WO2021250455A1 - System and method to detect and prevent spread of a disease - Google Patents

System and method to detect and prevent spread of a disease Download PDF

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Publication number
WO2021250455A1
WO2021250455A1 PCT/IB2020/057350 IB2020057350W WO2021250455A1 WO 2021250455 A1 WO2021250455 A1 WO 2021250455A1 IB 2020057350 W IB2020057350 W IB 2020057350W WO 2021250455 A1 WO2021250455 A1 WO 2021250455A1
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WO
WIPO (PCT)
Prior art keywords
disease
people
disinfectant
detection module
combination
Prior art date
Application number
PCT/IB2020/057350
Other languages
French (fr)
Inventor
Vivek Dubey
Awadhesh Pratap Singh
Mayank Mathur
Rashmi Mathur
Nisarg Vasani
Pankaj Kumar Rai
Prashant Dubey
Akshay Dubey
Abhilash Shrivastava
Namrata Choukse
Rohit Shrikrishna Walimbe
Rakesh Roshan Sonar
Anindya Mohanty
Original Assignee
Vivek Dubey
Awadhesh Pratap Singh
Mayank Mathur
Rashmi Mathur
Nisarg Vasani
Pankaj Kumar Rai
Prashant Dubey
Akshay Dubey
Abhilash Shrivastava
Namrata Choukse
Rohit Shrikrishna Walimbe
Rakesh Roshan Sonar
Anindya Mohanty
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Vivek Dubey, Awadhesh Pratap Singh, Mayank Mathur, Rashmi Mathur, Nisarg Vasani, Pankaj Kumar Rai, Prashant Dubey, Akshay Dubey, Abhilash Shrivastava, Namrata Choukse, Rohit Shrikrishna Walimbe, Rakesh Roshan Sonar, Anindya Mohanty filed Critical Vivek Dubey
Publication of WO2021250455A1 publication Critical patent/WO2021250455A1/en

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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
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/22—Social work or social welfare, e.g. community support activities or counselling services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/174—Facial expression recognition
    • 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
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • 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
    • G16H50/20—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
    • 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
    • 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
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • 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
    • G16H50/80—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

Definitions

  • Embodiments of a present invention relate to detection of disease, and more particularly, to a system and method to detect and prevent spread of a disease.
  • testing kits need to stored in a proper manner to ensure the accuracy of results.
  • the symptoms of the disease are also rarely seen, which is a reason for huge outbreak of such disease. Due to such limitations, the detection, prevention and cure of such disease is becoming challenging at a global level. Also, the spread of the disease cannot be controlled instantaneously in such situations through such conventional methods. Hence there is a need for an improved system and method to detect and prevent spread of a disease to address the aforementioned issues.
  • a system to detect and prevent spread of a disease includes at least one image capturing unit installed in a pre-defined location.
  • the at least one image capturing unit is configured to capture one of a video, one or more images or a combination thereof of people within the pre-defined location, at every pre-defined time interval.
  • the system also includes one or more processors operatively coupled to the at least one image capturing unit.
  • the processor includes an disease detection module configured to analyse at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof on analysing one of the video, the one or more images or a combination thereof, using an artificial intelligence technique to detect the disease within the pre defined location.
  • the disease detection module is also configured to generate an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people in real time.
  • the processor also includes a disease risk detection module operatively coupled to the disease detection module.
  • the disease risk detection module is configured to determine a risk factor corresponding to the disease for each of the people within the pre-defined location in real time.
  • the processor also includes a disease spread detection module operatively coupled to the disease risk detection module.
  • the disease spread detection module is configured to determine a contagiosity of the disease from the affected people to the disease-free people of people in real time.
  • the processor also includes a disinfectant spray activation module operatively coupled to the disease spread detection module.
  • the disinfectant spray activation module is configured to generate a notification to initiate spraying of disinfectant within the pre-defined location.
  • the disinfectant spray activation module is also configured to transfer the notification to one or more disinfectant spray units installed within the pre-defined location to activate the one or more disinfectant spray units to spray the disinfectant to prevent the spread of the disease among the people.
  • a method for detecting and prevent spreading of a disease includes capturing one of a video, one or more images or a combination thereof of people within the pre-defined location, at every pre-defined time interval.
  • the method also includes analysing at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof analysing one of the video, the one or more images or a combination thereof, using an artificial intelligence technique, for detecting the disease within the pre-defined location.
  • the method also includes generating an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people.
  • the method also includes determining a risk factor representative of the disease for each of the people within the pre-defined location.
  • the method also includes determining a contagiosity of the disease from the affected people to the disease-free people of people.
  • the method also includes generating a notification to initiate spraying of disinfectant within the pre-defined location.
  • the method also includes transferring the notification to one or more disinfectant spray units installed within the pre-defined location to activate the one or more disinfectant spray units to spray the disinfectant to prevent the spread of the disease among the people.
  • FIG. 1 is a block diagram representing a system to detect and prevent spread of a disease in accordance with an embodiment of the present disclosure
  • FIG. 2 is a block diagram representation of an exemplary embodiment of the system in a health care unit of FIG. 1 in accordance with an embodiment of the present disclosure
  • FIG. 3 is a block diagram representation of a processing subsystem located on a local server or on a remote server in accordance with an embodiment of the present disclosure.
