CN112842328A - Integrated gait, distance, relative weight and brain wave synchronous sensing analysis system based on Internet of things - Google Patents
Integrated gait, distance, relative weight and brain wave synchronous sensing analysis system based on Internet of things Download PDFInfo
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Abstract
The invention discloses an integrated gait, distance, relative weight and brain wave synchronous sensing analysis system based on the Internet of things, which comprises: the system comprises a sensor, an amplifying and converting circuit processing unit, a single chip computer, a wireless communication control unit and an internet data server. The internet data server includes a processor adapted to implement instructions, and a storage device adapted to store a plurality of instructions. The processor processes the received data transmitted by each mobile terminal in real time, wherein the data comprises eye movement electric waves, forehead electric waves, acceleration signals during walking and pressure waveform data, generates the data analysis report and transmits the data analysis report to the doctor central workstation in an electronic mode. The invention can solve the problem of automatic analysis and calculation of the Internet of things cloud based on the walking gait, distance, relative weight and brain wave synchronization characteristics of a person by utilizing the wireless mobile Internet technology in various environment states.
Description
Technical Field
The invention relates to a wearable system which can automatically calculate walking gait, distance, relative weight and brain wave characteristics by utilizing a multi-channel wireless mobile internet technology under various environmental states.
Background
The internet technology and the communication technology are one of the supporting forces leading scientific and technical progress in the modern times, and the integration and exchange of medical information are realized on an internet platform by a wireless communication means in combination with clinical medical services, so that a novel medical service mode is created. The novel mode of realizing medical service by utilizing the public communication and internet service platform realizes stable signal transmission on the unstable mobile internet, can save a large amount of investment for creating the service, reduces the cost of users, and can expand the medical service into rural areas, remote areas and other areas. Is one of the trends of the development of medical informatization. Under the condition of realizing a new diagnosis and treatment mode, the diagnosis and treatment process of a medical unit can be changed. The service range of a medical unit is expanded, the diagnosis process can be refined and divided, a large number of technical means and technical service posts are provided, and under the assistance of the biomedical signal processing technology, the implementation of intelligent diagnosis and treatment technologies such as automatic diagnosis, information management and multi-center concurrent information processing can be realized, so that doctors can realize active medical service for the families of patients by means of wearable and internet technologies, and even the doctors can develop the diagnosis and treatment service under the condition of returning to the homes. Aiming at the health problems in the elderly and the quality of life and the health state of the medical prognosis of the old, the method provides important information analysis content and diagnosis and treatment. The method establishes a home medical (HOME CARE) service system, forms a service mode of home-community-large medical center integration, is deep and specific of a regional medical system based on a central hospital and taking an information technology as a support platform, and has important significance for the improvement of the whole system architecture.
The living quality of the elderly and the coordination ability of the brain for walking are main contents reflecting the occurrence and rehabilitation of diseases of the elderly, particularly the weight change and the walking distance, and are main indexes for predicting the life span of the elderly and the disease prognosis. The brain coordination ability advantage is an effective means for predicting the occurrence of brain diseases of the old, particularly, the big data of the brain wave characteristic expression of the walking of the old is established, the brain wave characteristic basic value of the old is obtained, and the method plays an important role in predicting the cerebral infarction and the myocardial infarction. At present, a wearable walking distance brain cooperation and relative weight monitoring system for the elderly is in a blank state, related research does not relate to characteristic performance of brain waves during walking, a wearable sensor capable of meeting the requirements and an internet cloud computing and big data real-time monitoring and analyzing system are invented, and the wearable sensor and the internet cloud computing and big data real-time monitoring and analyzing system have great social significance and medical and intelligent values for nursing the aged.
The invention content is as follows:
the invention aims to provide an integrated gait, distance, relative weight and brain wave synchronous sensing analysis system based on the Internet of things, which can solve the problem of automatic analysis and calculation of the Internet of things cloud of the walking gait, distance, relative weight and brain wave synchronous characteristics of a person by utilizing a wireless mobile Internet technology under various environmental states.
