AU2021104281A4 - A smart wearable device for early detection and identification of COVID-19 cases. - Google Patents

A smart wearable device for early detection and identification of COVID-19 cases. Download PDF

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AU2021104281A4
AU2021104281A4 AU2021104281A AU2021104281A AU2021104281A4 AU 2021104281 A4 AU2021104281 A4 AU 2021104281A4 AU 2021104281 A AU2021104281 A AU 2021104281A AU 2021104281 A AU2021104281 A AU 2021104281A AU 2021104281 A4 AU2021104281 A4 AU 2021104281A4
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covid
cases
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Amit Baghel
Pushpendra Kumar Chandra
Gurpreet Singh Chhabra
Satish Kumar Negi
Devendra Kumar Singh
Vishnu Kant Soni
Poonam Yerpude
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Chhabra Gurpreet
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    • 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
    • G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
    • A—HUMAN NECESSITIES
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    • 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
    • A—HUMAN NECESSITIES
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    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
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    • A61B5/14542—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring blood gases
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • 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
    • A61B5/6802—Sensor mounted on worn items
    • 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
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • 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
    • 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
    • A61B5/7282—Event detection, e.g. detecting unique waveforms indicative of a medical condition
    • A—HUMAN NECESSITIES
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    • 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
    • A61B5/7465—Arrangements for interactive communication between patient and care services, e.g. by using a telephone network
    • A61B5/747—Arrangements for interactive communication between patient and care services, e.g. by using a telephone network in case of emergency, i.e. alerting emergency services
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    • 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
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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    • A61B5/083—Measuring rate of metabolism by using breath test, e.g. measuring rate of oxygen consumption
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Abstract

[1] The world has been facing the challenge of COVID-19 since the end of 2019. It is expected that the world will need to battle the COVID-19 pandemic with precautious measures, until an effective vaccine is developed. [2] This work proposes a real-time COVID-19 detection and identification system. The proposed system would employ an Internet of Things (IoTs) framework to collect real time symptom data from users to early identify suspected coronaviruses cases, to monitor the treatment response of those who have already recovered from the virus, and to understand the nature of the virus by collecting and analyzing relevant data. [3] The framework consists of five main components: Symptom Data Collection and Uploading (using wearable sensors), Quarantine/Isolation Center, Data Analysis Center (that uses machine learning algorithms), Health Physicians, and Cloud Infra-structure. To quickly identify potential coronaviruses cases from this real-time symptom data, using machine learning algorithms. An experiment was conducted to test this system on a real COVID-19 symptom dataset, after selecting the relevant symptoms. la. Collection of Relevant Symptoms through 2a. Identification and Predictions of suspected 3a. Monitor and Review Suspected Cases. Wearable Devices, such as Fever, Cough, Fatigue cases through executing machine learning F Sore Throat, and Shortness of Breath. In addition algorithm, such as SVM, Neural Network, to user information such as Travel and Contact. Naive Bayes, K-Star, Decision Table, and __________________________________Decision Stump. Symptom Data Collection and Uploading if suspected Data Analysis Center Health Physicians Quarantine/Isolation Center _ S1 00 00 D E 2b. Continuation of learning new models 3b. Contact Suspected Cases and health using digital data from health care centers, as well as from users. care centers. 1b. Keeping digital records of people 2c. Analyzing and Monitoring of the collected in quarantine and isolation, both data. confirmed and contact cases Figure 1: Deep learning based system for detection of covid-19 disease of patient at infection risk

