Individualized walking rehabilitation training device and control method thereof

Abstract

A personalized walking rehabilitation training device and a control method thereof are provided. The system includes a lower limb rehabilitation exoskeleton, an auxiliary cane, a controller, and a fulcrum indication device. The data collection module on the auxiliary cane is fixed to the index and middle fingers of the user's left hand and collects skin conductivity signals. The data transmission module transmits the collected skin conductivity signals to a data receiving module. The control calculation module on the controller processes the skin conductivity signals to construct a psychological trust measure model, calculates optimal solutions for control parameters for personalized walking rehabilitation training, and generates a joint movement trajectory and an optimized cane fulcrum for the personalized lower limb rehabilitation exoskeleton. The output module transmits the joint movement trajectory and the cane fulcrum to the lower limb rehabilitation exoskeleton and the fulcrum indication device, respectively, to control the movements of both.

Description

[Technical Field]

[0001] The present invention relates to the field of rehabilitation training devices, and more particularly to a personalized gait rehabilitation training device and a control method thereof. [Background technology]

[0002] In recent years, lower limb rehabilitation exoskeletons have been widely used in the motor rehabilitation of stroke patients.

[0003] Users with lower limb dysfunction lack muscle strength in their lower limbs and have inferior motor function in their lower limbs compared to people with normal limbs.

[0004] Therefore, walking rehabilitation training using a lower limb rehabilitation exoskeleton requires auxiliary support from a cane, and it is necessary to increase the overall stability between the exoskeleton, one cane for each person, and the three parties involved.

[0005] For example, Chinese patent document CN102499859A discloses an exoskeleton rehabilitation robot for lower limbs in the field of medical equipment technology, and Chinese patent document CN114028176A discloses an exoskeleton mechanism for lower limb rehabilitation with multiple degrees of freedom.

[0006] In conventional lower limb rehabilitation exoskeletons, the reference trajectory of the movement is a preset value, and the support point of the cane is freely specified by the user. However, due to dynamic characteristics and individual differences in users, the movement of the lower limb rehabilitation exoskeleton places a psychological burden on the user, making it impossible to achieve safe, efficient, and reliable walking rehabilitation training.

[0007] Therefore, it is necessary to design walking rehabilitation training parameters such as individualized walking speed and cane support point according to the user's physical condition and rehabilitation progress, thereby increasing the psychological trust when training while wearing a lower limb rehabilitation exoskeleton, helping users overcome their psychological fears, and increasing their motivation to participate in rehabilitation training.

[0008] However, due to differences in perception, it is difficult for users to quantitatively evaluate their psychological confidence in walking rehabilitation training parameters with clear numerical values. This makes it impossible to create walking rehabilitation training that responds to the individual preferences of users, significantly limiting the widespread use and use of lower limb exoskeletons in actual rehabilitation training. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] Chinese Patent Publication CN102499859A [Patent Document 2] China Patent Publication Publication CN114028176A Summary of the Invention [Problem to be solved by the invention]

[0010] In order to solve the problems existing in the background art described above, the present invention aims to provide an individualized walking rehabilitation training device and a control method thereof, which enables accurate quantitative evaluation of the psychological confidence in walking rehabilitation training parameters and prediction of individualized lower limb rehabilitation gait that suits the user's preferences, thereby helping the user overcome psychological fear, increasing their motivation to participate in rehabilitation training, providing safe and efficient rehabilitation training, and promoting the improvement and recovery of the user's limb motor function. [Means for solving the problem]

[0011] The individualized walking rehabilitation training device according to the present invention comprises a lower limb rehabilitation exoskeleton, an assistive cane, a controller, and a support point indication device.

[0012] The exoskeleton for lower limb rehabilitation is attached to the user's legs, and the support point indicator emits a laser toward the ground to indicate the optimal support point for the support point.

[0013] The assisting stick is provided with a data collection module and a data transmission module, and the controller is provided with a data receiving module, a control calculation module and an output module.

