CN105068654A - Motion capturing system and method based on CAN bus and inertial sensor - Google Patents

Motion capturing system and method based on CAN bus and inertial sensor Download PDF

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CN105068654A
CN105068654A CN201510502709.6A CN201510502709A CN105068654A CN 105068654 A CN105068654 A CN 105068654A CN 201510502709 A CN201510502709 A CN 201510502709A CN 105068654 A CN105068654 A CN 105068654A
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data
inertia sensing
node
sensing node
attitude
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CN105068654B (en
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陈东义
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JINAN ZHONGJING ELECTRONIC TECHNOLOGY Co Ltd
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JINAN ZHONGJING ELECTRONIC TECHNOLOGY Co Ltd
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Abstract

The present invention provides a motion capturing system and method based on a CAN bus and an inertial sensor, for tracking and capturing motion information of a human body in a real-time manner. The system comprises an inertial sensing node combination, a data aggregation node, and a central computer. The inertial sensing node combination contains 18 inertial sensing nodes, collects an accelerated speed, an angular speed, and geomagnetic information data of each bone point part in a real-time manner, and performs attitude estimation by using a complementary integration filtering algorithm, so as to obtain data, such as a quaternion and an euler angle, of an three-dimensional attitude of the current bone point. The data aggregation node is connected to the 18 inertial sensing nodes, is configured to collect, in a time-sharing manner, three-dimensional attitude data of each bone point obtained by the 18 inertial sensing nodes, and send the collected data to the central computer by using a wireless wifi module. A main task of the central computer is to complete functions of collecting motion data transmitted from the data aggregation node and driving a virtual 3D person model by using the motion data.

Description

Based on action capture systems and the method for CAN and inertial sensor
Technical field
The present invention relates to the fields such as human cinology, sensor technology, health medical treatment, particularly relate to a kind of action capture systems based on CAN and inertial sensor.
Background technology
Action capture technique is a kind of novel human-computer interaction technology, and it has a extensive future in fields such as rehabilitation medical, video display, animation, game.Utilizing human action capture technique, on rehabilitation medical, the patient because causing limbs or other particular body portion to lose motion function after disease or traffic accident can being assisted to carry out rehabilitation; In addition, action capture technique can also be applied on production of film and TV, can simplify the difficulty of film making, improves the degree true to nature of video display personage, as the virtual portrait in film " A Fanda " utilizes action capture technique to be made.Action capture technique is applied in game can introduce body sense interaction technique, enables player utilize the exercise data of health and virtual game environment to carry out interaction.
As described in patent CN104461013A, the attitude information that the action capture systems based on wifi is made up of 17 inertia sensing nodes obtains system, wifi wireless routing and central computer three part composition.Wherein inertia sensing node is arranged on multiple positions of human body respectively, is used for acceleration, the information such as angular velocity and earth magnetism at each position of Real-time Collection, and utilizes blending algorithm to obtain the attitude informations such as his attitude angle, hypercomplex number and Eulerian angle.Then the information collected and the information after merging are sent to central computer by wifi mode by wifi wireless router, the attitude information data that central computer merges each inertia sensing node complete the reconstruct of human action.
Action capture systems based on SPI is also by 15 ~ 17 the inertia sensing nodes being distributed in each major joint point of whole body, and convergence node also has central computer three part composition.Wherein convergence node utilizes respectively a heel piece to select signal wire to connect on inertia sensing node, being used for acquisition time is arranged on three-dimensional attitude data on each major joint point of whole body again.After having gathered the three-dimensional attitude data of each node, these data summarization are packaged into an exercise data frame, and send to central computer, central computer utilizes the attitude information data of each sensing node to complete the reconstruct of human action.
The defect that such scheme mainly exists have following some:
(1) use each inertia sensing node of wireless solution must be equipped with a lithium battery to power separately, when the battery electric quantity deficiency of single node or several node cannot work, whole system becomes and cannot use.
(2) each inertia sensing node of action capture systems of SPI scheme is used must to connect a chip selection signal line, the quantity directly caused along with inertia sensing node increases by this, the line being connected to convergence node on action capture systems increases, and whole system arrangement of conductors is complicated;
(3) use wireless solution, each node size and weight are not easy to reduce again, can hinder motion to a certain extent.
