CN105007636A - Wearable wireless sensing network node device oriented to athletic rehabilitation - Google Patents
Wearable wireless sensing network node device oriented to athletic rehabilitation Download PDFInfo
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
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Abstract
The invention discloses a wearable wireless sensing network node device oriented to athletic rehabilitation. The wearable wireless sensing network node device comprises multiple nodes and a monitoring terminal. The nodes comprise a transmission node and a gateway node. The transmission node comprises a human body information acquisition module, a signal processing module, and a communication module. The human body information acquisition module comprises a body temperature acquisition module, a sphygmus acquisition module, a myoelectricity acquisition module, a cutaneogalvanic acquisition module, an electrocardio acquisition module, a motion attitude acquisition module, first to sixth signal conditioning circuits, and an AD circuit. The signal processing module comprises a master processor, a slave processor, and a memory unit. The communication module comprises a Bluetooth unit and a wireless sensor network unit. A customized Tiny-OS system operates in each node. The wearable wireless sensing network node device may achieve Ad-hoc networks between nodes and between the node and the gateway, has a MAC protocol and an active channel jumping function, coexists with other wireless communication ways, and reduces performance interference.
Description
Technical Field
The invention relates to the technical field of intelligent monitoring, in particular to a wearable wireless sensor network node device for sports rehabilitation.
Background
Given the severe aging population and the scarcity of medical resources, modern medical systems face new challenges. According to the American demographic agency forecast, the world's elderly population (65 years and older) will increase 2 times today by 2025. At this time, the overall medical expenditure will be 3 times that of the present, about 20% of the global GDP. The upcoming medical crisis forces researchers, entrepreneurs, and economists to formulate efficient and fast health solutions. The wireless sensor network health monitoring system capable of updating the physiological characteristics of the patient in real time provides an effective solution for the purpose. In physical activities, emergency, military and healthcare, it is becoming increasingly important for wireless sensor network health monitoring systems to remotely monitor body conditions and the surrounding environment.
The wireless sensor network node integrating intellectualization, miniaturization and low power for detecting human body signals and the surrounding environment can be positioned on the body surface, in the body or around the human body. Each node has sufficient capacity to face and process the corresponding signal. The wireless sensor network node can perform long-term health detection on a human body on the premise of not interfering with daily actions of the human body, can be used for developing an intelligent and cheap health care system, and can be used for maintaining chronic diseases, supervising recovery of surgical operations and processing a part of emergency events.
Considering a doctor examining the blood pressure of a visiting patient, he may be anxious, thereby raising the pressure leading to an inaccurate diagnosis. Thus, if a patient can install a simple monitoring system without intervention, a map of the blood pressure changes is created during his normal business. This will give the physician a better accurate and real-time result. To fulfill these requirements, monitoring of movement and bodily functions is essential. Such monitoring systems require that the sensors and wireless systems be very portable and integrated.
The small medical wireless sensor node developed by Qingdao Dongyi information technology Limited company can realize continuous monitoring of single physiological parameter; tamura T, Mizukura I and the like design a family portable blood pressure detector. NguyenK D, Chen I and the like design a sensor node for monitoring the motion trail of the arm; the Korean Kangqing national university communication engineering laboratory successfully develops the T-shirt embedded with pulse and body temperature monitoring nodes. Compared with the common wireless sensor nodes in the market, the following defects still exist: (1) multiple items of information cannot be monitored simultaneously; (2) the wire transmission is inconvenient for users to move and wear, and the application range is small; (3) large-scale nodes and high cost.
Disclosure of Invention
The invention aims to solve the technical problem of overcoming the defects of the prior art and providing a wearable wireless sensor network node device for sports rehabilitation, wherein the wireless sensor network node is wearable, is compatible with the traditional node function and abandons the wearing complexity; on the other hand, a Bluetooth mode is added while a wireless sensing network is used.
