AU2021106583A4 - IoT AND MACHINE LEARNING BASED AUTOMATED SYSTEM FOR COVID - 19 DETECTION - Google Patents
IoT AND MACHINE LEARNING BASED AUTOMATED SYSTEM FOR COVID - 19 DETECTION Download PDFInfo
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- 208000025721 COVID-19 Diseases 0.000 title abstract description 14
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- 241000711573 Coronaviridae Species 0.000 abstract description 19
- 206010011224 Cough Diseases 0.000 abstract description 16
- 241000700605 Viruses Species 0.000 abstract description 13
- 230000036772 blood pressure Effects 0.000 abstract description 12
- 241001678559 COVID-19 virus Species 0.000 abstract description 5
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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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
- A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
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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
- A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
- A61B5/0004—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by the type of physiological signal transmitted
- A61B5/0008—Temperature signals
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
- A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
- A61B5/0022—Monitoring a patient using a global network, e.g. telephone networks, internet
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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
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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
- A61B5/021—Measuring pressure in heart or blood vessels
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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
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/02416—Detecting, measuring or recording pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
- A61B5/02427—Details of sensor
- A61B5/02433—Details of sensor for infrared radiation
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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
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/0823—Detecting or evaluating cough events
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
- A61B2562/02—Details of sensors specially adapted for in-vivo measurements
- A61B2562/0204—Acoustic sensors
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
- A61B2562/02—Details of sensors specially adapted for in-vivo measurements
- A61B2562/0271—Thermal or temperature sensors
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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
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/02438—Detecting, measuring or recording pulse rate or heart rate with portable devices, e.g. worn by the patient
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- A—HUMAN NECESSITIES
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6813—Specially adapted to be attached to a specific body part
- A61B5/6825—Hand
- A61B5/6826—Finger
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Abstract
A. S. Karthik Kannan, S. Appavu Alias Balamurugan, S. Sasikala,
G. M. K. Thirumoorthy & K Sham Vishnu
IoT AND MACHINE LEARNING BASED AUTOMATED SYSTEM FOR COVID - 19
DETECTION
ABSTRACT OF THE INVENTION
Present invention relates to a device which can be used to measure the temperature of the
person and analyze his/her cough which can be further used to check the person whether he/she
has contracted the Corona Virus. The device can be used as a first level screening system to
identify a person who has contracted the Corona Virus or COVID-19 virus. It can be used to find
the contact of Corona Virus and COVID-19 within the people and help to contain the spread of
Corona Virus and to break the chain of spread.
A major problem in its treatment is its initial examination and diagnosis, which will
determine its treatment. The virus's screening is extremely tough due of its rapid contactless
dissemination. The medical community requires efficient methods for detecting the virus in the
least amount of time. In this study, we invent a medical gadget that uses composable sensors to
monitor the health condition of people who show the symptoms of the coronavirus or infected. In
our invention, the medical device comprises wearable medical sensors which are merged with
Raspberry PI hardware interfacing and a smartphone application. At the backend, an IoT
framework is deployed, allowing multiple devices to connect in real time. Temperature, cough
sound, heartbeat, breath sound, and oxygen concentration (SpO2) are examined using our
ensemble machine learning algorithm to establish the patient's critical status of the impacts of the
coronavirus or its symptoms.
A. S. Karthik Kannan, S. Appavu Alias Balamurugan, S. Sasikala,
G. M. K. Thirumoorthy & K. Sham Vishnu
IoT AND MACHINE LEARNING BASED AUTOMATED SYSTEM FOR COVID - 19
DETECTION
FIGURES
Temperature Blood Pressure Heart Beat Cough Sounds Breath Sounds
Sensor Sensor Sensor Reader Reader
RASPBERRY PI
WiFi Module
DatsetEnsemble .rie ahn
T dModel Tachne - COVID-19 Prediction
Tonstuctio -*Learning Model
Samples Construction
Figure 1: Proposed System Model
Description
A. S. Karthik Kannan, S. Appavu Alias Balamurugan, S. Sasikala, G. M. K. Thirumoorthy & K. Sham Vishnu
IoT AND MACHINE LEARNING BASED AUTOMATED SYSTEM FOR COVID - 19 DETECTION
Temperature Blood Pressure Heart Beat Cough Sounds Breath Sounds Sensor Sensor Sensor Reader Reader
WiFi Module
DatsetEnsemble .rie ahn T dModel Tachne - COVID-19 Prediction Tonstuctio -*Learning Model Samples Construction
Figure 1: Proposed System Model
EDITORIAL NOTE 2021106583
There are 6 pages of description only.
