CN114654498B - A knee exoskeleton motion monitoring method based on inertial sensor - Google Patents
A knee exoskeleton motion monitoring method based on inertial sensor Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 28
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- 210000003127 knee Anatomy 0.000 title description 6
- 230000005021 gait Effects 0.000 claims abstract description 50
- 210000003141 lower extremity Anatomy 0.000 claims abstract description 28
- 210000002414 leg Anatomy 0.000 claims abstract description 25
- 230000001133 acceleration Effects 0.000 claims abstract description 15
- 210000000629 knee joint Anatomy 0.000 claims abstract description 12
- 210000002683 foot Anatomy 0.000 claims description 17
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
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- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
- A61B5/112—Gait analysis
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
- B25J19/02—Sensing devices
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
- A61B2562/02—Details of sensors specially adapted for in-vivo measurements
- A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
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Abstract
The knee joint exoskeleton motion monitoring method based on the inertial sensor comprises the steps of after the inertial sensor is installed, designing gait cycle, firstly obtaining the movement tracks of the joints and the tail ends of the legs of a wearer, integrating acceleration data acquired by the lower leg part exoskeleton sensor to obtain the movement track of the lower leg part exoskeleton, calculating the sole movement track according to the obtained movement track, combining the obtained tracks to obtain the whole movement track of the lower limbs, and completing the movement data acquisition and analysis of the exoskeleton.
Description
Technical Field
The invention relates to a knee joint exoskeleton motion monitoring method based on an inertial sensor, and belongs to the field of novel exoskeleton design.
Background
Light-load power-assisted exoskeleton, which is a novel exoskeleton, is increasingly being focused and studied by various parties with higher maneuverability and smaller energy consumption. Compared with the traditional heavy-duty exoskeleton, the light-duty exoskeleton provides comprehensive assistance for the limbs of the human body, and the light-duty exoskeleton provides assistance for one or more specific joints. In order to reduce the weight of the exoskeleton, improve the maneuverability of the system, the light-load exoskeleton is driven by a motor to drive a flexible or light-rigid structure to move, and simultaneously, the torque of the motor is reduced, the motor is not used as main power to drive the human body and the exoskeleton to move, but the action of a user is used as main power, and the exoskeleton provides auxiliary power for specific muscle groups when the muscles of the user exert force, so that the movement effect of the user is improved, and the movement consumption of the user is reduced. The light-load exoskeleton is more focused on the perception of human motion by the control system than the conventional heavy-load exoskeleton, which requires that the control system provide corresponding assistance torque to the joint-related muscle groups at a particular time. The perception of the power assisting interval and the power assisting magnitude becomes an important factor for optimizing the power assisting effect of the light-load exoskeleton, and the detection of the motion state of a user becomes a key technology.
Knee exoskeleton, as a light load exoskeleton for a particular joint, can provide assistance to the wearer during walking and climbing stairs. The walking and stair climbing processes are used as main facing situations of human leg walking, and can be used as reference to approximately simulate the activities of most of human legs. The knee joint exoskeleton can be applied to the aged, disabled, field marching, mountain climbing exploration and fire rescue in the future.
The existing knee exoskeleton sensing mode mainly adopts a lower limb exoskeleton sensing mode, namely, inertial sensors are installed on thighs, calves and feet, the mode proves to be effective in a laboratory, but an additional foot sensing module is required for the knee exoskeleton, a certain challenge is brought to knee joint productization, and the product structure is more complicated due to the design. But would result in reduced perceived accuracy if the foot sensor module were not used, thereby affecting the decision of the control algorithm in terms of data accuracy.
Disclosure of Invention
Aiming at the defects of the knee joint exoskeleton motion monitoring method in the prior art, the invention provides a knee joint exoskeleton motion monitoring method based on an inertial sensor.
The invention solves the technical problems by the following technical proposal:
The knee joint exoskeleton movement monitoring method based on the inertial sensor comprises the following steps:
(1) The inertial sensor is arranged outside the leg exoskeleton according to a specified installation direction;
(2) Measuring the angle of the joint of the thigh part and the shank part of the leg exoskeleton according to an angle meter, measuring the size of the lower limb of a wearer, calculating the real-time movement position of the lower limb of the wearer on a sagittal plane, generating the movement track of the joints and the tail ends of the leg of the wearer, namely the heel part excluding the sole, and generating a connecting rod structure according to the obtained movement track;
(3) Integrating acceleration data acquired by a lower leg part exoskeleton sensor to obtain a lower leg part exoskeleton movement track, and calculating a sole movement track according to the obtained movement track;
(4) And (3) combining the motion trajectories obtained in the step (2) and the step (3) to obtain the overall motion trajectory of the lower limb.