[0014] The data collection module is fixed to the index finger and middle finger of the user's left hand to acquire the skin conductivity signal of the user, and the data transmission module transmits the acquired skin conductivity signal to the data receiving module.

[0015] The control and calculation module processes the skin conductivity signal to construct a psychological confidence measurement model, calculates optimal training control parameters for personalized walking rehabilitation training, and generates a personalized joint motion trajectory and optimized cane support point of the lower limb rehabilitation exoskeleton, where the training control parameters refer to walking frequency and cane support distance.

[0016] The output module transmits the joint movement trajectory and the optimized cane support point of the individualized lower limb rehabilitation exoskeleton to the lower limb rehabilitation exoskeleton and the support point indication device, respectively, and controls the lower limb rehabilitation exoskeleton and the support point indication device.

[0017] A method for controlling a personalized gait rehabilitation training device, comprising the following steps: Step S1 The calibration technician wears the lower limb rehabilitation exoskeleton on his / her legs and holds the assistive cane with both hands. The data collection module on the assistive cane is fixed to the index and middle fingers of the calibration technician's left hand and is used to acquire the calibration technician's skin electrical conductance signal.

[0018] Step S2 The calibration technician performs calibration walking and is assisted by a walking stick. The data collection module acquires the calibration technician's electrical skin conductance signal, and the control and calculation module generates a reference model of psychological reliability in walking rehabilitation training.

[0019] Step S3 The exoskeleton for lower limb rehabilitation is attached to the user's legs, and the user holds the assistive cane with both hands. The data collection module attached to the assistive cane is fixed to the index and middle fingers of the user's left hand and is used to acquire the user's skin electrical conductance signal.

[0020] Step S4 The user walks using the assistive cane during a test walk, and the data collection module acquires the user's skin electrical conductance signal. Based on the reference model of psychological confidence, the control and calculation module calculates a psychological confidence correction coefficient and generates a user's psychological confidence measurement model.

[0021] Step S5 With the goal of maximizing the value of the psychological reliability measurement model, the control calculation module calculates optimal training control parameters for individualized walking rehabilitation training, outputs the joint movement trajectory of the lower limb rehabilitation exoskeleton and the optimized support point of the assistive cane, and controls the lower limb rehabilitation exoskeleton and the support point indication device.

[0022] The specific content of the calibration walk in step S2 is as follows.

[0023] The natural walking posture of the calibration person is used as a reference gait, and the corresponding reference gait is processed to obtain reference parameters for gait training, including a reference walking frequency and a reference cane support distance, where the reference cane support distance is the distance from the foremost tip of the front toe of the front foot to the point where the corresponding auxiliary cane contacts the ground, and the distance is 1 / 2 of the stride length.

[0024] The calibration test parameters were obtained by adjusting the baseline parameters of the gait training: the baseline walking frequency was decreased by 10%, decreased by 5%, increased by 5%, or increased by 10%, and the baseline cane support distance was decreased by 15%, decreased by 10%, increased by 10%, or increased by 15%.

[0025] Based on the calibration test parameters, joint angle trajectories and support points of the assistive cane for the calibration test are generated and input to the lower limb rehabilitation exoskeleton and support point indication device, respectively. The calibration technician wears the lower limb rehabilitation exoskeleton and walks with the assistive cane according to the instructions of the support point indication device.

[0026] In step S2, a reference model of psychological confidence in rehabilitation training walking is generated. The specific formula is as follows: JPEG0007769999000002.jpg13168

[0027] Here, Sp(d r ,w s ) represents the function of psychological distrust that different walking frequencies and cane support distances cause to the wearer, d r is the fulcrum distance of the cane, w s indicates the walking frequency during training walking.

[0028] The above equation is obtained using polynomial approximation, and z0, a p , b p , c p , d p , e pare the coefficients of determination, respectively.

[0029] In step S4, the test walk is performed as follows.

