Walking pattern acquisition method and system for an exoskeleton device

Abstract

This invention belongs to the field of exoskeleton device technology and provides a method and system for acquiring walking patterns of an exoskeleton device. The method includes: Step S1: Detecting muscle movement pressure information in the thigh and calf muscles, as well as angle information of the thigh and calf, during walking. Step S2: Sampling the muscle movement pressure data based on the angle information to obtain denoised muscle movement pressure data. Step S3: Obtaining a scatter plot of the angle information and the denoised muscle movement pressure data. Step S4: Obtaining a walking pattern model based on the scatter plot. According to this invention, muscle movement pressure is detected in a specific area of ​​muscle distribution. Based on the detected pressure data and angle information, the wearer's walking pattern is obtained, ensuring that the exoskeleton device's drive matches the user's walking pattern, thus reducing the user's walking fatigue.

Description

Technical Field

[0001] This invention belongs to the field of exoskeleton device technology, specifically, it relates to a method and system for obtaining walking patterns of an exoskeleton device. Background Technology

[0002] Recently, with the development of robotics technology, muscle assistive devices to aid human movement are being developed in this field. Some muscle assistive devices utilize the rotation of motors to generate motion power. For example, Korean Patent Publication No. 10-2019-0004854 discloses a wearable muscle assistive device and its control method. The aforementioned prior art document includes: a main body supporting the wearer's upper body, multiple leg pulleys arranged on both sides of the main body, and multiple steel wires connected to the leg pulleys, providing tension to the multiple steel wires and having the driving force to rotate the multiple leg pulleys.

[0003] Most muscle-assistive devices include batteries, speed reducers for reducing motor speeds, and pulleys to replace human joints. They generate driving force based on pre-stored drive controls to drive these joints. Existing technology has the following main drawbacks: when using a muscle-assistive device (exoskeleton), if the exoskeleton's drive is different from the user's walking pattern, it will increase the user's walking fatigue. Summary of the Invention

[0004] The purpose of this invention is to provide a method for obtaining the walking pattern of an exoskeleton device, so as to solve the technical problems existing in the prior art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for acquiring walking patterns of an exoskeleton device, comprising: Step S1: Detect the muscle movement pressure information of the thigh and calf muscles, as well as the angle information of the thigh and calf, when the exoskeleton device wearer walks. Step S2: Sample the muscle movement pressure data based on angle information to obtain denoised muscle movement pressure data; Step S3: Obtain the scatter plot of angle information and denoised muscle movement pressure data; Step S4: Obtain the walking pattern model based on the scatter plot.

[0006] In one embodiment, in step S1, the angle information of the thigh and calf is detected by an upper angle detection sensor and a lower angle detection sensor installed on the exoskeleton device; and the muscle movement pressure information of the thigh and the calf is detected by a pressure sensor worn on the wearer's legs.

[0007] In one embodiment, step S1, detecting muscle movement pressure information of the thigh includes: the movement of at least one muscle among the iliotibial band, rectus femoris, sartorius, vastus intermedius, and iliopsoas muscle constituting the anterior thigh muscles; and the movement of at least one muscle among the gluteus maximus, vastus adductor, semitendinosus, gracilis, two semimembranosus muscles, and biceps femoris muscle constituting the posterior thigh muscles; detecting muscle movement pressure information of the calf includes the movement of the gastrocnemius or soleus muscle constituting the posterior calf muscles.

[0008] In one implementation, the specific method of step S2 is as follows: Step S2.1: Obtain the covariance matrix of muscle movement pressure data based on angle information, and calculate the data interval distance based on the covariance matrix according to formula (1): (1) In the formula, Represents the covariance matrix. Represents a data vector. Represents the average vector. Indicates transpose; Step S2.2: Calculate the cutoff region using the chi-square distribution; Step S2.3: Data exceeding the cutoff area is identified as noise, and data within the cutoff area is sampled as valid data to obtain denoised muscle movement pressure data.