The three-axis installation direction of the inertial sensor is respectively an advancing direction, a direction vertical to a sagittal plane and a direction vertical to the ground.
The gait cycle of the movement track of the tail end of the joint and the leg and the movement track of the sole is the same, and one single-foot gait cycle is that:
The method comprises the steps of judging according to the angular velocity of an axis of an inertial sensor perpendicular to a sagittal plane, determining a segmentation time point of a gait cycle for segmenting the leg exoskeleton, taking a supporting state midpoint as a gait cycle starting point in the single-foot movement process of the exoskeleton, simultaneously recording acceleration data integral data and lower limb tail end position data at the beginning of the gait cycle, and obtaining the final stride length according to the maximum distance of the lower limb tail end and the calculated integral result after the gait cycle is ended.
The integral data is calculated and recorded from the starting point of the gait cycle until the sole is not safely separated from the ground, and the integral result is calculated according to the recorded integral data before the gait cycle is finished.
The integral data is obtained according to acceleration integral calculation, a motion track in a specific time period is selected in each gait cycle through a return-to-zero correction method, approximate conversion is carried out according to the angular velocity of the sensor in the direction perpendicular to the sagittal plane, and the calculation is carried out again in the next gait cycle so as to avoid accumulation of gait cycle errors.
Compared with the prior art, the invention has the advantages that:
According to the knee joint exoskeleton motion monitoring method based on the inertial sensor, provided by the invention, the lower limb motion state is analyzed through the combination of the geometric method and the kinematic integration mode, gait data with higher accuracy are synthesized, and different functions of the multi-axis inertial sensor are used in a combined mode to judge each time point in the human walking gait cycle, so that the foot motion can be estimated approximately through the leg sensor without adding the foot sensor, and meanwhile, the knee joint exoskeleton motion monitoring method is installed on an exoskeleton structure without adding an additional structure.
Drawings
FIG. 1 is a schematic view of a knee exoskeleton inertial sensor installation provided by the present invention;
FIG. 2 is a diagram for resolving human lower limb movement provided by the invention;
FIG. 3 is a graph of the motion of the foot calculated from the motion trajectory of the IMU provided by the present invention;
FIG. 4 is a schematic view of the invention for selecting an integral interval of an angular velocity curve in walking;
FIG. 5 is a schematic view of the integration interval selected by the angular velocity curve when going upstairs;
Detailed Description
The knee joint exoskeleton movement monitoring method based on the inertial sensor is particularly used for monitoring movement data acquisition and analysis of a user in the exoskeleton using process, and can realize measurement of data such as step length, step frequency, step speed, gradient and the like, so that the data are fed back to a control system, and the specific monitoring flow is as follows:
(1) The inertial sensor is arranged outside the leg exoskeleton according to a specified installation direction;
the three-axis installation direction of the inertial sensor is respectively an advancing direction, a direction vertical to a sagittal plane and a direction vertical to the ground;
(2) Measuring the angle of the joint of the thigh part and the shank part of the leg exoskeleton according to an angle meter, measuring the size of the lower limb of a wearer, calculating the real-time movement position of the lower limb of the wearer on a sagittal plane, generating movement tracks of the joints and the tail ends of the legs of the wearer, and generating a connecting rod structure according to the obtained movement tracks;
(3) Integrating acceleration data acquired by a lower leg part exoskeleton sensor to obtain a lower leg part exoskeleton movement track, and calculating a sole movement track according to the obtained movement track;
The gait cycle of the movement track of the tail end of the joints and the legs and the movement track of the soles is the same, and one single-foot gait cycle is as follows:
Determining a segmentation time point of a gait cycle of the segmented leg exoskeleton according to the angular velocity of an axis of the inertial sensor perpendicular to the sagittal plane, taking a supporting state midpoint as a gait cycle starting point in the exoskeleton single-foot movement process, and simultaneously recording acceleration data integral data and lower limb tail end position data at the beginning of the gait cycle;
The integral data is calculated and recorded from the starting point of the gait cycle until the sole does not leave the ground safely;
The integral data is obtained according to acceleration integral calculation, a motion track in a specific time period is selected in each gait cycle through a return-to-zero correction method, golden wire conversion is carried out according to the angular velocity of the sensor in the direction perpendicular to the sagittal plane, and the calculation is carried out again in the next gait cycle so as to avoid gait cycle error accumulation;
(4) And (3) combining the motion trajectories obtained in the step (2) and the step (3) to obtain the overall motion trajectory of the lower limb.