A. S. Karthik Kannan, S. Appavu Alias Balamurugan, S. Sasikala,
G. M. K. Thirumoorthy & K Sham Vishnu
IoT AND MACHINE LEARNING BASED AUTOMATED SYSTEM FOR COVID - 19 DETECTION
Field of Invention and use of the Invention:
Present invention pertains to a device which can be used to measure the temperature of the person and analyze his/her cough which can be further used to check the person whether he/she has contracted the Corona Virus. The device can be used as a first level screening system to identify a person who has contracted the Corona Virus or COVID-19 virus. It can be used to find the contact of Corona Virus and COVID-19 within the people and help to contain the spread of Corona Virus and to break the chain of spread.
Novelty of the Invention:
In this proposed invention, we used the various medical sensor to check whether the person has contracted the Corona Virus. The IoT-based infrastructure and ensemble machine learning model helps to detect COVID-19 based on clinical sensor information. The following medical sensor device are combined in our invention,
1. Temperature sensor 2. Heartbeat sensor 3. Cough Sound reader 4. Breath Sound reader 5. Blood pressure sensor The gathered clinical sensor information is sent to the well-trained ensemble model. The ensemble model is used to diagnosis the COVID-19 based on the medical data. The proposed system model is shown in Figure 1 of drawing section.
A. S. Karthik Kannan, S. Appavu Alias Balamurugan, S. Sasikala,
G. M. K. Thirumoorthy & K Sham Vishnu
Background of the Invention:
In the current scenario the screening of the Corona Virus or COVID-19 is done by checking the temperature of the person and if it is nonnal the person is not tested for Corona Virus. This is a basic screening test that can be used to identify people who may be infected with the virus. The most common symptoms of coronavirus are fever, dry cough and fatigue. Hence by testing the temperature of the person he/she can be guided for further testing if the initial screening results that he/she may have contracted the Virus.
If the person takes a fever-reducing medicine, they may not show a temperature difference because the medicine will suppress the fever. Hence, a second screening criteria must be done to ensure the person with the virus does not go unnoticed. The second screening method is in which the cough of the person is analyzed and is used to predict whether the person has contracted the virus or not.
The cough of the person is recorded using a device and it is processed by a machine learning algorithm which then returns result of the analysis as whether further attention is required for the person or not. The blood pressure sensor will sense the heartbeat rate of the person while the screening. The breath sound reader will sense the breathing sensation of the person while screening. The heart beat sensor will sense the pressure level of the person while screening. The gathered sensors information's are sent to the well-trained ensemble machine learning model by Raspberry pi WiFi module. The ensemble techniques which combine the diagnosis decision from the multiple trained machine learning model.
Drawbacks of Existing state-of-art and how the invention addresses the drawbacks:
Modem COVID-19 detectors use only the temperature sensors for person screening. In this invention, we designed the IoT based device which integrated the temperature sensor, blood pressure sensor, heart beat sensor, cough sound reader and breath sound reader. Finally, the ensemble-based machine learning model is used.
A. S. Karthik Kannan, S. Appavu Alias Balamurugan, S. Sasikala,
G. M. K. Thirumoorthy & K Sham Vishnu
Objectives of the Invention:
The principal object of the present invention is to serve as a device which can be used to screen the people and direct them to a health official for testing of the Virus. Another object of the present invention is to provide a way for fool proof testing where there are no loop holes through which the people cannot slip away with the virus.
The present invention relates to a temperature gun which is used to measure the temperature of the person to check for the contact of the Corona Virus or COVID-19 Virus. The present invention adds an extra aspect to the already existing temperature gun that is the microphone which is used to record the cough of the person and feed the recorded sound to the Machine Learning model which will be used to analyze the cough and show whether the person may or may not have contracted the virus.