The invention adopts the following technical scheme for solving the technical problems:
the wearable wireless sensor network node device for the sport rehabilitation comprises a plurality of nodes and a monitoring terminal; the nodes comprise transmission nodes and gateway nodes, wherein the transmission nodes comprise a human body information acquisition module, a signal processing module and a communication module; the human body information acquisition module comprises a body temperature acquisition module, a pulse acquisition module, a myoelectricity acquisition module, a skin electricity acquisition module, an electrocardio acquisition module, a motion posture acquisition module, a first signal conditioning circuit, a second signal conditioning circuit, a third signal conditioning circuit, a fourth signal conditioning circuit, a fifth signal conditioning circuit, a sixth signal conditioning circuit and an AD circuit; the signal processing module comprises a main processor, a slave processor and a storage unit; the communication module comprises a Bluetooth unit and a wireless sensor network unit; the monitoring terminal comprises a handheld monitoring terminal and a computer monitoring terminal; wherein,
the body temperature acquisition module is used for outputting the acquired body temperature signal to the first signal conditioning circuit;
the pulse acquisition module is used for outputting the acquired pulse signals to the second signal conditioning circuit;
the myoelectric acquisition module is used for outputting the acquired myoelectric signals to the third signal conditioning circuit;
the bioelectricity acquisition module is used for outputting the acquired bioelectricity signals to the fourth signal conditioning circuit;
the electrocardio acquisition module is used for outputting the acquired electrocardiosignals to the fifth signal conditioning circuit;
the motion attitude acquisition module is used for outputting the acquired motion attitude signal to the sixth signal conditioning circuit;
the first signal conditioning circuit is used for conditioning the received body temperature signal and outputting the conditioned body temperature signal to the AD circuit;
the second signal conditioning circuit is used for conditioning the received pulse signals and outputting the conditioned pulse signals to the AD circuit;
the third signal conditioning circuit is used for conditioning the received electromyographic signals and outputting the conditioned electromyographic signals to the AD circuit;
the fourth signal conditioning circuit is used for conditioning the received skin electric signal and outputting the conditioned skin electric signal to the AD circuit;
the fifth signal conditioning circuit is used for conditioning the received electrocardiosignals and outputting the conditioned electrocardiosignals to the AD circuit;
the sixth signal conditioning circuit is used for conditioning the received motion attitude signal and outputting the conditioned motion attitude signal to the AD circuit;
the AD circuit is used for converting the conditioned body temperature signal, the conditioned pulse signal, the conditioned myoelectric signal, the conditioned skin electric signal, the conditioned electrocardiosignal and the conditioned motion posture signal into digital signals and outputting the digital signals to the main processor;
the main processor is used for outputting a data packet to the storage unit after processing the digital signal;
a storage unit for outputting the stored data packet to the slave processor;
the slave processor is used for converting the data packet into a data frame in a wireless information format and outputting the data frame to the Bluetooth unit or the wireless sensor network unit;
the wireless sensor network unit is used for outputting the received wireless information format data frame to the gateway node;
the Bluetooth unit is used for outputting the received wireless information format data frame to the handheld monitoring terminal;
and the gateway node is used for outputting the wireless information format data frame to the computer monitoring terminal through the serial port.
As a further optimization scheme of the wearable wireless sensor network node device for the sport rehabilitation, the signal conditioning circuit comprises a pre-amplification circuit, a low-pass filter circuit, a high-pass filter circuit, a power frequency trap circuit and a post-amplification circuit which are sequentially connected.
As a further optimization scheme of the wearable wireless sensor network node device for athletic rehabilitation, the model of the wireless sensor network unit is AT86RF 230.
The wearable wireless sensor network node device for the sports rehabilitation further comprises a 4-layer three-dimensional circuit board, wherein the communication module is arranged on the first layer of the three-dimensional circuit board, the signal processing module is arranged on the second layer of the three-dimensional circuit board, the human body information acquisition module is arranged on the third layer of the three-dimensional circuit board, and a battery is arranged on the fourth layer of the three-dimensional circuit board; and each layer on the three-dimensional circuit board is provided with a flip-type FPC connector, and the layers on the three-dimensional circuit board are connected through the FPC connectors.
As a further optimization scheme of the wearable wireless sensor network node device for athletic rehabilitation, the main processor is a TMS 3205000 series DSP.
As a further optimization scheme of the wearable wireless sensor network node device for athletic rehabilitation, the slave processor is an ATmegal1280 processor.
As a further optimization scheme of the wearable wireless sensor network node device for the sports rehabilitation, the Bluetooth unit is a master-slave integrated BCO-4B module.
As a further optimization scheme of the wearable wireless sensor network node device for athletic rehabilitation, the wireless sensor network unit is based on IEEE802.25.4 protocol, has 16 channels in total, has a working frequency band of 2.4GHz, is in a unified frequency band with WiFi and Zigbee, and has an anti-interference function.