In order to achieve the purpose of the invention, the technical scheme adopted by the invention is as follows:
integration gait, distance, relative weight, brain wave synchronous sensing analytic system based on thing networking, its characterized in that includes:
the sensor collects signals containing electroencephalogram signals, walking pressure signals and walking acceleration signals in real time;
the amplifying and converting circuit processing unit is used for carrying out pre-amplifying and analog-to-digital conversion processing on the output signal of the sensor;
the single chip computer encrypts and compresses the data output by the sensor and sends the data to the wireless communication control unit;
the wireless communication control unit is used for packaging the data output by the singlechip through a TCP/IP protocol and directly sending the data to the interconnected network data server;
an internet data server comprising a processor adapted to implement instructions; and
a storage device adapted to store a plurality of instructions, the instructions loaded and executed by a processor:
receiving all data sent by mobile information acquisition and transmission terminals consisting of a sensor, an amplification and conversion circuit processing unit and a single chip microcomputer, wherein each mobile information acquisition and transmission terminal is set with a unique address code and consists of a machine number and a fixed IP address of a server:
processing the received data sent by each mobile terminal in real time, wherein the processing comprises automatically decomposing eye movement electric waves and frontal muscle electric waves mixed in the brain wave data;
calculating the characteristic value of an acceleration waveform of an acceleration signal during walking by adopting a composite algorithm, pattern recognition, waveform recognition and calculus; extracting data including slope, change rate, waveform area and inflection point in the waveform, integrating the data into an index and reflecting the walking distance;
for pressure waveform data, calculating the time interval between two points, calculating the amplitude values of the two points in real time, and solving the ratio of the current amplitude to the standard amplitude;
and generating the data analysis report and electronically transmitting the data analysis report to a doctor central workstation.
An integrated gait, distance, relative weight and brain wave synchronous sensing analysis system based on the Internet of things is characterized in that a sensor comprises a first unit, a physiological and life signal acquisition transducer, a second unit and a life information acquisition and transmission terminal host; the waveform signal acquisition transducer of the brain electricity, weight and acceleration sensors continuously acquires brain waves on two sides of the forehead and gravity and acceleration during walking, and comprises a group of metal electrode plates and pressure and acceleration sensors; the metal electrode is divided into two electroencephalogram signal guide electrodes at the upper part of a left side frame and the upper part of a right side frame and two electroencephalogram signal reference electrodes at the ear lobe parts, and the electroencephalogram signals of the left brain and the right brain are collected respectively after combination; the gravity and acceleration sensors are arranged at the sole and ankle parts and respectively collect acceleration waveform and gravity pressure waveform signals during walking.
The method is characterized in that eye movement electric waves and frontal muscle electric waves mixed in brain wave data are automatically decomposed, discretization processing with the sampling frequency of 500/s, the sampling time window of 2.5s and the sampling precision of 10 bits is performed, waveform identification and power spectrum analysis algorithms are adopted, and a power spectrum formula is adopted:
various components of the power spectrum in the brain wave can be obtained, including the values of the α β δ θ wave band, the edge frequencies such as Fsef and Fmax, the dominant frequency, and the like:
F＝{α，β，δ，θ，Fsef，Fmax}；
aiming at the original waveform data of the brain waves, solving the conventional rhythm and the high-frequency rhythm distributed in the electroencephalogram; extracting the characteristic points of the data by adopting a waveform identification algorithm in a pattern identification algorithm:
T(x)∈z；
t: a vector of eigenvalues; x: brain wave discrete data; z: a time domain space;
the feature data in vector t (x) includes specific points, amplitudes, variations, slopes, areas, and the calculations are from the basic algorithm: data sequence:
y(t)＝(f(j)-f(j-1))/Δt
j: discrete data subscripts; f (j): brain wave raw data function
Obtaining the maximum value in the sequence y (t) to obtain one of the characteristic indexes, wherein the positive and negative inversion points are special points, and the number of the special points is represented by the value of t;
applying an iterative differentiation algorithm to the f (j) sequence data:
d(j，k)＝∑(f(j+k)-f(j+k-1)/(Δt+k))
Δ t: a sampling time interval; k: delta of Δ t, from 1.. N, j: numerical serial numbers;
for each vector in the matrix d (j, k), data points in the vectors are sorted and added, the maximum value and the sum in each vector are selected as the slope and the amplitude, and the feature vector in the electroencephalogram wave time domain is obtained:
T(x)∈z；
combining the characteristic vectors obtained by waveform identification to form a group of data vectors covering time domain and frequency domain:
G(x)＝{T，F}；
j: a serial number of the characteristic value;
a data vector g (x) set, which is a primary processing result of brain waves, a metadata set named brain wave primary processing, may be used as basic data for secondary calculation; through data weighting, the following calculation formula is obtained:
E＝{c}*{G}；
c: a weighting coefficient;
applying a normalization calculation to the E data:
the walking brain wave characteristic index D ═ (exp (e)) × 100.
