CN114271812A - Three-dimensional gait analysis system and method based on inertial sensor - Google Patents
Three-dimensional gait analysis system and method based on inertial sensor Download PDFInfo
- Publication number
- CN114271812A CN114271812A CN202111478306.4A CN202111478306A CN114271812A CN 114271812 A CN114271812 A CN 114271812A CN 202111478306 A CN202111478306 A CN 202111478306A CN 114271812 A CN114271812 A CN 114271812A
- Authority
- CN
- China
- Prior art keywords
- inertial sensor
- data
- gait
- acceleration
- axis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000005021 gait Effects 0.000 title claims abstract description 143
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000004458 analytical method Methods 0.000 title claims abstract description 24
- 230000001133 acceleration Effects 0.000 claims abstract description 72
- 230000033001 locomotion Effects 0.000 claims abstract description 43
- 210000000544 articulatio talocruralis Anatomy 0.000 claims description 45
- 210000003141 lower extremity Anatomy 0.000 claims description 43
- 210000002414 leg Anatomy 0.000 claims description 39
- 238000005259 measurement Methods 0.000 claims description 33
- 238000006073 displacement reaction Methods 0.000 claims description 32
- 210000000629 knee joint Anatomy 0.000 claims description 32
- 238000012545 processing Methods 0.000 claims description 19
- 210000002683 foot Anatomy 0.000 claims description 17
- 210000000689 upper leg Anatomy 0.000 claims description 13
- 238000012937 correction Methods 0.000 claims description 11
- 230000003068 static effect Effects 0.000 claims description 11
- 210000004394 hip joint Anatomy 0.000 claims description 9
- 238000001914 filtration Methods 0.000 claims description 8
- 230000005484 gravity Effects 0.000 claims description 8
- 244000309466 calf Species 0.000 claims description 7
- 238000004891 communication Methods 0.000 claims description 7
- 210000003371 toe Anatomy 0.000 claims description 7
- 238000004140 cleaning Methods 0.000 claims description 5
- 238000009499 grossing Methods 0.000 claims description 5
- 230000001360 synchronised effect Effects 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 4
- 238000012935 Averaging Methods 0.000 claims description 3
- 210000003423 ankle Anatomy 0.000 claims description 3
- 230000000295 complement effect Effects 0.000 claims description 3
- 238000000513 principal component analysis Methods 0.000 claims description 3
- 230000000284 resting effect Effects 0.000 claims description 3
- 239000004576 sand Substances 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 description 11
- 238000012549 training Methods 0.000 description 10
- 238000012544 monitoring process Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 210000003414 extremity Anatomy 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 4
- 230000000007 visual effect Effects 0.000 description 3
- 210000000988 bone and bone Anatomy 0.000 description 2
- 230000036544 posture Effects 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- QBWCMBCROVPCKQ-UHFFFAOYSA-N chlorous acid Chemical compound OCl=O QBWCMBCROVPCKQ-UHFFFAOYSA-N 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 210000001624 hip Anatomy 0.000 description 1
- 210000003127 knee Anatomy 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 210000003205 muscle Anatomy 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
Images
Abstract
The invention discloses a three-dimensional gait analysis system and a method based on an inertial sensor, which comprises the following steps: the system comprises wearable equipment, a data acquisition client, a server platform and a gait display client; the invention is based on the inertial sensor with low price, and can effectively reproduce three-dimensional gait motion and calculate the related gait parameters by only utilizing the acceleration and angular velocity data of the key parts in the gait motion process.
Description
Technical Field
The invention belongs to the technical field of inertial sensing and gait rehabilitation monitoring, and particularly relates to a three-dimensional gait analysis system and method based on an inertial sensor.
Background
In the current medical system, the monitoring mode of gait rehabilitation training still adopts the real-time supervision and guidance mode of medical care personnel, namely, the medical care personnel firstly carries out gait analysis on a patient, namely, the gait analysis is carried out on various factors such as the walking posture, the walking speed, the joint and muscle activity, the balance coordination control and the like of the patient so as to determine the characteristics and reasons of the current gait, thereby timely and effectively helping the patient select an auxiliary appliance and make a corresponding training scheme. In this process, the medical staff usually records the training data of the patient by experience and directly guides the training data through visual observation.
Although the manual real-time monitoring mode is simple and effective, the burden of patients and medical care is inevitably increased at the same time, and the method has the following great disadvantages: (1) for the patients who are in follow-up visit, the patients generally need to make an appointment in advance and go to a professional medical care center; if the road is far away, the traffic is inconvenient, or the peak period is met, the long time is spent on the round trip and the queuing, and the experience is extremely poor. (2) For medical care, the manual real-time monitoring mode is low in efficiency and heavy in burden, the number of patients capable of being monitored simultaneously is limited, and the manual data recording mode is limited by the service level of medical care personnel and lacks of objective data support.
In addition, in real life, due to the limited medical care personnel and training fields, the patient may not obtain timely rehabilitation training guidance and even training at any time and any place, and the recovery of normal functions can be further influenced in the past.
The automatic monitoring techniques currently available for gait rehabilitation training can be mainly classified into the following three categories: image processing based technologies, wireless signal based technologies, and wearable device based technologies. The method comprises the following specific steps:
(1) image processing based techniques: depending on whether or not a marker is used, the technique can be divided into two categories: a gait monitoring and analyzing technology with marked points and a technology without the marked points. The technology with marked points is based on optical motion capture systems such as Vicon, OptiTrack and the like, and tracking is carried out by utilizing reflective marked points. The technology without the mark points directly captures the positions of the joint points by using a depth camera or extracts the motion postures of limbs from a common image sequence, and the gait parameters are calculated on the basis. The technical disadvantage based on image processing is that the user is restricted by sight, the user must move within the visual angle range of the camera, no shelters can be found, the equipment cost is high, and the privacy problem exists.