  • FIG. 4a and FIG. 4b are flow charts representing steps involved in a method for detecting and prevent spreading of a disease in accordance with an embodiment of the present disclosure.
  • Embodiments of the present disclosure relate to a system and method for detecting and prevent spreading of a disease.
  • the term ‘disease’ is defined as an abnormal condition which affects normal structure functioning of the body which is usually caused by external foreign bodies.
  • the disease may be a coronavirus or the like.
  • the disease is contagious from one from to another through saliva or any such bodily fluids.
  • the disease is referred to a contagious disease.
  • FIG. 1 is a block diagram representing a system (10) to detect and prevent spread of a disease in accordance with an embodiment of the present disclosure.
  • the system (10) includes at least one image capturing unit (20) installed in a pre-defined location.
  • the at least one image capturing unit (20) may include a camera, a video camera, an infrared (IR) camera, or the like.
  • the pre-defined location may include an indoor location or an outdoor location of a pre defined dimension.
  • the at least one image capturing unit (20) is configured to capture one of a video, one or more images or a combination thereof of people within the pre defined location, at every pre-defined time interval.
  • the people may be located within the pre-defined location.
  • the people may be at stationary, may be in motion within the pre-defined location.
  • the at least one image capturing (20) unit may be coupled to an internet of things (IoT) platform.
  • IoT internet of things
  • the system includes one or more processors (30) operatively coupled to the at least one image capturing unit (20).
  • the one or more processors (30) include a disease detection module (40).
  • the disease detection module (40) is configured to analyse one of the video, the one or more images or a combination thereof of the people within the pre-defined location captured by the at least one image capturing unit (20), using an artificial intelligence technique.
  • the analysis of the one or more image or the video may be compared with a pre-set of reference data.
  • the one or more images or the video may be analysed using an image processing technique.
  • an analysis technique may be one of a machine learning technique, deep learning technique, or the like.
  • an autonomous model may be used which may be a self teaching and self-governing model which may be trained using the deep learning technique. Broadly the autonomous model practices existing data to teach the model to detect patterns and later apply the detected patterns on a real time data to execute predictions.
  • the autonomous model is trained using the deep learning technique to recognize facial expressions with respect to symptoms of the detected disease based on historic data. The historic data may be collected based on feeding of millions of pictures, images, videos that contain symptoms of the detected disease, this would be one of the categories of symptoms.
  • the autonomous model may start building multiple patterns based on one of a plurality of clusters or a plurality of classifications of the one or more images or the video in real time and may compare the built pattern with the pre-defined to determine the disease using one or more layers of an artificial neural network of the machine learning technique the one or more layers may be distinguished based on the plurality of parameters or a plurality of hyperparameters.
  • the disease detection module (40) is also configured to analyse at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof upon analysing one of the video, the one or more images or a combination thereof, using the artificial intelligence technique to detect the disease within the pre-defined location.
  • the artificial intelligence technique may be one of the machine learning technique, the artificial intelligence technique, or the like.
  • the analysis technique may be the image processing technique.
  • the one or more images or the video may be divided into a plurality a sectors, and the plurality of sectors may be compared with a pre-defined set of reference data to identify the symptoms associated with the disease.
  • the facial expression may include restlessness, anxiety, or the like.
  • the at least one image capturing unit (20) may be an IoT device.
  • the one or more images or videos captured by such a device may be installed with the artificial intelligence technique.
  • a depth-map of face of the people within the location is created using the AI neural network technique based on various data points identified.
  • multiple network layers are created in order to identify the plurality of parameters and the plurality of hyper parameters.
  • the plurality of hyper parameters mean the configurable values which can be configured as per the pandemic symptoms occurred, parameters are the learned values that are the weightage realized while comparing the symptoms and self-learning process continues using the real time data and the historic data respectively.
  • the disease detection module (40) may also be configured to generate an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people.
  • the analysis result may data representing a number of disease-affected people, number of disease -free people among the people within the pre-defined location in real time.
  • the processor (30) includes a disease risk detection module (50) operatively coupled to the disease detection module (40).
  • the disease risk detection module (50) is configured to determine a risk factor representative of the disease for each of the people within the pre-defined location.
  • the risk factor may be a percentage determining a change to get the disease for the disease- free people from the disease affected people within the pre-defined location at an instant of time.
  • the risk factor may be determined using one of the machine learning technique, the artificial intelligence technique, or the like.
  • the risk factor may be based on and is directly proportional to one of one or more real time identified symptoms, the one or more facial expressions, rate of spread of the disease or a combination thereof.
  • the processor (30) also includes a disease spread detection module (60) operatively coupled to the disease risk detection module (50).
  • the disease spread detection module (60) is configured to determine a contagiosity of the disease from the affected people to the disease-free people of people in real time.
  • the contagiosity of the disease may be determined based on the risk factor.
  • the contagiosity of the disease may be associated to rate of disease-free people catching the disease within a set interval of time.
  • the processor (20) includes a disinfectant spray activation module (80) operatively coupled to the disease spread detection module (70).
  • the disinfectant spray activation module (80) is configured to generate a notification to initiate spraying of disinfectant within the pre-defined location.
  • the notification may be an electrical signal.
  • the disinfectant spray activation module (80) is also configured to transfer the notification to one or more disinfectant spray units (85) installed within the pre-defined location to activate the one or more disinfectant spray units (85) to spray the disinfectant to prevent the spread of the disease among the people.