Summary of the invention
For solving the problems of the technologies described above, the present invention proposes a kind of action capture systems based on CAN and inertial sensor, adopt the wireless mode of the wired and part of part to carry out data transmission, under ensureing to affect the minimum prerequisite of human motion, improve the convenience of the transfer rate of data, the stability of system and use.
Movable information due to human body forms complicated, need to place multiple inertia sensing node, so just there is higher requirement to the message transmission rate of whole system, in order to avoid classic method causes each inertia sensing node data transfer rate unstable, some method lines are complicated, the shortcomings such as outward appearance is indecency, the invention provides a kind of multi-channel data transmission scheme based on CAN, to guarantee that the message transmission rate of the inertia sensing node of whole action capture systems is stablized, mode of connection is simple, the plurality of advantages such as fashionable appearance, efficiently solve the inconvenience that current techniques is brought.
Specifically, the invention provides a kind of action capture systems based on CAN and inertial sensor: described system comprises inertia sensing node group, convergence node and central computer; It is characterized in that:
Described inertia sensing node group comprises 18 inertia sensing nodes, is distributed in human body major skeletal point place respectively, and for the three-dimensional attitude data of Real-time Collection human body major skeletal point, the data describing three-dimensional attitude mainly comprise hypercomplex number or Eulerian angle; Described convergence node, it is the convergence center of inertia sensing node attitude algorithm data, it completes the collecting work to 18 inertia sensing nodes, and the attitude data collected is packaged into exercise data frame and sends the data to central computer by wireless wifi module.
Preferably, each inertia sensing node of described inertia sensing node group comprises power module, 3-axis acceleration sensor, three-axis gyroscope sensor, three axle magnetometer sensors and CAN protocol process module; Wherein, described 3-axis acceleration sensor, three-axis gyroscope sensor all use I2C communication protocol to be connected with embedded microprocessor with three axle magnetometer sensors; Acceleration information, angular velocity data and geomagnetic data that the above-mentioned nine axle inertial sensors of described embedded microprocessor Real-time Collection export, and the data collected are carried out filtering, calculate, and apply mechanically Mutually fusion filtering algorithm and draw the hypercomplex number and Eulerian angle data that describe current inertia sensing Nodes Three-dimensional spatial attitude information more accurately.
Preferably, described Mutually fusion filtering method carries out the step of attitude algorithm and is:
Step one, system electrification, initialization 3-axis acceleration sensor, three axle magnetometer sensors and three-axis gyroscope sensor, and start timer, the data collected also are carried out simple filtering and are removed noise by the data of embedded microprocessor Real-time Collection nine axle inertial sensor;
Step 2, the angular velocity data utilizing three-axis gyroscope sensor to export and timer T, carry out integrated motion to angular velocity data, draw elapsed time T, the angle value that current time is crossed relative to a upper moment inertia sensing Nodes Three-dimensional Space Rotating;
Step 3, utilize three axle magnetometer sensor and 3-axis acceleration sensors, the angle value utilizing newton's method of steepest descent to derive three dimensions to rotate through;
The three-dimensional attitude data that step 4, the three-dimensional attitude data utilizing Mutually fusion filtering algorithm fusion three-axis gyroscope sensor to calculate and 3-axis acceleration magnetometer sensor calculate, the complementary filter function of employing is:
F ( t ) = F 1 ( t ) + F 2 ( t ) = τ τ + d t g y r o ( t ) + ( 1 - τ τ + d t ) a c c e l _ m a g ( t )
Wherein gyro (t) is the value after gyro sensor attitude algorithm, accel_mag (t) for acceleration magnetometer sensor attitude resolve after value, dt is the sampling time, and τ is constant.The filter function used is:
F(t)=0.99gyro(t)+0.01accel_mag(t)
Preferably, described 18 inertia sensing nodes are mainly distributed in the major skeletal point place of human body, for Real-time Collection, the three-dimensional space position calculating each major skeletal point, so that central computer carries out action reconstruct; Wherein, described 18 inertia sensing nodes are worn on 18 positions of human body respectively, these 18 positions respectively: head, the left hand palm, lower-left arm, left upper arm, left shoulder, the left foot palm, left leg, left thigh, the right hand palm, bottom right arm, right upper arm, right shoulder, the right crus of diaphragm palm, right leg, right thigh, neck, the upper back of the body and lower back; Described 18 inertia sensing nodes can catch the exercise data of each major joint point of human body accurately, can solve the action Trapped problems of whole body.