Further description is provided below with reference to specific examples:
In the current embodiment, the knee exoskeleton is composed of 4 9-axis inertial sensors, and these 9 axes are three-axis acceleration, three-axis angular velocity, and three-axis angle, respectively. In exoskeleton walking, an inertial sensor is arranged on the outer side of a leg, and a real-time lower limb rod-shaped graph of human walking can be built by using a sagittal plane angle meter through real-time acquisition of 9-axis data. And the gait state is judged by the angular velocity, and the displacement caused by the foot motion is calculated by the acceleration integration.
As shown in fig. 1, the sensors are respectively mounted on the outer leg exoskeleton structure in a manner shown by a module indicated by an arrow in the figure. The three axes of the sensor have a fixed mounting direction, one pointing in the forward direction, one perpendicular to the sagittal plane and the other perpendicular to the ground.
The installation inevitably generates an angle error, so the method corrects the coordinate axis of the sensor through an initialization algorithm, and the aim is to reach the set installation direction.
As shown in fig. 2, according to the angle between the thigh and the calf measured by the angle meter and the size of the lower limb of the wearer, the real-time movement position of the lower limb of the human body is calculated on the sagittal plane, the movement track of the joint and the tail end of the leg is generated, and the connecting rod structure is generated according to the experimental result.
By monitoring at specific time points, the method can determine the segmentation points during the two gait cycles. As shown in fig. 4 and 5, it is possible to determine certain specific time points during the walking of the user based on the angular velocity of the axis of the inertial sensor perpendicular to the sagittal plane, and use one of the time points for dividing the gait cycle.
Gait cycle setting, for a single foot cycle, the real-time monitoring system takes the midpoint of the support state as a starting point, and acceleration is zero at the time point. At the beginning of a gait cycle, integral data and lower extremity position data are recorded simultaneously. The integration is calculated from the starting point of the cycle until the sole is completely lifted off, then the gait cycle is not finished, and the integration result is calculated in the period, and when the gait cycle is finished, the maximum distance of the tail end of the lower limb is added with the integration result after conversion, and the final stride length can be considered. For a bipedal cycle, the cycle start point of both feet can be set as the bipedal cycle start point, and the cycle end point of the other foot can be set as the end point.
The integration period determination is that the angular velocity image according to fig. 4 can be used to find the integration period for deducing the motion of the foot, starting from the black point (midpoint of the support state) and ending at the red star (sole lift), and the motion track of the inertial sensor can be obtained by integration. Fig. 5 shows the integration period set during the ascending stairs, as during the walking.
And the integral error is removed, namely the motion trail generated by acceleration integral has the problem of error accumulation, and the error is accumulated continuously along with the increase of the integral length. In order to avoid the problem of error accumulation, a return-to-zero correction method is adopted, a motion track in a specific time period is selected in each gait cycle according to the angular velocity to perform approximate conversion, and the motion track is recalculated in the same manner in the next gait cycle, so that the error accumulation in the previous gait cycle is avoided. In the integration selection of the gait cycle, in order to avoid the accumulated error caused by the calculation of the initial velocity, the initial point of the total integration period is advanced, and the integration is started from the point of time when the velocity approaches zero, thereby avoiding the dependence on the initial velocity and thus avoiding the accumulated error.
As shown in fig. 3, the motion trajectory of the inertial sensor may be approximated to the ankle motion (i.e., foot motion) trajectory based on the ankle geometry. The conversion relation between the sensor displacement and the displacement of the lower extremity joint is that the ankle joint rotation angle is assumed to be ignored, and the positions of the sensor and the toe joint (rotation axis) can be obtained according to the body size and the exoskeleton size, so that the sensor displacement and the displacement of the lower extremity joint become the proportional arc length relation with the same angle and different radiuses, and the approximate displacement of the lower extremity can be deduced according to the body size.
Finally, the motion trail of the tail end of the leg deduced by the connecting rod structure is combined with the motion trail of the approximate foot, so that the motion distance of the lower limb in one gait cycle, namely the stride, can be completely estimated, and the pace of the gait cycle can be deduced according to the duration of the gait cycle. The same can be applied to the stair climbing process, and data such as the stepping height, the stair slope and the like can be obtained.