  • the electrical signal may be transmitted to the one or more disinfectant spray units (85) to remove virus causing the identified disease within the pre-defined location.
  • the disinfectant spray activation module (80) may be configured to generate and transfer a stop notification to the one or more disinfectant spray units (85) to de-activate the one or more disinfectant spray units (85) to stop the spray of the disinfectant, wherein the stop notification is generated based on one or more parameters.
  • the one or more parameters may include one of time duration of spaying of the disinfectant, intensity of spaying of the disinfectant, the analysis result, or a combination thereof.
  • the one or more disinfectant spray units (85) may be operatively coupled to an autonomous vehicle which may be driven using the artificial intelligence technique. The autonomous vehicles may be trained to move about a pre-defined location in order to spray the disinfectant within the pre-defined location.
  • the disease detection module (40) may be configured to detect a stage of the disease upon detecting at least one of the one or more symptoms of the disease, the one or more facial expressions of the people, the risk factor or a combination thereof.
  • the stage may be categorised into high, medium and low categories respectively.
  • the high risk may be defined when the rate of spread calculated against the people is high which may be greater than about 10%.
  • the medium risk may be defined when the rate of spread calculated against the people is medium which may be between about 5% to 10%.
  • the low risk may be defined when the rate of spread calculated against the people is high which may be lesser than about 5%.
  • FIG. 2 is a block diagram representation of an exemplary embodiment of the system (90) in a health care unit of FIG. 1 in accordance with an embodiment of the present disclosure.
  • the health care unit may include 4 cameras (20) installed within the health care unit in four different locations within the health care unit. The 4 cameras (20) capture a plurality of images of the X people at every instant of time. The captured images of X people are analysed using an artificial intelligence technique by a disease detection module of a processor (30).
  • symptoms of the disease is analysed based on one or more facial expressions of the X people, one or more behaviours of the X people using the artificial intelligence technique using the disease detection module (40).
  • an analysis report is generated by the disease detection module (40).
  • a risk factor for the spread of the disease from the N disease-affected people to the other disease-free people of the X people is determined by a disease risk detection module (50) within the health care unit.
  • a disease risk detection module 50
  • contagiosity of disease from the N number of disease-affected people to other disease-free people is determined by a disease spread detection module (60). Consequently, a notification is generated to initiate spraying of disinfectant within the health care unit by a disinfectant spray activation module (80). Further the notification is transferred to a disinfectant spray unit (85) installed within the health care unit to activate the disinfectant spray unit (85) to spray the disinfectant to prevent the spread of the disease among the X people.
  • the disinfectant spray activation module (80) will generate a stop notification and is transferred to the disinfectant spray unit (85) to de-activate the same in order to stop the spraying of the disinfectant.
  • the camera (20), the processor (30), the disease detection module (40), the disease risk detection module (50), the disease spread detection module (60), the disinfectant spray activation module (80) and the disinfectant spray unit (85) are substantially similar to at least one image capturing module (20), a processor (30), an disease detection module (40), a disease risk detection module (50), a disease spread detection module (60), a disinfectant spray activation module (80) and a disinfectant spray unit (85) of FIG. 1.
  • FIG. 3 is a block diagram representation of a processing subsystem located on a local server or on a remote server in accordance with an embodiment of the present disclosure.
  • the server (110) includes processor(s) (120), and memory (130) operatively coupled to the bus (140).
  • the processor(s) (120), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.
  • the memory (130) includes a plurality of modules stored in the form of executable program which instructs the processor (120) to perform the method steps illustrated in FIG. 4a and FIG. 4b.
  • the memory (130) is substantially similar to the system (10) of FIG.l.
  • the memory (130) has the following modules: an disease detection module (40), a disease risk detection module (50), a disease spread detection module (60), and a disinfectant spray activation module (80).
  • the image analysis (40) module is configured to analyse one of the video, the one or more images or a combination thereof of the people within the pre-defined location, analyse at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof and to generate an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people.
  • the disease risk detection module (50) is configured to determine a risk factor representative of the disease for each of the people within the pre-defined location.
  • the disease spread detection module (60) configured to determine a contagiosity of the disease from the affected people to the disease-free people of people.
  • the disinfectant spray activation module (80) is configured to generate a notification to initiate spraying of disinfectant within the pre-defined location and to transfer the notification to one or more disinfectant spray units installed within the pre defined location to activate the one or more disinfectant spray units to spray the disinfectant to prevent the spread of the disease among the people.
  • FIG. 4a and FIG. 4b are flow charts representing steps involved in a method (150) for detecting and prevent spreading of a disease in accordance with an embodiment of the present disclosure.
  • the method (150) includes capturing one of a video, one or more images or a combination thereof of people within the pre-defined location, at every pre-defined time interval in step 160.
  • capturing one of a video, one or more images comprises capturing one of a video, one or more images using at least one image capturing unit.
  • the method (150) includes analysing at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof upon analysing one of the video, one or more images or a combination thereof, using the artificial intelligence technique for detecting the disease within the pre-defined location in step 180.
  • analysing the at least one of the one or more symptoms of the disease, the one or more facial expressions of the people, the one or more behaviours of the people or a combination thereof may include analysing the at least one of the one or more symptoms of the disease, the one or more facial expressions of the people, the one or more behaviours of the people or a combination thereof by the disease detection module.