Calculating a characteristic numerical value of an acceleration waveform of the acceleration signal during walking, wherein the calculation adopts a composite algorithm, mode identification, waveform identification and calculus; extracting data including a slope, a change rate, a waveform area and an inflection point from a waveform, integrating the data into an index, and reflecting the walking distance; and generating a vector group of waveform signals by discretization with 1500/s sampling frequency, continuous data point calculation and 10-bit sampling precision:
Xi＝[x1 x2 x3 … xm-2 xm-1 xm]
wherein m is the number of elements in the vector, the value of m is variable, and the value when one extreme value of the waveform data is changed is obtained as the standard; an element x: the amplitude of a certain point of the waveform is equal, and the time interval delta t between two adjacent elements is equal;
for the vector numerical values, calculating the numerical value of each point of the feature set { a, b, c, d }; differential calculation is performed on the vectors:
y(j)＝x(j)-x(j-1)/Δt
j＝0,1,2,,m
obtaining the maximum value in y (j), obtaining a characteristic finger (a), wherein the positive and negative inversion points are the characteristic value (b), the characteristic value (d) and the characteristic value (c), and applying an iterative differential algorithm to vector data:
y(j，i)＝∑(x(j+i)-x(j+i-1)/(Δt+i))
i is the increment of Δ t, from 1.. N, j is the numerical sequence number from point (b) to point (d);
for each vector in the matrix y (j, i), selecting the minimum value in the vectors, wherein the corresponding j point represents the point (c) in the graph;
calculating the slope and integral between points (a) and (b):
H＝[a-b]/Δt
T＝∫y(i)*Δt i＝a,,b
calculate the integral between points (b) and (c):
T1＝∫y(i)*Δt i＝b,,c
calculate the integral between points (c) and (d):
T2＝∫y(i)*Δt i＝c,,d
obtaining a gait index:
P＝((T*H)+T1+T2)*L/G
l is the distance between point (a) and point (d) and G is a selected rate of variation constant;
indexing P to obtain gait index
Bi＝(1-1/exp(P))×100。
For pressure waveform data, calculating the time interval delta t of points a and d, calculating the amplitude value h of the points a and b in real time, and solving the distance and relative weight taken one step:
L＝(Δt*Bi)*p；
W＝h*q；
p: a distance conversion factor, constant; bi: a gait index; Δ t: a time interval; h: an amplitude value; q: relative weight coefficient, constant.
The invention has the advantages and positive effects that:
the invention can realize the purpose of analyzing and monitoring the walking state of the guardian. The system comprises a mobile physiological signal acquisition and transmission terminal, a relevant central data server, a central processing workstation, a wireless internet and the like. The invention can analyze the living quality of the old people at home and the coordination ability of the brain to walking in real time, further reflect the conditions of the old people in the process of disease occurrence and rehabilitation, particularly the weight change and the walking distance, and play an important role in predicting the life of the old people and the disease prognosis. The brain coordination ability advantage is an effective means for predicting the occurrence of brain diseases of the old, particularly, the big data of the brain wave characteristic expression of the walking of the old is established, the brain wave characteristic basic value of the old is obtained, and the method plays an important role in predicting the cerebral infarction and the myocardial infarction. Fills the blank state of the prior monitoring system aiming at the wearable walking distance brain coordination and relative weight of the elderly, and has great social significance and medical and intelligent values for nursing the aged.
Drawings
Fig. 1 is a schematic block diagram of a system circuit according to the present disclosure.
Fig. 2 is a waveform diagram of walking acceleration.
FIG. 3 is a graphical representation of pressure waveform data.
Detailed Description
The system composition of the invention is shown in figure 1 and comprises: the system comprises a sensor, an amplifying and converting circuit processing unit, a single chip computer, a wireless communication control unit and an internet data server.
The wireless mobile life data acquisition and transmission terminal adopts integrated multi-lead physiological signals to acquire sensor output (adopting HXD series module products) containing electroencephalogram signals, walking pressure signals and walking acceleration signals in real time, the sensor output enters a single chip computer unit through preamplification and analog-to-digital conversion, then is packaged by a TCP/IP protocol of a wireless communication control unit and is directly transmitted to an Internet data server after being encrypted and compressed by data, and a software system in the server processes, stores and forwards the received physiological signals in real time, wherein a calculating part adopts fuzzy recognition analysis, fuzzy analysis, and the like, And a plurality of algorithms of correlation analysis, wavelet analysis and spectrum analysis are used for extracting gait change, weight change and distance change in walking and walking characteristic expression of brain nerve function, and the calculation result is transmitted to a central workstation or a mobile communication terminal of a doctor in real time through a wireless internet platform or an intranet of a hospital for displaying. And (3) triggering an alarm function by data which are dangerous to life, such as falling and brain dysfunction, in the calculation result, sending alarm information to an internet platform in real time through a server, and triggering an alarm service when the alarm information reaches a computer or a mobile phone terminal of a doctor or a family or a service staff in real time.
The sensor comprises: the acceleration sensor, the pressure sensor and the brain wave sensor are integrated on a hollow tube line, the signal output is connected to the single-chip computer unit, the brain wave sensing signal is also accessed to the single-chip computer unit, and the unit integrates and sends the signal to the internet platform. The unit can be worn on the waist of a human body.