The present technique which is used for the screening the person for contact of the corona virus is done by checking the temperature of the person which is used to check if he has fever which is a symptom for the person contacting the Corona Virus or COVID-19 Virus. On the other hand when the screening is done for Corona Virus, if the person has consumed any medication for fever, then it may go unnoticed and the person may have corona and it is not detected by the temperature gun and the person may lead to further spread of the virus.
In the present invention , for screening the person for contact of the corona virus is done by checking the temperature of the person and also recording the cough of the person which can be used to distinguish the cough for whether the person has been affected with the virus or not. Hence if found to be affected then he/she can be sent to the hospitals for further medication.
Summary of the Invention:
This invention proposes an IOT based architecture for screening the COVID-19 virus using literature-based standards. The framework employs sensors in the form of a gadget that can be owned by anyone to determine in a matter of seconds whether a person is healthy or has a high risk of contracting a disease.
A. S. Karthik Kannan, S. Appavu Alias Balamurugan, S. Sasikala,
G. M. K. Thirumoorthy & K Sham Vishnu
Detailed Description of the Invention:
Raspberry Pi 3 - Model B - ARMv8 with 1G RAM Figure 2 of drawing section shows the Raspberry Pi 3 processing unit. It is a third generation based single board minicomputer. This credit card sized board contains Bluetooth and Wireless LAN connectivity. This model is used for our invention to predict the COVID-19 virus infection.
SparkFun Electret Microphone Breakout In order to observe the cough of the human, our invention model use SparkFun Electret Microphone which is depicted in Figure 3 of drawing section. The observed cough sound is analysed by our proposed machine learning model. The Electret Mic Breakout converts the sound waves into electrical waves by capturing cough sound.
DS18B20 1-Wire Temperature Sensor The waterproof DS18B20 temperature sensor is depicted in Figure 4 of drawing section. It requires a ground dataline for communication. It does not require any external components. This sensor can measure the temperature in the range of [-55°C to +125°C (-67°F to +257°F)].
LCD 16x212C LCD based output screen of our model is shown in Figure 5 of drawing section. This output screen is used to display the analysis results based on the various sensor data. It contains the 2 display lines. Each display line can show the 16 characters. LCD screen is integrated with Raspberry kit by using the12C interface. The 12C interface reduce the connection complexity.
Finger Heart beat Sensor
A. S. Karthik Kannan, S. Appavu Alias Balamurugan, S. Sasikala,
G. M. K. Thirumoorthy & K Sham Vishnu
Figure 6 of drawing section shows the KY-039 Finger Detection Heartbeat measuring sensor module. In order to detect the pulse of the finger, it uses infrared LED and a phototransistor. For each pulse, it flashes the red LED.
Blood Pressure Sensor Module The pressure of the blood in the arteries is calculated to measure the blood pressure. Blood pressure is represented by two numbers as follows, i Systolic pressure ii. Diastolic pressure (relaxes between beats). Figure 7 of drawing section shows a serial measurement of Systolic, Diastolic, and Pulse from the Sunrom-1437 Blood Pressure sensor. It operates at a 9600 Baud rate. +Vc, ground, and Serial Output are the three terminals.
Brief description of Figures
Figure 2 of drawing section shows the Raspberry Pi 3, a development board which has ARMv8 architecture and a gigabyte RAM. The development kit of Raspberry Pi 3 uses the ARMv8 based motherboard architecture with a Giga byte RAM.
Figure 3 of drawing section shows the standard environmental sounds such as voices, knocks, and claps can be detected by using a microphone. It returns the observed sound in the form of scalar value with the range from 0 (no sound) to 1023 (maximal sound).
Figure 4 of drawing section shows a wire temperature sensor measures the temperature with the range from -55°C to +125°C (-67°F to +257°F).
Figure 5 of drawing section displays the short messages in the mini-LCD screen The mini-LCD screen uses the 12C interface for the rendering the output signal.
Figure 6 of drawing section shows the heart beat sensor contains the photo diode and IR sensor. This sensor works by sending infrared light to one side of the finger, which is then received by a photo diode. It takes a 30-second pulse reading.
A. S. Karthik Kannan, S. Appavu Alias Balamurugan, S. Sasikala,
G. M. K. Thirumoorthy & K Sham Vishnu
Figure 7 of drawing section shows the digital blood pressure sensor which measures the blood pressure. The reading of sensors involves 15 bytes at 9600 baud rates. The output of the sensor contains three values (Systolic, Diastolic and Pulse) separated by comna.