  • the analysis technique may include one of the machine learning technique, the artificial intelligence technique or the like.
  • the method (150) also includes generating an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people in step 190.
  • generating the analysis result may include generating the analysis result by the disease detection module.
  • generating the analysis result may include generating the analysis result including data representing a number of disease-affected people, number of disease - free people among the people within the pre-defined location.
  • the method (150) also includes determining a risk factor representative of the disease for each of the people within the pre-defined location in step 200.
  • determining the risk factor may include determining the risk factor by a disease risk detection module.
  • the method (150) also includes determining a contagiosity of the disease from the affected people to the disease-free people of people using in step 210.
  • determining the contagiosity of the disease may include determining the contagiosity of the disease by a disease spread detection module.
  • the method (150) also includes generating a notification to initiate spraying of disinfectant within the pre-defined location.
  • generating the notification may include generating the notification by a disinfectant spray activation module.
  • generating the notification may include generating an electrical signal.
  • the method also includes transferring the notification to one or more disinfectant spray units installed within the pre-defined location to activate the one or more disinfectant spray units to spray the disinfectant to prevent the spread of the disease among the people.
  • transferring the notification may include transferring the notification by the disinfectant spray activation module.
  • the method (150) may further include generating and transferring a stop notification to the one or more disinfectant spray units to de-activate the one or more disinfectant spray units to stop the spray the disinfectant, wherein the stop notification is generated based on one or more parameters.
  • generating and transferring the stop notification may include generating and transferring the stop notification by the disinfectant spray activation module.
  • the one or more parameters may include one of time duration of spaying of the disinfectant, intensity of spaying of the disinfectant, the analysis result, or a combination thereof.
  • Various embodiments of the present disclosure enable the system and method to detect and prevent spread of a disease enables the system to determine the disease-affected people in real time.
  • the system also measures the risk of spread of the disease among healthier people and also the contagiosity of such deadly diseases.
  • the system can generate a real time guidance and precautionary measures for people within a particular geographical sector.
  • the system upon determining the conditions in real time, the system initiates to spray the disinfectant on the people within the location, thereby making the system more efficient and reliable as the system uses machine learning or artificial intelligence techniques to function in real time.
  • the system can be implemented in areas like customer communication, health care, city administration, police, robotic process automation, water supply, electricity, fintech, smart speakers and the like.

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  • Apparatus For Disinfection Or Sterilisation (AREA)

Abstract

System and method to detect and prevent spread of a disease are provided. The system includes an image capturing unit to capture video, images of people, a processor. The processor includes an disease detection module to analyse at least symptoms of the disease, facial expressions of the people, behaviours of the people and to generate an analysis result; a disease risk detection module to determine a risk factor representative of the disease for each of the people, a disease spread detection module to determine a contagiosity of the disease, a disinfectant spray activation module to generate a notification and to transfer the notification to disinfectant spray units to activate the disinfectant spray units to spray the disinfectant.

Description

SYSTEM AND METHOD TO DETECT AND PREVENT SPREAD OF A
DISEASE
This International Application claims priority from a Complete patent application filed in India having Patent Application No. 202021024545, filed on June 11, 2020, and titled “SYSTEM AND METHOD TO DETECT AND PREVENT SPREAD OF A DISEASE”.
FIELD OF INVENTION
Embodiments of a present invention relate to detection of disease, and more particularly, to a system and method to detect and prevent spread of a disease.
BACKGROUND
In the recent days, one of the newest diseases by name COVID-19 was discovered which spreads through saliva or any similar bodily mucus. On December 8, 2019, the first new coronavims case was discovered, and an intensive outbreak incepted in the followed days. Virologicalists and epidemiologists predicted that it would reach a peak in about 90 days and fade away till the end in about 4 months. However, the rate of cases is increasing rapidly everyday across the globe. The number of deaths is also increasing due the disease. However, until now the reliable technique to determine an infected person is through Swab Test, Nasal aspirate, Tracheal aspirate, Sputum Test, and Blood test. All these testing require elaborate testing kits and trained professionals to use the same, which makes the process of testing costly and difficult to deploy in areas having limited resources. Further, such testing kits need to stored in a proper manner to ensure the accuracy of results. In most of the cases of affected people, the symptoms of the disease are also rarely seen, which is a reason for huge outbreak of such disease. Due to such limitations, the detection, prevention and cure of such disease is becoming challenging at a global level. Also, the spread of the disease cannot be controlled instantaneously in such situations through such conventional methods. Hence there is a need for an improved system and method to detect and prevent spread of a disease to address the aforementioned issues.