(2) Wireless signal based technologies: the technique utilizes wireless signals that change during walking to monitor and analyze gait. Since most wireless signal receivers have a limited working range, this limits the range of motion of the subject; and more importantly, the wireless signals are susceptible to the surrounding environment.
(3) Wearable device based technologies: the technique utilizes a variety of wearable sensors placed on different parts of the body, such as the hip, knee, and foot, to collect data in multiple dimensions, on the basis of which gait is monitored and analyzed. Currently, inertial sensors are most frequently used, but the existing scheme has disadvantages that unstable magnetometers are used for fusing data, a large amount of data is collected to estimate gait parameters based on machine learning, or a user is required to measure a priori knowledge, such as lower limb length. More importantly, none of them achieve a complete gait tracking task.
Therefore, based on the above considerations, there is a need for an innovative gait monitoring and analyzing system, which uses an economically feasible, convenient and stable device to effectively reconstruct actual gait movement and record objective data in the movement process for auxiliary analysis. The gait training system can meet the daily gait training requirement and has a great promoting effect on the construction and development of an intelligent medical system.
Disclosure of Invention
In view of the above-mentioned deficiencies of the prior art, the present invention aims to provide a three-dimensional gait analysis system and method based on an inertial sensor, so as to alleviate the situations of high manpower overhead and difficult data acquisition in the traditional gait rehabilitation monitoring technology, and solve the problems of high cost, and unstable or incomplete system in the existing automation technology; the invention is based on the inertial sensor with low price, and can effectively reproduce three-dimensional gait motion and calculate the related gait parameters by only utilizing the acceleration and angular velocity data of the key parts in the gait motion process.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention discloses a three-dimensional gait analysis system based on an inertial sensor, which comprises: the system comprises wearable equipment, a data acquisition client, a server platform and a gait display client; wherein the content of the first and second substances,
the wearable device is arranged at 4 key parts of the lower limb of the user, and is used for receiving an instruction sent by the data acquisition client, acquiring sensor data in the movement process of the user and sending the sensor data to the data acquisition client;
the data acquisition client receives sensor data sent by the wearable device through wireless communication, synchronizes the time of the data and uploads the data to the server platform;
the server platform is used for acquiring and processing data uploaded by the data acquisition client and storing the processed gait result data in the database;
and the gait display client acquires the gait result data of the user from the server platform and displays the gait motion and the gait parameters.
Further, the wearable apparatus includes: the system comprises an inertial sensor, a wireless communication module, a single chip microcomputer and a mobile power supply;
the inertial sensor is used for acquiring acceleration and angular velocity data of a user;
the wireless communication module is used for instruction receiving and data transmission;
the single chip microcomputer is used for controlling the acquisition of acceleration and angular velocity data and storing the acquired data into a cache queue for transmission according to a time sequence relation;
and the mobile power supply is used for supplying power to the components.
Furthermore, the 4 key parts are respectively above the knee joint of the left leg, at the ankle joint of the left leg, above the knee joint of the right leg and at the ankle joint of the right leg.
Further, the specific method for processing data by the server platform is as follows:
31) data cleaning: processing data by adopting a smoothing filtering method to reduce noise, and eliminating the offset of acceleration and angular velocity according to the data when an inertial sensor in the wearable equipment is static at the initial moment;
32) detecting a turning point in the walking process according to the angular speed data of the inertial sensor, segmenting the data according to the turning point, and ensuring that the moving direction of each segment of data is a straight line;
33) synchronizing coordinate systems of inertial sensor data of 4 key parts under each section of data;
34) determining gait cycles according to the periodicity of the rotation angle of the inertial sensor at the ankle joint and dividing the data of the inertial sensor, detecting key events in the gait cycles according to the rotation angle of the inertial sensor in each gait cycle, and calculating the displacement of the inertial sensor at the ankle joint by utilizing zero-speed correction and acceleration secondary integral so as to calculate the length of the lower limb of a human body;
35) correcting the displacement of an inertial sensor at the ankle joint by using the length of the lower limb and the rotation angle of the inertial sensor according to the basic structure of a lower limb framework and taking the hip joint as a fulcrum, deducing the displacement of the inertial sensor above the knee joint according to the displacement of the inertial sensor at the ankle joint, and storing the displacement and rotation angle data of each inertial sensor into a database;
36) in different gait cycles, the length of the lower limbs of the human body and the rotation angle of the inertial sensor are used for calculating each gait parameter, and the result is stored in a database according to the time sequence relation.
Further, the gait parameters include: swing phase ratio, step length, step speed, step span, step frequency and symmetry; the specific definition is as follows:
swing phase ratio: the swing phase time accounts for the proportion of the corresponding gait cycle; the gait cycle is the time from the lift-off of one toe to the lift-off of the toe again in the walking process; the swing phase time is the time of the foot in the air in one gait cycle;
step length: during walking, the distance between the landing points of the two feet in the advancing direction when the two feet land simultaneously;
the pace speed is as follows: speed of travel during walking;
step-span length: the distance between the foot parts on the same side in the advancing direction is continuously twice;
step frequency: the frequency of foot landing during walking;
symmetry: the degree of the difference between the movements of the left limb and the right limb in the walking process can be described by the ratio of the step length of the left leg to the step length of the right leg.
The invention discloses a three-dimensional gait analysis method based on an inertial sensor, which is based on the system and comprises the following steps:
1) wearable devices arranged at 4 key parts of the lower limbs of a user respectively acquire gait data of the user;
2) the data acquisition client receives sensor data sent by each wearable device, synchronizes time and uploads the sensor data to the server platform;
3) processing the data uploaded by the data acquisition client, and storing the processed gait result data in a database;
4) and acquiring gait result data of the user from the server platform, and displaying gait motion and gait parameters.
Furthermore, the 4 key parts are respectively above the knee joint of the left leg, at the ankle joint of the left leg, above the knee joint of the right leg and at the ankle joint of the right leg.