As a further optimization scheme of the wearable wireless sensor network node device for the sports rehabilitation, the sports attitude acquisition module comprises a three-axis acceleration sensor, a geomagnetic sensor and a gyroscope.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
(1) the problem that the application range of the sensing node in the wireless sensor network system is narrow is effectively solved, and the system is low in investment cost, high in cost performance, good in real-time performance, safe, reliable and convenient to use and maintain;
(2) the human body signal acquisition module and the signal processing and communication module jointly form a sensing node, and the human body signal acquisition module can acquire human body temperature, pulse, myoelectricity, skin electricity and electrocardio signals, so that the node can realize different functions;
(3) the main processor is stored in the memory to carry out calculation, transformation, analysis, filtering, verification and feature extraction algorithms on the human body information data, so that the data size can be effectively controlled without influencing the real effect of the data, and the transmission rate and the system power consumption are reduced;
(4) the wireless communication unit adopts Zigbee and radio frequency transceiving chips of IEEE802.15.4 protocol standards, the chips have the characteristics of low power consumption, low cost and the like, and the constructed network has self-organization and self-healing properties, so that the sensing nodes can be widely deployed and reliably communicate in practical application, and most of application occasions of the wireless sensor network can be met; and Bluetooth is added, so that point-to-point high-speed communication between the gateway node and the terminal is realized.
(5) The operation of a Tiny-OS system on each node can realize the self-networking between the nodes and the gateway, has the functions of MAC protocol and active channel hopping, can realize the coexistence with other wireless communication modes, and reduces the performance interference.
Drawings
Fig. 1 is a structural block diagram of a wearable wireless sensor network node device for athletic rehabilitation and an operation method of the wearable wireless sensor network node device.
FIG. 2 is a schematic diagram of the circuitry in the body signal acquisition module of the present invention; wherein (a) is an AD circuit and (b) is a conditioning circuit.
FIG. 3 is a schematic circuit diagram of a portion of a signal processing and communication module according to the present invention; wherein, (a) is the circuit diagram of the master processor and the storage unit, and (b) is the circuit diagram of the slave processor and the communication module.
Fig. 4 is a software flow diagram of the signal processing and communication module of the transmission node in the operation method of the present invention.
Fig. 5 is a software flow diagram of the signal processing and communication module of the gateway node in the method of operation of the present invention.
Fig. 6 is an experimental diagram of the present invention when multiple wireless communication systems coexist; the WBAN alone presence map (a), the coexistence map without anti-interference WBAN and WiFi, and the coexistence map with anti-interference WBAN and WiFi are shown in (c).
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
as shown in fig. 1, the wearable wireless sensor network node apparatus for sports rehabilitation of the present invention includes a plurality of nodes and a monitoring terminal; the nodes comprise transmission nodes and gateway nodes, wherein the transmission nodes comprise a human body information acquisition module, a signal processing module and a communication module; the human body information acquisition module comprises a body temperature acquisition module, a pulse acquisition module, a myoelectricity acquisition module, a skin electricity acquisition module, an electrocardio acquisition module, a motion posture acquisition module, a first signal conditioning circuit, a second signal conditioning circuit, a third signal conditioning circuit, a fourth signal conditioning circuit, a fifth signal conditioning circuit, a sixth signal conditioning circuit and an AD circuit; the signal processing module comprises a main processor, a slave processor and a storage unit; the communication module comprises a Bluetooth unit and a wireless sensor network unit; the monitoring terminal comprises a handheld monitoring terminal and a computer monitoring terminal; wherein,
the body temperature acquisition module is used for outputting the acquired body temperature signal to the first signal conditioning circuit;
the pulse acquisition module is used for outputting the acquired pulse signals to the second signal conditioning circuit;
the myoelectric acquisition module is used for outputting the acquired myoelectric signals to the third signal conditioning circuit;
the bioelectricity acquisition module is used for outputting the acquired bioelectricity signals to the fourth signal conditioning circuit;
the electrocardio acquisition module is used for outputting the acquired electrocardiosignals to the fifth signal conditioning circuit;
the motion attitude acquisition module is used for outputting the acquired motion attitude signal to the sixth signal conditioning circuit;
the first signal conditioning circuit is used for conditioning the received body temperature signal and outputting the conditioned body temperature signal to the AD circuit;
the second signal conditioning circuit is used for conditioning the received pulse signals and outputting the conditioned pulse signals to the AD circuit;
the third signal conditioning circuit is used for conditioning the received electromyographic signals and outputting the conditioned electromyographic signals to the AD circuit;
the fourth signal conditioning circuit is used for conditioning the received skin electric signal and outputting the conditioned skin electric signal to the AD circuit;
the fifth signal conditioning circuit is used for conditioning the received electrocardiosignals and outputting the conditioned electrocardiosignals to the AD circuit;
the sixth signal conditioning circuit is used for conditioning the received motion attitude signal and outputting the conditioned motion attitude signal to the AD circuit;
the AD circuit is used for converting the conditioned body temperature signal, the conditioned pulse signal, the conditioned myoelectric signal, the conditioned skin electric signal, the conditioned electrocardiosignal and the conditioned motion posture signal into digital signals and outputting the digital signals to the main processor;
the main processor is used for outputting a data packet to the storage unit after processing the digital signal;
a storage unit for outputting the stored data packet to the slave processor;
the slave processor is used for converting the data packet into a data frame in a wireless information format and outputting the data frame to the Bluetooth unit or the wireless sensor network unit;
the wireless sensor network unit is used for outputting the received wireless information format data frame to the gateway node;
the Bluetooth unit is used for outputting the received wireless information format data frame to the handheld monitoring terminal;
and the gateway node is used for outputting the wireless information format data frame to the computer monitoring terminal through the serial port.