[0009] In one implementation, the specific method of step S3 is as follows: Step S3.1: Calculate the covariance of the angle information and the denoised muscle movement pressure data; Step S3.2: Normalize the covariance and calculate the correlation coefficient using formula (2). : (2) In the formula, represents the angle information, and represents the denoised muscle movement pressure data; Step S3.3: Obtain the scatter plot corresponding to the angle information based on the correlation coefficient.

[0010] In one implementation, the specific method of step S4 is as follows: According to formula (3), the scatter plot is applied to a single detection data point to obtain a walking pattern model: (3) In the formula, For detection data, This is the angle correction constant. A scatter plot of individual detection data. Represents walking model data, Represents a constant.

[0011] To achieve the above objectives, the present invention provides a walking pattern acquisition system for implementing the walking pattern acquisition method of the exoskeleton device described above, comprising: The pressure detection unit includes an upper pressure detection unit for detecting the movement of the wearer's thigh muscles and a lower pressure detection unit for detecting the movement of the wearer's calf muscles. The exoskeleton device includes a main body and left and right connecting parts; An angle detection unit is installed on the exoskeleton device and is used to detect the angle information between the wearer's waistline and thigh line and the angle information between the thigh line and calf line; the angle detection unit includes an upper angle detection sensor and a lower angle detection sensor installed on the exoskeleton device; The controller includes a signal sampling module that samples muscle movement pressure information based on angle information, a correlation calculation module that calculates the angle information and the scatter plot of the detection data, and a walking pattern acquisition module that obtains a walking pattern model based on the scatter plot and muscle movement pressure information.

[0012] Furthermore, the left and right connecting bodies include a middle support body, an upper support body connected to the upper side of the middle support body, a lower support body connected to the lower side, and a foot connected to the lower support body; the upper angle detection sensor is used to detect the angle formed by the middle support body and the upper support body, and the lower angle detection sensor is used to detect the angle formed by the middle support body and the lower support body.

[0013] Furthermore, a battery is installed on the main body, and the controller is disposed on the main body.

[0014] Furthermore, the intermediate support body is provided with an upper driving part for driving the upper support body and a lower driving part for driving the lower support body.

[0015] Compared with the prior art, the present invention has the following beneficial effects: (1) According to the present invention, the movement pressure of muscles is detected in a certain area of ​​muscle distribution, and the wearer’s walking pattern is obtained based on the detected pressure data and angle information, so that the drive of the exoskeleton device is the same as the user’s walking pattern, reducing the user’s walking fatigue, thereby effectively solving the problems existing in the prior art. (2) According to the present invention, the drive control of the exoskeleton device can be performed using the leg muscle movement pressure information of the wearer wearing the exoskeleton device, and the angle control optimized for the wearer's walking pattern can be performed. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the wearing state in Embodiment 1 of the present invention.

[0017] Figure 2 This is a schematic diagram of the pressure detection unit in Embodiment 1 of the present invention.

[0018] Figure 3 This is a schematic diagram of the exoskeleton device in Embodiment 1 of the present invention.

[0019] Figure 4 This is a schematic diagram of the left and right connecting bodies in Embodiment 1 of the present invention.

[0020] Figure 5 This is a schematic diagram of the controller in Embodiment 1 of the present invention.

[0021] Figure 6 This is a schematic diagram illustrating the correlation between the detection data from the pressure sensor and the angle data.

[0022] Figure 7 This is a flowchart of Embodiment 2 of the present invention.

[0023] The corresponding names of the reference numerals in the attached drawings are as follows: 100-Pressure detection unit; 200-Exoskeleton device; 300-Angle detection unit; 400-Controller; 110 - Upper pressure detection unit; 120 - Lower pressure detection unit; 1101 - Pressure sensor; 1102 - Velcro; 210-Main body, 220-Left and right connecting parts; 221-Middle support body, 222-Upper support body, 223-Lower support body, 224-Foot base, 225-Connecting platform; 2211 - Upper drive unit, 2212 - Lower drive unit, 2213 - Cover; 2241 - Fixing ring, 2242 - Bending part; 410 - Signal sampling module, 420 - Correlation calculation module, 430 - Walking mode acquisition module. Detailed Implementation

[0024] To enable those skilled in the art to have a clearer understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described below are merely for illustrative purposes and to facilitate understanding. The technical solutions provided by the present invention are not limited to those provided in the following embodiments, nor should they limit the scope of protection of the present invention.