Further, the specific method for processing data in step 3) is as follows:
31) data cleaning: processing data by adopting a smoothing filtering method to reduce noise, and eliminating the offset of acceleration and angular velocity according to the data when an inertial sensor in the wearable equipment is static at the initial moment;
32) detecting a turning point in the walking process according to the angular speed data of the inertial sensor, segmenting the data according to the turning point, and ensuring that the moving direction of each segment of data is a straight line;
33) synchronizing coordinate systems of inertial sensor data of 4 key parts under each section of data;
34) determining gait cycles according to the periodicity of the rotation angle of the inertial sensor at the ankle joint and dividing the data of the inertial sensor, detecting key events in the gait cycles according to the rotation angle of the inertial sensor in each gait cycle, and calculating the displacement of the inertial sensor at the ankle joint by utilizing zero-speed correction and acceleration secondary integral so as to calculate the length of the lower limb of a human body;
35) correcting the displacement of an inertial sensor at the ankle joint by using the length of the lower limb and the rotation angle of the inertial sensor according to the basic structure of a lower limb framework and taking the hip joint as a fulcrum, deducing the displacement of the inertial sensor above the knee joint according to the displacement of the inertial sensor at the ankle joint, and storing the displacement and rotation angle data of each inertial sensor into a database;
36) in different gait cycles, the length of the lower limbs of the human body and the rotation angle of the inertial sensor are used for calculating each gait parameter, and the result is stored in a database according to the time sequence relation.
Further, the specific method for eliminating the offset between the acceleration and the angular velocity in step 31) is as follows:
311) let the mean value of the acceleration measurements in the stationary time period be a ═ ax，ay，az]Theoretical acceleration measurement value a ' ═ a ' is calculated from the tilt attitude of the inertial sensor 'x，a′y，a′z]Wherein, in the step (A),
g is the acceleration of gravity, axComponent a representing the mean value of the acceleration measurements along the X-axis of the coordinate system of the inertial sensor unityComponent a representing the mean value of the acceleration measurements along the Y-axis of the coordinate system of the inertial sensor unitzA 'representing a component of the mean value of the acceleration measurements along the Z-axis of the inertial sensor device coordinate system'xRepresenting the component, a ', of the theoretical acceleration measurement along the X-axis of the inertial sensor device coordinate system'yRepresenting the component, a ', of the theoretical acceleration measurement along the Y-axis of the inertial sensor device coordinate system'zRepresenting the component of the theoretical acceleration measurement value along the Z axis of the coordinate system of the inertial sensor equipment, and obtaining the offset of the acceleration by using a-a';
312) averaging the measurement values of the gyroscope in the static time period of the inertial sensor to obtain the offset of the angular velocity;
313) all measured data of acceleration versus angular velocity are subtracted by the corresponding offset.
Further, the method for detecting the turning point in the walking process in the step 32) comprises the following steps: the rotation angle is estimated by using the angular velocity integral of the Z-axis of the inertial sensor device coordinate system, and when the absolute value of the rotation angle within a time window exceeds pi/2, steering is considered to have occurred.
Further, the specific method for performing coordinate system synchronization on the inertial sensor data in step 33) is as follows:
331) let the device coordinate system of each wearable device be Xl，Yl，ZlThe reference coordinate system is Xr，Yr，Zr，xrIs XlProjection on a horizontal plane, ZrIn the direction opposite to gravity, YrIs cross multiplied by Zr×XrThe direction of (a); unified global coordinate system of Xg，Yg，Zg，YgThe initial orientation of the human body, XgIn the right direction of the body, ZgIn the direction opposite to gravity;
332) calculating the initial tilt attitude of the inertial sensor according to the static acceleration data of n seconds at the initial moment, wherein n is more than or equal to 1, obtaining the rotation quaternion from the initial moment equipment coordinate system to the reference coordinate system, updating the rotation quaternion by fusing the acceleration data and the angular velocity data of the inertial sensor by using self-adaptive complementary filtering, and at the moment, 4 inertial sensor data have the same Z after rotation transformationrAxis, i.e. ZgA shaft;
333) removing the Z-edge from the acceleration measurementsgDetermining Y from the direction of maximum acceleration fluctuation using principal component analysis algorithmgDetermining X axis or from direction of greatest angular velocity fluctuationgAxis, respectively obtained from YrTo YgAngle of rotation of and from XrTo XgThe angle of rotation of (a); rotation angle calculated for two waysAnd carrying out weighted average to enable the data of the 4 inertial sensors to be synchronized to the global coordinate axis after rotation transformation.
Further, the key events in the gait cycle in step 34) include: the detection method comprises the following steps:
341) separating the toes from the ground: corresponding to the moment of maximum posterior thigh swing in one gait cycle, i.e. the sensor above the knee joint is around XgMinimum point of axis rotation angle;
342) the sole touches the ground: when the sole is completely touched, the shank is perpendicular to the ground, and the inertial sensor winds around X corresponding to the ankle jointgThe rotational angle of the shaft crosses zero.
Further, the specific method of zero-speed correction in step 34) is as follows:
343) let t be the time when two successive sole touchdown events occursAnd te，tsTime edge YgThe speed of the shaft is v (t)s) Along YgAcceleration of the shaft is ag(t), then teTime edge YgSpeed v (t) of the shafte) Expressed as:
wherein v (t) can be substituted for the first time the sole touches the ground, i.e. the initial resting times)＝0；
344) Theoretical sole edge YgThe speed of the shaft is zero, resulting in v (t) during motion due to errors in the acceleration measurementse) Not zero, assuming the acceleration error is a constant δ, and can be calculated by:
345) by ag(t) -delta correction edge YgAcceleration of the shaft.