Preferably, described inertia sensing node comprises power management module, nine axle inertial sensor modules and CAN data transmission module; Described power management module is powered to inertia sensing node for providing stable 3.3v voltage, and can determine separately the power on/off function of inertia sensing node; Described nine axle inertial sensor modules comprise 3-axis acceleration sensor, three-axis gyroscope sensor and three axle magnetometer sensors, for the data of the three-dimensional acceleration of each crucial skeleton point of Real-time Collection human body, three-dimensional angular velocity and magnetic direction, these data are utilized to calculate the three-dimensional attitude information of current inertia sensing node; Described CAN data transmission module is data after gathering 18 inertia sensing node attitude algorithms and is aggregated into the bus communication protocol that convergence node adopts; Wherein, described 18 CAN are divided into three groups, often organize and comprise 6 inertia sensing nodes, often organize CAN node and connect respectively in the CAN of convergence transmission node again.
Preferably, described convergence node comprises three CAN communication lines, every bar CAN communication line comprises upper and lower two ends CAN interface, upper end CAN interface is for connecting the inertia sensing node of human body upper body limbs, and the CAN interface of lower end is for connecting the inertia sensing node of lower part of humanbody limbs; Described three CAN communication lines effectively can improve the attitude uploading rate of each inertia sensing node, because the node communication speed of CAN communication protocol can reduce along with the increase of the number of nodes of carry in bus, adopt the mode of three CAN communication lines, effectively reduce the quantity of the inertia sensing node of inertia sensing node carry in wall scroll CAN, thus ensure that the data communication rates of inertia sensing node.Described convergence node also comprises wireless wifi module, and the steering order of wireless wifi communication module receiving center computing machine is also uploaded to central computer according to the data of corresponding steering order collection 18 inertia sensing nodes.
Preferably, the exercise data frame that described central computer image data aggregation node sends, the reconstructing method according to the three-dimensional space position Eulerian angle of each node and the three-dimensional space position data separate human action of abdomen node is reconstructed.The step of described human action reconstructing method is:
Step one: according to the affiliation of human skeleton point, set up skeleton dot structure hierarchical model, native system establishes three skeleton point hierarchy Model;
Step 2, drafting human joint points, the position of initialization human joint points and direction, and draw human skeleton according to the direction of articulation point and utilize spheric coordinate system that bone is rotated to suitable position;
Step 3, utilize Real-time Collection to exercise data frame to drive each articulation point of human body, and utilize spherical co-ordinate rotation formula will rotate each skeleton point successively.
Meanwhile, the invention allows for a kind of action catching method based on CAN and inertia sensing node, it realizes based on the system as described in arbitrary in claim 1-9, and the method comprises the following steps:
(1) power on to the inertia sensing node group be connected in CAN, complete the system initialization work of 18 inertia sensing nodes;
(2) power on to convergence node, complete the connection of convergence node and central computer;
(3) judge whether starting operation capture systems, if then enter (4) step, otherwise wait for;
(4) inertia sensing node gathers the data such as acceleration, angular velocity and earth magnetism, carries out first filtering, then carries out three-dimensional attitude according to Mutually fusion filtering algorithm to current inertia sensing node and resolve, and be stored into memory headroom;
(5) judge whether to receive CAN data and send order, if it is the three-dimensional attitude data hypercomplex number after resolving or Eulerian angle are sent to convergence node, otherwise enter (4) step;
(6) circulation of convergence node carries out data acquisition to 18 inertia sensing nodes, when becoming exercise data frame to send to central computer by wifi module these 18 groups of data encapsulation after the three-dimensional attitude data receiving 18 inertia sensing nodes;
(7) central computer judges whether to receive data, if the data of receiving, enters step (8), otherwise enters (6) step;
(8) data acquisition terminates, and central computer is resolved the exercise data frame sended over by human action reconstructing method.
The present invention has following beneficial effect:
(1) native system transducer sensitivity is high, attitude algorithm speed is fast, message transmission rate reliable, this means that virtual 3D personage's motor reorgan is reliable and stable.
