Gait training system, control method, and control program

Abstract

To provide a gait training system, a control method, and a control program capable of obtaining a load value appropriately.SOLUTION: A gait training system 1 includes: a treadmill 131 having a belt 132 onto which a user lands; a load distribution sensor 152 having a plurality of sensors 155 arranged in a two-dimensional array, for detecting a load applied through the best 132; a sensor output acquisition unit 171 for acquiring sensor output from the sensors of the load distribution sensor; a correction unit 172 for correcting the sensor output from each sensor 155 on the basis of a moving speed of the belt 132; and a load value calculation unit 174 for calculating the load value received from the user on the basis of the correction value obtained by the correction by the correction unit 172.SELECTED DRAWING: Figure 6

Description

[Technical field]

[0001] The present invention relates to a walking training system, a control method, and a control program. [Background technology]

[0002] Patent Document 1 discloses a pressure distribution detection device. This pressure distribution detection device includes a plurality of drive coils and a plurality of detection coils. It also includes spacer means for maintaining a constant distance between the drive coils and the detection coils. The drive coil is provided with a cushioning material and an electromagnetic shielding body. The detection coil is provided with a cushioning material and an electromagnetic coupling variable body. The electromagnetic coupling variable body is a sheet-shaped magnetic material sheet. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2007-218892 A Summary of the Invention [Problem to be solved by the invention]

[0004] When detecting pressure distribution, pressure (load) is detected from the amount of deformation (deformation stress) of the viscoelastic body. If the viscoelastic body is formed into a sheet, the viscoelastic body will also deform in the surrounding area of ​​the point where the load is actually applied. Since the load is also detected in the surrounding area where no load is applied, it is difficult to detect the load with high accuracy.

[0005] In addition, in a walking training device using a treadmill or the like, the timing in the walking cycle is estimated according to the load distribution. Then, the drive control is performed according to the timing in the walking cycle. Therefore, by detecting the load distribution with high accuracy, more appropriate drive control can be performed.

[0006] The present invention has been made in consideration of the above background, and has an object to provide a walking training system, a control method, and a control program that can appropriately calculate a load value. [Means for solving the problem]

[0007] The walking training system in this embodiment comprises a treadmill having a belt on which the user lands, a load distribution sensor having a plurality of sensors arranged in a two-dimensional array and detecting the load applied through the belt, a sensor output acquisition unit acquiring sensor output from the sensors of the load distribution sensor, a correction unit correcting the sensor output from each of the sensors based on the moving speed of the belt, and a load value calculation unit calculating a load value received from the user based on the correction value corrected by the correction unit.

[0008] In the above walking training system, the correction unit may determine the sensor at the front end of the area where the user lands for each line, and correct the sensor output according to the sensor address in the forward / backward direction based on the sensor address of the sensor at the front end.

[0009] In the walking training system described above, the correction unit may correct the sensor outputs of one row of the sensors along the front-rear direction by using a correction coefficient according to a sensor address in the front-rear direction.

[0010] In the walking training system described above, the load distribution sensor may be configured to sequentially read out outputs from sensors arranged in a row in the front-rear direction.

[0011] The walking training system may further include a robot leg attached to the user's leg, and a drive control unit that drives the robot leg based on the load value.

[0012] In the above walking training system, the load value calculation unit may include a CNN that performs a convolution process using two-dimensional data including the correction values ​​of a plurality of cells as an input.

[0013] The control method for a walking training system in this embodiment is a control method for a walking training system equipped with a treadmill having a belt on which a user lands, and a load distribution sensor having a plurality of sensors arranged in a two-dimensional array and detecting the load applied through the belt, and includes the steps of acquiring sensor output from the load distribution sensor, obtaining correction values ​​by correcting the sensor output from each of the sensors based on the moving speed of the belt, and calculating a load value received from the user based on the correction value.

[0014] In the above-mentioned control method for the walking training system, the correction step may determine the sensor at the front end of the area where the user lands for each line, and correct the sensor output according to the sensor address in the forward / backward direction based on the sensor address of the sensor at the front end.

[0015] In the walking training system described above, the sensor outputs of the sensors in one row along the front-rear direction may be corrected using a correction coefficient according to a sensor address in the front-rear direction.

[0016] In the above walking training system, the load distribution sensor may sequentially read out outputs from sensors arranged in a row in the front-rear direction.

[0017] The walking training system may further include a robot leg attached to the user's leg, and a drive control unit that drives the robot leg based on the load value.

[0018] In the above walking training system, the step of calculating the load value may include a CNN that performs a convolution process using two-dimensional data including the correction values ​​of a plurality of cells as an input.

[0019] The control program in this embodiment is a control program that causes a computer to execute a control method for controlling a walking training system that includes a treadmill having a belt on which a user lands, and a load distribution sensor having a plurality of sensors arranged in a two-dimensional array and detecting the load applied through the belt, the control method including the steps of acquiring sensor output from the load distribution sensor, obtaining correction values ​​by correcting the sensor output from each of the sensors based on the moving speed of the belt, and calculating a load value received from the user based on the correction value. Effect of the Invention

[0020] According to the present disclosure, it is possible to provide a walking training system, a control method, and a control program capable of appropriately calculating a load value. [Brief description of the drawings]