[0025] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "comprising" or "including," and similar expressions used in this application, mean that the element or object preceding the term encompasses the elements or objects listed following the term and their equivalents, without excluding other elements or objects. Positional relation terms such as "upper," "lower," "left," "right," "front," and "rear" are determined based on the layout orientation of the accompanying drawings and are used only to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. Example

[0026] like Figures 1-5 As shown, this embodiment provides a walking pattern acquisition system, including a pressure detection unit, an exoskeleton device, an angle detection unit, and a controller. The pressure detection unit is used to detect pressure information based on the wearer's leg muscle movements. The exoskeleton device is worn. The angle detection unit is mounted on the exoskeleton device and is used to detect the angle information between the wearer's waistline and thigh line, and the angle information between the thigh line and calf line. The controller is used to receive data, process data, generate and send control signals, etc.

[0027] In this embodiment, the pressure detection unit includes an upper pressure detection unit for detecting the movement of the wearer's thigh muscles and a lower pressure detection unit for detecting the movement of the wearer's calf muscles. The upper and lower pressure detection units can each be worn in pairs on the wearer's left and right legs. In this embodiment, the upper and lower pressure detection units have the same structure. Taking the upper pressure detection unit as a descriptive reference, it includes a pad with a strip shape and at least two pressure sensors mounted on the pad. In a further preferred embodiment, Velcro can be attached to one and the other side of the pad. Using Velcro, the upper and lower pressure detection units can be tightly fitted around the thigh and calf.

[0028] Preferably, the upper detection unit can detect the movement of at least one of the following muscles that make up the muscles of the front thigh: iliotibial band, rectus femoris, sartorius, vastus intermedius, and iliopsoas; and can detect the movement of at least one of the following muscles that make up the muscles of the back thigh: gluteus maximus, vastus adductor, semitendinosus, gracilis, two semimembranosus muscles, and biceps femoris. The lower detection unit can detect the movement of the gastrocnemius or soleus muscle that makes up the muscles of the back calf.

[0029] It should be noted that different pressure values ​​can be detected for any muscle object based on its location. Based on the aforementioned detection objects, preferably, the pressure sensor is placed in the area where the front thigh muscle, back thigh muscle, and back calf muscle are located. In this embodiment, the focus is on the magnitude of pressure data based on muscle movement at location; therefore, the pressure sensor can be installed in the area where a specific muscle is expected to be present, and in the area where the muscle is present, it can be attached anywhere.

[0030] The pressure information of muscle movement detected by the upper pressure detection unit and the lower pressure detection unit, i.e., pressure data, is sent to the controller.

[0031] In this example, the exoskeleton device includes a main body and left and right connecting bodies. The main body is installed on the back of the human body when worn. The main body includes a shell, which contains a battery and a controller. The battery is used to power the pressure detection unit, the angle detection unit and the controller. At the same time, the battery also powers the drive of the exoskeleton device. Preferably, the battery is a rechargeable battery. The shape of the shell is not particularly limited and can generally adopt a regular rectangular or other conventional shape.

[0032] The main body and the left and right connecting bodies are connected by connecting platforms on the left and right sides, respectively. The connecting platforms extend from the lower part of the main body from the left and right sides, then bend forward and then bend downward. The specific structure is as follows: the connecting platform includes a first L-shaped structural member and a second L-shaped structural member. The first L-shaped structural member is located at the lower part of the main body and is perpendicularly connected to it at one end and perpendicularly connected to one end of the second L-shaped structural member at the other end. The other end of the second L-shaped structural member is connected to the left and right connecting bodies.

[0033] The left and right connecting bodies include a middle support body, an upper support body connected to the upper side of the middle support body, a lower support body connected to the lower side, and a foot connected to the lower support body.