Further, the method for calculating the length of the lower limb in the step 34) is as follows:
346) for the same foot, between two adjacent sole landing events, the inertial sensor at the ankle joint is along YgThe second integral of the axial acceleration is the first step length d (i);
347) one step length is equal to the sum of the step lengths of two consecutive times, i.e. s (i) + s (i + 1); from the lower limb geometric model, the step length is expressed as the thigh length l at the time of toe-off eventtLength of calf lsRelation to the respective rotation angle:
wherein alpha isr(i) And phir(i) Respectively shows the rotation angles alpha of the inertial sensors above the knee joint and at the ankle joint of the right leg when the sole of the right leg lands on the ground in the ith stepl(i) And phil(i) The rotation angles of the inertial sensors above the knee joint and at the ankle joint of the left leg at the ith step when the foot tip of the left leg is off are respectively shown, and the subscript X shows the rotation angle around XgThe component of the axis, the subscript Y denoting the rotation angle about YgA component of the axis;
348) calculating the thigh length l by introducing a regression modeltLength of shank ls：
Further, the lower limb geometric model in the step 347) is as follows: the thigh and the calf are regarded as rigid bodies to move by taking the hip joint as a common fulcrum of the left leg and the right leg, wherein the rotation angle and the displacement of the thigh by taking the hip joint as the fulcrum are captured by the inertial sensor above the knee joint, and the rotation angle and the displacement of the calf by taking the knee joint as the fulcrum are captured by the inertial sensor at the ankle joint. Further, the showing process of the gait motion and the gait parameters in the step 4) comprises presenting 3D bone playback of the gait motion and further showing the variation waveform of the related gait parameters along with time;
3D skeleton playback of gait movement, namely, defining that knee joint movement of lower limb skeleton is consistent with movement of an inertial sensor above an actual knee joint, defining that ankle joint movement of the lower limb skeleton is consistent with movement of the inertial sensor at the actual ankle joint by constructing human three-dimensional skeleton, and acquiring rotation angle and displacement of the corresponding inertial sensor in a database to reproduce the gait movement;
the gait parameters are output in a dynamic curve graph form according to a time sequence relation, and correspond to the playback process of the 3D skeleton, namely, the curve graph outputs the gait parameters in a period every time the 3D skeleton completes the movement of one gait period.
The invention has the beneficial effects that:
1. three-dimensional reconstruction of gait movement: the lower limb geometric model is established, the three-dimensional reproduction of the gait can be realized according to the rotation angle and the displacement of the inertial sensor, and the actual gait condition is intuitively restored while the privacy of the user is protected.
2. High-precision calculation of gait parameters: high-frequency jitter and drift errors of the inertial sensors are relieved through an algorithm, data of a plurality of sensors are synchronized to calculate the length of the lower limb, objective gait parameters such as step length, pace, stride length, step frequency and the like can be accurately calculated on the basis, and the calculation error of each parameter is about 5%.
3. Visual interface aided analysis, use manpower sparingly: the server platform provides 3D skeleton playback of gait movement and a dynamic curve graph of gait parameters, a curve is synchronous with gait playback, and data points are refreshed when each gait cycle is completed; the graph can provide specific detailed numerical values, so that the analysis is better assisted and the energy is saved.
4. The cost is low: the hardware of the wearable device adopts commercial elements, is economical and small, and has low power consumption; the data acquisition client can be installed on the existing smart phone of the user.
Drawings
FIG. 1 is a system framework diagram of the present invention;
fig. 2 is a wearable device deployment diagram;
FIG. 3 is a geometric model of a lower limb;
FIG. 4 is a flow chart of the method of the present invention;
FIG. 5 is a global coordinate system diagram;
FIG. 6 is a diagram of key events in a gait cycle;
FIG. 7 is a diagram of a model for lower limb length calculation;
fig. 8 is a schematic diagram of a three-dimensional skeleton of a human body.
Detailed Description
In order to facilitate understanding of those skilled in the art, the present invention will be further described with reference to the following examples and drawings, which are not intended to limit the present invention.
Referring to fig. 1 and 2, the invention provides a three-dimensional gait analysis system based on an inertial sensor, comprising: the system comprises wearable equipment, a data acquisition client, a server platform and a gait display client; wherein the content of the first and second substances,
the wearable device is arranged at 4 key parts of the lower limb of the user, and is used for receiving an instruction sent by the data acquisition client, acquiring sensor data in the movement process of the user and sending the sensor data to the data acquisition client;
wherein the wearable device includes: the system comprises an inertial sensor, a wireless communication module, a single chip microcomputer and a mobile power supply;
the inertial sensor is used for acquiring acceleration and angular velocity data of a user;
the wireless communication module is used for instruction receiving and data transmission;
the single chip microcomputer is used for controlling the acquisition of acceleration and angular velocity data and storing the acquired data into a cache queue for transmission according to a time sequence relation;
and the mobile power supply is used for supplying power to the components.
In the example, the 4 key parts are respectively above the knee joint of the left leg, at the ankle joint of the left leg, above the knee joint of the right leg and at the ankle joint of the right leg.
The system comprises a data acquisition client (mobile phone), a server platform and a server, wherein the data acquisition client (mobile phone) receives sensor data sent by wearable equipment through wireless communication, synchronizes the time of the data and uploads the data to the server platform;
the server platform is used for acquiring and processing data uploaded by the data acquisition client and storing the processed gait result data in the database;
the specific method for processing data by the server platform comprises the following steps:
31) data cleaning: processing data by adopting a smoothing filtering method to reduce noise, and eliminating the offset of acceleration and angular velocity according to the data when an inertial sensor in the wearable equipment is static at the initial moment;
32) detecting a turning point in the walking process according to the angular speed data of the inertial sensor, segmenting the data according to the turning point, and ensuring that the moving direction of each segment of data is a straight line;
33) synchronizing coordinate systems of inertial sensor data of 4 key parts under each section of data;
34) determining gait cycles according to the periodicity of the rotation angle of the inertial sensor at the ankle joint and dividing the data of the inertial sensor, detecting key events in the gait cycles according to the rotation angle of the inertial sensor in each gait cycle, and calculating the displacement of the inertial sensor at the ankle joint by utilizing zero-speed correction and acceleration secondary integral so as to calculate the length of the lower limb of a human body;
35) correcting the displacement of an inertial sensor at the ankle joint by using the length of the lower limb and the rotation angle of the inertial sensor according to the basic structure of a lower limb framework and taking the hip joint as a fulcrum, deducing the displacement of the inertial sensor above the knee joint according to the displacement of the inertial sensor at the ankle joint, and storing the displacement and rotation angle data of each inertial sensor into a database;
36) in different gait cycles, the length of the lower limbs of the human body and the rotation angle of the inertial sensor are used for calculating each gait parameter, and the result is stored in a database according to the time sequence relation.