[0021] [Figure 1] FIG. 1 is a schematic perspective view of a walking training system. [Diagram 2] FIG. 2 is a side cross-sectional view showing the configuration of the treadmill. [Diagram 3] FIG. 2 is a top view showing a schematic configuration of a load distribution sensor and a belt. [Figure 4] 5A to 5C are cross-sectional views illustrating the deformation of a viscoelastic sheet in the load distribution sensor. [Diagram 5] 10A to 10C are cross-sectional views showing schematic deformation of a viscoelastic sheet during operation of a treadmill. [Figure 6] FIG. 2 is a functional block diagram showing the main configuration of a control system of the system. [Figure 7] FIG. 13 is a diagram for explaining a cell arrangement and a leading cell in each line. [Figure 8] 4 is a graph showing a sensor output before correction and a correction value obtained by a correction unit. [Figure 9] 11 is a diagram for explaining a learning model used in a load value calculation unit. FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] The present invention will be described below through embodiments of the invention, but the invention according to the claims is not limited to the following embodiments. In addition, not all of the configurations described in the embodiments are necessarily essential as means for solving the problems. For clarity of explanation, the following description and drawings are omitted and simplified as appropriate. In each drawing, the same elements are given the same reference numerals, and repeated explanations are omitted as necessary.

[0023] (System Configuration) 1 is an overall conceptual diagram showing an example of the configuration of a rehabilitation support system according to an embodiment of the present invention. The rehabilitation support system (walking training system 1) according to this embodiment is mainly composed of a walking training device 100 and a leg brace 120.

[0024] The walking training device 100 is a specific example of a rehabilitation support device that supports the rehabilitation of a trainee (user) 900. The walking training device 100 is a device that allows the trainee 900, who is a hemiplegic patient suffering from paralysis in one leg, to perform walking training under the guidance of a training staff member 901. Here, the training staff member 901 can be a therapist (physiotherapist) or a doctor, and can also be called a training instructor, training assistant, training assistant, etc., since the training staff member supports the trainee's training by providing guidance or assistance.

[0025] The walking training device 100 mainly comprises a control panel 133 attached to a frame 130 forming the overall skeleton, and a treadmill 131 on which the trainee 900 walks. Furthermore, a leg brace 120 is attached to the affected leg of the trainee 900, which is the paralyzed leg. In FIG. 1, the leg brace 120 is attached to the right leg of the trainee 900. The leg brace 120 is a robotic leg equipped with an actuator and the like. For example, the leg brace 120 has a joint drive mechanism that assists the movement of the knee joint of the trainee 900.

[0026] The frame 130 is erected on a treadmill 131 that is installed on the floor. The treadmill 131 rotates a ring-shaped belt 132 by a motor (not shown). The treadmill 131 is a device that encourages the trainee 900 to walk, and the trainee 900 who is undergoing walking training gets on the belt 132 and tries to walk in accordance with the movement of the belt 132. Note that the training staff 901 can stand on the belt 132 behind the trainee 900 and walk together as shown in FIG. 1, for example, but it is usually preferable for the training staff 901 to be in a position that makes it easy to assist the trainee 900, such as standing astride the belt 132. The treadmill 131 has a load distribution sensor, as will be described later.

[0027] The frame 130 supports a control panel 133 and a training monitor 138. The control panel 133 houses an overall control unit 210 that controls the motors and sensors. The training monitor 138 is, for example, a liquid crystal panel, and displays the progress of training and the like to the trainee 900. The frame 130 also supports a front pulling unit 135 near the front of the upper part of the trainee 900's head, a harness pulling unit 112 near the upper part of the head, and a rear pulling unit 137 near the rear of the upper part of the head. The frame 130 also includes a handrail 130a for the trainee 900 to grasp.

[0028] The handrails 130a are arranged on both the left and right sides of the trainee 900. Each handrail 130a is arranged in a direction parallel to the walking direction of the trainee 900. The handrails 130a are adjustable in vertical and horizontal positions. In other words, the handrails 130a can include a mechanism for changing their height and width. Furthermore, the handrails 130a can be configured so that their inclination angle can be changed, for example, by adjusting the height to be different between the front side and the rear side in the walking direction. For example, the handrails 130a can be given an inclination angle that gradually increases along the walking direction.

[0029] The handrail 130a is also provided with a handrail sensor 218 that detects the load received from the trainee 900. For example, the handrail sensor 218 can be a resistance change detection type load detection sheet in which electrodes are arranged in a matrix. The handrail sensor 218 can also be a six-axis sensor that combines a three-axis acceleration sensor (x, y, z) and a three-axis gyro sensor (roll, pitch, yaw). However, the type and installation position of the handrail sensor 218 are not important.

[0030] The camera 140 functions as an imaging unit for observing the entire body of the trainee 900. The camera 140 is installed near the training monitor 138 so as to face the trainee. The camera 140 takes still images and videos of the trainee 900 during training. The camera 140 includes a lens and imaging element set that provides an angle of view sufficient to capture the entire body of the trainee 900. The imaging element is, for example, a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor, and converts an optical image formed on an imaging surface into an image signal.

[0031] The coordinated operation of the front pulling section 135 and the rear pulling section 137 offsets the load of the leg brace 120 so that it does not become a burden on the affected leg, and further assists the swinging motion of the affected leg according to the set degree.

[0032] One end of the front wire 134 is connected to a winding mechanism of the front pulling unit 135, and the other end is connected to the leg brace 120. The winding mechanism of the front pulling unit 135 turns on / off a motor (not shown) to wind or unwind the front wire 134 in response to the movement of the affected leg. Similarly, one end of the rear wire 136 is connected to a winding mechanism of the rear pulling unit 137, and the other end is connected to the leg brace 120. The winding mechanism of the rear pulling unit 137 turns on / off a motor (not shown) to wind or unwind the rear wire 136 in response to the movement of the affected leg. Such coordinated operation of the front pulling unit 135 and the rear pulling unit 137 offsets the load of the leg brace 120 so that the load does not become a burden on the affected leg, and further assists the swinging motion of the affected leg according to the set degree.