(2) the design adopts the mode of three CAN, and line is simple, and wiring is convenient, and fashionable appearance is less to the constraint sense of user.
(3) plurality of devices such as system uses wireless wifi module to be sent by the exercise data of human body, can directly supply PC computer, mobile phone like this, dull and stereotyped use.
(4) inertia sensing design of node size is little, thus ensure that the convenience worn, and environment for use is simple, and dressing powers on uses, thus makes common unprofessional user also can use native system easily.
(5) 18 inertia sensing nodes are adopted, be compared to the action capture device of 15 ~ 17 inertia sensing nodes, he can gather the movable information of left shoulder, right shoulder and neck, better can describe the exercise data of Whole Body, can present better quality reconstruction to user.
Accompanying drawing explanation
Fig. 1 is human body inertia sensing Node distribution figure in the present invention;
Fig. 2 is system inertia sensing node bus and data transmission nodal connection layout in the present invention;
Fig. 3 is inertia sensing node structure block diagram in the present invention;
Fig. 4 is convergence node structure block diagram in the present invention.
Embodiment
For making the technical problem to be solved in the present invention, technical scheme and advantage clearly, be described in detail below in conjunction with the accompanying drawings and the specific embodiments.Those skilled in the art should know, following specific embodiment or embodiment, to be the present invention be explains the set-up mode of the series of optimum that concrete summary of the invention is enumerated further, and all can be combined with each other or interrelated use between those set-up modes, cannot carry out associating with other embodiment or embodiment and arrange unless clearly proposed wherein some or a certain specific embodiment or embodiment in the present invention or jointly use.Meanwhile, following specific embodiment or embodiment only as optimized set-up mode, and not as limiting the understanding of protection scope of the present invention.
Embodiment 1
As shown in Figure 1, a kind of action capture systems based on CAN and inertial sensor is made up of 18 inertia sensing nodes, convergence node and central computer.These 18 inertia sensing nodes are placed on the skeleton point place of mark as said in Fig. 1 respectively, and convergence node placement is at the waist location of human body.Convergence node sends the data to central computer by the data after the attitude algorithm of these 18 inertia sensing nodes of collection by wireless wifi module.
As shown in Figure 2,18 inertia sensing nodes are divided into 3 groups, and often group comprises 6 nodes, belongs to Bus1 respectively, Bus2 and Bus3.Wherein Bus1, Bus2 and Bus3 are also divided into two sections: epimere and lower end, and design can make entire system line minimum and connect up conveniently like this, simply.18 inertia sensing nodes are placed on the head of user, the left hand palm, lower-left arm, left upper arm, left shoulder, the left foot palm, left leg, left thigh, the right hand palm, bottom right arm, right upper arm, right shoulder, the right crus of diaphragm palm, right leg, right thigh, neck, the position such as the upper back of the body and lower back respectively.
As shown in Figure 3, inertia sensing node comprises main control module, external memory module, power module, CAN protocol process module and nine axle inertia sensing modules.Wherein power module provides the function of a stable 3.3V voltage; External memory module is used for the calibration data of storage nine axle inertial sensor node and the numbering data of node; Main control module comprises embedded microprocessor, clock circuit and reset circuit; CAN module comprises CAN level shifting circuit, and CAN level shifting circuit is connected with the CAN protocol processing unit of embedded microprocessor; Nine axle inertial sensors comprise 3-axis acceleration sensor, three-axis gyroscope sensor and three axle magnetometer sensors, and this nine axles inertial sensor is connected with embedded microprocessor by i2c agreement.
As shown in Figure 4, convergence node comprises wireless wifi sending module, main control module, three CAN protocol process module and power module composition, and what wherein wireless wifi sending module adopted is USR-wifi module, is connected by serial ports and main control module; Article three, CAN protocol module mainly comprises two kinds of patterns, CANbus1 and CANbus3 adopts external CAN protocol processes chip and the combination of CAN level transferring chip, and CANbus2 adopts the integrated CAN protocol processing unit of embedded microprocessor and CAN level transferring chip composition.The external CAN protocol processes chip that wherein CANbus1 with CANbus3 adopts uses spi bus to be connected with embedded microprocessor respectively.