The following material is required for designing as;
1. Temperature Sensor 2. Finger Heart beat Sensor 3. Blood pressure Sensor 4. Microphone 5. Rasberry PI mother board 6. WiFi module Cough analysis algorithm is used to analyse the recorded coughing clip to predict whether he/she has contracted the Virus or not. Breath sound analysis algorithm is used to analyse the recorded breathing clip to predict whether he/she has contracted the Virus or not.
EDITORIAL NOTE 2021106583
There is 1 page of claims only.
A. S. Karthik Kannan, S. Appavu Alias Balamurugan, S. Sasikala,
G. M. K. Thirumoorthy & K Sham Vishnu
IoT AND MACHINE LEARNING BASED AUTOMATED SYSTEM FOR COVID - 19 DETECTION
1. We claim that the temperature sensor used to take the temperature of the person by keeping the sensor near the person so that there will be no external factors affecting the temperature of the person to be high or low than the original value.
2. We claim that the surrounding of the person where the screening is done need not be silent for the microphone to get accurate values since the microphone will be kept in such a design in which the microphone is kept where it does not gets affected by the external noises other than the persons noise.
3. We claim that the Machine Learning (ML) model will be well trained with a higher accuracy so that the results are accurate and no mistake are not being done at the screening.
4. We claim that the blood pressure sensor will sense the heartbeat rate of the person during the screening, the breath sound reader will sense the breathing sensation of the person while screening and the heart beat sensor will sense the pressure level of the person during the screening.
5. We claim that the gathered sensors information's are sent to the well-trained ensemble machine learning model by Raspberry pi WiFi module and the ensemble techniques which combines the diagnosis decision from the multiple trained machine learning model.
A. S. Karthik Kannan, S. Appavu Alias Balamurugan, S. Sasikala, G. M. K. Thirumoorthy & K. Sham Vishnu 23 Aug 2021
IoT AND MACHINE LEARNING BASED AUTOMATED SYSTEM FOR COVID - 19 DETECTION
FIGURES 2021106583
Figure 1: Proposed System Model
A. S. Karthik Kannan, S. Appavu Alias Balamurugan, S. Sasikala, G. M. K. Thirumoorthy & K. Sham Vishnu 23 Aug 2021 2021106583
Figure 2. Raspberry Pi 3 - Model B - ARMv8 with 1G RAM
Figure 3. SparkFun Electret Microphone Breakout
A. S. Karthik Kannan, S. Appavu Alias Balamurugan, S. Sasikala, G. M. K. Thirumoorthy & K. Sham Vishnu 23 Aug 2021 2021106583
Figure 4. DS18B20 1-Wire Temperature Sensor
Figure 5. LCD 16x2 I2C
A. S. Karthik Kannan, S. Appavu Alias Balamurugan, S. Sasikala, G. M. K. Thirumoorthy & K. Sham Vishnu 23 Aug 2021 2021106583
Figure 6: Heart beat Sensor
Figure 7: Blood Pressure Sensor Module
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| AU2021106583A AU2021106583A4 (en) | 2021-08-23 | 2021-08-23 | IoT AND MACHINE LEARNING BASED AUTOMATED SYSTEM FOR COVID - 19 DETECTION |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE202022101331U1 (en) | 2022-03-10 | 2022-05-05 | Kailas Aute | Wearable Omicron symptom detection device based on biomedical and proximity sensors and artificial intelligence |
| IT202200008198A1 (en) | 2022-04-26 | 2023-10-26 | Stefania Galdiero | SENSOR FOR THE DETECTION OF VIRAL INFECTIONS |
-
2021
- 2021-08-23 AU AU2021106583A patent/AU2021106583A4/en not_active Ceased
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE202022101331U1 (en) | 2022-03-10 | 2022-05-05 | Kailas Aute | Wearable Omicron symptom detection device based on biomedical and proximity sensors and artificial intelligence |
| IT202200008198A1 (en) | 2022-04-26 | 2023-10-26 | Stefania Galdiero | SENSOR FOR THE DETECTION OF VIRAL INFECTIONS |
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