CN112768069A - Intelligent old-age robot system design method based on AD-SVM - Google Patents
Intelligent old-age robot system design method based on AD-SVM Download PDFInfo
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- 238000013461 design Methods 0.000 title claims abstract description 17
- 238000000034 method Methods 0.000 title claims abstract description 17
- 238000012544 monitoring process Methods 0.000 claims abstract description 7
- 230000036772 blood pressure Effects 0.000 claims abstract description 4
- 230000036760 body temperature Effects 0.000 claims abstract description 4
- 238000001514 detection method Methods 0.000 claims abstract description 4
- 238000005286 illumination Methods 0.000 claims abstract description 4
- 238000012706 support-vector machine Methods 0.000 claims description 22
- 238000012549 training Methods 0.000 claims description 9
- 238000004891 communication Methods 0.000 claims description 6
- 230000002159 abnormal effect Effects 0.000 claims description 4
- 238000013139 quantization Methods 0.000 claims description 3
- 238000004806 packaging method and process Methods 0.000 claims description 2
- 230000032683 aging Effects 0.000 abstract description 3
- 238000012545 processing Methods 0.000 abstract description 2
- 230000003044 adaptive effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000033764 rhythmic process Effects 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
- A61B5/02055—Simultaneously evaluating both cardiovascular condition and temperature
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
- G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
Abstract
An intelligent endowment robot system design method based on an AD-SVM comprises a body health monitoring module, an environment monitoring module, a CO state danger alarm module, a cloud platform data processing module, a screen upper computer display module and the like. The robot system mainly comprises an upper computer and a lower computer, wherein the lower computer monitors physical health sign parameters of a user in real time: blood pressure, heart rate, body temperature; monitoring the temperature, humidity and illumination intensity of the environment in real time, uploading the detection data to a cloud platform in real time and storing the data; the upper computer mainly monitors data in real time, and the invention provides the electrocardiogram abnormity detection algorithm based on the AD-SVM, which can greatly increase the identification rate of the electrocardiogram and ensure the life safety of users. The invention aims at the situation that the aging problem is more and more severe at present, the requirements of people on home safety and health management are deepened, and higher requirements on the functions of the old-age care service and the quality of home old-care life are provided.
Description
Technical Field
The invention relates to the field of robots, in particular to an intelligent old-age robot system design method based on an AD-SVM.
Background
Since China has come to an aging society since the end of the last century, the population of the elderly has grown rapidly and the innovation is high frequently. As the developing countries with the most population in the world, the aging problem of China is not only a century problem which needs to be solved urgently but also a century problem which is concerned strongly in the world, but the problem has a more serious situation at present.
Disclosure of Invention
To solve the above existing problems. The invention provides an intelligent endowment robot system design method based on an AD-SVM. To achieve this object:
the invention provides an intelligent endowment robot system design method based on an AD-SVM, which is characterized by comprising the following steps:
step 1: establishing an intelligent old-age robot system design system, wherein the robot system mainly comprises an upper computer and a lower computer;
step 2: the lower computer data acquisition system mainly comprises a real-time monitoring user body health sign parameter: the blood pressure, the heart rate and the body temperature are monitored in real time, the temperature, the humidity and the illumination intensity of the environment are monitored in real time, and the communication module is used for packaging and sending the sampled data to the upper computer;
and step 3: the upper computer module mainly comprises a data real-time display module and a data identification module, and after receiving data sent by the lower computer, the upper computer module firstly displays the data on the display module and then identifies the heart rate data;
and 4, step 4: the heart rate identification module firstly preprocesses block data, divides the data into blocks X ═ X1 x2…xn]；
And 5: using an analysis dictionary to reduce the dimension of the data to obtain a characteristic vector Z ═ Z1 z2…zn]；
Step 6: carrying out self-adaptive threshold denoising on the characteristic value to obtain a support characteristic set D ═ D1 d2…dn]；
And 7: identifying the support feature set by using an SVM (support vector machine) to obtain the health condition of the user;
and 8: and if the heart rate detection is abnormal, alarming.
As a further improvement of the present invention, in step 2, the communication data between the upper computer and the lower computer is encoded, and the encoding method is expressed as:
D＝DCT(w) (1)
h＝huffman(C) (3)
where DCT (-) represents a discrete cosine transform, round (-) a rounding function, T is a quantization threshold, and huffman (-) is a huffman coding function.
As a further improvement of the invention, the step 5 analysis dictionary training model comprises the following steps:
wherein Z is [ Z ]1,z2,…,zi]Representing sparse coefficients, phi representing an analysis dictionary, X representing training data, | · luminance |, n0Denotes a norm of 0, T0Representing the Z sparse coefficient threshold.
As a further improvement of the invention, the step 5 data dimension reduction is represented as:
Z＝XΦ (5)
where Φ denotes an analysis dictionary and X denotes training data.
As a further improvement of the invention, the step 6 adaptive threshold denoising formula is as follows:
wherein, TZAnd representing a sparse coefficient denoising threshold value.
As a further improvement of the present invention, the support vector machine formula in step 7 is:
Y＝SVM(D) (7)
wherein, SVM (·) is a support vector machine function, Y represents a support vector machine output, "Y ═ 1" represents that the heart rate is normal, and "Y ═ 0" represents that the heart rate is abnormal.
The intelligent endowment robot system design method based on the AD-SVM has the beneficial effects that:
1. according to the invention, the accurate recognition of the robot is increased by using the AD-SVM wave.