Embodiment 2
Based on a system for the action capture systems of CAN and inertial sensor, comprise inertia sensing node group, convergence node and central computer.The main task of inertia sensing node group has been the data acquisition of the 3-axis acceleration of nine axle inertia sensing nodes, three-axis gyroscope and three axle magnetometer sensors, filtering and attitude algorithm, obtains the hypercomplex number that describes present node three-dimensional attitude information or Eulerian angle describe after attitude algorithm.The hypercomplex number obtained or Eulerian angle data converge to convergence node by the form of CAN, and encapsulate data into exercise data frame by wireless wifi module and send to central computer.First central computer starts to set up virtual 3D personage, and initialization action and human action are consistent, the real-time action of real-time animation demonstration user after receiving exercise data frame.
Job step based on the action capture systems of CAN and inertial sensor is as follows:
(1) power on to the inertia sensing node group be connected in CAN, complete the system initialization work of 18 inertia sensing nodes;
(2) power on to convergence node, complete the connection of convergence node and central computer;
(3) judge whether starting operation capture systems, if then enter (4) step, otherwise wait for;
(4) inertia sensing node gathers the data such as acceleration, angular velocity and earth magnetism, carries out first filtering, then carries out three-dimensional attitude according to Mutually fusion filtering algorithm to current inertia sensing node and resolve, and be stored into memory headroom;
(5) judge whether to receive CAN data and send order, if it is the three-dimensional attitude data hypercomplex number after resolving or Eulerian angle are sent to convergence node, otherwise enter (4) step;
(6) circulation of convergence node carries out data acquisition to 18 inertia sensing nodes, when becoming exercise data frame to send to central computer by wifi module these 18 groups of data encapsulation after the three-dimensional attitude data receiving 18 inertia sensing nodes;
(7) central computer judges whether to receive data, if the data of receiving, enters step (8), otherwise enters (6) step;
(8) data acquisition terminates, and central computer is resolved the exercise data frame sended over by human action reconstructing method.
The above is the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the prerequisite not departing from principle of the present invention; can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.

Claims (11)

1., based on an action capture systems for CAN and inertia sensing node, described system comprises inertia sensing node group, convergence node and central computer, it is characterized in that:
Described inertia sensing node group comprises the inertia sensing node of a predetermined quantity, be separately positioned on human body major skeletal point, for the three-dimensional attitude data of Real-time Collection human body major skeletal point, the data describing three-dimensional attitude mainly comprise hypercomplex number or Eulerian angle;
Described convergence node, it is the convergence center of inertia sensing node attitude algorithm data, it completes the collecting work of the inertia sensing node to above-mentioned predetermined quantity, and the attitude data collected is packaged into exercise data frame and sends the data to central computer by wireless WIFI module.
2. action capture systems as claimed in claim 1, is characterized in that:
Each inertia sensing node of described inertia sensing node group comprises power module, 3-axis acceleration sensor, three-axis gyroscope sensor, three axle magnetometer sensors and CAN protocol process module; Wherein,
Described 3-axis acceleration sensor, three-axis gyroscope sensor all use I2C communication protocol to be connected with embedded microprocessor with three axle magnetometer sensors;
Acceleration information, angular velocity data and geomagnetic data that the above-mentioned nine axle inertial sensors of described embedded microprocessor Real-time Collection export, and the data collected are carried out filtering, calculate, and apply mechanically Mutually fusion filtering algorithm and draw the hypercomplex number and Eulerian angle data that describe current inertia sensing Nodes Three-dimensional spatial attitude information more accurately.