SUMMARY OF INVENTION
In accordance with one embodiment of the disclosure, a system to detect and prevent spread of a disease is disclosed. The system includes at least one image capturing unit installed in a pre-defined location. The at least one image capturing unit is configured to capture one of a video, one or more images or a combination thereof of people within the pre-defined location, at every pre-defined time interval. The system also includes one or more processors operatively coupled to the at least one image capturing unit. The processor includes an disease detection module configured to analyse at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof on analysing one of the video, the one or more images or a combination thereof, using an artificial intelligence technique to detect the disease within the pre defined location. The disease detection module is also configured to generate an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people in real time. The processor also includes a disease risk detection module operatively coupled to the disease detection module. The disease risk detection module is configured to determine a risk factor corresponding to the disease for each of the people within the pre-defined location in real time. The processor also includes a disease spread detection module operatively coupled to the disease risk detection module. The disease spread detection module is configured to determine a contagiosity of the disease from the affected people to the disease-free people of people in real time. The processor also includes a disinfectant spray activation module operatively coupled to the disease spread detection module. The disinfectant spray activation module is configured to generate a notification to initiate spraying of disinfectant within the pre-defined location. The disinfectant spray activation module is also configured to transfer the notification to one or more disinfectant spray units installed within the pre-defined location to activate the one or more disinfectant spray units to spray the disinfectant to prevent the spread of the disease among the people. In accordance of another embodiment of the disclosure, a method for detecting and prevent spreading of a disease is disclosed. The method includes capturing one of a video, one or more images or a combination thereof of people within the pre-defined location, at every pre-defined time interval. The method also includes analysing at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof analysing one of the video, the one or more images or a combination thereof, using an artificial intelligence technique, for detecting the disease within the pre-defined location. The method also includes generating an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people. The method also includes determining a risk factor representative of the disease for each of the people within the pre-defined location. The method also includes determining a contagiosity of the disease from the affected people to the disease-free people of people. The method also includes generating a notification to initiate spraying of disinfectant within the pre-defined location. The method also includes transferring the notification to one or more disinfectant spray units installed within the pre-defined location to activate the one or more disinfectant spray units to spray the disinfectant to prevent the spread of the disease among the people.
To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF THE DRAWINGS
The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
FIG. 1 is a block diagram representing a system to detect and prevent spread of a disease in accordance with an embodiment of the present disclosure; FIG. 2 is a block diagram representation of an exemplary embodiment of the system in a health care unit of FIG. 1 in accordance with an embodiment of the present disclosure;
FIG. 3 is a block diagram representation of a processing subsystem located on a local server or on a remote server in accordance with an embodiment of the present disclosure; and
FIG. 4a and FIG. 4b are flow charts representing steps involved in a method for detecting and prevent spreading of a disease in accordance with an embodiment of the present disclosure.
Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.
DETAILED DESCRIPTION
For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.
The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.
Embodiments of the present disclosure relate to a system and method for detecting and prevent spreading of a disease. As used herein, the term ‘disease’ is defined as an abnormal condition which affects normal structure functioning of the body which is usually caused by external foreign bodies. In one exemplary embodiment, the disease may be a coronavirus or the like. In such embodiment, the disease is contagious from one from to another through saliva or any such bodily fluids. In the present disclosure, the disease is referred to a contagious disease.
FIG. 1 is a block diagram representing a system (10) to detect and prevent spread of a disease in accordance with an embodiment of the present disclosure. The system (10) includes at least one image capturing unit (20) installed in a pre-defined location. In one embodiment, the at least one image capturing unit (20) may include a camera, a video camera, an infrared (IR) camera, or the like. In one exemplary embodiment, the pre-defined location may include an indoor location or an outdoor location of a pre defined dimension. The at least one image capturing unit (20) is configured to capture one of a video, one or more images or a combination thereof of people within the pre defined location, at every pre-defined time interval. In such embodiment, the people may be located within the pre-defined location. Also, the people may be at stationary, may be in motion within the pre-defined location. In one specific embodiment, the at least one image capturing (20) unit may be coupled to an internet of things (IoT) platform.
[0001] Further, the system includes one or more processors (30) operatively coupled to the at least one image capturing unit (20). The one or more processors (30) include a disease detection module (40). The disease detection module (40) is configured to analyse one of the video, the one or more images or a combination thereof of the people within the pre-defined location captured by the at least one image capturing unit (20), using an artificial intelligence technique. In one embodiment, the analysis of the one or more image or the video may be compared with a pre-set of reference data. In another embodiment, the one or more images or the video may be analysed using an image processing technique. In one exemplary embodiment, an analysis technique may be one of a machine learning technique, deep learning technique, or the like. As used herein, the term “artificial intelligence” is defined as a study of computer algorithms that improve automatically through experience. Also, the term ‘artificial intelligence’ is defined as a technique demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. In one exemplary embodiment, an autonomous model may be used which may be a self teaching and self-governing model which may be trained using the deep learning technique. Broadly the autonomous model practices existing data to teach the model to detect patterns and later apply the detected patterns on a real time data to execute predictions. Here the autonomous model is trained using the deep learning technique to recognize facial expressions with respect to symptoms of the detected disease based on historic data. The historic data may be collected based on feeding of millions of pictures, images, videos that contain symptoms of the detected disease, this would be one of the categories of symptoms. Further the autonomous model may start building multiple patterns based on one of a plurality of clusters or a plurality of classifications of the one or more images or the video in real time and may compare the built pattern with the pre-defined to determine the disease using one or more layers of an artificial neural network of the machine learning technique the one or more layers may be distinguished based on the plurality of parameters or a plurality of hyperparameters.