[0030] The baseline parameters of the gait training were adjusted to obtain the test parameters, specifically, the baseline cane support distance was decreased or increased by 15%.

[0031] The natural walking posture of the calibrator is used as the reference walking posture, and test joint angle trajectories and test cane support points are generated based on the test parameters and input into the lower limb rehabilitation exoskeleton and support point indication device, respectively. The user wears the lower limb rehabilitation exoskeleton and walks using an auxiliary cane according to the cane support point indication.

[0032] In step S4, the psychological reliability correction coefficient ζ is expressed by the following equation. JPEG0007769999000003.jpg16164where Sp1 * (d r ,w s ) and Sp2 * (d r ,w s ) are the user's skin electrical conductance signal data values ​​obtained when the reference cane fulcrum distance is decreased by 15% and increased by 15%, respectively; Sp1(d r ,w s ) and Sp2(d r ,w s ) are the psychological trust standard models Sp(d r ,w s ) is the calculated value.

[0033] Next, the user's psychological trust measurement model is defined by the following equation: JPEG0007769999000004.jpg17118where, s r represents the degree of familiarity of the user with the exoskeleton, and l r indicates the extent of the patient's injury.

[0034] d is the distance between the rehabilitation instructor and the user, ε * is the offset coefficient of static psychological trust.

[0035] The user's level of familiarity with the exoskeleton includes the following five rating scales:

[0036] Very high: value=0.9 High: Value=0.75 Normal: Value=0.5 Low: value=0.25 Very low: value=0.1

[0037] The extent of the patient's injury is assessed using five grades: Very High: Value=10 High: Value=7.5 Normal: Number=5 Low: value=2.5 Very low: Number=1

[0038] Offset coefficient ε of static psychological reliability * is defined by the following formula: JPEG0007769999000005.jpg17148 where Sp1 * (d r ) and Sp2 * (d r ) are the electrical skin conductance signal data values ​​obtained when the reference cane fulcrum distance is decreased by 15% and increased by 15%, respectively, and the user is in a stationary state; Sp1(d r ) and Sp2(d r ) are the psychological trust standard models Sp(d r ,w s )(However, s =0). [Effects of the Invention]

[0039] Compared with the prior art, the present invention has the following beneficial effects. 1. By expressing psychological confidence using electrodermal signals, the present invention is more reliable in determining the optimal walking style and more suited to the user's thinking habits than evaluation using numerical scores.

[0040] 2. The present invention establishes a quantitative psychological confidence measurement model to accurately calculate the degree of psychological fear that users have regarding the cane support distance. Furthermore, by modifying the model using a psychological confidence correction coefficient and a static psychological confidence offset coefficient, the present method can better realize the generalization and transferability of the psychological confidence measurement model, and the reflected information is more comprehensive.

[0041] 3. The present invention significantly reduces the trial process for finding the user's optimal walking style and shortens the experiment time, making it highly practical for users with physical disabilities.

[0042] As described above, the present invention solves the problem of reduced effectiveness of walking rehabilitation training due to inaccurate subjective assessment of the stability assisted by a cane by the user during walking rehabilitation training, and by generating optimal control parameters for individualized walking rehabilitation training that increases the psychological confidence of the user, it is possible to provide safe and efficient walking rehabilitation training and improve and restore motor function of the limbs. [Brief explanation of the drawings]

[0043] [Figure 1] 1 is a flowchart of a control method for a personalized walking rehabilitation training device according to the present invention. [Figure 2] 1 is a schematic diagram of walking rehabilitation training in an embodiment of the present invention. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0044] The present invention will be described in more detail below with reference to the accompanying drawings and examples. However, it should be noted that the examples described below are for the purpose of facilitating understanding of the present invention and are not intended to limit the present invention.

[0045] The present invention provides a personalized walking rehabilitation training device that includes a lower limb rehabilitation exoskeleton, an auxiliary cane equipped with a data collection module and a data transmission module, a controller equipped with a data receiving module, a control and calculation module, and an output module, and a fulcrum indicator equipped with a laser emission module. The lower limb rehabilitation exoskeleton is worn on a user's legs, the auxiliary cane is held by the user with both hands, and the fulcrum indicator emits a laser toward the ground to indicate the optimal fulcrum for the auxiliary cane.