3. action capture systems as claimed in claim 1, is characterized in that:
The step that described Mutually fusion filtering algorithm carries out attitude algorithm is:
Step one: system electrification, initialization 3-axis acceleration sensor, three axle magnetometer sensors and three-axis gyroscope sensor, and start timer, the data collected also are carried out simple filtering and are removed noise by the data of embedded microprocessor Real-time Collection nine axle inertial sensor;
Step 2: the angular velocity data utilizing three-axis gyroscope sensor to export and timer T, carries out integrated motion to angular velocity data, draws elapsed time T, the angle value that current time is crossed relative to a upper moment inertia sensing Nodes Three-dimensional Space Rotating;
Step 3: utilize three axle magnetometer sensor and 3-axis acceleration sensors, the angle value utilizing newton's method of steepest descent to derive three dimensions to rotate through;
Step 4: the three-dimensional attitude data that the three-dimensional attitude data utilizing Mutually fusion filtering algorithm fusion three-axis gyroscope sensor to calculate and 3-axis acceleration magnetometer sensor calculate, the complementary filter function of employing is:
F ( t ) = F 1 ( t ) + F 2 ( t ) = τ τ + d t g y r o ( t ) + ( 1 - τ τ + d t ) a c c e l _ m a g ( t )
Wherein gyro (t) is the value after gyro sensor attitude algorithm, accel_mag (t) for acceleration magnetometer sensor attitude resolve after value, dt is the sampling time, and τ is constant;
The filter function used is:
F(t)=0.99gyro(t)+0.01accel_mag(t)。
4., as the action capture systems as described in arbitrary in claim 1-3, it is characterized in that:
Described inertia sensing node comprises power management module, nine axle inertial sensor modules and CAN data transmission module;
Described power management module is powered to inertia sensing node for providing stable voltage, and can determine separately the power on/off function of inertia sensing node;
Described nine axle inertial sensor modules comprise 3-axis acceleration sensor, three-axis gyroscope sensor and three axle magnetometer sensors, for the data of the three-dimensional acceleration of each crucial skeleton point of Real-time Collection human body, three-dimensional angular velocity and magnetic direction, these data are utilized to calculate the three-dimensional attitude information of current inertia sensing node;
Described CAN data transmission module is data after the inertia sensing node attitude algorithm of predetermined quantity for gathering and is aggregated into the bus communication protocol that convergence node adopts;
Wherein, the CAN of described predetermined quantity is divided into three groups, often organizes the inertia sensing node comprising equal number, often organizes CAN node and connects respectively, then by the CAN of convergence transmission node.
5. action capture systems as claimed in claim 1, is characterized in that:
Described convergence node comprises three CAN communication lines, every bar CAN communication line comprises upper and lower two ends CAN interface, upper end CAN interface is for connecting the inertia sensing node of human body upper body limbs, and the CAN interface of lower end is for connecting the inertia sensing node of lower part of humanbody limbs;
Described convergence node also comprises wireless WIFI communication module, and the steering order of wireless WIFI communication module receiving center computing machine also gathers the inertia sensing node data of predetermined quantity according to corresponding steering order and is uploaded to central computer.
6. action capture systems as claimed in claim 1, is characterized in that:
The exercise data frame that described central computer image data aggregation node sends, the reconstructing method according to the three-dimensional space position Eulerian angle of each node and the three-dimensional space position data separate human action of abdomen node is reconstructed.
7., as the action capture systems as described in arbitrary in claim 1-6, it is characterized in that:
The inertia sensing node of described predetermined quantity is mainly distributed in the major skeletal point place of human body, for Real-time Collection, the three-dimensional space position calculating each major skeletal point, so that central computer carries out action reconstruct.
8., as the action capture systems as described in arbitrary in claim 1-6, it is characterized in that:
Described predetermined quantity is 18,18 described inertia sensing nodes are worn on 18 positions of human body respectively, these 18 positions respectively: head, the left hand palm, lower-left arm, left upper arm, left shoulder, the left foot palm, left leg, left thigh, the right hand palm, bottom right arm, right upper arm, right shoulder, the right crus of diaphragm palm, right leg, right thigh, neck, the upper back of the body and lower back.
9., based on an action catching method for CAN and inertia sensing node, it realizes based on the system as described in arbitrary in claim 1-8, and the method comprises the following steps:
(1) power on to the inertia sensing node group be connected in CAN, complete the system initialization work of the inertia sensing node of predetermined quantity;
(2) power on to convergence node, complete the connection of convergence node and central computer;
(3) judge whether starting operation capture systems, if then enter (4) step, otherwise wait for;
(4) inertia sensing node gathers the data such as acceleration, angular velocity and earth magnetism, carries out first filtering, then carries out three-dimensional attitude according to Mutually fusion filtering algorithm to current inertia sensing node and resolve, and be stored into memory headroom;
(5) judge whether to receive CAN data and send order, if it is the three-dimensional attitude data hypercomplex number after resolving or Eulerian angle are sent to convergence node, otherwise enter (4) step;
(6) circulation of convergence node carries out data acquisition to the inertia sensing node of above-mentioned predetermined quantity, sends to central computer when encapsulating data into exercise data frame after the three-dimensional attitude data receiving inertia sensing node by WIFI module;
(7) central computer judges whether to receive data, if the data of receiving, enters step (8), otherwise enters (6) step;
(8) data acquisition terminates, and central computer is resolved the exercise data frame sended over by human action reconstructing method.