[0002] The disease detection module (40) is also configured to analyse at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof upon analysing one of the video, the one or more images or a combination thereof, using the artificial intelligence technique to detect the disease within the pre-defined location. In one embodiment, the artificial intelligence technique may be one of the machine learning technique, the artificial intelligence technique, or the like. In another embodiment, the analysis technique may be the image processing technique. In such embodiment, the one or more images or the video may be divided into a plurality a sectors, and the plurality of sectors may be compared with a pre-defined set of reference data to identify the symptoms associated with the disease. In such embodiment, the facial expression may include restlessness, anxiety, or the like. In one exemplary embodiment, the at least one image capturing unit (20) may be an IoT device. The one or more images or videos captured by such a device may be installed with the artificial intelligence technique. In such embodiment, a depth-map of face of the people within the location is created using the AI neural network technique based on various data points identified. Furthermore, using the one or more images and the data points, multiple network layers are created in order to identify the plurality of parameters and the plurality of hyper parameters. The plurality of hyper parameters mean the configurable values which can be configured as per the pandemic symptoms occurred, parameters are the learned values that are the weightage realized while comparing the symptoms and self-learning process continues using the real time data and the historic data respectively.
[0003] The disease detection module (40) may also be configured to generate an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people. In one embodiment, the analysis result may data representing a number of disease-affected people, number of disease -free people among the people within the pre-defined location in real time.
[0004] Furthermore, the processor (30) includes a disease risk detection module (50) operatively coupled to the disease detection module (40). The disease risk detection module (50) is configured to determine a risk factor representative of the disease for each of the people within the pre-defined location. In one embodiment, the risk factor may be a percentage determining a change to get the disease for the disease- free people from the disease affected people within the pre-defined location at an instant of time. In one exemplary embodiment, the risk factor may be determined using one of the machine learning technique, the artificial intelligence technique, or the like. In one exemplary embodiment, the risk factor may be based on and is directly proportional to one of one or more real time identified symptoms, the one or more facial expressions, rate of spread of the disease or a combination thereof.
[0005] The processor (30) also includes a disease spread detection module (60) operatively coupled to the disease risk detection module (50). The disease spread detection module (60) is configured to determine a contagiosity of the disease from the affected people to the disease-free people of people in real time. In one embodiment, the contagiosity of the disease may be determined based on the risk factor. In such embodiment, the contagiosity of the disease may be associated to rate of disease-free people catching the disease within a set interval of time.
[0006] Furthermore, the processor (20) includes a disinfectant spray activation module (80) operatively coupled to the disease spread detection module (70). The disinfectant spray activation module (80) is configured to generate a notification to initiate spraying of disinfectant within the pre-defined location. In one embodiment, the notification may be an electrical signal. The disinfectant spray activation module (80) is also configured to transfer the notification to one or more disinfectant spray units (85) installed within the pre-defined location to activate the one or more disinfectant spray units (85) to spray the disinfectant to prevent the spread of the disease among the people. Referring to the above-mentioned embodiment, the electrical signal may be transmitted to the one or more disinfectant spray units (85) to remove virus causing the identified disease within the pre-defined location.
[0007] In one exemplary embodiment, the disinfectant spray activation module (80) may be configured to generate and transfer a stop notification to the one or more disinfectant spray units (85) to de-activate the one or more disinfectant spray units (85) to stop the spray of the disinfectant, wherein the stop notification is generated based on one or more parameters. In such embodiment, the one or more parameters may include one of time duration of spaying of the disinfectant, intensity of spaying of the disinfectant, the analysis result, or a combination thereof. In one exemplary embodiment, the one or more disinfectant spray units (85) may be operatively coupled to an autonomous vehicle which may be driven using the artificial intelligence technique. The autonomous vehicles may be trained to move about a pre-defined location in order to spray the disinfectant within the pre-defined location.
[0008] In one exemplary embodiment, the disease detection module (40) may be configured to detect a stage of the disease upon detecting at least one of the one or more symptoms of the disease, the one or more facial expressions of the people, the risk factor or a combination thereof. In such embodiment, the stage may be categorised into high, medium and low categories respectively. In one embodiment, the high risk may be defined when the rate of spread calculated against the people is high which may be greater than about 10%. In another embodiment, the medium risk may be defined when the rate of spread calculated against the people is medium which may be between about 5% to 10%. In yet another embodiment, the low risk may be defined when the rate of spread calculated against the people is high which may be lesser than about 5%.
[0009] FIG. 2 is a block diagram representation of an exemplary embodiment of the system (90) in a health care unit of FIG. 1 in accordance with an embodiment of the present disclosure. In the health care unit, consider a number of people to be ‘X’, out of which ‘N’ be a number of disease-affected people. Also, the health care unit may include 4 cameras (20) installed within the health care unit in four different locations within the health care unit. The 4 cameras (20) capture a plurality of images of the X people at every instant of time. The captured images of X people are analysed using an artificial intelligence technique by a disease detection module of a processor (30).
[00010] Further upon analysing the plurality of images symptoms of the disease is analysed based on one or more facial expressions of the X people, one or more behaviours of the X people using the artificial intelligence technique using the disease detection module (40). Upon analysing the plurality of images, an analysis report is generated by the disease detection module (40).
[00011] Furthermore, based on the analysis result, a risk factor for the spread of the disease from the N disease-affected people to the other disease-free people of the X people is determined by a disease risk detection module (50) within the health care unit. On determining the risk factor, contagiosity of disease from the N number of disease-affected people to other disease-free people is determined by a disease spread detection module (60). Consequently, a notification is generated to initiate spraying of disinfectant within the health care unit by a disinfectant spray activation module (80). Further the notification is transferred to a disinfectant spray unit (85) installed within the health care unit to activate the disinfectant spray unit (85) to spray the disinfectant to prevent the spread of the disease among the X people.