And the gait display client acquires the gait result data of the user from the server platform and displays the gait motion and the gait parameters.
Wherein the gait parameters include: swing phase ratio, step length, step speed, step span, step frequency and symmetry; the specific definition is as follows:
swing phase ratio: the swing phase time accounts for the proportion of the corresponding gait cycle; the gait cycle is the time from the lift-off of one toe to the lift-off of the toe again in the walking process; the swing phase time is the time of the foot in the air in one gait cycle;
step length: during walking, the distance between the landing points of the two feet in the advancing direction when the two feet land simultaneously;
the pace speed is as follows: speed of travel during walking;
step-span length: the distance between the foot parts on the same side in the advancing direction is continuously twice;
step frequency: the frequency of foot landing during walking;
symmetry: the degree of the difference between the movements of the left limb and the right limb in the walking process can be described by the ratio of the step length of the left leg to the step length of the right leg.
Referring to fig. 4, the three-dimensional gait analysis method based on inertial sensor of the invention, based on the above system, includes the following steps:
1) wearable devices arranged at 4 key parts of the lower limbs of a user respectively acquire gait data of the user;
2) the data acquisition client receives sensor data sent by each wearable device, synchronizes time and uploads the sensor data to the server platform;
3) processing the data uploaded by the data acquisition client, and storing the processed gait result data in a database;
4) and acquiring gait result data of the user from the server platform, and displaying gait motion and gait parameters.
Wherein, 4 key parts are knee joint top of left leg, ankle joint department of left leg, knee joint top and the ankle joint department of right leg respectively.
Specifically, the specific method for processing data in step 3) is as follows:
31) data cleaning: processing data by adopting a smoothing filtering method to reduce noise, and eliminating the offset of acceleration and angular velocity according to the data when an inertial sensor in the wearable equipment is static at the initial moment;
32) detecting a turning point in the walking process according to the angular speed data of the inertial sensor, segmenting the data according to the turning point, and ensuring that the moving direction of each segment of data is a straight line;
33) synchronizing coordinate systems of inertial sensor data of 4 key parts under each section of data;
34) determining gait cycles according to the periodicity of the rotation angle of the inertial sensor at the ankle joint and dividing the data of the inertial sensor, detecting key events in the gait cycles according to the rotation angle of the inertial sensor in each gait cycle, and calculating the displacement of the inertial sensor at the ankle joint by utilizing zero-speed correction and acceleration secondary integral so as to calculate the length of the lower limb of a human body; as shown with reference to FIG. 6;
35) correcting the displacement of an inertial sensor at the ankle joint by using the length of the lower limb and the rotation angle of the inertial sensor according to the basic structure of a lower limb framework and taking the hip joint as a fulcrum, deducing the displacement of the inertial sensor above the knee joint according to the displacement of the inertial sensor at the ankle joint, and storing the displacement and rotation angle data of each inertial sensor into a database;
36) in different gait cycles, the length of the lower limbs of the human body and the rotation angle of the inertial sensor are used for calculating each gait parameter, and the result is stored in a database according to the time sequence relation.
Specifically, the specific method for eliminating the offset between the acceleration and the angular velocity in step 31) is as follows:
311) let the mean value of the acceleration measurements in the stationary time period be a ═ ax，ay，az]Theoretical acceleration measurement value a ' ═ a ' is calculated from the tilt attitude of the inertial sensor 'x，a′y，a′z]Wherein, in the step (A),
g is gravityAcceleration, axComponent a representing the mean value of the acceleration measurements along the X-axis of the coordinate system of the inertial sensor unityComponent a representing the mean value of the acceleration measurements along the Y-axis of the coordinate system of the inertial sensor unitzA 'representing a component of the mean value of the acceleration measurements along the Z-axis of the inertial sensor device coordinate system'xRepresenting the component, a ', of the theoretical acceleration measurement along the X-axis of the inertial sensor device coordinate system'yRepresenting the component, a ', of the theoretical acceleration measurement along the Y-axis of the inertial sensor device coordinate system'zRepresenting the component of the theoretical acceleration measurement value along the Z axis of the coordinate system of the inertial sensor equipment, and obtaining the offset of the acceleration by using a-a';
312) averaging the measurement values of the gyroscope in the static time period of the inertial sensor to obtain the offset of the angular velocity;
313) all measured data of acceleration versus angular velocity are subtracted by the corresponding offset.
Specifically, the method for detecting the turning point in the walking process in the step 32) includes: the rotation angle is estimated by using the angular velocity integral of the Z-axis of the inertial sensor device coordinate system, and when the absolute value of the rotation angle within a time window exceeds pi/2, steering is considered to have occurred.