[0033] The front wire 134 and the front pulling unit 135 constitute a first pulling means for pulling the legs of the trainee 900 upward and forward. The rear wire 136 and the rear pulling unit 137 constitute a second pulling means for pulling the legs of the trainee 900 upward and backward. The front pulling unit 135 and the rear pulling unit 137 pull the front wire 134 and the rear wire 136 with a pulling force according to the walking phase, as described below. Also, a pulling force operation pattern may be set according to the walking phase.

[0034] For example, the training staff 901, as an operator, sets a high assist level for a trainee with severe paralysis. When the assist level is set high, the front pulling unit 135 winds up the front wire 134 with a relatively large force in accordance with the timing of the swing of the affected leg. As the training progresses and assistance is no longer necessary, the training staff 901 sets the assist level to the minimum. When the assist level is set to the minimum, the front pulling unit 135 winds up the front wire 134 with just enough force to cancel the weight of the leg orthosis 120 in accordance with the timing of the swing of the affected leg.

[0035] The walking training device 100 is equipped with a fall prevention harness device as a safety device, the main components of which are an orthosis 110, a harness wire 111, and a harness tensioning part 112. The orthosis 110 is a belt that is wrapped around the abdomen of the trainee 900 and is fixed to the waist by, for example, a hook and loop fastener. The orthosis 110 is equipped with a connecting hook 110a that connects one end of the harness wire 111, which is a hanging device, and can also be called a hanger belt. The trainee 900 wears the orthosis 110 so that the connecting hook 110a is located at the rear of the trainee.

[0036] One end of the harness wire 111 is connected to the connecting hook 110a of the harness 110, and the other end is connected to a winding mechanism of the harness tensioning unit 112. The winding mechanism of the harness tensioning unit 112 winds and unwinds the harness wire 111 by turning on and off a motor (not shown). With this configuration, when the trainee 900 is about to fall, the fall prevention harness device winds up the harness wire 111 in accordance with an instruction from the overall control unit 210 that detects the movement, and the harness 110 supports the upper body of the trainee 900, preventing the trainee 900 from falling.

[0037] The orthosis 110 includes a posture sensor 217 for detecting the posture of the trainee 900. The posture sensor 217 is, for example, a combination of a gyro sensor and an acceleration sensor, and outputs the inclination angle of the abdomen on which the orthosis 110 is worn with respect to the direction of gravity.

[0038] The management monitor 139 is attached to the frame 130, and is a display input device mainly used for monitoring and operation by the training staff 901. The management monitor 139 is, for example, a liquid crystal panel, and a touch panel is provided on its surface. The management monitor 139 displays various menu items related to training settings, various parameter values ​​during training, training results, etc. In addition, an emergency stop button 232 is provided near the management monitor 139. When the training staff 901 presses the emergency stop button 232, the walking training device 100 is brought to an emergency stop.

[0039] The overall control unit 210 generates rehabilitation data that may include setting parameters related to training settings, various data related to leg movements output from the leg brace 120 as training results, etc. This rehabilitation data may include data indicating the training staff 901 or their years of experience and level of proficiency, data indicating the symptoms, walking ability, recovery level, etc. of the trainee 900, various data output from sensors provided outside the leg brace 120, etc.

[0040] At least a part of the control of the overall control unit 210 may be executed by a computer program. For example, the overall control unit 210 includes a memory for storing the program, a processor for executing the program, and the like.

[0041] The overall control unit 210 can control actuators such as motors based on the detection results of the load distribution sensor. For example, the overall control unit 210 determines the phase (timing) of a walking cycle based on the change in load detected by the load distribution sensor. The overall control unit 210 generates a control signal for controlling the actuator based on the determination result. The joint drive mechanism of the leg brace 120 performs an assisting operation of the knee joint based on the control signal. In this way, an appropriate assisting operation can be performed, and effective training can be achieved. The overall control unit 210 may also control the front tension unit 135 and the rear tension unit 137 according to the phase of the walking cycle. The control based on the detection results of the load distribution sensor will be described later.

[0042] Next, the configuration of the load distribution sensor provided on the treadmill 131 will be described with reference to Figures 2 and 3. Figure 2 is a side cross-sectional view showing the configuration of the treadmill 131 and the load distribution sensor 152. Figure 3 is a top view showing a schematic arrangement of the load distribution sensor 152 on the treadmill 131.

[0043] The treadmill 131 includes at least a ring-shaped belt (moving plate) 132, a pulley 153, and a motor (not shown). The belt 132 is an endless belt and is formed in a ring shape in a side view. Two pulleys 153 are arranged inside the ring-shaped belt 132. The two pulleys 153 are arranged at a distance in the front-rear direction. The rotation axis of the pulley 153 is parallel to the left-right direction. Therefore, when the motor rotates the pulley 153, the belt 132 is driven in the front-rear direction. When the trainee 900 or the training staff 901 sets the moving speed, the motor rotates the pulley 153 so that the belt 132 moves at the moving speed.

[0044] Furthermore, a load distribution sensor 152 is arranged on the inside of the ring-shaped belt 132 (below the belt 132 on which the trainee 900 rides) so as not to be linked to the belt 132. The load distribution sensor 152 is arranged on the inside of the ring of the endless belt. The load distribution sensor 152 is composed of a plurality of sensors 155. The plurality of sensors 155 are arranged in a matrix on the underside of the belt 132 that supports the soles of the feet of the trainee 900 during walking training. By using the plurality of sensors 155, the load distribution sensor 152 can detect the distribution of surface pressure (load distribution) received from the feet FT of the trainee 900 riding on the belt 132.