[00012] Subsequently, once the disinfectant is sprayed for a pre-set amount of time, the disinfectant spray activation module (80) will generate a stop notification and is transferred to the disinfectant spray unit (85) to de-activate the same in order to stop the spraying of the disinfectant.
[00013] Furthermore, the camera (20), the processor (30), the disease detection module (40), the disease risk detection module (50), the disease spread detection module (60), the disinfectant spray activation module (80) and the disinfectant spray unit (85) are substantially similar to at least one image capturing module (20), a processor (30), an disease detection module (40), a disease risk detection module (50), a disease spread detection module (60), a disinfectant spray activation module (80) and a disinfectant spray unit (85) of FIG. 1.
[0001] FIG. 3 is a block diagram representation of a processing subsystem located on a local server or on a remote server in accordance with an embodiment of the present disclosure. The server (110) includes processor(s) (120), and memory (130) operatively coupled to the bus (140).
[0002] The processor(s) (120), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.
[00014] The memory (130) includes a plurality of modules stored in the form of executable program which instructs the processor (120) to perform the method steps illustrated in FIG. 4a and FIG. 4b. The memory (130) is substantially similar to the system (10) of FIG.l. The memory (130) has the following modules: an disease detection module (40), a disease risk detection module (50), a disease spread detection module (60), and a disinfectant spray activation module (80).
[00015] The image analysis (40) module is configured to analyse one of the video, the one or more images or a combination thereof of the people within the pre-defined location, analyse at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof and to generate an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people.
[00016] The disease risk detection module (50) is configured to determine a risk factor representative of the disease for each of the people within the pre-defined location. The disease spread detection module (60) configured to determine a contagiosity of the disease from the affected people to the disease-free people of people. The disinfectant spray activation module (80) is configured to generate a notification to initiate spraying of disinfectant within the pre-defined location and to transfer the notification to one or more disinfectant spray units installed within the pre defined location to activate the one or more disinfectant spray units to spray the disinfectant to prevent the spread of the disease among the people.
[00017] FIG. 4a and FIG. 4b are flow charts representing steps involved in a method (150) for detecting and prevent spreading of a disease in accordance with an embodiment of the present disclosure. The method (150) includes capturing one of a video, one or more images or a combination thereof of people within the pre-defined location, at every pre-defined time interval in step 160. In one embodiment, capturing one of a video, one or more images comprises capturing one of a video, one or more images using at least one image capturing unit.
[00018] Furthermore, the method (150) includes analysing at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof upon analysing one of the video, one or more images or a combination thereof, using the artificial intelligence technique for detecting the disease within the pre-defined location in step 180. In one embodiment, analysing the at least one of the one or more symptoms of the disease, the one or more facial expressions of the people, the one or more behaviours of the people or a combination thereof may include analysing the at least one of the one or more symptoms of the disease, the one or more facial expressions of the people, the one or more behaviours of the people or a combination thereof by the disease detection module. In such embodiment, the analysis technique may include one of the machine learning technique, the artificial intelligence technique or the like.
[00019] The method (150) also includes generating an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people in step 190. In one embodiment, generating the analysis result may include generating the analysis result by the disease detection module. In one exemplary embodiment, generating the analysis result may include generating the analysis result including data representing a number of disease-affected people, number of disease - free people among the people within the pre-defined location.
[00020] The method (150) also includes determining a risk factor representative of the disease for each of the people within the pre-defined location in step 200. In one embodiment, determining the risk factor may include determining the risk factor by a disease risk detection module. The method (150) also includes determining a contagiosity of the disease from the affected people to the disease-free people of people using in step 210. In one embodiment, determining the contagiosity of the disease may include determining the contagiosity of the disease by a disease spread detection module.
[00021] The method (150) also includes generating a notification to initiate spraying of disinfectant within the pre-defined location. In one embodiment, generating the notification may include generating the notification by a disinfectant spray activation module. In one exemplary embodiment, generating the notification may include generating an electrical signal. The method also includes transferring the notification to one or more disinfectant spray units installed within the pre-defined location to activate the one or more disinfectant spray units to spray the disinfectant to prevent the spread of the disease among the people. In one embodiment, transferring the notification may include transferring the notification by the disinfectant spray activation module. [00022] In one embodiment, the method (150) may further include generating and transferring a stop notification to the one or more disinfectant spray units to de-activate the one or more disinfectant spray units to stop the spray the disinfectant, wherein the stop notification is generated based on one or more parameters. In such embodiment, generating and transferring the stop notification may include generating and transferring the stop notification by the disinfectant spray activation module. In one embodiment, the one or more parameters may include one of time duration of spaying of the disinfectant, intensity of spaying of the disinfectant, the analysis result, or a combination thereof.
[00023] Various embodiments of the present disclosure enable the system and method to detect and prevent spread of a disease enables the system to determine the disease-affected people in real time. The system also measures the risk of spread of the disease among healthier people and also the contagiosity of such deadly diseases. In addition, the system can generate a real time guidance and precautionary measures for people within a particular geographical sector. Also, upon determining the conditions in real time, the system initiates to spray the disinfectant on the people within the location, thereby making the system more efficient and reliable as the system uses machine learning or artificial intelligence techniques to function in real time. Further, the system can be implemented in areas like customer communication, health care, city administration, police, robotic process automation, water supply, electricity, fintech, smart speakers and the like.