2. The invention utilizes the analysis dictionary to increase the anti-interference capability of the system.
3. The algorithm of the invention has low complexity and strong real-time performance.
4. The hardware system of the invention is simple to realize and has low cost.
Drawings
FIG. 1 is a flow diagram of the system;
FIG. 2 is a system cloud platform processing module flow diagram;
Detailed Description
The invention provides an intelligent endowment robot system design method based on an AD-SVM, which comprises the following specific steps:
the invention is further described in the following detailed description with reference to the drawings in which:
firstly, as shown in fig. 1, an intelligent elderly people care robot system design system is established, the robot system mainly comprises an upper computer and a lower computer, and a lower computer data acquisition system mainly comprises a real-time monitoring module for monitoring physical health sign parameters of a user: blood pressure, rhythm of the heart, body temperature, the temperature, humidity, the illumination intensity of real-time supervision environment to and communication module, the data packing that will sample is sent the host computer, and host computer module mainly includes data real-time display module and data identification module, receives at first data display on display module behind the host computer sending data, then carries out rhythm of the heart data discernment.
The communication data of the upper computer and the lower computer are coded, and the coding method is expressed as follows:
D＝DCT(w) (1)
h＝huffman(C) (3)
where DCT (-) represents a discrete cosine transform, round (-) a rounding function, T is a quantization threshold, and huffman (-) is a huffman coding function.
Next, as shown in fig. 2, the heart rate recognition module first performs block data preprocessing to divide the data into blocks X ═ X1 x2…xn]And reducing the dimension of the data by using an analysis dictionary to obtain a characteristic vector Z ═ Z1 z2…zn]And carrying out self-adaptive threshold denoising on the characteristic value to obtain a support characteristic set D ═ D1 d2…dn]。
The analysis dictionary training model is as follows:
wherein Z is [ Z ]1,z2,…,zi]Representing sparse coefficients, phi representing an analysis dictionary, X representing training data, | · luminance |, n0Denotes a norm of 0, T0Representing the Z sparse coefficient threshold.
The data dimensionality reduction is represented as:
Z＝XΦ (5)
where Φ denotes an analysis dictionary and X denotes training data.
The adaptive threshold denoising formula is as follows:
wherein, TZAnd representing a sparse coefficient denoising threshold value.
And finally, identifying the support feature set by using an SVM (support vector machine) to obtain the health condition of the user, and alarming if the heart rate is detected abnormally.
The support vector machine formula is:
Y＝SVM(D) (7)
wherein, SVM (·) is a support vector machine function, Y represents the output of the support vector machine, "Y ═ 1" represents heart rate normality, and "Y ═ 0" represents heart rate abnormality
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.
Claims (6)
1. An intelligent endowment robot system design method based on an AD-SVM (analog-to-digital support vector machine) comprises the following specific steps:
step 1: establishing an intelligent old-age robot system design system, wherein the robot system mainly comprises an upper computer and a lower computer;
step 2: the lower computer data acquisition system mainly comprises a real-time monitoring user body health sign parameter: the blood pressure, the heart rate and the body temperature are monitored in real time, the temperature, the humidity and the illumination intensity of the environment are monitored in real time, and the communication module is used for packaging and sending the sampled data to the upper computer;
and step 3: the upper computer module mainly comprises a data real-time display module and a data identification module, and after receiving data sent by the lower computer, the upper computer module firstly displays the data on the display module and then identifies the heart rate data;
and 4, step 4: the heart rate identification module firstly preprocesses block data, divides the data into blocks X ═ X1 x2 … xn]；
And 5: using an analysis dictionary to reduce the dimension of the data to obtain a characteristic vector Z ═ Z1 z2 … zn]；
Step 6: carrying out self-adaptive threshold denoising on the characteristic value to obtain a support characteristic set D ═ D1 d2 … dn]；
And 7: identifying the support feature set by using an SVM (support vector machine) to obtain the health condition of the user;
and 8: and if the heart rate detection is abnormal, alarming.
2. The intelligent elderly-care robot system design method based on AD-SVM of claim 1, wherein:
in the step 2, the communication data of the upper computer and the lower computer is coded, and the coding method is expressed as follows:
D＝DCT(w) (1)
h＝huffman(C) (3)
where DCT (-) represents a discrete cosine transform, round (-) a rounding function, T is a quantization threshold, and huffman (-) is a huffman coding function.
3. The intelligent elderly-care robot system design method based on AD-SVM of claim 1, wherein:
the step 5 of analyzing the dictionary training model comprises the following steps:
wherein Z is [ Z ]1,z2,…,zi]Representing sparse coefficients, phi representing an analysis dictionary, X representing training data, | · luminance |, n0Denotes a norm of 0, T0Representing the Z sparse coefficient threshold.
4. The intelligent elderly-care robot system design method based on AD-SVM of claim 1, wherein:
the step 5 data dimension reduction is represented as:
Z＝XΦ (5)
where Φ denotes an analysis dictionary and X denotes training data.
5. The intelligent elderly-care robot system design method based on AD-SVM of claim 1, wherein:
the step 6 self-adaptive threshold denoising formula is as follows:
wherein, TZAnd representing a sparse coefficient denoising threshold value.
6. The intelligent elderly-care robot system design method based on AD-SVM of claim 1, wherein:
the formula of the support vector machine in the step 7 is as follows:
Y＝SVM(D) (7)
wherein, SVM (·) is a support vector machine function, Y represents a support vector machine output, "Y ═ 1" represents that the heart rate is normal, and "Y ═ 0" represents that the heart rate is abnormal.
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Application publication date: 20210507 |