Specifically, the specific method for performing coordinate system synchronization on the inertial sensor data in step 33) is as follows:
331) let the device coordinate system of each wearable device be Xl，Yl，ZlThe reference coordinate system is Xr，Yr，Zr，XrIs XlProjection on a horizontal plane, ZrIn the direction opposite to gravity, YrIs cross multiplied by Zr×XrThe direction of (a); unified global coordinate system of Xg，Yg，Zg，YgThe initial orientation of the human body, XgIn the right direction of the body, ZgIn the direction opposite to gravity; as shown with reference to FIG. 5;
332) calculating the initial tilt attitude of the inertial sensor according to the static acceleration data of n seconds at the initial time, wherein n is more than or equal to 1, and obtaining the coordinates of the equipment at the initial timeAnd (3) a rotation quaternion tied to a reference coordinate system, and updating the rotation quaternion by fusing the acceleration and angular velocity data of the inertial sensor by using adaptive complementary filtering, wherein the data of 4 inertial sensors have the same Z after rotation transformationrAxis, i.e. ZgA shaft;
333) removing the Z-edge from the acceleration measurementsgDetermining Y from the direction of maximum acceleration fluctuation using principal component analysis algorithmgDetermining X axis or from direction of greatest angular velocity fluctuationgAxis, respectively obtained from YrTo YgAngle of rotation of and from XrTo XgThe angle of rotation of (a); and carrying out weighted average on the rotation angles calculated by the two modes, so that the data of the 4 inertial sensors are synchronized to the global coordinate axis after rotation transformation.
Specifically, the key events in the gait cycle in the step 34) include: the detection method comprises the following steps:
341) separating the toes from the ground: corresponding to the moment of maximum posterior thigh swing in one gait cycle, i.e. the sensor above the knee joint is around XgMinimum point of axis rotation angle;
342) the sole touches the ground: when the sole is completely touched, the shank is perpendicular to the ground, and the inertial sensor winds around X corresponding to the ankle jointgThe rotational angle of the shaft crosses zero.
Specifically, the specific method of zero-speed correction in step 34) is as follows:
343) let t be the time when two successive sole touchdown events occursAnd te，tsTime edge YgThe speed of the shaft is v (t)s) Along YgAcceleration of the shaft is ag(t), then teTime edge YgSpeed v (t) of the shafte) Expressed as:
wherein v (t) can be substituted for the first time the sole touches the ground, i.e. the initial resting times)＝0；
344) Theoretical sole edge YgThe speed of the shaft is zero, resulting in v (t) during motion due to errors in the acceleration measurementse) Not zero, assuming the acceleration error is a constant δ, and can be calculated by:
345) by ag(t) -delta correction edge YgAcceleration of the shaft.
Specifically, referring to fig. 7, the method for calculating the length of the lower limb in step 34) is as follows:
346) for the same foot, between two adjacent sole landing events, the inertial sensor at the ankle joint is along YgThe second integral of the axial acceleration is the first step length d (i);
347) one step length is equal to the sum of the step lengths of two consecutive times, i.e. s (i) + s (i + 1); from the lower limb geometric model, the step length is expressed as the thigh length l at the time of toe-off eventtLength of calf lsRelation to the respective rotation angle: as shown with reference to FIG. 3;
wherein alpha isr(i) And phir(i) Respectively shows the rotation angles alpha of the inertial sensors above the knee joint and at the ankle joint of the right leg when the sole of the right leg lands on the ground in the ith stepl(i) And phil(i) The rotation angles of the inertial sensors above the knee joint and at the ankle joint of the left leg at the ith step when the foot tip of the left leg is off are respectively shown, and the subscript X shows the rotation angle around XgThe component of the axis, the subscript Y denoting the rotation angle about YgA component of the axis;
348) calculating the thigh length l by introducing a regression modeltLength of shank ls：
Specifically, the lower limb geometric model in the step 347) is as follows: the thigh and the calf are regarded as rigid bodies to move by taking the hip joint as a common fulcrum of the left leg and the right leg, wherein the rotation angle and the displacement of the thigh by taking the hip joint as the fulcrum are captured by the inertial sensor above the knee joint, and the rotation angle and the displacement of the calf by taking the knee joint as the fulcrum are captured by the inertial sensor at the ankle joint. Further, the showing process of the gait motion and the gait parameters in the step 4) comprises presenting 3D bone playback of the gait motion and further showing the variation waveform of the related gait parameters along with time;
3D skeleton playback of gait movement, namely, defining that knee joint movement of lower limb skeleton is consistent with movement of an inertial sensor above an actual knee joint, defining that ankle joint movement of the lower limb skeleton is consistent with movement of the inertial sensor at the actual ankle joint by constructing human three-dimensional skeleton, and acquiring rotation angle and displacement of the corresponding inertial sensor in a database to reproduce the gait movement; as shown with reference to FIG. 8;
the gait parameters are output in a dynamic curve graph form according to a time sequence relation, and correspond to the playback process of the 3D skeleton, namely, the curve graph outputs the gait parameters in a period every time the 3D skeleton completes the movement of one gait period.
While the invention has been described in terms of its preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.
Claims (10)
1. An inertial sensor-based three-dimensional gait analysis system, comprising: the system comprises wearable equipment, a data acquisition client, a server platform and a gait display client;
the wearable device is arranged at 4 key parts of the lower limb of the user, and is used for receiving an instruction sent by the data acquisition client, acquiring sensor data in the movement process of the user and sending the sensor data to the data acquisition client;
the data acquisition client receives sensor data sent by the wearable device through wireless communication, synchronizes the time of the data and uploads the data to the server platform;
the server platform is used for acquiring and processing data uploaded by the data acquisition client and storing the processed gait result data in the database;
and the gait display client acquires the gait result data of the user from the server platform and displays the gait motion and the gait parameters.
2. The inertial sensor-based three-dimensional gait analysis system according to claim 1, characterized in that the 4 key parts are above the knee joint of the left leg, at the ankle joint of the left leg, above the knee joint of the right leg and at the ankle joint of the right leg, respectively.
3. A three-dimensional gait analysis method based on inertial sensors, based on the system of any one of claims 1-2, comprising the steps of:
1) wearable devices arranged at 4 key parts of the lower limbs of a user respectively acquire gait data of the user;
2) the data acquisition client receives sensor data sent by each wearable device, synchronizes time and uploads the sensor data to the server platform;
3) processing the data uploaded by the data acquisition client, and storing the processed gait result data in a database;
4) and acquiring gait result data of the user from the server platform, and displaying gait motion and gait parameters.