[0045] 3, the load distribution sensor 152 has a plurality of sensors 155 arranged in a matrix. The load distribution sensor 152 has a sheet shape parallel to a horizontal plane. The sensor 155 may be of an electromagnetic induction type, an electrical resistance type, a capacitance type, or a load cell type. Of course, the type of the sensor 155 is not particularly limited.

[0046] For example, the load distribution sensor 152 is assumed to have m (m is an integer of 2 or more) sensors 155 arranged in the left-right direction and n (n is an integer of 2 or more) sensors 155 arranged in the front-rear direction. In Fig. 3, the XY coordinates of the sensor 155 at the left front corner are (1,1), and the XY coordinates of the sensor 155 at the left rear corner are (1,n). The XY coordinates of the sensor 155 at the right front corner are (m,1), and the XY coordinates of the sensor 155 at the right rear corner are (m,n). Each sensor 155 has the same size. In this manner, an XY address (XY coordinates) is assigned to each sensor 155.

[0047] The sensors 155 are arranged at equal intervals in the front-rear and left-right directions. The sensing area of ​​the load distribution sensor 152 is therefore rectangular. The output from the load distribution sensor 152 is therefore m×n pieces of two-dimensional map data.

[0048] One sensor 155 is also referred to as a cell. The load distribution sensor 152 detects pressure (load) for each cell. The load distribution sensor 152 detects the distribution of the load received from the trainee 900 by sequentially reading out the outputs of the multiple sensors 155. Each sensor 155 detects a two-dimensional distribution of the pressure received from the foot FT via the belt 132. The m×n pieces of two-dimensional data are also referred to as one frame of data.

[0049] The overall control unit 210 estimates a load value from the foot FT based on the detection result of the load distribution sensor 152. The load value here indicates the total load received from the entire foot FT. For example, the output values ​​detected by the multiple sensors 155 can be converted into pressure using a conversion map. Then, the pressure can be converted into a load value by integrating it. The overall control unit 210 makes a decision regarding the walking phase based on the load value. For example, the overall control unit 210 decides that it is the timing to transition from the stance leg to the swing leg.

[0050] Load distribution sensor 152 is a sheet-like sensor and has a viscoelastic body. For example, as shown in Fig. 4, load distribution sensor 152 has substrate 1511, viscoelastic sheet 1512, and conductive sheet 1513. Note that Fig. 4 is a side cross-sectional view that typically shows the cross-sectional configuration of load distribution sensor 152. Here, load distribution sensor 152 will be described as being an electromagnetic induction type sensor. Also, treadmill 131, belt 132, etc. are omitted in Fig. 4. In Fig. 4, it will be described that treadmill 131 is not operating.

[0051] Load distribution sensor 152 includes substrate 1511, viscoelastic sheet 1512, and conductive sheet 1513. Substrate 1511, viscoelastic sheet 1512, and conductive sheet 1513 are arranged in this order from the bottom side. Viscoelastic sheet 1512 is provided between conductive sheet 1513 and substrate 1511 of load distribution sensor 152. Viscoelastic sheet 1512 is made of a resin material such as urethane foam. The thickness direction of viscoelastic sheet 1512 is the up-down direction.

[0052] The substrate 1511 has a coil pattern having a plurality of coils. The coil pattern has a coil provided for each cell. The plurality of coils are arranged in an array on the substrate 1511. The conductive sheet 1513 is, for example, an aluminum sheet, and is formed over the entire load distribution sensor 152 so as to cover the plurality of coils. The distance between the conductive sheet 1513 and the substrate 1511 changes depending on the load. In other words, the greater the load, the more the viscoelastic sheet 1512 is crushed and becomes thinner. When the viscoelastic sheet 1512 deforms, the electrical characteristics of the substrate 1511 change.

[0053] The load distribution sensor 152 can determine the amount of deformation by measuring electrical characteristics such as electromagnetic induction for each cell. The load distribution sensor 152 converts the amount of deformation of the viscoelastic sheet 1512 in the thickness direction into a load. The amount of deformation can be converted into a load for each cell. In this way, the electrical characteristics change according to the amount of deformation of the viscoelastic sheet 1512, so that each sensor 155 can detect the load. Of course, the load distribution sensor 152 is not limited to the electromagnetic induction type, and a load distribution sensor of a capacitance type, an electrical resistance type, or the like may also be used.

[0054] Fig. 5 shows the deformation of the viscoelastic sheet 1512 when the treadmill is operating. The left side of Fig. 5 is the front in the walking direction, and the belt 132 (not shown) moves from left to right. As the belt 132 moves, the detection position of the load distribution sensor gradually moves backward when the foot FT is on the ground.

[0055] For example, at time T1 when foot FT lands, the fore-aft position of the front end of foot FT on the treadmill 131 is position P1, and the fore-aft position of the rear end is position P2. Then, at time T2, a predetermined time after time T1, the belt 132 is moving with foot FT still on the ground. Therefore, the fore-aft position of the front end of the foot is position P3, and the fore-aft position of the rear end is position P4.

[0056] Here, position P2 and position P3 coincide with each other on the treadmill 131. In this case, at position P3, a load is constantly applied to the load distribution sensor 152 between timing T1 and timing T2. In other words, the viscoelastic sheet 1512 is constantly deformed between timing T1 and timing T2.

[0057] On the other hand, at position P4, a new load is applied to load distribution sensor 152 at timing T2. Therefore, at timing T2, deformation of viscoelastic sheet 1512 begins at position P4. Position P4 becomes a new region where viscoelastic sheet 1512 begins to be crushed. Therefore, it takes time for viscoelastic sheet 1512 to be completely crushed at position P4.