The signal conditioning circuit comprises a pre-amplification circuit, a low-pass filter circuit, a high-pass filter circuit, a power frequency trap circuit and a post-amplification circuit which are sequentially connected.
The monitoring terminal is a computer terminal or a handheld terminal.
The wireless sensor network element is of the type AT86RF 230.
The invention also comprises a 4-layer three-dimensional circuit board, wherein the communication module is arranged on the first layer of the three-dimensional circuit board, the signal processing module is arranged on the second layer of the three-dimensional circuit board, the human body information acquisition module is arranged on the third layer of the three-dimensional circuit board, and a battery is arranged on the fourth layer of the three-dimensional circuit board; and each layer on the three-dimensional circuit board is provided with a flip-type FPC connector, and the layers on the three-dimensional circuit board are connected through the FPC connectors.
The host processor is a TMS 3205000 series DSP.
The slave processor is an ATmegal1280 processor.
The Bluetooth unit is a master-slave integrated BCO-4B module.
The wireless sensor network unit is based on IEEE802.25.4 protocols, has 16 channels in total, has a working frequency band of 2.4GHz, is in a unified frequency band with WiFi and Zigbee, and has an anti-interference function.
The motion attitude acquisition module comprises a three-axis acceleration sensor, a geomagnetic sensor and a gyroscope.
The signal conditioning circuit comprises a pre-amplification circuit, a low-pass filter circuit, a high-pass filter circuit, a power frequency trap circuit and a post-amplification circuit which are sequentially connected. The monitoring terminal is a computer terminal or a handheld terminal. The wireless sensor network element is of the type AT86RF 230. The communication module is arranged on the first layer of the three-dimensional circuit board, the signal processing module is arranged on the second layer of the three-dimensional circuit board, the human body information acquisition module is arranged on the third layer of the three-dimensional circuit board, and a battery is arranged on the fourth layer of the three-dimensional circuit board; and each layer on the three-dimensional circuit board is provided with a flip-type FPC connector, and the layers on the three-dimensional circuit board are connected through the FPC connectors. The host processor is a TMS 3205000 series DSP. The slave processor is an ATmegal1280 processor. The Bluetooth unit is a master-slave integrated BCO-4B module. The wireless sensor network unit is based on IEEE802.25.4 protocols, has 16 channels in total, has a working frequency band of 2.4GHz, is in a unified frequency band with WiFi and Zigbee, and has an anti-interference function.
Each node comprises an information acquisition module, a signal processing module and a communication module, and different modules are connected through an FPC (flexible printed circuit); all modules and elements are powered by the same 5V rechargeable lithium battery.
Wherein: the body temperature acquisition module adopts a digital body temperature sensor and can directly output the processed signals to the main processor.
The pulse acquisition module, the myoelectricity acquisition module, the skin electricity acquisition module and the electrocardio acquisition module adopt physiological pole pieces and can be converted into digital signals only through corresponding conditioning circuits and AD circuits.
The signal processing and communication module comprises a main processor, a slave processor, a wireless transceiving unit, Bluetooth and a storage unit; the storage unit stores various algorithms corresponding to the data acquired by the human body signal acquisition module and the data acquired by the main processor.
FIG. 2 is a schematic diagram of the circuitry in the body signal acquisition module of the present invention; fig. 2 (a) shows an AD circuit, and fig. 2 (b) shows a conditioning circuit. Because signals such as electrocardio, myoelectricity, picoelectricity and the like are mv-level signals, and the range of signals which can be received by an I/O port of the processor is mostly V-level, the signals need to be amplified, and the signals are very easily interfered, especially easily interfered by power frequency, so that a power frequency trap circuit is specially designed, and a software filter is also designed during subsequent algorithm processing, so that the combination of the software filter and the hardware filter is achieved.