4. The inertial sensor-based three-dimensional gait analysis method according to claim 3, characterized in that the specific method for processing data in step 3) is as follows:
31) data cleaning: processing data by adopting a smoothing filtering method to reduce noise, and eliminating the offset of acceleration and angular velocity according to the data when an inertial sensor in the wearable equipment is static at the initial moment;
32) detecting a turning point in the walking process according to the angular speed data of the inertial sensor, segmenting the data according to the turning point, and ensuring that the moving direction of each segment of data is a straight line;
33) synchronizing coordinate systems of inertial sensor data of 4 key parts under each section of data;
34) determining gait cycles according to the periodicity of the rotation angle of the inertial sensor at the ankle joint and dividing the data of the inertial sensor, detecting key events in the gait cycles according to the rotation angle of the inertial sensor in each gait cycle, and calculating the displacement of the inertial sensor at the ankle joint by utilizing zero-speed correction and acceleration secondary integral so as to calculate the length of the lower limb of a human body;
35) correcting the displacement of an inertial sensor at the ankle joint by using the length of the lower limb and the rotation angle of the inertial sensor according to the basic structure of a lower limb framework and taking the hip joint as a fulcrum, deducing the displacement of the inertial sensor above the knee joint according to the displacement of the inertial sensor at the ankle joint, and storing the displacement and rotation angle data of each inertial sensor into a database;
36) in different gait cycles, the length of the lower limbs of the human body and the rotation angle of the inertial sensor are used for calculating each gait parameter, and the result is stored in a database according to the time sequence relation.
5. The inertial sensor-based three-dimensional gait analysis method according to claim 4, characterized in that the specific method for eliminating the offset between acceleration and angular velocity in step 31) is as follows:
311) let the mean value of the acceleration measurements in the stationary time period be a ═ ax，ay，az]Theoretical acceleration measurement value a ' ═ a ' is calculated from the tilt attitude of the inertial sensor 'x，a′y，a′z]Wherein, in the step (A),
g is the acceleration of gravity, axComponent a representing the mean value of the acceleration measurements along the X-axis of the coordinate system of the inertial sensor unityComponent a representing the mean value of the acceleration measurements along the Y-axis of the coordinate system of the inertial sensor unitZA 'representing a component of the mean value of the acceleration measurements along the Z-axis of the inertial sensor device coordinate system'xRepresenting the component, a ', of the theoretical acceleration measurement along the X-axis of the inertial sensor device coordinate system'yRepresenting the component, a ', of the theoretical acceleration measurement along the Y-axis of the inertial sensor device coordinate system'zRepresenting the component of the theoretical acceleration measurement value along the Z axis of the coordinate system of the inertial sensor equipment, and obtaining the offset of the acceleration by using a-a';
312) averaging the measurement values of the gyroscope in the static time period of the inertial sensor to obtain the offset of the angular velocity;
313) all measured data of acceleration versus angular velocity are subtracted by the corresponding offset.
6. The three-dimensional gait analysis method based on inertial sensors according to claim 4, characterized in that the detection method of the turning point in the walking process in the step 32) is as follows: the rotation angle is estimated by using the angular velocity integral of the Z-axis of the inertial sensor device coordinate system, and when the absolute value of the rotation angle within a time window exceeds pi/2, steering is considered to have occurred.
7. The inertial sensor-based three-dimensional gait analysis method according to claim 4, characterized in that the specific method for performing coordinate system synchronization on the inertial sensor data in step 33) is as follows:
331) let the device coordinate system of each wearable device be Xl，Yl，ZlThe reference coordinate system is Xr，Yr，Zr，XrIs XlProjection on a horizontal plane, ZrIn the direction opposite to gravity, YrIs cross multiplied by Zr×XrThe direction of (a); unified global coordinate system of Xg，Yg，Zg，YgThe initial orientation of the human body, XgIn the right direction of the body, ZgIn the direction opposite to gravity;
332) calculating the initial tilt attitude of the inertial sensor according to the static acceleration data of n seconds at the initial moment, wherein n is more than or equal to 1, obtaining the rotation quaternion from the initial moment equipment coordinate system to the reference coordinate system, updating the rotation quaternion by fusing the acceleration data and the angular velocity data of the inertial sensor by using self-adaptive complementary filtering, and at the moment, 4 inertial sensor data have the same Z after rotation transformationrAxis, i.e. ZgA shaft;
333) removing the Z-edge from the acceleration measurementsgDetermining Y from the direction of maximum acceleration fluctuation using principal component analysis algorithmgDetermining X axis or from direction of greatest angular velocity fluctuationgAxis, respectively obtained from YrTo YgAngle of rotation of and from XrTo XgThe angle of rotation of (a); and carrying out weighted average on the rotation angles calculated by the two modes, so that the data of the 4 inertial sensors are synchronized to the global coordinate axis after rotation transformation.
8. The inertial sensor-based three-dimensional gait analysis method according to claim 4, characterized in that the key events in the gait cycle in step 34) include: the detection method comprises the following steps:
341) separating the toes from the ground: corresponding to the moment of maximum posterior thigh swing in one gait cycle, i.e. the sensor above the knee joint is around XgMinimum point of axis rotation angle;
342) the sole touches the ground: when the sole is completely touched, the shank is perpendicular to the ground, and the inertial sensor winds around X corresponding to the ankle jointgThe rotational angle of the shaft crosses zero.
9. The inertial sensor-based three-dimensional gait analysis method according to claim 4, characterized in that the zero velocity correction in step 34) is carried out by the following specific methods:
343) let t be the time when two successive sole touchdown events occursAnd te，tsTime edge YgThe speed of the shaft is v (t)s) Along YgAcceleration of the shaft is ag(t), then teTime edge YgSpeed v (t) of the shafte) Expressed as:
wherein v (t) can be substituted for the first time the sole touches the ground, i.e. the initial resting times)＝0；
344) Theoretical sole edge YgThe speed of the shaft is zero, resulting in v (t) during motion due to errors in the acceleration measurementse) Not zero, assuming the acceleration error is a constant δ, and can be calculated by:
345) by ag(t) -delta correction edge YgAcceleration of the shaft.