[0058] A new load is applied to the rear end of the foot FT due to the movement of the belt 132. Meanwhile, the point that was directly under the front end of the foot FT is now directly under the rear end of the foot FT due to the movement of the belt. In such a case, the relationship between the actually applied load and the detected load that becomes the sensor output is misaligned between the front and rear ends of the foot FT. For example, even if the entire foot FT is receiving a uniform load, the sensor output of the cell at the front end of the foot may be greater than the sensor output of the cell at the rear end.

[0059] Therefore, there is a risk that the load value cannot be detected with high accuracy. If the load value detection accuracy is low, there is a risk that the drive timing of the leg orthosis 120 will deviate from the timing of the walking cycle. Therefore, in this embodiment, the following process is performed to estimate the load value with high accuracy. Specifically, the overall control unit 210 corrects the sensor output according to the moving speed of the belt 132. The process according to this embodiment will be described below.

[0060] 6 is a functional block diagram for explaining the control according to this embodiment. The overall control unit 210 includes a sensor output acquisition unit 171, a correction unit 172, a load value calculation unit 174, a determination unit 175, and a drive control unit 176. The joint drive unit 221 is provided in the leg orthosis 120. Specifically, the joint drive unit 221 includes a motor and a rotation mechanism for assisting the knee joint movement of the trainee 900.

[0061] The treadmill driving unit 131a drives the treadmill 131. The treadmill driving unit 131a has a motor or the like for rotating the pulley 153. The treadmill 131 operates so that the belt 132 moves at a moving speed set by the training staff 901 or the like.

[0062] The treadmill 131 is provided with a load distribution sensor 152. The load distribution sensor 152 outputs a detection signal (sensor output) indicating the detection result to the overall control unit 210. The sensor output may be digital data or analog data. In other words, the load distribution sensor 152 may have an A / D converter that converts the analog detection signal into digital data. Alternatively, the sensor output acquisition unit 171 may have the A / D converter.

[0063] The sensor output includes an output value for each cell. The sensor output acquisition unit 171 acquires the sensor output from the load distribution sensor 152 for each cell. That is, data indicating the sensor output is associated with a cell address and an output value. The sensor output acquisition unit 171 may acquire the sensor output data in the read order of the load distribution sensor 152. The sensor outputs from all the cells constitute one frame of data. The load distribution sensor 152 reads out data for each line or each cell.

[0064] The correction unit 172 corrects the cell output of the load distribution sensor 152 based on the moving speed of the belt 132 of the treadmill 131. The correction unit 172 uses a correction formula based on the cell address of the load distribution sensor 152. The correction unit 172 corrects the sensor output using the correction formula.

[0065] The sensor output value after correction is called the correction value C. If the cell output before correction is S and the correction coefficient is α(L, V), the correction value C is expressed by the following formula (1). C = S × α(L, V) (1)

[0066] Here, L is the number of cells in the forward / backward direction from a reference cell (a leading cell 156 described later), and V is the treadmill speed. The reference cell can be the cell at the front end of the area where foot FT lands. The correction coefficient α is a coefficient that changes depending on the number of cells L and the treadmill speed V. Specifically, α(L, V) can be expressed as the following equation (2). α(L,V)=(1+L×β+V×γ) ···(2)

[0067] β and γ are recorded in the gain setting unit 178. β is a preset travel direction gain, which can be a constant value. γ is a preset treadmill speed gain, which is a constant value. β and γ are both numbers greater than 0. In this way, the faster the moving speed of the belt 132, the larger the correction coefficient α. Also, the larger the number of cells L, the larger the correction coefficient α. The correction unit 172 corrects the sensor output for each line based on the correction formula (1). Therefore, the correction unit 172 can appropriately correct the sensor output.

[0068] Furthermore, the correction coefficient α(L, V) for determining the correction value C may be the following formula (3): In other words, instead of the α shown in formula (2), the α shown in formula (3) can be used. α(L,V)=(1+(V×γ)L) ···(3)

[0069] The correction process of the correction unit 172 will be described in detail with reference to Fig. 7. Fig. 7 is a top view showing a schematic diagram of the cell arrangement of the load distribution sensor 152. Fig. 7 shows only the sensors 155 in the area where the foot FT lands and the surrounding area, among the sensors 155 of the load distribution sensor 152. Specifically, Fig. 7 shows 8 sensors 155 on the left and right × 16 sensors 155 on the front and back, and the load from the foot FT is applied to some of them. In other words, since no load is applied to the sensors 155 outside the area where the foot FT lands, the sensor output is 0.

[0070] In FIG. 7, the leftmost column is designated as line L1. Line L1 has a plurality of sensors (cells) 155 arranged in the front-rear direction. Similarly, the second to eighth columns from the left are designated as lines L2 to L8, respectively. The load distribution sensor 152 reads out data in order from the leftmost column. For example, after finishing reading out data from line L1, the load distribution sensor 152 reads out data from line L2. The load distribution sensor 152 reads out data in the order of L1, L2, L3, L4, L5, L6, L7, and L8.

[0071] The cell addresses of the 8×16 sensors 155 of the load distribution sensor 152 are indicated by XY addresses. Specifically, the X address indicates the position in the left-right direction, and the Y address indicates the position in the front-rear direction. The X address of the leftmost sensor 155 is 1, and the X address of the rightmost sensor is 8. The Y address of the frontmost sensor 155 is 1, and the Y address of the rearmost sensor is 16. Therefore, the XY address of the sensor 155 at the upper left corner is (1,1). The XY address of the sensor 155 at the upper right corner is (8,1). The XY address of the sensor 155 at the lower left corner is (1,16). The XY address of the sensor 155 at the lower right corner is (8,16). Line L1 includes sensors 155 (1,1) to (1,16).