[0034] The intermediate support body is equipped with an upper drive unit and a lower drive unit. The upper drive unit drives the upper support body, and the lower drive unit drives the lower support body. The upper and lower drive units have the same structure. Taking the upper drive unit as the description reference, the upper drive unit includes a drive motor and a reducer connected to it. During assembly, the drive motor and reducer are covered by a cover. The drive motor is powered by a battery and generates rotational force. The reducer reduces the number of rotations of the drive motor and increases the rotational torque.

[0035] The footrest includes a retaining ring that wraps around the wearer's ankle and bent portions fixed to both sides of the retaining ring and supporting the wearer's foot. The retaining ring may consist of a fixed semi-circular ring fixed to a lower support and a rotating semi-circular ring connected to the fixed semi-circular ring. As an example, the rotating semi-circular ring connects to the fixed semi-circular ring, securing the wearer's ankle inside the fixed semi-circular ring, and then closes onto the fixed semi-circular ring.

[0036] In this embodiment, a connection port is installed on the main body of the exoskeleton device, which can be firmly fixed to the upper body of the human body. In addition, a foot seat is connected at the wearer's ankle. Therefore, the upper support and the lower support are fixed to the upper body and the ankle respectively, and when walking, the upper support, the middle support and the lower support can be connected at a predetermined angle.

[0037] The angle detection unit is installed on the exoskeleton device. Specifically, the angle detection unit is installed on the left and right connecting bodies of the exoskeleton device to detect the angle information between the wearer's waistline and thigh line, and the angle information between the thigh line and calf line.

[0038] In this embodiment, the angle detection unit includes an upper angle detection sensor and a lower angle detection sensor installed on the left and right connecting bodies; wherein, the upper angle detection sensor is used to detect the angle formed by the middle support body and the upper support body, and the lower angle detection sensor is used to detect the angle formed by the middle support body and the lower support body.

[0039] The pressure and angle information of the detected muscle movements are sent to the controller. At the same time, the controller can generate control commands based on the received information to drive the upper and lower drive units, thereby realizing the drive control of the exoskeleton device.

[0040] In this embodiment, based on functional modules, the controller includes a signal sampling module for sampling muscle movement pressure information based on angle information, a correlation calculation module for calculating the scatter plot of angle information and detection data, and a walking pattern acquisition module for obtaining a walking pattern model based on the scatter plot and muscle movement pressure information. It should be noted that the controller includes a CPU, specifically a Samsung Artik-710. The controller's hardware structure utilizes existing mature hardware architecture. The aforementioned functional modules, to implement the corresponding programs, can be integrated into the CPU; therefore, its hardware structure will not be described in detail here. Example

[0041] like Figure 6 and 7 As shown, where, Figure 6 The vertical axis represents pressure, and the horizontal axis represents angle. This embodiment provides a method for obtaining the walking pattern of an exoskeleton device, which includes the following steps: Step S1: Detect the muscle movement pressure information of the thigh and calf muscles, as well as the angle information of the thigh and calf, when the exoskeleton device wearer walks. In this step, the angle information of the thigh and calf is detected by the upper angle detection sensor and the lower angle detection sensor installed on the exoskeleton device; the muscle movement pressure information of the thigh and the muscle movement pressure information of the calf are detected by the pressure sensor worn on the wearer's legs.

[0042] The detection of muscle movement pressure information in the thigh includes: the movement of at least one of the following muscles that make up the front thigh muscles: iliotibial band, rectus femoris, sartorius, vastus intermedius, and iliopsoas; and the movement of at least one of the following muscles that make up the back thigh muscles: gluteus maximus, vastus adductor, semitendinosus, gracilis, two semimembranosus muscles, and biceps femoris. The detection of muscle movement pressure information in the calf includes the movement of the gastrocnemius or soleus muscle that makes up the back calf muscles.