The user wears the sensor when the walking, monitors the walking characteristic of gait, distance, weight and brain nerve function, through wireless mobile internet platform, utilizes a plurality of GPRS, 3G passageway, transmits data center server to in real time, and its characterized in that: in multiple communication modes, the 4G and 5G, WIFI modes can be selected to realize a far-short range, multi-environment, two-way sharing and real-time interaction multi-channel data exchange mode, and for the collected multi-lead physiological information, a central computing control unit of a single chip computer processes, encrypts, compresses and packages in real time to realize dynamic storage queue management, identify communication lines, automatically switch communication modes and control communication transmission. The real-time data packet is directly sent to the data server through the wireless internet platform. In the data transmission process, a data structure of address information and time information are overlapped by data units, the data units are repeatedly sent, the loss of effective data caused by packet loss generated by internet communication errors is avoided, and the integrity and the reliability of data receiving of a server side are ensured.
The invention has the specific structure and the working principle that:
the waveform signal acquisition transducer of the electroencephalogram, weight and acceleration sensors integrated into a whole continuously acquires brain waves on two sides of the forehead and gravity and acceleration during walking, and comprises a group of metal electrode plates and pressure and acceleration sensors. The metal electrode is divided into two electroencephalogram signal guide electrodes at the upper part of the left side frame and the upper part of the right side frame, and two electroencephalogram signal reference electrodes at the ear lobe parts. The combined brain wave signals of the left and right brains are collected respectively, the electrode design is subjected to physicochemical treatment, the application requirements of the dry electrode on waveform signal collection are met in shape and potential polarization, the brain wave signals meeting the medical standard can be conveniently collected without professional treatment on the skin near a signal collection point, and the gravity and acceleration sensors are arranged at the foot bottom and the ankle part and respectively collect acceleration waveform and gravity pressure waveform signals during walking.
The terminal host machine completes the work of amplification, shaping, conversion receiving, encryption, packaging, communication and the like of the physiological signals and the vital signs signals. The system comprises a signal shaping and amplifying circuit, a central computing control management circuit, a communication interface circuit, a dynamic data link cache circuit, a wireless internet access control and automatic packet-dividing uploading circuit, a power circuit and the like, wherein the signal shaping and amplifying circuit comprises a signal conversion circuit, a signal amplifying circuit, a filter circuit and the like; the connection relationship among the parts is as follows: through the human brain electricity, weight in walking, acceleration when taking a step and other physiological and physical signals, convert to corresponding electrical signal and computer numerical signal, the electrical signal is sent into the filtering, noise control, amplifying input part of the guardianship circuit, after calculating separately and processing through the corresponding guardianship module (HXD _ I), and then via the analog signal input port of the module, send into the module of the central calculation control management circuit, number conversion circuit separately, the life numerical signal is sent into the communication interface of the central calculation control management circuit via the RS232 interface circuit; after the central computing control management circuit obtains the digitalized life data, respectively encrypting and compressing the digitalized life data; obtaining a processed data stream, and sending the processed data stream into a dynamic data link cache queue; the dynamic data link cache queue is a changing data storage and output structure; according to different network states, the structures of the data in the storage areas are different; after obtaining the interrupt event trigger of the network state change, the calculation control management part controls different permutation and combination of the acquired data; under the control of a write instruction, writing into a data buffer area of a storage queue; under the control of the read instruction of the calculation control management circuit, the data in the queue is output to the wireless internet access control circuit through the data port; the internet access control circuit part completes the functions of automatic dialing, network state identification, TCP/IP mode signal modulation, sub-packaging and output to the network and uploads the functions to the data storage server and the data calculation server;
the data center server, the mobile physiological signal acquisition and transmission terminal and the center processing workstation in the system realize data exchange through the Internet, and adopt the wireless Internet to access the GPRS transmission technology (a middle mobile phone communication mode) to transmit the physiological multi-lead signals of the patient to the data center server and the center processing workstation in real time through the Internet in a wireless mode, thereby completing interactive monitoring tasks such as real-time interactive signal acquisition and adjustment, physiological multi-lead signal storage, real-time data automatic pre-analysis and the like. The central processing workstation completes tasks of data review and browsing, automatic data analysis, manual analysis result correction, report document generation and the like. The central workstation can be any computer connected with the internet, and can also be any doctor workstation which is accessed and mapped to the network in the hospital through the hospital information center internet. The functions of concurrent multi-terminal data acquisition and multi-center diagnosis and treatment monitoring, analyzing and processing in a wireless networking area are realized, and the real-time online management, analysis, statistics and storage recording of diagnosis and treatment information related to sleep respiratory diseases are realized through wireless network transmission. The transmission information will be integrated into the courtyard HIS system in a standardized format. The time length of information collection, recording and monitoring is not limited, and the whole process of disease diagnosis and treatment can be run through. The wireless mobile multi-lead physiological signal data acquisition and transmission terminal works in a direct current power supply mode, and diagnosis and treatment safety is fully guaranteed. The architecture of the platform is schematically shown in fig. 2.