10. action catching method according to claim 9, is characterized in that:
The step of described human action reconstructing method is:
Step one: according to the affiliation of human skeleton point, set up skeleton dot structure hierarchical model, native system establishes three skeleton point hierarchy Model;
Step 2: draw human joint points, the position of initialization human joint points and direction, and draw human skeleton according to the direction of articulation point and utilize spheric coordinate system that bone is rotated to suitable position;
Step 3: utilize Real-time Collection to exercise data frame to drive each articulation point of human body, and utilize spherical co-ordinate rotation formula will rotate each skeleton point successively.
Action catching method described in 11. claims 9 or 10, is characterized in that:
The step that described Mutually fusion filtering algorithm carries out attitude algorithm is:
Step one: system electrification, initialization 3-axis acceleration sensor, three axle magnetometer sensors and three-axis gyroscope sensor, and start timer, the data collected also are carried out simple filtering and are removed noise by the data of embedded microprocessor Real-time Collection nine axle inertial sensor;
Step 2: the angular velocity data utilizing three-axis gyroscope sensor to export and timer T, carries out integrated motion to angular velocity data, draws elapsed time T, the angle value that current time is crossed relative to a upper moment inertia sensing Nodes Three-dimensional Space Rotating;
Step 3: utilize three axle magnetometer sensor and 3-axis acceleration sensors, the angle value utilizing newton's method of steepest descent to derive three dimensions to rotate through;
Step 4: the three-dimensional attitude data that the three-dimensional attitude data utilizing Mutually fusion filtering algorithm fusion three-axis gyroscope sensor to calculate and 3-axis acceleration magnetometer sensor calculate, the complementary filter function of employing is:
F ( t ) = F 1 ( t ) + F 2 ( t ) = τ τ + d t g y r o ( t ) + ( 1 - τ τ + d t ) a c c e l _ m a g ( t )
Wherein gyro (t) is the value after gyro sensor attitude algorithm, accel_mag (t) for acceleration magnetometer sensor attitude resolve after value, dt is the sampling time, and τ is constant;
The filter function used is:
F(t)=0.99gyro(t)+0.01accel_mag(t)。
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CN108108016A (en) * 2017-12-07 2018-06-01 浙江大学 Gesture perceptron
CN108170268A (en) * 2017-12-26 2018-06-15 浙江大学 A kind of Whole Body motion capture devices based on Inertial Measurement Unit
CN108198383A (en) * 2017-12-26 2018-06-22 深圳市宇恒互动科技开发有限公司 The high-precision Activity recognition method, apparatus and system of a kind of multi sensor combination
CN109343713A (en) * 2018-10-31 2019-02-15 重庆子元科技有限公司 A kind of human action mapping method based on Inertial Measurement Unit
CN109859597A (en) * 2019-03-07 2019-06-07 哈工大机器人(合肥)国际创新研究院 A kind of rehabilitation simulation people's pose acquisition system and method

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101357061A (en) * 2007-08-01 2009-02-04 圣美申医疗科技(上海)有限公司 No-electrode line wireless physiological phenomenon description and record system
CN201229355Y (en) * 2008-07-07 2009-04-29 李乔峰 Wireless body sport attitude detection system
CN102008290A (en) * 2010-09-28 2011-04-13 深圳市倍泰健康测量分析技术有限公司 Equipment and method for collecting and analyzing human body health
CN201949599U (en) * 2011-01-12 2011-08-31 东北农业大学 Sports data wireless sensor
CN103021129A (en) * 2012-12-04 2013-04-03 东南大学 System and method for monitoring falling down of old people
CN103079289A (en) * 2013-01-18 2013-05-01 浙江大学 Wireless multi-person motion data collector and collection method
CN103889065A (en) * 2014-02-28 2014-06-25 中国农业大学 Wireless body area network data transmission scheduling method and device