[0043] It should be noted that different pressure values ​​can be detected for any muscle object based on its location. Based on the aforementioned detection objects, preferably, the pressure sensor is placed in the area where the front thigh muscle, back thigh muscle, and back calf muscle are located. In this embodiment, the focus is on the magnitude of pressure data based on muscle movement at location; therefore, the pressure sensor can be installed in the area where a specific muscle is expected to be present, and in the area where the muscle is present, it can be attached anywhere.

[0044] The detected pressure and angle information is sent to the controller, where the data is processed.

[0045] Step S2: Sample muscle movement pressure data based on angle information to obtain denoised muscle movement pressure data. This step is performed in the signal sampling module of the controller, and the specific method is as follows: Step S2.1 Obtain the covariance matrix of muscle movement pressure data based on angle information, and calculate the data interval distance based on the covariance matrix according to formula (1): (1) In the formula, Represents the covariance matrix. Represents a data vector. Represents the average vector. Indicates transpose; Step S2.2: Calculate the cutoff region using the chi-square distribution; Step S2.3: Data outside the cutoff area is identified as noise, and data within the cutoff area is sampled as valid data to obtain denoised muscle movement pressure data.

[0046] Figure 6 This diagram illustrates the correlation between the pressure sensor's detection data and the angle data. Figure 6 In the diagram, the icon symbol R represents the cutoff area.

[0047] Step S3: Obtain the scatter plot of angle information and denoised muscle movement pressure data. This step is performed in the relevant calculation module of the controller, and the specific method is as follows: Step S3.1: Calculate the covariance of the angle information and the denoised muscle movement pressure data; Step S3.2: Normalize the covariance and calculate the correlation coefficient using formula (2). : (2) In the formula, It is angle information. These are noise-reduced muscle movement stress data; Step S3.3: Obtain the scatter plot corresponding to the angle information based on the correlation coefficient.

[0048] The calculated correlation coefficient is used as a scatter plot of individual sensed data for angle information. The calculated correlation coefficient can be a value between 0.4 and 0.95.

[0049] Step S4: Obtain the walking pattern model based on the scatter plot. This step is performed in the walking pattern acquisition module of the controller. According to formula (3), the scatter plot is applied to a single detection data to obtain the walking pattern model: (3) In the formula, For detection data, This is the angle correction constant. A scatter plot of individual detection data. Represents walking model data, Represents a constant.

[0050] In formula (3), the detection data can be selected from at least 3 to 5 sensors in order of size.

[0051] In this embodiment, the walking pattern acquisition method can be implemented through a combination of hardware and software. Several walking pattern models are pre-stored in the walking pattern acquisition module. During walking, the system reads data from angle and pressure sensors and calculates the optimal walking model. The purpose is to control the exoskeleton to operate at the optimal angle based on the walking pattern when used with the exoskeleton, allowing the wearer to walk with the most suitable gait. The aforementioned hardware can be a data processing device including a processor, and the hardware-driven software can be a program running on the processor.

[0052] By using the above method, after obtaining the walking pattern of the wearer wearing the exoskeleton device, the exoskeleton device can be driven to execute a walking drive that conforms to the wearer's walking pattern, thereby controlling the optimal walking angle of the wearer.

[0053] The muscle movements generated during walking involve different muscles in the thighs and calves for each person and each step. If walking aids are driven based on uniform muscle movements, the wearer will make unnatural and ill-fitting walking movements, eventually causing muscle strain and easy fatigue. According to the method of the present invention, the movement pressure of muscles detected in certain areas of specific muscle distribution regions is analyzed, and the wearer's walking pattern is obtained based on the detected pressure data and angle information, which can effectively reduce the wearer's walking fatigue.

[0054] The above description represents the preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements without departing from the design principles and technical solutions of the present invention, and these improvements should also be considered within the scope of protection of the present invention.