The diagnosis and treatment process of the wide-area wireless mobile gait, distance, relative weight and brain wave walking characteristic index networked monitoring analysis computing system based on wireless internet communication is as follows:
1) the patient family or in hospital occasion by diagnosing service personnel wear electrode sensor for the patient, service personnel substitutes mobile computer, can receive the signal of gathering in real time, observe signal quality, simultaneously, central monitoring workstation watches the signal of gathering in step, utilize instant messaging mode, interactive adjustment sensor position, ensure signal acquisition's quality.
2) The mobile information acquisition and transmission terminal automatically finishes information acquisition and synchronously sends the information to the data center server and the system center workstation for real-time analysis and storage.
3) When the information transmission of the remote terminal has problems, the central monitoring workstation gives an alarm by sound and light to prompt the operator on duty to handle abnormal conditions.
4) After the signal collection is finished, the data analysis workstation is used for calling monitoring data of walking of related patients, automatic analysis and manual correction are carried out, a data analysis report is generated, and the data analysis report is transmitted to the doctor center workstation in an electronic mode.
With a typical multi-center distributed service model, the coverage of the wireless networking information function area can be a community center, a district, a county, or even nationwide. The network server arranged in the functional area plays roles of data forwarding, storing and sharing. The wireless mobile physiological signal acquisition and transmission terminals in the functional area can be independent unique identification addresses, and the number of the maximum physiological signal acquisition and transmission terminals is set in the functional area. Data transmission is realized in an encryption caching mode, and the completeness and privacy protection of data are guaranteed. In order to ensure the stable and reliable work of the network server, a dual-computer hot backup external optical fiber disk array mode is proposed for redundancy, and meanwhile, a manual or automatic tape machine or DVD external storage backup mode can be performed in stages, so that the disaster recovery reliability is further improved. The most important function of the network server is that the Internet is accessed through a broadband, so that various wireless mobile diagnosis, treatment and monitoring terminals arranged at the sides of patients can upload data in real time through the wireless Internet, a Beijing-Unicom DDN special line access mode can be adopted in consideration of implementation environment and geographical position, and the access bandwidth is required to be not less than 4M. The supporting equipment required for accessing the internet may relate to a router, a switch, a firewall and the like, and can be customized according to different configuration requirements and security protection requirements.
The central processing workstation arranged in the medical institution can be realized by adopting a common computer to complete the analysis, diagnosis, monitoring and feedback regulation and control of physiological multi-lead signals of patients in charge of the central processing workstation. Each computer needs to access to the internet or be mapped to a fixed-point network node of an internal network in a hospital through a broadband, a wired access mode can be selected according to the geographical position and the placement condition of each workstation, and wireless internet access equipment such as HSDPA, TD-SCDMA, EVDO (CDMA2000) or WCDMA (wideband code division multiple Access) can also be adopted to directly access the internet. The data of the central diagnosis and treatment analysis and monitoring workstation are all derived from real-time data of wireless mobile physiological signal acquisition and transmission terminal equipment forwarded by a network server arranged in the functional area.
The central workstation is used as an application software carrier. According to the function division, a diagnosis data analysis center or a monitoring treatment data control center and a doctor diagnosis workstation can be formed. The data analysis and processing workstation has the functions of automatic analysis and manual correction on data, can be used as a data analysis and processing workstation by a plurality of computers at the same time, and jointly develops physiological multi-derivative data analysis of one patient or independent data analysis of a plurality of patients in a synchronous and coordinated mode. The results are sent back to the data center server and diagnostic computer workstations of each of the associated physicians.
The data center consisting of the central diagnosis and treatment analysis and monitoring workstation and the server is networked in a C/S mode. And the real-time requirement of data processing and display is met. A B/S mode can be adopted between the central diagnosis and treatment monitoring workstation and the doctor personal computer or the doctor diagnosis workstation, and the analysis and processing results can be obtained in a browser mode.
In wider areas, such as the intercity, the provincial level, even the national and international ranges, information exchange of different functional areas can be realized by sharing information of a plurality of functional areas, namely different application service software is configured on respective central diagnosis and treatment monitoring workstations, and corresponding functional area names are clicked under the authority matching principle and the safety principle, so that the clinical medical information acquisition, sharing and analysis in the wide area range are realized, and the specific realization mode can be realized through the internet or a private network according to requirements.