[0046] The lower limb rehabilitation exoskeleton includes four Maxon EC90F1 motors, each used to control the hip and knee joints. Two springs are also provided for passive control of the ankle joints, and the motors are controlled by a Maxon EPOS4 position controller.

[0047] The auxiliary wand contains one set of The Shimmer3 GSR+ sensors used to collect skin conductivity signal data.

[0048] The calibrator performs calibration walking using the assistive cane, collects skin conductivity signal data, and generates a psychological confidence measurement standard model for rehabilitation training walking. The user performs test walking using the assistive cane, collects skin conductivity signal data, calculates a psychological confidence correction coefficient, and generates a psychological confidence measurement model corresponding to the user. With the goal of maximizing the psychological confidence measurement model value, the system calculates optimal solutions for training control parameters for personalized walking rehabilitation training, generates joint motion trajectories for the personalized lower limb rehabilitation exoskeleton, and outputs the motion trajectories and optimized cane fulcrums to control the lower limb rehabilitation exoskeleton and fulcrum indication device, respectively.

[0049] As shown in FIG. 1, the control method for a personalized walking rehabilitation training device according to the present invention includes the following steps.

[0050] S1: The calibrator wears a lower limb rehabilitation exoskeleton on his / her legs, holds the assistive stick with both hands, and fixes the collection ends of the electrodermal activity signal acquisition module to the index and middle fingers of the calibrator's left hand.

[0051] S2: The calibrator performs a calibration walk using an assistive cane, collects skin conductivity signal data, and generates a psychological reliability metric model for rehabilitation training walks.

[0052] S3: The lower limb rehabilitation exoskeleton is attached to the user's legs, the user holds the assistive stick with both hands, and the collection end of the electrodermal activity signal acquisition module is fixed to the index finger and middle finger of the user's left hand.

[0053] S4: The user performs a test walk using the assistive cane, skin conductivity signal data is collected, a psychological confidence correction coefficient is calculated, and a psychological confidence measurement model corresponding to the user is generated.

[0054] S5: Calculate the optimal training control parameters for individualized gait rehabilitation training.

[0055] A schematic diagram of walking rehabilitation training is shown in Figure 2. The figure shows a left lower leg 1, a left thigh 2, a torso 3, a right lower leg 4, a right thigh 5, a left arm 6, a right arm 7, a left auxiliary cane 8, a right auxiliary cane 9, and the ground 10.

[0056] In step S2, the calibration gait is embodied as follows: using the natural walking of the calibrator as the reference gait, generating a calibration joint angle trajectory and a calibration cane installation position based on the calibration test parameters, inputting them into the lower limb rehabilitation exoskeleton and the installation point indication device, respectively, the calibrator wears the lower limb rehabilitation exoskeleton, and performs assisted walking with the cane according to the instructions of the cane installation point.

[0057] The calibration test parameters are obtained by adjusting the walking training reference parameters, specifically by decreasing the reference walking frequency by 10%, decreasing by 5%, increasing by 5%, or increasing by 10%, and by decreasing the reference cane support distance by 15%, decreasing by 10%, increasing by 10%, or increasing by 15%.

[0058] The walking training reference parameters are gait characteristic parameters obtained from the calibrator's natural walking, including the reference walking frequency and the reference cane support distance, which is the distance from the tip of the front toe to the ground contact point of the corresponding auxiliary cane, and is 50 cm.

[0059] Psychological confidence standard model Sp(d) for psychological confidence in rehabilitation training walking r , ws) is constructed by polynomial approximation, and the specific formula is as follows: JPEG0007769999000006.jpg11159Here, Sp(d r ,w s ) represents the function of psychological distrust that different walking frequencies and cane support distances cause to the wearer, d r is the fulcrum distance of the cane, w s indicates the walking frequency during training walking.