[0072] The range in which foot FT lands is from line L2 to line L7. Sensor 155 on line L1 and sensor 155 on line L8 do not detect the load. Here, among the cells (sensors 155) that detect the load on lines L2 to L7, the cell located most forward is defined as a leading cell 156.

[0073] For example, the leading cell 156 in line L2 is the fourth sensor 155 from the front, and the leading cell 156 in line L3 is the second sensor 155 from the front. That is, the XY address of the leading cell 156 in line L2 is (2,4), and the XY address of the leading cell 156 in line L3 is (3,3). Similarly, the addresses of the leading cells 156 in lines L4 to L7 are (4,2), (5,2), (6,3), and (7,4), respectively. The leading cell 156 is the reference for correction. The Y address of the leading cell 156 is also called the leading address. Thus, L increases as one moves backward, with the leading cell 156 being 1.

[0074] The correction unit 172 calculates the correction value C by applying formula (1) for each line. The correction unit 172 corrects the sensor output according to the sensor address in the forward and backward directions. Specifically, the correction unit 172 obtains the sensor at the front end of the landing area for each line. Then, the correction unit 172 sets the sensor 155 at the front end of each line as the leading cell 156 as a reference.

[0075] For example, as shown in FIG. 7, the Y address of the leading cell 156 is different for each line. The leading address is different depending on the line. The correction unit 172 determines the leading address for each line. For example, the correction unit 172 can set the Y address of the cell located at the front side among the cells whose sensor output is equal to or greater than the threshold value in one line as the leading address. The correction unit 172 corrects the sensor output for each line using the leading address for each line as a reference. The correction unit 172 corrects the sensor output of the sensor 155 for one line using a correction coefficient α according to the sensor address in the forward and backward directions using the leading address as a reference.

[0076] As shown in formula (2), the correction coefficient α increases the further rearward from the reference leading cell 156. Therefore, appropriate correction can be performed. As explained with reference to FIG. 5, the sensor output tends to be larger at the rear end of the foot FT than at the front end. Therefore, appropriate correction can be performed. FIG. 8 is a graph showing the sensor output S before correction and the sensor output (correction value C) after correction. The horizontal axis is the number of cells L, and the vertical axis is the sensor output S or the correction value C. FIG. 8 also shows the measurement results when a uniform load is applied to the load distribution sensor 152.

[0077] 8, data D1 indicates data when the treadmill 131 is not operating. That is, data D1 indicates data when the moving speed of the belt 132 is 0 km / h. Data D2 to D5 indicate data when the moving speed of the belt 132 is 2 km / h. Also, the cell address of the first cell is set to cell number "1".

[0078] The sensor output S is lower in the pre-correction data D2 to D5 compared to the data D1. In particular, in the pre-correction data D2 to D5, the sensor output S decreases as the number of cells increases. Therefore, as described above, the correction coefficient α increases as the number of cells L increases. In this way, the correction value C becomes uniform regardless of the number of cells. The correction unit 172 can appropriately correct the cell output.

[0079] Returning to the explanation of FIG. 6, the load value calculation unit 174 calculates the load value received from the foot based on the correction value C for each cell. The correction value C is two-dimensional data since it is calculated for each cell. The load value calculation unit 174 can calculate the load value by integrating the correction value C of the two-dimensional data. The load value calculation unit 174 calculates the load value based on the sum of the correction values ​​of the two-dimensional data. The load value calculation unit 174 calculates a load value indicating the load received from the foot FT, and outputs it to the determination unit 175.

[0080] The determination unit 175 performs a determination regarding the phase of the walking cycle based on the estimated load value. The determination unit 175 compares the load value with a preset threshold value. Then, the overall control unit 210 determines the timing at which the load value falls below the threshold value as the switching timing for transitioning from the standing leg to the swing leg. The determination unit 175 performs the determination in order to detect the timing for lifting weight from the affected leg.

[0081] The drive control unit 176 controls the joint driving unit 221 according to the determination result by the determination unit 175. For example, the drive control unit 176 outputs a control signal to the joint driving unit 221 so that the joint driving unit 221 operates at the switching timing. Then, an actuator of the joint driving unit 221 operates according to the control signal. The joint driving unit 221 can bend the knee joint at the switching timing, that is, at the timing of lifting weight from the affected leg.

[0082] In this way, the joint driving unit 221 can apply an assist force in accordance with the walking phase. Therefore, the trainee 900 can perform effective walking training. Note that the control target controlled in accordance with the judgment result is not limited to the joint driving unit 221. For example, the drive control unit 176 may control the front pulling unit 135 and the rear pulling unit 137 so that the pulling forces of the front wires 134 and the rear wires 136 operate in accordance with the walking phase. This allows the front pulling unit 135 and the rear pulling unit 137 to assist in swinging forward at the timing of unloading.

[0083] Even when the treadmill is in operation, the load value can be improved with high accuracy. That is, even when the load distribution sensor 152 that detects the load applied via the belt 132 is used, the walking motion can be analyzed appropriately. The drive control unit 176 can perform drive control at appropriate timing. The trainee 900 can perform effective training.

[0084] Furthermore, the weight value calculation unit 174 may have a CNN that performs convolution processing using two-dimensional data including the correction value C for each cell as input. In this way, the weight value can be estimated with higher accuracy. This point will be described.

