper limb motion trajectory information set and the standard motion trajectory information are evaluated and processed to obtain an evaluation and analysis result; Using the human body information collection module, a human body parameter information set of the user is collected when the user performs upper limb rehabilitation training using the upper limb rehabilitation training module; The feedback processing module is used to perform fusion feedback calculation processing on the strength value sequence, the human body parameter information set and the evaluation and analysis result to obtain a fusion feedback output value set.

6. The fusion feedback processing method for upper limb rehabilitation training according to claim 5, characterized in that: The evaluating and distinguishing processing of the upper limb motion trajectory information set and the standard motion trajectory information to obtain an evaluation and analysis result includes: Using the upper limb motion trajectory information set, a motion trajectory matrix is ​​constructed; the row vector of the motion trajectory matrix is ​​an upper limb motion trajectory information sequence; The standard motion trajectory information is used as a row vector, and the row vector is copied in a column direction to obtain a standard motion matrix; the dimension of the standard motion matrix is ​​the same as the dimension of the motion trajectory matrix; Subtracting the motion trajectory matrix from the standard motion matrix to obtain a difference matrix; Performing evaluation and calculation processing on the difference matrix to obtain an evaluation and analysis result; The expression of the evaluation calculation process is: Among them, h is the evaluation analysis result value, A ij is the element of the i-th row and j-th column of the standard motion matrix, B ij is the element of the i-th row and j-th column of the motion trajectory matrix, is the mean of the i-th row of the standard motion matrix, M and N are the row and column dimensions of the motion trajectory matrix, respectively.

7. The fusion feedback processing method for upper limb rehabilitation training according to claim 6, characterized in that: The fusion feedback calculation process is performed on the strength value sequence, the human body parameter information set and the evaluation analysis result to obtain a fusion feedback output value set, including: Performing a first feedback calculation process on the strength value sequence and the evaluation analysis result to obtain first feedback result information; Performing a second feedback calculation process on the first feedback result information, the human body parameter information set and the evaluation and analysis result to obtain a force feedback value for adjusting the force applied by the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training and a tactile feedback value for adjusting the tactile stimulation amount of the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training; The force feedback value and the tactile feedback value are used to construct a fusion feedback output value set.

8. The fusion feedback processing method for upper limb rehabilitation training according to claim 7, characterized in that: The performing a first feedback calculation process on the strength value sequence and the evaluation analysis result to obtain first feedback result information includes: The feedback processing module obtains the standard force value f0; Performing a first feedback calculation process on the standard force value f0, the force value sequence and the evaluation analysis result h to obtain first feedback result information p1; The expression of the first feedback calculation process is: Among them, F is the number of elements contained in the force value sequence, f i is the i-th element of the strength value sequence, is the average value of all elements of the force magnitude sequence, and π is the circumference constant.

9. The fusion feedback processing method for upper limb rehabilitation training according to claim 8, characterized in that: The second feedback calculation processing is performed on the first feedback result information, the human body parameter information set and the evaluation and analysis result to obtain a force feedback value for adjusting the force applied by the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training and a tactile feedback value for adjusting the tactile stimulation amount of the lower limb rehabilitation training module to the user when the user performs lower limb rehabilitation training, including: Obtaining a standard information set of artificial parameters; the standard information set of artificial parameters includes a standard value of body fat percentage, a standard value of total body water, a standard value of total protein, a standard value of total inorganic salts, a standard value of right upper muscle weight, a standard value of left upper muscle weight, a standard value of right lower muscle weight, a standard value of left lower muscle weight, and a standard value of extracellular water ratio; The human body parameter information set and the artificial parameter standard information set are represented as a first parameter sequence and a second parameter sequence respectively; The first parameter sequence and the second parameter sequence are respectively subjected to high-order self-cumulants calculation to obtain a high-order self-cumulants set; the high-order self-cumulants set includes a first high-order self-cumulants and a second high-order self-cumulants; the first high-order self-cumulants include a first parameter fourth-order cumulant RX4, a first parameter sixth-order cumulant RX6 and a first parameter eighth-order cumulant RX8; the second high-order self-cumulants include a second parameter third-order cumulant RY3, a second parameter fifth-order cumulant RY5 and a second parameter seventh-order cumulant RY7; the calculation process of the high-order self-cumulants includes: Wherein, FFT represents Fourier transform, the elements in the first parameter sequence and the second parameter sequence are represented as x(n) and y(n), respectively, n represents the number of numerical values ​​in the sequence, n=1, 2, ..., N, and N represents the number of numerical values ​​contained in the parameter sequence; The first parameter sequence and the second parameter sequence are respectively subjected to high-order cross-cumulant calculation to obtain a high-order cross-cumulant set; the high-order cross-cumulant set includes a first cross-cumulant RXY 34 , the second mutual accumulation amount RXY 56 and the third cross-cumulant RXY 78 ; The calculation process of the high-order cross-cumulant is expressed as: Performing fusion calculation processing on the high-order mutual cumulative quantity set and the high-order self-cumulative quantity set to obtain a human body parameter evaluation value a1; Performing force feedback calculation processing on the human body parameter evaluation value a1, the first feedback result information p1 and the evaluation analysis result h to obtain a force feedback value for adjusting the force exerted by the lower limb rehabilitation training module on the user when the user performs lower limb rehabilitation training; The human body parameter evaluation value a1, the first feedback result information p1 and the evaluation analysis result h are processed by tactile feedback calculation 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 performs lower limb rehabilitation training.

10. The fusion feedback processing method for upper limb rehabilitation training according to claim 9, characterized in that: The expression of the force feedback calculation process is: Wherein, L1 is the force feedback value, β1 and β2 are respectively the preset first weighting factor and the second weighting factor; The expression for the tactile feedback calculation process is: Wherein, L2 is the tactile feedback amount, and exp represents the power function of the constant e.

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