Claims

1. A walking pattern acquisition system for implementing a walking pattern acquisition method for an exoskeleton device, characterized in that, include: The pressure detection unit (100) includes an upper pressure detection unit (110) for detecting the movement of the wearer's thigh muscles and a lower pressure detection unit (120) for detecting the movement of the wearer's calf muscles. An exoskeleton device (200) includes a main body (210) and left and right connecting bodies (220). A battery is installed on the main body (210). The left and right connecting bodies (220) include a middle support body (221), an upper support body (222) connected to the upper side of the middle support body (221), a lower support body (223) connected to the lower side, and a foot (224) connected to the lower support body (223). The middle support body (221) is provided with an upper driving part (2211) for driving the upper support body (222) and a lower driving part (2212) for driving the lower support body (223). An angle detection unit (300) is installed on the exoskeleton device (200) and is used to detect the angle information between the wearer's waistline and thigh line and the angle information between the thigh line and calf line; the angle detection unit (300) includes an upper angle detection sensor and a lower angle detection sensor installed on the exoskeleton device (200); the upper angle detection sensor is used to detect the angle formed by the intermediate support (221) and the upper support (222), and the lower angle detection sensor is used to detect the angle formed by the intermediate support (221) and the lower support (223); A controller (400) is disposed on the main body (210) and includes a signal sampling module (410), a correlation calculation module (420), and a walking pattern acquisition module (430); The signal sampling module (410) is configured to: obtain the covariance matrix of muscle movement pressure data based on angle information, calculate the data interval distance based on the covariance matrix, calculate the cutoff region using chi-square distribution, judge the data outside the cutoff region as noise, sample the data within the cutoff region as valid data, and obtain the denoised muscle movement pressure data. The relevant calculation module (420) is configured to: calculate the covariance of angle information and denoised muscle movement pressure data, normalize the covariance and calculate the correlation coefficient, and obtain the scatter plot corresponding to the angle information based on the correlation coefficient; The walking pattern acquisition module (430) is configured to obtain a walking pattern model based on the scatter plot.

2. A method for acquiring walking patterns of an exoskeleton device, characterized in that, include: Step S1: Detect the muscle movement pressure information of the thigh and calf muscles, as well as the angle information of the thigh and calf, when the exoskeleton device wearer walks. The detection of muscle movement pressure information in the thigh includes: the movement of at least one of the following muscles that make up the front thigh muscles: iliotibial band, rectus femoris, sartorius, vastus intermedius, and iliopsoas; and the movement of at least one of the following muscles that make up the back thigh muscles: gluteus maximus, vastus adductor, semitendinosus, gracilis, two semimembranosus muscles, and biceps femoris. The detection of muscle movement pressure information in the calf includes the movement of the gastrocnemius or soleus muscle that makes up the back calf muscles. Step S2: Sample muscle movement pressure data based on angle information to obtain denoised muscle movement pressure data; specifically including: Step S2.1: Obtain the covariance matrix of muscle movement pressure data based on angle information, and calculate the data interval distance based on the covariance matrix according to formula (1): （1） In the formula, Represents the covariance matrix. Represents a data vector. Represents the average vector. Indicates transpose; Step S2.2: Calculate the cutoff region using the chi-square distribution; Step S2.3: Data exceeding the cutoff area is identified as noise, and data within the cutoff area is sampled as valid data to obtain denoised muscle movement pressure data; Step S3: Obtain the scatter plot of angle information and denoised muscle movement pressure data; specifically including: Step S3.1: Calculate the covariance of the angle information and the denoised muscle movement pressure data; Step S3.2: Normalize the covariance and calculate the correlation coefficient using formula (2): （2） In the formula, It is angle information. These are noise-reduced muscle movement stress data; Step S3.3: Obtain the scatter plot corresponding to the angle information based on the correlation coefficient; Step S4: Obtain the walking pattern model based on the scatter plot; specifically including: According to formula (3), the scatter plot is applied to a single detection data point to obtain a walking pattern model: （3） In the formula, For detection data, This is the angle correction constant. A scatter plot of individual detection data. Represents walking model data, Represents a constant; In step S1, the angle information of the thigh and calf is detected by the upper angle detection sensor and the lower angle detection sensor installed on the exoskeleton device; the muscle movement pressure information of the thigh and the muscle movement pressure information of the calf are detected by the pressure sensor worn on the wearer's legs.

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