The data center server manages data sent by all the mobile information acquisition and transmission terminals, each terminal sets a unique address code and consists of a machine number and a fixed IP address of the server:
terminal machine address is unique address number in networking range + fixed IP address of networking server
65535 maximum machine number in the network
The mobile information acquisition and transmission terminal carries out encryption processing and compression on the data converted into the digital signals. The integrated data stream enters a link store queue. The data window is 8 beta
L stream window ═ wavelet (m1+ m2+ m3+ m4+ addr + asyn + data1+ data2)
m1, m2, m3 and m4 are data transmitted by the module, asyn is synchronization, and data is data and an encrypted packet
The personal computer of the medical staff can directly obtain the walking information of different users under corresponding authority through the internet, and a special software package is required to be installed on the personal computer. The authority management is to be classified into the overall management authority range of a hospital or a health management center. Meanwhile, all operation records are sent back to the data center server and stored as original data for tracking, mining and analyzing. The real-time online interaction between the doctors and the patients and between the first-line and second-line doctors is realized by the computers or the personal mobile communication terminals connected to the server end, and the communication information is bidirectionally forwarded by the server to achieve the purpose of real-time bidirectional communication. The real-time interaction function is realized by the system software function.
The central workstation processes the received data sent by each mobile terminal in real time, automatically decomposes the eye movement electric wave and the frontal muscle electric wave mixed in brain wave data, carries out discretization processing with the sampling frequency of 500/s, the sampling time window of 2.5s and the sampling precision of 10 bits, adopts waveform identification and power spectrum analysis algorithms, and adopts a power spectrum formula:
various components of the power spectrum in the brain wave can be obtained, including the values of the α β δ θ wave band, the edge frequencies such as Fsef and Fmax, the dominant frequency, and the like:
F＝{α，β，δ，θ，Fsef，Fmax}；
aiming at the original waveform data of the brain waves, solving the conventional rhythm and the high-frequency rhythm distributed in the electroencephalogram; extracting the characteristic points of the data by adopting a waveform identification algorithm in a pattern identification algorithm:
T(x)∈z；
t: a vector of eigenvalues; x: brain wave discrete data; z: a time domain space;
the feature data in vector t (x) includes the specific points, amplitudes, variations, slopes, areas, and the calculations are from the basic algorithm:
data sequence:
y(t)＝(f(j)-f(j-1))/Δt
j: discrete data subscripts; f (j): brain wave raw data function
Obtaining the maximum value in the sequence y (t) to obtain one of the characteristic indexes, wherein the positive and negative inversion points are special points, and the number of the special points is represented by the value of t;
applying an iterative differentiation algorithm to the f (j) sequence data:
d(j，k)＝∑(f(j+k)-f(j+k-1)/(Δt+k))
Δ t: a sampling time interval; k: delta of Δ t, from 1.. N, j: numerical serial numbers;
for each vector in the matrix d (j, k), data points in the vectors are sorted and added, the maximum value and the sum in each vector are selected as the slope and the amplitude, and the feature vector in the electroencephalogram wave time domain is obtained:
T(x)∈z；
combining the characteristic vectors obtained by waveform identification to form a group of data vectors covering time domain and frequency domain:
G(x)＝{T，F}；
j: the serial number of the characteristic value.
A data vector g (x) set, which is a primary processing result of brain waves, a metadata set named brain wave primary processing, may be used as basic data for secondary calculation; through data weighting, the following calculation formula is obtained:
E＝{c}*{G}；
c: a weighting coefficient;
applying a normalization calculation to the E data:
walking brain wave characteristic index D ═ (exp (e)) × 100
Calculating the characteristic value of an acceleration waveform of an acceleration signal during walking by adopting a composite algorithm, pattern recognition, waveform recognition and calculus; the extracted waveform comprises slope, change rate, waveform area and inflection point data, and is integrated into an index to reflect the walking distance.
For the walking acceleration waveform shown in fig. 2, a vector group of waveform signals is generated by discretization processing with a sampling frequency of 1500/sec, continuous data point calculation and sampling precision of 10 bits bit:
Xi＝[x1 x2 x3 … xm-2 xm-1 xm]
wherein m is the number of elements in the vector, the value of m is variable, and the value when one extreme value of the waveform data is changed is obtained. An element x: the amplitude of a certain point of the waveform is equal, and the time interval delta t between two adjacent elements is equal.