In order to facilitate the maintenance and the upgrade of the system, communication debugging ports are arranged on the master processor and the slave processor to download a running program and a debugging program for the system.
Fig. 3 shows a schematic circuit diagram of a signal processing and communication module according to the present invention, where (a) in fig. 3 is a circuit diagram of a master processor and a memory cell, and (b) in fig. 3 is a circuit diagram of a slave processor and a communication module. The main processor adopts a TMS320C5509A processor of TI company; the slave processor and the wireless transceiver unit adopt a CrossbowOEM module produced by ATMEL company, the CrossbowOEM module integrates an 8-bit RSIC processor and a wireless communication module AT86RF230, and can be fully compatible with 2.4GHz and IEEE802.15.4 transceivers, provide rich analog quantity and digital peripheral interfaces, and can be used as an MCU with very strong performance besides wireless communication;
the human body signal acquisition sensor part is an external expansion device of the whole node, is connected with the signal conditioning circuit by a detachable conducting wire, is designed into an FPC and is convenient to wear.
Because the slave processor runs the TinyOS system, the functions of networking, data transmission and the like of the nodes can be realized, and the anti-interference performance of the wireless sensor network when multiple wireless communication modes coexist can be improved by designing an MAC protocol and a channel hopping program.
All modules of the node are powered by the same 5V rechargeable lithium battery.
In the signal processing module and the communication module, a TMS 3205000 series DSP is adopted as a main processor, an ATmegal128V processor is adopted as a slave processor, an AT86RF230 chip is adopted for wireless communication, and a master-slave integrated BCO-4B module is adopted for wireless communication Blutooth. The master processor, the slave processor and the wireless transceiver unit can also be composed of other 5000/6000 series DSPs with 32-bit low-power consumption RSIC processor cores, an 8-bit MCU capable of running a TinyOS system, a CC243X series wireless transceiver module and peripheral circuits.
In order to reduce power consumption and transmission rate, the main processor performs auxiliary online processing and feature extraction on the human body signals, and processes and packages data into data packets meeting a wireless communication protocol.
In order to optimize the signal transmission route and reduce the power, each node automatically selects the transmission path according to the specific situation.
The gateway node receives different data from other nodes in a wireless communication mode, performs data fusion on the data to reduce the size of the data, transmits the fused data to handheld software through Bluetooth and transmits the fused data to computer software through a serial port.
The invention relates to an operation method of a wearable wireless sensor network node device for sports rehabilitation, which comprises the following steps:
1. wearing the node on the body or placing the node around the body, attaching the sensor to a monitoring part, and then starting the power management module to electrify the device;
2. each processor unit in the signal processing and communication module is automatically initialized;
3. the gateway node receives the monitoring software command and controls various works of other data nodes according to the command;
4. the node slave processor waits for receiving a command issued by the gateway node, extracts, calculates, transforms, analyzes, filters and checks the command, and judges the command meaning;
(1) wake-up command: the node is in a working state and waits for other commands; (2) sleep command: the node enters an energy-saving mode and waits for a wake-up command; (3) sending a command: the nodes send human body information;
6. after the command message is received, if the node receives a sending command, the signal processing and communication module starts to wait for the acquisition result signal data sent by the corresponding part in the signal acquisition module;
7. the main processor processes, extracts, calculates, transforms, analyzes, filters, checks and extracts the characteristics of the data according to the actual requirements, and then stores the data in the memory unit;
8. the slave processor reads the data in the memory, converts the data of the stored information format data frame into a wireless information format data frame, and then sends the wireless information format data frame to other nodes in the wireless sensor network through the wireless transceiving unit;
9. after receiving data from other nodes, the gateway node correspondingly processes the received data, and then combines the data into a data packet to be sent to the monitoring software;
10. and after receiving the data from the gateway node, the monitoring terminal extracts, calculates, transforms, analyzes, filters, checks and stores the received data, judges the type of the data to obtain a corresponding parameter acquisition object and displays the corresponding parameter acquisition object.