[00024] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.
[00025] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.

Claims

V WE CLAIM:
1. A system (10) to detect and prevent spread of a contagious disease, comprising: at least one image capturing unit (20) installed in a pre-defined location, and configured to capture one of a video, one or more images or a combination thereof of people within the pre-defined location, at every pre-defined time interval; one or more processors (30) operatively coupled to the at least one image capturing unit (20), wherein the one or more processors (30) comprises: a disease detection module (40) configured to: analyse at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof upon analysing one of the video, the one or more images or a combination thereof, using an artificial intelligence technique to detect the disease within the pre defined location; and generate an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people in real time; a disease risk detection module (50) operatively coupled to the disease detection module (40), and configured to determine a risk factor corresponding to the disease for each of the people within the pre-defined location in real time; a disease spread detection module (60) operatively coupled to the disease risk detection module (50), and configured to determine contagiosity of the disease from the affected people to the disease-free people of people in real time; and a disinfectant spray activation module (80) operatively coupled to the disease spread detection module (70), and configured to: generate a notification to initiate spraying of disinfectant within the pre-defined location; and transfer the notification to one or more disinfectant spray units (85) installed within the pre-defined location to activate the one or more disinfectant spray units (85) to spray the disinfectant to prevent the spread of the disease among the people.
2. The system (10) as claimed in claim 1, wherein the at least one medical assistant comprises a doctor, a nurse, a healthcare assistant, or a combination thereof.
3. The system (10) as claimed in claim 1, wherein the disinfectant spray activation module (80) is configured to generate and transfer a stop notification to the one or more disinfectant spray units (85) to de-activate the one or more disinfectant spray units (85) to stop the spray of the disinfectant, wherein the stop notification is generated based on one or more parameters;
4. The system (10) as claimed in claim 3, wherein the one or more parameters comprises one of time duration of spaying of the disinfectant, intensity of spaying of the disinfectant, the analysis result, or a combination thereof.
5. The system (10) as claimed in claim 1, wherein the artificial intelligence technique comprises an artificial intelligence image processing technique.
6. The system (10) as claimed in claim 1, wherein the disease detection module (40) is configured to detect a stage of the disease upon detecting at least one of the one or more symptoms of the disease, the one or more facial expressions of the people, the risk factor or a combination thereof.
7. A method (150) for detecting and prevent spreading of a disease comprising: capturing, by at least one image capturing unit, one of a video, one or more images or a combination thereof of people within the pre-defined location, at every pre-defined time interval; (160) analysing, by the disease detection module, at least one of one or more symptoms of the disease, one or more facial expressions of the people, one or more behaviours of the people or a combination thereof upon analysing one of the video, the one or more images or a combination thereof, using an artificial intelligence technique, for detecting the disease within the pre-defined location; (180) generating, by the disease detection module, an analysis result representative of one of disease affected people, disease free people, or a combination thereof among the people; (190) determining, by a disease risk detection module, a risk factor representative of the disease for each of the people within the pre-defined location; (200) determining, by a disease spread detection module, a contagiosity of the disease from the affected people to the disease-free people of people using a disease detection technique; (210) generating, by a disinfectant spray activation module, a notification to initiate spraying of disinfectant within the pre-defined location; and (230) transferring, by the disinfectant spray activation module, the notification to one or more disinfectant spray units installed within the pre-defined location to activate the one or more disinfectant spray units to spray the disinfectant to prevent the spread of the disease among the people. (240)
8. The method (150) as claimed in claim 7, wherein generating the at least one holographic image of the at least one medical assistant comprises generating the at least one holographic images of a doctor, a nurse, a healthcare assistant, or a combination thereof.
9. The method (150) as claimed in claim 7, comprising to generating, by the disinfectant spray activation module, and transferring a stop notification to the one or more disinfectant spray units to de-activate the one or more disinfectant spray units to stop the spray the disinfectant, wherein the stop notification is generated based on one or more parameters.
10. The method (150) as claimed in claim 8, wherein the one or more parameters comprises one of time duration of spaying of the disinfectant, intensity of spaying of the disinfectant, the analysis result, or a combination thereof.
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HASSAN M.A.; MALIK A.S.; FOFI D.; KARASFI B.; MERIAUDEAU F.: "Towards health monitoring using remote heart rate measurement using digital camera: A feasibility study", MEASUREMENT., INSTITUTE OF MEASUREMENT AND CONTROL. LONDON, GB, vol. 149, 4 September 2019 (2019-09-04), GB , XP085862247, ISSN: 0263-2241, DOI: 10.1016/j.measurement.2019.07.032 *
MADANDOLA OLATUNDE O, TUMURBAATAR ALTANSUREN, ABBU SAITEJA, TAN LIANGYU, CHARLES LAUREN E: " Camera-based, mobile disease surveillance using Convolutional Neural Networks", ONLINE JOURNAL OF PUBLIC HEALTH INFORMATICS, vol. 11, no. 1, 30 May 2019 (2019-05-30), pages e376, XP055882596 *

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20230144246A (en) * 2022-04-07 2023-10-16 박건우 Apparatus and methods for measuring virus exposure risk
KR102821954B1 (en) 2022-04-07 2025-06-17 박건우 Apparatus and methods for measuring virus exposure risk

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