For the above vector values, the values of the respective points of the feature set { a, b, c, d } are found. Differential calculation is performed on the vectors:
y(j)＝x(j)-x(j-1)/Δt
j＝0,1,2,,m
obtaining the maximum value in y (j), obtaining a characteristic finger (a), wherein the positive and negative inversion points are the characteristic value (b), the characteristic value (d) and the characteristic value (c), and applying an iterative differential algorithm to vector data:
y(j，i)＝∑(x(j+i)-x(j+i-1)/(Δt+i))
i is the increment of Δ t, and from 1.. N, j is the numerical index from point (b) to point (d).
For each vector in the matrix y (j, i), the minimum value in the vector is selected, and the corresponding j point represents the (c) point in the graph.
Calculating the slope and integral between points (a) and (b):
H＝[a-b]/Δt
T＝∫y(i)*Δt i＝a,,b
calculate the integral between points (b) and (c):
T1＝∫y(i)*Δt i＝b,,c
calculate the integral between points (c) and (d):
T2＝∫y(i)*Δt i＝c,,d
obtaining a gait index:
P＝((T*H)+T1+T2)*L/G
l is the distance between point (a) and point (d) and G is a selected variability constant.
Indexing P to obtain gait index
Bi＝(1-1/exp(P))×100
Referring to fig. 3, for the pressure waveform data, the time interval Δ t between points a and d is calculated, the amplitude value h of points a and b is calculated in real time, and the distance and relative weight of one step are obtained:
L＝(Δt*Bi)*p；
W＝h*q；
p: a distance conversion factor, constant; bi: a gait index; Δ t: a time interval; h: an amplitude value; q: relative weight coefficient, constant.
Claims (5)
1. Integration gait, distance, relative weight, brain wave synchronous sensing analytic system based on thing networking, its characterized in that includes:
the sensor collects signals containing electroencephalogram signals, walking pressure signals and walking acceleration signals in real time;
the amplifying and converting circuit processing unit is used for carrying out pre-amplifying and analog-to-digital conversion processing on the output signal of the sensor;
the single chip computer encrypts and compresses the data output by the sensor and sends the data to the wireless communication control unit;
the wireless communication control unit is used for packaging the data output by the singlechip through a TCP/IP protocol and directly sending the data to an Internet data server;
an internet data server comprising a processor adapted to implement instructions; and a storage device adapted to store a plurality of instructions, the instructions loaded and executed by the processor:
receiving all data sent by mobile information acquisition and transmission terminals consisting of a sensor, an amplification and conversion circuit processing unit and a single chip microcomputer, wherein each mobile information acquisition and transmission terminal is set with a unique address code and consists of a machine number and a fixed IP address of a server:
processing the received data sent by each mobile terminal in real time, wherein the processing comprises automatically decomposing eye movement electric waves and frontal muscle electric waves mixed in the brain wave data;
calculating the characteristic value of an acceleration waveform of an acceleration signal during walking by adopting a composite algorithm, pattern recognition, waveform recognition and calculus; the extracted waveform comprises slope, change rate, waveform area and inflection point data, and is integrated into an index to reflect the walking distance.
For pressure waveform data, calculating the time interval between two points, calculating the amplitude values of the two points in real time, and solving the ratio of the current amplitude to the standard amplitude;
and generating the data analysis report and electronically transmitting the data analysis report to a doctor central workstation.
2. The Internet of things based integrated gait, distance, relative weight and brain wave synchronous sensing analysis system according to claim 1; the waveform signal acquisition transducer of the electroencephalogram, weight and acceleration sensors continuously acquires brain waves on two sides of the forehead and gravity and acceleration during walking, and comprises a group of metal electrode plates and pressure and acceleration sensors; the metal electrode is divided into two electroencephalogram signal guide electrodes at the upper part of a left side frame and the upper part of a right side frame and two electroencephalogram signal reference electrodes at the ear lobe parts, and the electroencephalogram signals of the left brain and the right brain are collected respectively after combination; the gravity and acceleration sensors are arranged at the positions of the sole and the ankle and respectively collect acceleration waveform and gravity pressure waveform signals during walking.