Fig. 4 and 5 are combined to show a software processing flow of the signal processing and communication module, where fig. 4 is a software flow chart of the signal processing and communication module of the transmission node in the operation method of the present invention; fig. 5 is a software flow diagram of the signal processing and communication module of the gateway node in the method of operation of the present invention. The method comprises the following specific steps:
1. starting a power management module to power on the device; initializing each processor unit in the signal processing and communication module, setting a stack pointer and starting global interrupt;
2. the gateway node receives the terminal command, extracts, calculates, transforms, analyzes, filters and checks the command, judges the command meaning, converts the command into data meeting the wireless communication protocol, and finally issues the data through the wireless transceiving unit; the transmission/routing node waits for receiving a command issued by the gateway node, extracts, calculates, transforms, analyzes, filters and checks the command, and judges the command meaning;
3. the transmission/routing node receives the sending command, and the signal processing and communication module starts to wait for the acquisition result signal data sent by the corresponding part in the signal acquisition module; the main processor processes, extracts, calculates, transforms, analyzes, filters, checks and extracts the characteristics of the data according to the actual requirements, and then stores the data in the memory unit; reading the human body signal data in the memory from the processor, extracting, calculating, converting, analyzing, checking, filtering, constructing and storing the human body signal data, converting the data of the stored information format data frame into a wireless information format data frame, and then sending the wireless information format data frame to other nodes in the wireless sensor network through the wireless transceiving unit; finally converging the data to a gateway node;
4. after receiving data from other nodes, the gateway node extracts, calculates, transforms, analyzes, filters, checks and stores the received data, judges the type of the data to obtain a corresponding parameter acquisition object, extracts effective information of each data, fuses each data to form a data packet, and finally sends the data packet to monitoring software;
the gateway node directly transmits data to the computer monitoring software through the serial port and transmits the data to the handheld monitoring software through Bluetooth, the two types of monitoring software have data display and storage functions and an emergency reminding function, and the computer software also has remote transmitting and receiving functions and can realize data transmission between a local part and a remote end.
Fig. 6 is a data diagram of optimizing interference resistance when multiple wireless communication modes coexist, where by designing channel hopping and MAC protocol in the TinyOS system on a node, (a) in fig. 6 is a communication data diagram of a wireless sensor network alone, (b) in fig. 6 is a data diagram of a wireless sensor network before the interference resistance is optimized when other communication modes coexist, and (c) in fig. 6 is a data diagram of a wireless sensor network before the interference resistance is optimized when the other communication modes coexist, it is known that the interference resistance of the system is improved after the optimization by comparing a, b, and c.
The algorithm is as follows: skin electricity, electrocardio, myoelectricity and pulse: the collected skin electric signal, electrocardio signal, myoelectric signal and pulse signal can be expressed as follows:
x(n)＝s(n)+u(n)
wherein, s (n) is a useful signal, u (n) is superposition of various types of noise, and x (n) comprises the useful signal and a noise signal and is an acquired original signal to be processed. From the analysis of the surface myoelectric signal, the electrocardio signal and the pulse signal, it can be verified that: the useful signal is often represented as a low-frequency signal, and the noise signal is mainly high-frequency noise and power-frequency noise.
The algorithm process for denoising by utilizing wavelet transform mainly comprises the following three steps:
(1) wavelet decomposition of the signal. And selecting a proper wavelet basis and wavelet decomposition level N, and then performing N-level discrete scale wavelet decomposition on the noise-containing signal. For a one-dimensional discrete signal, its high frequency part affects the high frequency first layer of the wavelet decomposition, and its low frequency part affects the deepest layer of the wavelet decomposition and its low frequency layer. Through the analysis of the signals such as the surface myoelectric signals and the like, the energy of the physiological signals can be mostly concentrated on the 4-scale and the 5-scale, and the multi-resolution analysis on the two scales can not lose some important local singularities, so that the number of the selected decomposition layers is 5.
(2) After wavelet decomposition, threshold quantization processing is carried out on the high-frequency coefficient. And selecting a proper threshold value from each layer from 1 to N, and carrying out hard threshold value or soft threshold value quantization processing on the high-frequency coefficient of the layer to reduce Gibbs oscillation phenomenon generated by the noise-eliminated signal in the vicinity of a singular point. The method for determining the threshold value adopts an unbiased risk estimation threshold value (rigrsure) to determine, and comprises the following specific steps:
(a) taking the absolute value of each element in the signal z (i), then sorting the elements from small to large, taking the square value of each element, and obtaining a new sequence:
f(k)＝(sort(|z|))2,(k＝0,1,...,N-1)
z (i) is the original signal, f (k) is the ordered signal
(b) If the square root of the kth element with threshold f (k) is taken, then:
in addition, the risk of the threshold is:
(c) let k be the value corresponding to the minimum risk point on the Risk (k) curveminThen the threshold size determined by rigrsure is:
after thresholding, the wavelet coefficients may be thresholded, typically using a hard thresholding method. The hard thresholding method is to retain only wavelet coefficients that are greater than a threshold value, while leaving all other wavelet coefficients zero. The soft threshold method is to shrink the wavelet coefficient larger than the threshold value to zero and set the wavelet coefficient smaller than the threshold value to zero. The system carries out thresholding processing on the high-frequency coefficient by adopting a soft threshold method. Let DiFor the high-frequency detail component obtained after wavelet decomposition of the ith (i is less than or equal to N), after a threshold value P is given, the functional expression of thresholding is as follows:
(3) and (5) wavelet reconstruction. And performing wavelet reconstruction according to the N-th layer of low-frequency coefficients of the wavelet decomposition and the quantized high-frequency coefficients of each layer to obtain the physiological signals after noise elimination.