CN104348684A (en) * 2014-11-19 2015-02-11 成都理工大学 Method for reducing data transmission flow based on wireless sensor network nodes
CN104469903A (en) * 2014-11-12 2015-03-25 成都理工大学 Method for reducing data redundancy based on data storage in wireless sensor network

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101357061A (en) * 2007-08-01 2009-02-04 圣美申医疗科技(上海)有限公司 No-electrode line wireless physiological phenomenon description and record system
CN201229355Y (en) * 2008-07-07 2009-04-29 李乔峰 Wireless body sport attitude detection system
CN102008290A (en) * 2010-09-28 2011-04-13 深圳市倍泰健康测量分析技术有限公司 Equipment and method for collecting and analyzing human body health
CN201949599U (en) * 2011-01-12 2011-08-31 东北农业大学 Sports data wireless sensor
CN103021129A (en) * 2012-12-04 2013-04-03 东南大学 System and method for monitoring falling down of old people
CN103079289A (en) * 2013-01-18 2013-05-01 浙江大学 Wireless multi-person motion data collector and collection method
CN103889065A (en) * 2014-02-28 2014-06-25 中国农业大学 Wireless body area network data transmission scheduling method and device
CN104469903A (en) * 2014-11-12 2015-03-25 成都理工大学 Method for reducing data redundancy based on data storage in wireless sensor network
CN104348684A (en) * 2014-11-19 2015-02-11 成都理工大学 Method for reducing data transmission flow based on wireless sensor network nodes

Cited By (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107024976A (en) * 2016-01-30 2017-08-08 南京理工大学 Human body attitude detecting system and its detection method based on MEMS inertial sensor
CN107291265A (en) * 2016-04-05 2017-10-24 中科北控成像技术有限公司 Inertia action catches hardware system
CN106175687B (en) * 2016-07-20 2019-04-16 四川东鼎里智信息技术有限责任公司 Motion function sensing device in rehabilitation training
CN106175687A (en) * 2016-07-20 2016-12-07 四川东鼎里智信息技术有限责任公司 Motion function sensing device in rehabilitation training
CN106227368A (en) * 2016-08-03 2016-12-14 北京工业大学 A kind of human synovial angle calculation method and device
CN106227368B (en) * 2016-08-03 2019-04-30 北京工业大学 A kind of human synovial angle calculation method and device
CN106295616A (en) * 2016-08-24 2017-01-04 张斌 Exercise data analyses and comparison method and device
CN106295616B (en) * 2016-08-24 2019-04-30 张斌 Exercise data analyses and comparison method and device
CN107026778A (en) * 2017-03-29 2017-08-08 南京赛百联人防科技有限公司 Protective door detecting system, detection method and information transferring method based on CAN agreement
CN107063233B (en) * 2017-04-12 2020-06-02 无锡研测技术有限公司 Production line management and control device based on inertial sensor
CN107063233A (en) * 2017-04-12 2017-08-18 无锡研测技术有限公司 Producing line control device based on inertial sensor
CN107199566B (en) * 2017-06-02 2019-09-10 东南大学 A kind of remote control system of the space-oriented station robot based on virtual arm
CN107199566A (en) * 2017-06-02 2017-09-26 东南大学 A kind of remote control system of the space-oriented station robot based on virtual arm
CN107582062A (en) * 2017-08-31 2018-01-16 南京华苏科技有限公司 A kind of indoor human body movement locus and Posture acquisition rendering method and device
CN108108016A (en) * 2017-12-07 2018-06-01 浙江大学 Gesture perceptron
CN108170268A (en) * 2017-12-26 2018-06-15 浙江大学 A kind of Whole Body motion capture devices based on Inertial Measurement Unit
CN108198383A (en) * 2017-12-26 2018-06-22 深圳市宇恒互动科技开发有限公司 The high-precision Activity recognition method, apparatus and system of a kind of multi sensor combination
CN108198383B (en) * 2017-12-26 2020-08-07 深圳市宇恒互动科技开发有限公司 Multi-sensor combined high-precision behavior recognition method, device and system
CN109343713A (en) * 2018-10-31 2019-02-15 重庆子元科技有限公司 A kind of human action mapping method based on Inertial Measurement Unit
CN109859597A (en) * 2019-03-07 2019-06-07 哈工大机器人(合肥)国际创新研究院 A kind of rehabilitation simulation people's pose acquisition system and method

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