[0085] 5 and other figures, the viscoelastic sheet 1512 also deforms at the boundary between the area where the foot FT is present and the area where the foot FT is not present. In other words, the viscoelastic sheet deforms at a location that is not directly under the foot FT and where the load is not applied from the foot FT (for example, a location where the conductive sheet 1513 is inclined forward of position P1). Therefore, at the boundary, the sensor 155 that is not directly under the foot FT detects the load (hereinafter, also referred to as the entrainment phenomenon).

[0086] In this way, the sensor 155 detects pressure in an area larger than the actual ground contact size of the foot FT. The viscoelastic sheet 1512 deforms and detects pressure (load) in a number of cells greater than the number of cells actually located directly under the foot FT. In other words, it is not possible to distinguish between cells that are crushed and cells that are not crushed based on the output value of each sensor 155 alone.

[0087] The weight value calculation unit 174 uses a machine learning model having a CNN. The correction values ​​of a plurality of cells are input as two-dimensional data to the CNN. Since the CNN has a weight called a two-dimensional kernel, it is possible to perform weighting taking into account the correction values ​​of adjacent cells.

[0088] The following describes the learning model used by the load value calculation unit 174. Fig. 9 is a conceptual diagram for explaining an example of the learning model. The learning model is an inference model generated by machine learning.

[0089] The learning model is a DL (Deep Learning) model having multiple layers. As described above, the input data of the learning model is two-dimensional data. Therefore, the input data is shown as a two-dimensional grayscale image.

[0090] The learning model is equipped with a Convolutional Neural Network (CNN) that performs convolution processing on two-dimensional data. The CNN has a convolution layer and a pooling layer. The latter stage of the CNN is equipped with a Fully Connected Network (FCN) that includes a fully connected layer. Of course, other networks may be used for the learning model.

[0091] CNN performs convolution processing on two-dimensional data that includes the correction value of each cell. The convolutional neural network CNN has weights called two-dimensional kernels, which allows weighting processing that takes adjacent cells into account. The convolutional neural network extracts features according to the two-dimensional data. This makes it possible to improve the estimation accuracy of the weight value. The weights of the CNN can be calculated by machine learning.

[0092] In this way, the load distribution sensor 152 has a plurality of sensors 155 arranged in a two-dimensional array, and detects the load distribution applied by the trainee 900. The load value calculation unit 174 receives two-dimensional data based on the correction value. The load value calculation unit 174 has a CNN that performs convolution calculation processing, and estimates the load value received from the user. Since the output value of each sensor 155 is multi-bit data, the input data to the load value calculation unit 174 becomes two-dimensional data similar to image data. In other words, the sensor output becomes similar to image data in which the correction value of one sensor 155 becomes pixel data for one pixel. As with processing such as image identification, the CNN can perform convolution processing. Since a CNN for image processing can be used, versatility can be improved.

[0093] This allows the load value calculation unit 174 to calculate the load value with high accuracy. The load value calculation unit 174 does not need to distinguish between an area that is actually crushed and an area that is deformed due to the current state of entrapment. This allows the walking phase to be properly analyzed, enabling effective training.

[0094] In this embodiment, the load distribution sensor 152 is disposed under the belt 132. The sensor 155 detects the load (pressure) applied through the belt 132. Even in such a case, the load value calculation unit 174 can estimate the load value with high accuracy. That is, each parameter such as the weight of the CNN is learned assuming that the belt 132 moves. That is, the weighting parameters are optimized in a state in which the belt 132 moves. This can improve the estimation accuracy of the load value. The load value calculation unit 174 uses a machine learning model having a CNN, so that the load value can be calculated with higher estimation accuracy. Therefore, the overall control unit 210 can appropriately analyze the walking phase, and the trainee 900 can perform effective training.

[0095] Furthermore, based on the load value estimated by the load value calculation unit 174, the drive control unit 176 controls the leg orthosis 120, which is the robot leg. For example, the drive control unit 176 outputs a control signal for controlling a joint drive unit 221 such as a knee joint. The drive control unit 176 can drive the leg orthosis 120 in accordance with the walking phase. Since the leg orthosis 120 can assist the walking motion with an appropriate assist force, the trainee 900 can perform effective training.

[0096] The degree of the entrapment phenomenon may change depending on the moving speed of the belt 132. Therefore, the moving speed of the belt 132 may be input to the load value calculation unit 174. In other words, the two-dimensional data and the moving speed become input data for the learning model. For example, the learning model may use different parameters depending on the moving speed of the belt 132. Specifically, the activation function, filter, weight, etc. may be switched. Alternatively, the load value calculation unit 174 may switch between a learning model for high speed and a learning model for low speed. When generating the learning model by machine learning, the learning device may perform machine learning for each moving speed of the belt 132. In other words, the learning device uses the data of the moving speed as learning data by associating it with the two-dimensional data of the sensor output.

[0097] The sensor output acquisition unit 171 may trim the sensor output from all the sensors 155 included in the load distribution sensor 152. Then, the sensor output acquisition unit 171 may input the trimmed two-dimensional data to the load value calculation unit 174. For example, the sensor output acquisition unit 171 may have a trimming size set in advance. Here, the trimming size can be (k×l) rectangular areas. k is greater than 1 and less than m, and l is greater than 1 and less than n. The sensor output acquisition unit 171 cuts out k×l two-dimensional data with the center of gravity of the load distribution of the load distribution sensor 152 as the center. That is, the sensor output becomes (k×l) two-dimensional map data. In this way, the amount of data processing can be reduced, and therefore the processing can be performed at a high speed. The load value calculation unit 174 can calculate the load value for each frame.