Orthogonal scale function alpha (t) and wavelet function beta (t)
In the formula, h0k，h1kIs the filter coefficient in the multi-resolution analysis, k is the number of the band of the j +1 th layer of the wavelet packet transformation, and t is the total number.
Further generalizing the two-scale resolution, the following recursion relationship is defined:
set of functions defined by the above equation wn(t)}n∈ZIs w0The wavelet packet determined by (t) is a function including a scaling function w0(t) and wavelet mother function w1(t) a set of functions having a relationship.
By the definition of the wavelet packet, a wavelet packet decomposition algorithm formula and a reconstruction algorithm formula can be obtained, wherein: k is the number of the frequency bands of the j +1 th layer of the wavelet packet transformation.
Wherein,is a wavelet of the k-th layer,is the total wavelet, and L is the total number.
The motion posture is as follows: when transformed from the local coordinate system (LSC) to the global coordinate system (GSC), its rotation vector is R:
the kinematic rotation vector change rate is:
where ω (t) is the local coordinate system rotation speed vector obtained from the gyroscope and r (t) is the global coordinate system rotation speed vector.
The integral is in the form: <math>
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<mi>r</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>r</mi>
<mrow>
<mo>(</mo>
<mn>0</mn>
<mo>)</mo>
</mrow>
<mo>+</mo>
<munderover>
<mo>∫</mo>
<mn>0</mn>
<mi>t</mi>
</munderover>
<mi>dθ</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
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<mo>(</mo>
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<mo>)</mo>
</mrow>
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</math>
after the direction is changed, the following can be obtained from the cosine square matrix: r isGSC(t+dt)＝rGSC(t)+rGSC(t)×dθ(t)
The diagonal matrix for 1 can be derived as:
finally, it can be found that:
θ＝-sin-1(R[3,1])
φ＝tan-1(R[3,2]/R[3,3])
ψ＝tan-1(R[2,1]/R[1,2])
psi is the angle of the X-axis from the Y-axis, phi is the angle of the Y-axis from the Z-axis, and theta is the angle of the X-axis from the Z-axis.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all should be considered as belonging to the protection scope of the invention.
Claims (9)
1. A wearable wireless sensor network node device for sports rehabilitation is characterized by comprising a plurality of nodes and a monitoring terminal; the nodes comprise transmission nodes and gateway nodes, wherein the transmission nodes comprise a human body information acquisition module, a signal processing module and a communication module; the human body information acquisition module comprises a body temperature acquisition module, a pulse acquisition module, a myoelectricity acquisition module, a skin electricity acquisition module, an electrocardio acquisition module, a motion posture acquisition module, a first signal conditioning circuit, a second signal conditioning circuit, a third signal conditioning circuit, a fourth signal conditioning circuit, a fifth signal conditioning circuit, a sixth signal conditioning circuit and an AD circuit; the signal processing module comprises a main processor, a slave processor and a storage unit; the communication module comprises a Bluetooth unit and a wireless sensor network unit; the monitoring terminal comprises a handheld monitoring terminal and a computer monitoring terminal; wherein,
the body temperature acquisition module is used for outputting the acquired body temperature signal to the first signal conditioning circuit;
the pulse acquisition module is used for outputting the acquired pulse signals to the second signal conditioning circuit;
the myoelectric acquisition module is used for outputting the acquired myoelectric signals to the third signal conditioning circuit;
the bioelectricity acquisition module is used for outputting the acquired bioelectricity signals to the fourth signal conditioning circuit;
the electrocardio acquisition module is used for outputting the acquired electrocardiosignals to the fifth signal conditioning circuit;
the motion attitude acquisition module is used for outputting the acquired motion attitude signal to the sixth signal conditioning circuit;
the first signal conditioning circuit is used for conditioning the received body temperature signal and outputting the conditioned body temperature signal to the AD circuit;
the second signal conditioning circuit is used for conditioning the received pulse signals and outputting the conditioned pulse signals to the AD circuit;
the third signal conditioning circuit is used for conditioning the received electromyographic signals and outputting the conditioned electromyographic signals to the AD circuit;