3. The integrated gait, distance, relative weight and brain wave synchronous sensing analysis system based on the internet of things according to claim 1, characterized in that the eye movement electric wave and the frontal muscle electric wave mixed in the brain wave data are automatically decomposed, and the discretization processing with the sampling frequency of 500/s, the sampling time window of 2.5s and the sampling precision of 10 bits is carried out by adopting waveform recognition and power spectrum analysis algorithms and adopting a power spectrum formula:
the components of the power spectrum in the brain wave can be obtained, including the values of the α β δ θ wave band, the edge frequencies such as Fsef and Fmax, the dominant frequency, and the like:
F＝{α，β，δ，θ，Fsef，Fmax}；
aiming at the original waveform data of the brain waves, solving the conventional rhythm and the high-frequency rhythm distributed in the electroencephalogram; extracting the characteristic points of the data by adopting a waveform identification algorithm in a pattern identification algorithm:
T(x)∈z；
t: a vector of eigenvalues; x: brain wave discrete data; z: a time domain space;
the feature data in vector t (x) includes specific points, amplitudes, variations, slopes, areas, and the calculations are from the basic algorithm:
data sequence:
y(t)＝(f(j)-f(j-1))/Δt
j: discrete data subscripts; f (j): brain wave raw data function
Obtaining the maximum value in the sequence y (t), and obtaining one of the characteristic indexes, wherein the positive and negative inversion points are special points, and the number of the special points is represented by the value of t;
applying an iterative differentiation algorithm to the f (j) sequence data:
d(j，k)＝∑(f(j+k)-f(j+k-1)/(Δt+k))
Δ t: a sampling time interval; k: delta of Δ t, from 1.. N, j: numerical serial numbers;
for each vector in the matrix d (j, k), data points in the vectors are sorted and added, the maximum value and the sum in each vector are selected as the slope and the amplitude, and the feature vector in the electroencephalogram wave time domain is obtained:
T(x)∈z；
combining the characteristic vectors obtained by waveform identification to form a group of data vectors covering time domain and frequency domain:
G(x)＝{T，F}；
j: a serial number of the characteristic value;
a data vector g (x) set, which is a primary processing result of brain waves, a metadata set named brain wave primary processing, may be used as basic data for secondary calculation; through data weighting, the following calculation formula is obtained:
E＝{c}*{G}；
c: a weighting coefficient;
applying a normalization calculation to the E data:
the walking brain wave characteristic index D ═ (exp (e)) × 100.
4. The integrated gait, distance, relative weight and brain wave synchronous sensing analysis system based on the internet of things according to claim 1, characterized in that the characteristic numerical value of the acceleration waveform is calculated for the acceleration signal during walking, and the calculation adopts a composite algorithm, mode identification, waveform identification and calculus; extracting data including a slope, a change rate, a waveform area and an inflection point from a waveform, integrating the data into an index, and reflecting the walking distance; and generating a vector group of waveform signals by discretization with 1500/s sampling frequency, continuous data point calculation and 10-bit sampling precision:
Xi＝[x1 x2 x3 … xm-2 xm-1 xm]
wherein m is the number of elements in the vector, the value of m is variable, and the value when one extreme value of the waveform data is changed is obtained as the standard; an element x: the amplitude of a certain point of the waveform is equal, and the time interval delta t between two adjacent elements is equal;
for the vector numerical values, calculating the numerical value of each point of the feature set { a, b, c, d }; differential calculation is performed on the vectors:
y(j)＝x(j)-x(j-1)/Δt
j＝0,1,2,,m
obtaining the maximum value in y (j), obtaining a characteristic finger (a), wherein the positive and negative inversion points are the characteristic value (b), the characteristic value (d) and the characteristic value (c), and applying an iterative differential algorithm to vector data:
y(j，i)＝∑(x(j+i)-x(j+i-1)/(Δt+i))
i is the increment of Δ t, from 1.. N, j is the numerical sequence number from point (b) to point (d);
for each vector in the matrix y (j, i), selecting the minimum value in the vectors, wherein the corresponding j point represents the point (c) in the graph;
calculating the slope and integral between points (a) and (b):
H＝[a-b]/Δt
T＝∫y(i)*Δt i＝a,,b
calculate the integral between points (b) and (c):
T1＝∫y(i)*Δt i＝b,,c
calculate the integral between points (c) and (d):
T2＝∫y(i)*Δt i＝c,,d
obtaining a gait index:
P＝((T*H)+T1+T2)*L/G
l is the distance between point (a) and point (d) and G is a selected rate of variation constant;
indexing P to obtain gait index
Bi＝(1-1/exp(P))×100。
5. The integrated gait, distance, relative weight and brain wave synchronous sensing analysis system based on the internet of things according to claim 1, characterized in that for the pressure waveform data, the time interval Δ t between the points a and d is calculated, the amplitude value h of the points a and b is calculated in real time, and the distance and relative weight of the walking step are obtained:
L＝(Δt*Bi)*p；
W＝h*q；
p: a distance conversion factor, constant; bi: a gait index; Δ t: a time interval; h: an amplitude value; q: relative body mass coefficient, constant.
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| CN113812964A (en) * | 2021-08-02 | 2021-12-21 | 杭州航弈生物科技有限责任公司 | Method and device for proxy measurement of electroencephalogram characteristics and pseudo-multi-mode frozen gait detection |
| CN114420251A (en) * | 2022-03-30 | 2022-04-29 | 南京麦澜德医疗科技股份有限公司 | Rehabilitation data transmission system and method suitable for ultrasonic uterine rehabilitation instrument |
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