[0060] The above equation is obtained using polynomial approximation, and z0, a p , b p , c p , d p , e p are the coefficients of determination, respectively.

[0061] The calibrator's skin conductivity (SCR) signal data is normalized and expressed as a ratio to the maximum value of the three skin conductivity signal data in each calibration test set. The specific expression is as follows: JPEG0007769999000007.jpg21151

[0062] Normalized Sp i The values ​​are shown in Table 1. Table 1 JPEG0007769999000008.jpg201170

[0063] The coefficient of determination obtained by polynomial approximation is as follows: JPEG0007769999000009.jpg10170Therefore, the psychological trust measure criterion model is expressed by the following equation: In step S4, the test walking is performed as follows: The calibrator's natural walking is used as the reference walking, and a test joint angle trajectory and test cane placement position are generated based on the test parameters and input into the lower limb rehabilitation exoskeleton and placement point indicator device, respectively. The user wears the lower limb rehabilitation exoskeleton and walks with the cane in accordance with the cane placement point indication.

[0064] The test parameters were obtained by adjusting the walking training reference parameters, specifically by decreasing or increasing the support distance of the reference cane by 15% or 15%.

[0065] The user's skin electrical conductance signal data is normalized, that is, the ratio of the three skin electrical conductance signal data in each test set to the maximum value, and the specific formula is as follows: JPEG0007769999000011.jpg21126

[0066] Normalized Sp i * The values ​​of are shown in Table 2. JPEG0007769999000012.jpg25170

[0067] JPEG0007769999000013.jpg18156ζ=(Sp1 * / Sp2+Sp2 * / Sp5) / 2 The calculated psychological trust correction coefficient is ζ=1.130.

[0068] The specific formula of the psychological trust measurement model is as follows: JPEG0007769999000014.jpg14138

[0069] where s r indicates the user's familiarity with the exoskeleton, and l r indicates the degree of injury of the patient, d indicates the distance between the rehabilitation therapist and the user, and ε * denotes the static psychological trust bias coefficient.

[0070] The natural language assessment data regarding the user's familiarity with the exoskeleton includes the following five assessment scales:

[0071] "Very high" = 0.9, "High" = 0.75, "Medium" = 0.5, "Low" = 0.25, "Very low" = 0.1 User proficiency s r =0.75 The natural language assessment data regarding the patient's injury level includes the following five assessment grades:

[0072] "Very high" = 10, "high" = 7.5, "moderate" = 5, "low" = 2.5, "very low" = 1.

[0073] Degree of damage to the user r =1.

[0074] Distance between rehabilitation therapist and user: d = 2 m.

[0075] Static psychological trust bias coefficient ε * The specific formula is as follows: JPEG0007769999000015.jpg17164

[0076] Here, Sp1 * (d r ) and Sp2 * (d r) are the electrical skin conductance signal data obtained when the reference base distance is decreased by 15% and increased by 15%, respectively, and the user is stationary. Sp1(d r ) and Sp2(d r ) is the psychological trust measure standard model Sp(d r ,w s ) is the calculated value of w s =0.

[0077] The signal data of the user's skin electrical conductance value when the reference base distance is decreased and increased by 15% and when the user is stationary is as follows.

[0078] Sp1 * (42.5)=0.9873, Sp2 * (57.5)=0.9921

[0079] The calculated values ​​of the psychological trust measure criterion model were Sp1(42.5)=0.9728, Sp2(57.5)=0.9953, w s =0.

[0080] Therefore, the offset coefficient of static psychological reliability ε * =0.00565 is obtained.

[0081] Therefore, the psychological trust measure model is expressed as follows: JPEG0007769999000016.jpg30170

[0082] The optimal cane support distance d was obtained by using a genetic algorithm to find the maximum value for the psychological trust measure model. r =50.1cm, walking frequency w s =39.76 steps / min, and the psychological trust measure model value SE=-2.1545 was obtained.