[0098] The sensor output acquisition unit 171 or the correction unit 172 may function as a pressure conversion unit that converts the sensor output or the correction value into a pressure value. In this case, the load value calculation unit 174 uses two-dimensional map data composed of pressure values ​​as input data. Specifically, the load distribution sensor 152 A / D converts the output values ​​(voltage values) output from each sensor 155 into digital data. Then, the sensor output acquisition unit 171 converts the sensor output, which is digital data, into a pressure value using a conversion formula or a conversion table. The load distribution sensor 152 has a conversion formula or a conversion table that converts a measured value of an electrical characteristic into a pressure value.

[0099] The conversion table or the conversion formula may indicate a linear conversion result or a non-linear conversion result. For example, the conversion formula may be a quadratic or higher polynomial. The conversion formula or the conversion table for converting the sensor output value into the pressure value may be different for each sensor 155, or may be common to a plurality of sensors 155. For example, the sensor output acquisition unit 171 may use a conversion formula calibrated for each sensor 155. This can improve the estimation accuracy.

[0100] The overall control unit 210 is not limited to being a single physical device. In other words, the sensor output acquisition unit 171, the load value calculation unit 174, the determination unit 175, the gain setting unit 178, and the drive control unit 176 may be distributed and arranged in a plurality of devices. In this case, data may be transmitted and received between the devices via a network. For example, the gain setting unit 178 may be mounted on a server or the like so that it can be used by a plurality of walking training devices 100.

[0101] In addition, a part or all of the above-mentioned control method can be realized by a computer program. The program can be stored using various types of non-transitory computer readable media and supplied to the computer. The non-transitory computer readable media includes various types of tangible storage media. The non-transitory computer readable media includes, for example, magnetic recording media, magneto-optical recording media, CD-ROM (Read Only Memory), CD-R, CD-R / W, and semiconductor memory. The magnetic recording media are, for example, flexible disks, magnetic tapes, hard disk drives, etc. The magneto-optical recording media are, for example, magneto-optical disks, etc. The semiconductor memory is, for example, mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, RAM (Random Access Memory), etc. In addition, the program may be supplied to the computer by various types of transitory computer readable media. Examples of the transitory computer readable media include electric signals, optical signals, and electromagnetic waves. The temporary computer-readable medium can supply the program to the computer via a wired communication path such as an electric wire or an optical fiber, or via a wireless communication path. [Explanation of symbols]

[0102] 1. Walking training system 100 Walking training device 120 Leg braces 131 Treadmill 132 Belt 152 Load distribution sensor 153 Pulley 155 Sensors 171 Sensor output acquisition unit 172 Correction section 174 Load value calculation section 175 Judgment section 176 Drive control unit 178 Gain setting section 210 Overall Control Unit 221 Joint drive unit 900 Trainee

Claims

1. a treadmill having a belt on which a user stands; a load distribution sensor having a plurality of sensors arranged in a two-dimensional array and detecting a load applied via the belt; a sensor output acquisition unit that acquires a sensor output from the load distribution sensor; a correction unit that corrects a sensor output from each of the sensors based on a moving speed of the belt; A walking training system comprising: a load value calculation unit that calculates a load value received from the user based on the correction value corrected by the correction unit.

2. The correction unit obtains a sensor at a front end of an area where the user lands for each line, 2. The walking training system according to claim 1, wherein the sensor output is corrected in accordance with a sensor address in the forward / rearward direction, with the sensor address of the front end sensor being used as a reference.

3. 3. The walking training system according to claim 1, wherein the correction unit corrects the sensor outputs of one row of the sensors along the front-rear direction by using a correction coefficient according to a sensor address in the front-rear direction.

4. 3. The walking training system according to claim 1, wherein the load distribution sensor sequentially reads out outputs from sensors arranged in a row in the front-rear direction.

5. A robot leg attached to the user's leg; The walking training system according to claim 1 , further comprising: a drive control unit that drives the robot leg based on the load value.

6. 3. The walking training system according to claim 1, wherein the load value calculation unit has a CNN that performs convolution processing using two-dimensional data including the correction values ​​of a plurality of cells as input.

7. a treadmill having a belt on which a user stands; A control method for a walking training system including a load distribution sensor having a plurality of sensors arranged in a two-dimensional array and detecting a load applied via the belt, comprising: acquiring a sensor output from the load distribution sensor; determining a correction value by correcting the sensor output from each of the sensors based on a moving speed of the belt; and calculating a load value received from the user based on the correction value.

8. In the step of correcting A sensor at the front end of the area where the user lands is obtained for each line; 8. The method for controlling a walking training system according to claim 7, further comprising the step of correcting the sensor output in accordance with a sensor address in the forward / rearward direction, based on a sensor address of the front end sensor as a reference.

9. 9. The control method for a walking training system according to claim 7, further comprising the step of correcting the sensor outputs of the sensors in one row along the front-rear direction using a correction coefficient according to a sensor address in the front-rear direction.

10. 9. The method for controlling a walking training system according to claim 7, wherein the load distribution sensor sequentially reads out outputs from sensors arranged in a row in the front-rear direction.

11. A robot leg attached to the user's leg; The method for controlling a walking training system according to claim 7 or 8, further comprising: a drive control unit that drives the robot leg based on the load value.

12. 9. The method for controlling a walking training system according to claim 7, wherein the step of calculating the load value includes a CNN that performs convolution processing using two-dimensional data including the correction values ​​of a plurality of cells as input.

13. a treadmill having a belt on which a user stands; A control program for causing a computer to execute a control method for controlling a walking training system including a load distribution sensor having a plurality of sensors arranged in a two-dimensional array and detecting a load applied via the belt, The control method includes: acquiring a sensor output from the load distribution sensor; determining a correction value by correcting the sensor output from each of the sensors based on a moving speed of the belt; and calculating a load value received from the user based on the correction value.

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