the fourth signal conditioning circuit is used for conditioning the received skin electric signal and outputting the conditioned skin electric signal to the AD circuit;
the fifth signal conditioning circuit is used for conditioning the received electrocardiosignals and outputting the conditioned electrocardiosignals to the AD circuit;
the sixth signal conditioning circuit is used for conditioning the received motion attitude signal and outputting the conditioned motion attitude signal to the AD circuit;
the AD circuit is used for converting the conditioned body temperature signal, the conditioned pulse signal, the conditioned myoelectric signal, the conditioned skin electric signal, the conditioned electrocardiosignal and the conditioned motion posture signal into digital signals and outputting the digital signals to the main processor;
the main processor is used for outputting a data packet to the storage unit after processing the digital signal;
a storage unit for outputting the stored data packet to the slave processor;
the slave processor is used for converting the data packet into a data frame in a wireless information format and outputting the data frame to the Bluetooth unit or the wireless sensor network unit;
the wireless sensor network unit is used for outputting the received wireless information format data frame to the gateway node;
the Bluetooth unit is used for outputting the received wireless information format data frame to the handheld monitoring terminal;
and the gateway node is used for outputting the wireless information format data frame to the computer monitoring terminal through the serial port.
2. The wearable wireless sensor network node device for athletic rehabilitation according to claim 1, wherein the signal conditioning circuit comprises a pre-amplifier circuit, a low-pass filter circuit, a high-pass filter circuit, a power frequency trap circuit and a post-amplifier circuit which are connected in sequence.
3. The wearable wireless sensor network node apparatus for athletic rehabilitation according to claim 1, wherein the wireless sensor network unit is AT86RF 230.
4. The wearable wireless sensor network node device for athletic rehabilitation according to claim 1, further comprising a 4-layer three-dimensional circuit board, wherein the communication module is disposed on a first layer of the three-dimensional circuit board, the signal processing module is disposed on a second layer of the three-dimensional circuit board, the human body information acquisition module is disposed on a third layer of the three-dimensional circuit board, and a battery is disposed on a fourth layer of the three-dimensional circuit board; and each layer on the three-dimensional circuit board is provided with a flip-type FPC connector, and the layers on the three-dimensional circuit board are connected through the FPC connectors.
5. The wearable wireless sensor network node apparatus for athletic rehabilitation according to claim 1, wherein the main processor is a TMS 3205000 series DSP.
6. The wearable wireless sensor network node apparatus for athletic rehabilitation according to claim 1, wherein the slave processor is an ATmegal1280 processor.
7. The wearable wireless sensor network node device for athletic rehabilitation according to claim 1, wherein the bluetooth unit is a master-slave integrated BCO-4B module.
8. The wearable wireless sensor network node apparatus for athletic rehabilitation according to claim 1, wherein the wireless sensor network unit is based on IEEE802.25.4 protocol, has 16 channels in total, has an operating frequency band of 2.4GHz, is in a unified frequency band with WiFi and Zigbee, and has an anti-interference function.
9. The wearable wireless sensor network node device for athletic rehabilitation according to claim 1, wherein the motion gesture collection module includes a three-axis acceleration sensor, a geomagnetic sensor and a gyroscope.
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| CN106452608A (en) * | 2016-10-31 | 2017-02-22 | 天津大学 | Radio-frequency transceiver being achieved by adopting notching filter and applied in wireless body local area network |
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| CN108309294A (en) * | 2018-02-07 | 2018-07-24 | 上海健康医学院 | A kind of human body active state movable recognition system |
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| CN109199367A (en) * | 2018-10-18 | 2019-01-15 | 杭州电子科技大学 | A kind of wearable monitoring device and monitoring method for wisdom endowment |
| CN109805912A (en) * | 2019-04-10 | 2019-05-28 | 燕山大学 | The multi signal synchronous acquisition device and method of a kind of human Body Physiology Multi-parameter and inertial signal |
| CN111265222A (en) * | 2020-03-24 | 2020-06-12 | 孙孟良 | ICU patient unplanned tube drawing early warning system |
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