[0083] At this time, the user's skin electrical conductance signal data Sp * =0.710. A comparison of the psychological trust measure model values ​​SE is shown in Table 3 below.

[0084] Table 3 JPEG0007769999000017.jpg32170

[0085] The Sp corresponding to the optimal walking frequency and cane support distance combination of 39.76 steps / min and 50.1 cm obtained by the present invention compared with a walking frequency of 40 steps / min and a cane support distance of 57.5 cm (standard cane support distance + 15%) or 42.5 cm (standard cane support distance - 15%). * The values ​​decreased by 24.9% and 27.8%, respectively, and the psychological trust measure model value SE increased by 24.8% and 27.7%, respectively.

[0086] As a result, when using the walking rehabilitation training device of the present invention, the user can feel more relaxed mentally and increase their psychological trust in the device, demonstrating the effectiveness of the technical means proposed by the present invention.

[0087] The embodiments disclosed above have described in detail the technical means and beneficial effects of the present invention, but they do not limit the present invention, and any modifications, supplements, and equivalent replacements made within the principle of the present invention should be construed as being included in the protection scope of the present invention.

Claims

1. A personalized walking rehabilitation training device including a lower limb rehabilitation exoskeleton, an assistive cane, a controller, and a fulcrum indicator, The exoskeleton for lower limb rehabilitation is attached to the user's legs, the auxiliary cane is held by both hands of the user, and the fulcrum indicating device emits a laser toward the ground to indicate an optimal fulcrum position of the auxiliary cane, The auxiliary stick is provided with a data collection module and a data transmission module; the controller is provided with a data receiving module, a control calculation module and an output module; The data collection module is fixed to the index finger and the middle finger of the user's left hand to collect the skin electrical conductance value signal of the user, and the data transmission module transmits the signal to the data receiving module; The control calculation module processes the skin electrical conductance value signal to construct a psychological trust measure model, calculate an optimal solution of training control parameters for personalized walking rehabilitation training, and generate a joint movement trajectory and an optimized cane support position of the personalized lower limb rehabilitation exoskeleton; wherein the training control parameters are walking frequency and cane support distance; A rehabilitation training device characterized in that the output module transmits the joint movement trajectory of the individualized lower limb rehabilitation exoskeleton and the optimized cane fulcrum position to the lower limb rehabilitation exoskeleton and the fulcrum indication device, respectively, and controls them.

2. 2. A method for controlling a rehabilitation training apparatus according to claim 1, comprising: Step S1: The lower limb rehabilitation exoskeleton is attached to the leg of the calibration worker, and the calibration worker holds the assisting stick with both hands. The data collection module on the assisting stick is fixed to the index finger and middle finger of the left hand of the calibration worker, and skin electrical conductance value signals are collected. Step S2: The calibration worker walks using the assistive cane through a calibration walk, the data collection module collects skin electrical conductance signals, and the control calculation module generates a psychological confidence measure reference model for the walking rehabilitation training; Step S3: The exoskeleton for lower limb rehabilitation is attached to the user's leg, the user holds the assisting stick with both hands, and the data collection module on the assisting stick is fixed to the index finger and middle finger of the user's left hand to collect the user's skin electrical conductance value signal. Step S4: The user walks using the assistive cane through a test walk, the data collection module collects skin electrical conductance signals, and the control calculation module calculates a psychological trust correction coefficient based on the psychological trust measure reference model to generate a user psychological trust measure model; Step S5: The control calculation module calculates optimal training control parameters for the personalized walking rehabilitation training, aiming to maximize the psychological trust measure model value, and outputs the joint movement trajectory and optimized cane support position of the lower limb rehabilitation exoskeleton, thereby controlling the lower limb rehabilitation exoskeleton and the support point indication device; A control method including the above steps.