10. The inertial sensor-based three-dimensional gait analysis method according to claim 4, characterized in that the calculation method of the lower limb length in step 34) is as follows:
346) for the same foot, between two adjacent sole landing events, the inertial sensor at the ankle joint is along YgThe second integral of the axial acceleration is the first step length d (i);
347) one step length is equal to the sum of the step lengths of two consecutive times, i.e. s (i) + s (i + 1); from the lower limb geometric model, the step length is expressed as the thigh length l at the time of toe-off eventtLength of calf lsRelation to the respective rotation angle:
wherein alpha isr(i) And phir(i) Respectively shows the rotation angles alpha of the inertial sensors above the knee joint and at the ankle joint of the right leg when the sole of the right leg lands on the ground in the ith stepl(i) And phil(i) The rotation angles of the inertial sensors above the knee joint and at the ankle joint of the left leg at the ith step when the foot tip of the left leg is off are respectively shown, and the subscript X shows the rotation angle around XgThe component of the axis, the subscript Y denoting the rotation angle about YgA component of the axis;
348) calculating the thigh length l by introducing a regression modeltLength of shank ls：
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111478306.4A CN114271812A (en) | 2021-12-06 | 2021-12-06 | Three-dimensional gait analysis system and method based on inertial sensor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111478306.4A CN114271812A (en) | 2021-12-06 | 2021-12-06 | Three-dimensional gait analysis system and method based on inertial sensor |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114271812A true CN114271812A (en) | 2022-04-05 |
Family
ID=80871143
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111478306.4A Pending CN114271812A (en) | 2021-12-06 | 2021-12-06 | Three-dimensional gait analysis system and method based on inertial sensor |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114271812A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116310083A (en) * | 2023-02-03 | 2023-06-23 | 杭州百世伽信息科技有限公司 | Human body posture depth image generation method and human body posture recognition system |
| CN116784838A (en) * | 2023-08-24 | 2023-09-22 | 中国科学技术大学 | Steering identification system, method, equipment and medium based on wearable inertial sensor |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108720841A (en) * | 2018-05-22 | 2018-11-02 | 上海交通大学 | Wearable lower extremity movement correction system based on cloud detection |
-
2021
- 2021-12-06 CN CN202111478306.4A patent/CN114271812A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108720841A (en) * | 2018-05-22 | 2018-11-02 | 上海交通大学 | Wearable lower extremity movement correction system based on cloud detection |
Non-Patent Citations (1)
| Title |
|---|
| 杨佩成: "面向术后康复的可穿戴步态监测与分析技术研究", 《中国优秀硕士学位论文全文数据库信息科技辑》, no. 4, pages 140 - 184 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116310083A (en) * | 2023-02-03 | 2023-06-23 | 杭州百世伽信息科技有限公司 | Human body posture depth image generation method and human body posture recognition system |
| CN116310083B (en) * | 2023-02-03 | 2023-11-14 | 杭州百世伽信息科技有限公司 | Human body posture depth image generation method and human body posture recognition system |
| CN116784838A (en) * | 2023-08-24 | 2023-09-22 | 中国科学技术大学 | Steering identification system, method, equipment and medium based on wearable inertial sensor |
| CN116784838B (en) * | 2023-08-24 | 2024-01-09 | 中国科学技术大学 | Steering identification system, method, equipment and medium based on wearable inertial sensor |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107174255B (en) | Three-dimensional gait information acquisition and analysis method based on Kinect somatosensory technology | |
| Schepers et al. | Xsens MVN: Consistent tracking of human motion using inertial sensing | |
| El-Gohary et al. | Human joint angle estimation with inertial sensors and validation with a robot arm | |
| Mariani et al. | Heel and toe clearance estimation for gait analysis using wireless inertial sensors | |
| Tong et al. | A practical gait analysis system using gyroscopes | |
| Ahmadi et al. | 3D human gait reconstruction and monitoring using body-worn inertial sensors and kinematic modeling | |
| CN108720841A (en) | Wearable lower extremity movement correction system based on cloud detection | |
| CN104757976A (en) | Human gait analyzing method and system based on multi-sensor fusion | |
| CN114271812A (en) | Three-dimensional gait analysis system and method based on inertial sensor | |
| CN102679964B (en) | Gait parameter measurement system and data processing device and method thereof | |
| Laudanski et al. | A concurrent comparison of inertia sensor-based walking speed estimation methods | |
| WO2015162158A1 (en) | Human motion tracking | |
| Bötzel et al. | Quantification of gait parameters with inertial sensors and inverse kinematics | |
| Lee et al. | Detection of spatio-temporal gait parameters by using wearable motion sensors | |
| CN110609621B (en) | Gesture calibration method and human motion capture system based on microsensor | |
| Yang et al. | IMU-Kinect: A motion sensor-based gait monitoring system for intelligent healthcare | |
| Hindle et al. | Inertial-based human motion capture: A technical summary of current processing methodologies for spatiotemporal and kinematic measures | |
| Hu et al. | Performance evaluation of lower limb ambulatory measurement using reduced inertial measurement units and 3R gait model | |
| Palani et al. | Real-time joint angle estimation using mediapipe framework and inertial sensors | |
| CN114663913A (en) | Human body gait parameter extraction method based on Kinect | |
| Hu et al. | A study on estimation of planar gait kinematics using minimal inertial measurement units and inverse kinematics | |
| Zhang et al. | Human back movement analysis using bsn | |
| WO2023169465A1 (en) | Human body motion monitoring method and device based on multi-source information fusion | |
| CN110731784A (en) | gait measurement system based on mobile platform and gait measurement method thereof | |
| CN114440883B (en) | Pedestrian positioning method based on foot and leg micro-inertia measurement unit |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20220405 |