Although the present invention has been described in terms of the preferred embodiments, it is not intended to be limited to the embodiments, and any person skilled in the art can make any possible variations and modifications to the technical solution of the present invention by using the methods and technical matters disclosed above without departing from the spirit and scope of the present invention, so any simple modifications, equivalent variations and modifications to the embodiments described above according to the technical matters of the present invention are within the scope of the technical matters of the present invention.
What is not described in detail in the present specification belongs to the known technology of those skilled in the art.
Claims (1)
1. The knee joint exoskeleton movement monitoring method based on the inertial sensor is characterized by comprising the following steps of:
(1) The inertial sensor is arranged outside the leg exoskeleton according to a specified installation direction;
(2) Measuring the angle of the joint of the thigh part and the shank part of the leg exoskeleton according to an angle meter, measuring the size of the lower limb of a wearer, calculating the real-time movement position of the lower limb of the wearer on a sagittal plane, generating the movement track of the joints and the tail ends of the leg of the wearer, namely the heel part excluding the sole, and generating a connecting rod structure according to the obtained movement track;
(3) Integrating acceleration data acquired by a lower leg part exoskeleton sensor to obtain a lower leg part exoskeleton movement track, and calculating a sole movement track according to the obtained movement track;
(4) Combining the motion trail obtained in the step (2) and the step (3) to obtain the overall motion trail of the lower limb;
the three-axis installation direction of the inertial sensor is respectively an advancing direction, a direction vertical to a sagittal plane and a direction vertical to the ground;
The gait cycle of the movement track of the tail end of the joint and the leg and the movement track of the sole is the same, and one single-foot gait cycle is that:
Determining a segmentation time point of a gait cycle of the segmented leg exoskeleton according to the angular velocity of an axis of the inertial sensor perpendicular to the sagittal plane, taking a supporting state midpoint as a gait cycle starting point in the exoskeleton single-foot movement process, and simultaneously recording acceleration data integral data and lower limb tail end position data at the beginning of the gait cycle;
the integral data is calculated and recorded from the starting point of the gait cycle until the sole does not leave the ground safely;
the integral data is obtained according to acceleration integral calculation, a motion track in a specific time period is selected from each gait cycle through a return-to-zero correction method, approximate conversion is carried out according to the angular velocity of the sensor in the direction perpendicular to the sagittal plane, and the calculation is carried out again in the next gait cycle so as to avoid accumulation of gait cycle errors;
In exoskeleton walking, the inertial sensors are arranged on the outer sides of legs, real-time acquisition of 9-axis data is carried out, a real-time lower limb rod-shaped graph of human walking is built by using a sagittal plane angle meter, gait state is judged through the angular velocity, and displacement caused by foot movement is calculated through acceleration integration;
for the double-foot period, setting that any one of the period starting points of the two feet is used as the starting point of the double-foot period and the other one is used as the end point;
The integration period judgment is that the integration period of foot motion is deduced according to the angular velocity image, the moment starts from the midpoint of the supporting state and ends when the sole leaves the ground, and the motion track of the inertial sensor can be obtained through integration;
According to the geometrical relationship of ankle part, the motion track of inertial sensor is approximately calculated to calculate the motion track of ankle joint, the motion track of leg end deduced by connecting rod structure is combined with the motion track of foot, the motion distance of lower limb in a gait cycle is completely estimated, i.e. the stride, and the pace speed of the gait cycle is deduced according to the length of the gait cycle, at the same time, the method is used for obtaining the data of step height and stair slope in the process of going upstairs.
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| CN112318490A (en) * | 2020-11-30 | 2021-02-05 | 北京精密机电控制设备研究所 | Light and flexible knee joint power assisting exoskeleton robot |
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| CN110543922A (en) * | 2019-10-15 | 2019-12-06 | 北京理工大学 | A Real-time Walking Pattern Recognition Method Based on Knee Joint Exoskeleton |
| CN110916679B (en) * | 2019-12-31 | 2022-07-15 | 复旦大学 | Device and method for detecting posture and gait of human lower limbs |
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| CN106419928A (en) * | 2016-11-03 | 2017-02-22 | 浙江大学 | A wearable device and a real-time step size measurement method for the device |
| CN111012358A (en) * | 2019-12-26 | 2020-04-17 | 浙江福祉医疗器械有限公司 | Human ankle joint motion trajectory measurement method and wearable device |
| CN112318490A (en) * | 2020-11-30 | 2021-02-05 | 北京精密机电控制设备研究所 | Light and flexible knee joint power assisting exoskeleton robot |
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