3. The calibration walk in step S2 is using the gait of the calibration worker's natural walking as a reference gait and processing it to obtain reference parameters for gait training; the reference parameters include a reference walking frequency and a reference cane support distance, the reference cane support distance being the distance from the foremost end of the front sole to the ground support point of the auxiliary cane on the corresponding side, and being 1 / 2 the length of the stride; adjusting the reference parameters to obtain calibration test parameters; Specifically, the standard walking frequency is set to 10% decrease, 5% decrease, 5% increase, or 10% increase, and the standard cane support distance is set to 15% decrease, 10% decrease, 10% increase, or 15% increase. generating a calibration test joint angle trajectory and a calibration test cane fulcrum based on the calibration test parameters, inputting them into the lower limb rehabilitation exoskeleton and the fulcrum indication device, respectively, and having the calibration worker walk using the assistive cane in accordance with the instructions of the fulcrum indication device; 3. The control method according to claim 2.

4. 3. The method for controlling an individualized walking rehabilitation training device according to claim 2, In the step S2, a psychological confidence measure reference model for rehabilitation training walking is generated by the following formula: where Sp(d ｒ , w ｓ ) is a function that indicates the psychological uncertainty that different walking frequencies and cane support distances cause to the wearer, and d ｒ is the support distance of the cane, w ｓ represents the walking frequency of the training walk, and the above equation is obtained using polynomial approximation, and z ０ , a ｐ , b ｐ , c ｐ , d ｐ , e ｐ are the coefficients of determination, 3. The control method according to claim 2.

5. The test walking in step S4 is Adjusting the walking training reference parameters to obtain test parameters, the reference cane support distance is reduced by 15% or increased by 15%; using the natural walking of the calibration worker as a reference walking, generating a test joint angle trajectory and a test cane fulcrum based on the test parameters, inputting them into the lower limb rehabilitation exoskeleton and the fulcrum indication device, respectively, and having the user walk using the assistive cane in accordance with the cane fulcrum indication.

4. The control method according to claim 3.

6. 3. The method for controlling an individualized walking rehabilitation training device according to claim 2, The psychological trust correction coefficient ζ in step S4 is expressed by the following formula: Here, Sp １ ＊ (d ｒ , w ｓ ) and Sp ２ ＊ (d ｒ , w ｓ ) are the user's skin electrical conductance signal data values ​​obtained when the reference cane support distance is decreased by 15% and increased by 15%, respectively; Sp １ (d ｒ , w ｓ ) and Sp ２ (d ｒ , w ｓ ) are the psychological trust standard models Sp(d ｒ , w ｓ ) is the calculated value of 3. The control method according to claim 2.

7. 3. The method for controlling an individualized walking rehabilitation training device according to claim 2, In step S4, the user's psychological trust measure model value SE is generated by the following formula: Here, s ｒ represents the user's degree of familiarity with the exoskeleton, and l ｒ indicates the extent of the patient's injury. d is the distance between the rehabilitation instructor and the user, ε ＊ is the offset coefficient of static psychological trust, 3. The control method according to claim 2.

8. The degree of familiarity of the user with the exoskeleton was assessed on a 5-point scale: Very high (value = 0.9), high (value = 0.75), moderate (value = 0.5), low (value = 0.25), very low (value = 0.1) That is, 8. The control method according to claim 7.

9. The degree of injury to the patient was rated on a 5-point scale: Very high (value = 10), high (value = 7.5), moderate (value = 5), low (value = 2.5), very low (value = 1) That is, 8. The control method according to claim 7.

10. The offset coefficient ε of the static psychological reliability ＊ is defined by the following formula: Here, Sp １ ＊ (d ｒ ) and Sp ２ ＊ (d ｒ ) are the electrical skin conductance signal data values ​​obtained when the reference cane support distance is decreased by 15% and increased by 15%, respectively, and the user is in a stationary state; Sp １ (d ｒ ) and Sp ２ (d ｒ ) are the psychological trust standard models Sp(d ｒ , w ｓ ) (However, ｓ = 0), 8. The control method according to claim 7.

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