Description

la. Collection of Relevant Symptoms through Wearable Devices, such as Fever, Cough, Fatigue Sore Throat, and Shortness of Breath. In addition 2a. Identification and Predictions of suspected cases through executing machine learning algorithm, such as SVM, Neural Network, F3a. Monitor and Review Suspected Cases.
to user information such as Travel and Contact. NaiveBayes, K-Star, Decision Table, and __________________________________Decision Stump. Symptom Data Collection and Uploading ifsuspected Data Analysis Center Health Physicians
Quarantine/Isolation Center _
S1 00 00 DE 2b. Continuation of learning new models 3b. Contact Suspected Cases and health using digital data from health care centers, as well as from users. care centers.
1b. Keeping digital records of people 2c. Analyzing and Monitoring of the collected in quarantine and isolation, both data. confirmed and contact cases
Figure 1: Deep learning based system for detection of covid-19 disease of patient at infection risk
Australian Government
IP Australia
Innovation Patent Australia
Title of the Invention: - A smart wearable device for early detection and identification of COVID-19 cases.
Name and address of patentees(s): 1. Dr. Gurpreet Singh Chhabra Shri Shankaracharya Institute of Professional Management and Technology, Raipur, 492015, Chhattisgarh, India 2. SatishKumar Negi Guru Ghasidas Vishwavidyalaya (A Central University) Bilaspur, 495009, Chhattisgarh, India 3. Amit Baghel Guru Ghasidas Vishwavidyalaya (A Central University) Bilaspur, 495009, Chhattisgarh, India 4. Devendra Kumar Singh Guru Ghasidas Vishwavidyalaya(A Central University), Bilaspur, 495009, Chhattisgarh, India 5. Pushpendra Kumar Chandra Guru Ghasidas Vishwavidyalaya(A Central University), Bilaspur, 495009, Chhattisgarh, India 6. PoonamYerpude Guru Ghasidas Vishwavidyalaya(A Central University), Bilaspur, 495009, Chhattisgarh, India 7. Vishnu Kant Soni Lakhmi Chand Institute of Technology, Bilaspur, 495220, Chhattisgarh, India
COMPLETE SPECIFICATION FIELD OF THE INVENTION
[1] The present invention relates to the field of computer science and engineering. The coronavirus outbreak is a threat to humanity in recent times that disrupts the global healthcare systems. The healthcare and government policies make utmost concern in preventing the infectious spread and in reducing the rate of infections in humans. In such cases machine Learning and IOT techniques use to monitor the COVID-19 patients efficiently stop the spread of the corona virus. More particularly, the present invention is related to a smart wearable device for early detection and identification of COVID-19.
BACKGROUND OF THE INVENTION
[2] Coronaviruses are a wide family of viruses, which cause disease in both animals and humans. The coronavirus, which is an infectious illness caused by the last discovered form of coronavirus, SARS-CoV-2, is caused by a novel coronavirus that was recently discovered. There are at least seven distinct human coronaviruses that have been identified thus far, with the number likely to rise in the future.
[3] When a sick person coughs, sneezes, or talks, COVID-19 tends to get transmitted via respiratory droplets emitted from the nose or mouth. These drops are relatively hefty and don't cover a great deal of ground. Rather, they tumble to the ground swiftly. If droplets are inhaled, COVID-19 may be contracted. As a result, it is important that we maintain a distance of at least a meter between ourselves and others. Around a sick individual, these droplets can be detected on items or surfaces.
[4] If anyone comes into contact with these surfaces and then contacts their eyes, nose, or mouth, COVID-19 may be contracted. COVID-19 coronavirus incubation time, or the time between the developments of first symptoms, is typically three to five days.
[5] The above information is presented as back ground information only to assist with an understanding of the present disclosure. Determination has been made, no assertion is made, and as to whether any of the abovemight be applicable as prior art regarding the present invention.
SUMMARY OF INVENTION
[6] Since its discovery in late December of 2019, there have been more than 14.5 million confirmed cases of COVID-19 reported in 185 coun-tries, as of July 21, 2020, with approximately a 2% daily increase. Among these cases there have been more than 95 thousand deaths, which represents an approximate 4.2% mortality rate. This novel coronavirus was characterized on March 11, 2020 as a pandemic by the World Health Organization
[7] Unfortunately, there is no successful treatment procedure or vaccine yet. It is expected that the development of an effective vaccine will take more than a year, especially since the nature of the virus has not yet been completely characterized.
[8] However, technology could also help slow its spread, through early identification (or prediction) and monitoring of new cases. Such technologies include big data, as well as cloud and fog capabilities, the use of data gathered through remote monitoring, such as mHealth, teleHealth, and real-time patient status follow-up. The present invention is a COVID-19 detection and monitoring system that would collect real-time symptom data from wearable sensor technologies.
[9] To quickly identify potential coronaviruses cases from this real-time data. This detection and identification system could be implemented with an IoT infrastructure that would monitor both potential and confirmed cases, as well as the treatment responses of patients who recover from the virus.
[10] In addition to real-time monitoring, this system could contribute to the understanding of the nature of the virus by collecting, analyzing and archiving relevant data. The proposed framework consists of five main components: (1) real- time symptom data collection (using wearable devices), (2) treatment and outcome records from quarantine/isolation centers, (3) a data analysis center that uses machine learning algorithms, (4) healthcare physicians.
[11] The aim of this framework, is to reduce mortality rates through early detection, following up on recovered cases, and a better understanding of the disease. This work conducts an experiment to test these invention on a real dataset.
OBJECTIVE OF THE INVENTION
[12] The principle objective of the present invention is to provide a machine learning with IOT based smart wearable device for early detection and identification of COVID 19.
BRIEF DESCRIPTION OF THE DIAGRAM
[13] The system non-invasively collects real-time user symptom data through wearable devices and sensors. Again, these symptoms are: Fever, Cough, Fatigue, Sore Throat, and Shortness of Breath. The Quarantine/Isolation Center also periodically submits data from their isolated and quarantined patients who are housed in the center. The content of that data is similar to the real-time data collected from users.
[14] The sensed symptom data are uploaded to the Data Analysis Center. Digital records from the health care center are also regularly sent to the Data Analysis Center. The Data Analysis Center hosts machine learning algorithms, which use the data received from the health care center to continuously update its models.
[15] The models are then used to identify potential cases, based on the real-time symptom data from each user. The data center also analyzes all its data, and presents the results on a real-time dash-board. That dashboard can be informative to physicians about the nature of the virus.
[16] If a potential case is identified, it will be sent to the relevant physician to follow up with the patient. The patient will then be called and encouraged to visit the health care center for clinical tests, such as the Polymerase Chain Reaction (PCR) test, which is used to identify positive cases. If it turns out that the case is confirmed, the patient can be isolated, and all contacts will be contacted and quarantined.
DETAIL DESCRIPTION
[17] Present invention a IoT-based frame-work, which could be used to detection and identify (or predict) potential coronaviruses cases, in real time. Equally important, this framework could be used to predict the treatment response of confirmed cases, as well as to better understand the nature of the COVID-19 disease.
[18] Fig. 1 shows the framework of our invention It consists of five main components: Symptom Data Collection and Uploading, a Quarantine/Isolation Center, a Data Analysis Center, an interface to Health Physicians, all of which are interconnected through a Cloud Infrastructure.
[19] Symptom data collection and uploading - The aim of this component is to collect real time symptom data through a set of wearable sensors on the user's body. The most relevant COVID-19 symptoms were identified, based on a real COVID-19 patient dataset.
[20] These identified symptoms were: Fever, Cough, Fatigue, Sore Throat, and Shortness of Breath. There are several biosensors available to detect these symptoms. For instance, temperature-based sensors can be used for the detection of Fever. Cough and its classifications for different ages can be detected using audio-based sensors with acoustic and aerodynamic models. Motion-based and heart-rate sensors can be used to detect Fatigue. Finally, oxygen-based sensors can be used to detect Shortness of Breath.
[21] Quarantine/isolation center - this component collects data records from users who have been quarantined or isolated in a health care center. These records include both health (or technical) and non-technical data. For health (or technical) data, each record includes time-series data of the above- mentioned symptoms, while for non-technical data, chronic diseases, age, gender, and any other relevant information, such as family history of illness. Each record would eventually also include the treatment response for each case.
[22] Data analysis center - The Data Center hosts data analysis and machine learning algorithms. These algorithms are used to build a model for COVID-19, and to provide a real-tine dashboard of the processed data. The model could then be used to quickly identify or predict potential COVID-19 cases, based on real-time data collected and uploaded from users. The model can also predict the patient's treatment response. Over time, the disease models developed from this data will provide useful information about the nature of the disease.
[23] Health physicians - Physicians will monitor suspected cases whose real-time uploaded symptom data indicates a possible infection by our proposed machine learning based identification/prediction model. The physicians will then be able to respond swiftly to these suspected cases by following up with any further clinical investigation needed to confirm the case. This allows the confirmed cases to be isolated and given appropriate health care.
[24] Prediction of potential cases - This predictive models, and the machine learning algorithms that will be employed in the Data Center component of the proposed IoT based framework. In particular, an experiment was conducted to investigate the possibility of using machine learning algorithms for quick identification (or prediction) of potential COVID-19 infections.
[25] Dataset - The data contains different types of information about each case. Our work focused on symptoms, travel history to suspicious areas, and contact history with potentially infected people. However, some of this information was missing for many of the cases documented within the database. Moreover, the data was not well structured for use by machine learning algorithms.
[26] Data preprocessing - the data was preprocessed and structured to be better suited for machine learning. The cases with documented symptoms were collected. This resulted in a list of 80 symptoms. How-ever, many of these symptoms were judged to be synonyms. Thus, the number of symptoms was reduced to 20. This merging of synonymous symptoms was done in an ad-hoc manner by two medical doctors, who are co-authors of this work. For example, "anorexia" and "loss of appetite" were merged together.

Claims (7)

  1. EDITORIAL NOTE
    2021104281
    THERE IS ONE PAGE OF CLAIMS ONLY
    WE CLAIMS
    [1] A system as claimed in claim 1, wherein the system is autonomous detection of infection level in humans using smart device technology.
  2. [2] A system as claimed in claim 2, wherein the system is a series of IoT sensor interconnections fitted within a smart device.
  3. [3] A system as claimed in claim 3, wherein the system connects the real-time condition of human beings associated with covid-19 infections.
  4. [4] A system as claimed in claim 4, wherein the system enables the individuals to monitor themselves the state of their oxygen level and physical stability.
  5. [5] A system as claimed in claim 5, wherein the clinician monitors the stability of covid-19 affected individuals and reporting their immediate diagnosis.
  6. [6] A system as claimed in claim 6, wherein the system assists the healthcare and local health ministry to track the covid-19 records of an individual.
  7. [7] A system as claimed in claim 7, wherein the system alerts the critical condition of patients using smart device technology to local health ministry and hospitals.
    Figure 1: Deep learning based system for detection of covid-19 disease of patient at infection risk
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115273908A (en) * 2022-08-05 2022-11-01 东北农业大学 Live pig cough sound identification method based on classifier fusion

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115273908A (en) * 2022-08-05 2022-11-01 东北农业大学 Live pig cough sound identification method based on classifier fusion
CN115273908B (en) * 2022-08-05 2023-05-12 东北农业大学 Live pig cough voice recognition method based on classifier fusion

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