AU2020103880A4 - An iot and deep learning enabled device for monitoring social distancing - Google Patents
An iot and deep learning enabled device for monitoring social distancing Download PDFInfo
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- AU2020103880A4 AU2020103880A4 AU2020103880A AU2020103880A AU2020103880A4 AU 2020103880 A4 AU2020103880 A4 AU 2020103880A4 AU 2020103880 A AU2020103880 A AU 2020103880A AU 2020103880 A AU2020103880 A AU 2020103880A AU 2020103880 A4 AU2020103880 A4 AU 2020103880A4
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- deep learning
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- social distancing
- iot
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- 238000013135 deep learning Methods 0.000 title claims abstract description 22
- 238000012544 monitoring process Methods 0.000 title claims abstract description 21
- 241000282412 Homo Species 0.000 claims abstract description 9
- 230000033001 locomotion Effects 0.000 claims abstract description 6
- 230000003044 adaptive effect Effects 0.000 claims abstract description 4
- 230000003068 static effect Effects 0.000 claims abstract description 3
- 240000007651 Rubus glaucus Species 0.000 claims description 4
- 235000011034 Rubus glaucus Nutrition 0.000 claims description 4
- 235000009122 Rubus idaeus Nutrition 0.000 claims description 4
- 238000013528 artificial neural network Methods 0.000 claims description 4
- 230000005855 radiation Effects 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000000034 method Methods 0.000 description 3
- 241000700605 Viruses Species 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 208000025721 COVID-19 Diseases 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000001010 compromised effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013481 data capture Methods 0.000 description 1
- 230000001771 impaired effect Effects 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 230000007480 spreading Effects 0.000 description 1
Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/19—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using infrared-radiation detection systems
- G08B13/191—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using infrared-radiation detection systems using pyroelectric sensor means
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
AN IOT AND DEEP LEARNING ENABLED DEVICE FOR MONITORING SOCIAL
DISTANCING
The present invention relates to an IOT and deep learning enabled device for monitoring
social distancing. The proposed invention comprises PIR sensor which is attached with band,
said PIR sensor collects the human's static and motion and will send to Raspberry Pi. Also
herein Euclidean distance is implemented to find the distance between humans, and a
Raspberry Pi with deep learning adaptive configuration enabled to classify the humans who
are violating the social distance and producing a louder alarm, alerting workers with ID when
they are within six feet of each other. Following invention described in details with the help
of figure 1 of sheet 1 illustrates architecture for an IOT and deep learning enabled device for
monitoring social distancing and Figure 2 of sheet 2 illustrates flow chart of proposed
invention.
2/2
MJIRIi ~w I, rrd Pfft1c 4w
Pz t a r j i r
'J -4 .
Figre
Description
2/2
MJIRIi ~w I, rrd Pfft1c 4w
Pz t a r j i r
'J -4 .
Figre
Technical field of invention:
[001] The present invention in general relates to personal protective equipment, and more specifically to an IOT and deep learning enabled device for monitoring social distancing.
Background of the invention:
[002] The background information herein below relates to the present disclosure but is not necessarily prior art.
[003] Owing to the recent spread and transmission of communicable viruses such as COVID-19, for example, doctors and governments around the world are asking individuals to maintain minimum recommended separation distances with other individuals. Although the concept of maintaining an appropriate "social distance" is easy to understand in the abstract, it is somewhat difficult to maintain in reality.
[004] One of the biggest reasons groups of people fail to maintain proper social distancing is because there is no easy way for everyone to immediately realize they are too close together. Indeed, when viewing gatherings of people, it is easy to see that the recommended guidelines are compromised very quickly. This is especially true for children and persons with disabilities such as the vision impaired. In addition to the above, there is no current system in place to provide individuals with an active notification (e.g., visual, audible or tactile) that they have come in close proximity less than the recommended distance guidelines, and therefore are not available for detective, after the fact action or analysis, as well as preventive measures.
[005] In light of the above, many establishments have implemented signs and other physical markings to provide some type of visual cue to maintain spacing. Although such items are useful, they do not afford individuals with any type of protection or notice when they are not in the actual merchant location. Additionally, without tracking data, such locations are not able to identify instances of overcrowding within their establishments.
[006] Accordingly, it would be beneficial to provide a device which can continually provide a visible safe zone about a user that represents the boundaries for proper social distancing. It would also be beneficial if the device was tied to a system and method of providing user notifications when the safe zone has been breached by another individual, and for providing analytics regarding social distancing, proximity and capacity within a given area, so as to alleviate the drawbacks described above.
[007] Although various attempts are made before, for providing device for monitoring social distancing and few of them are such as- US20160117329A1 discloses systems and methods for social recommendations, IN202011023853 discloses social distancing monitoring mobile kit/apps: alert and track the real time position and monitoring the social distancing, KR101875858B1 discloses integrated health data capture and analysis system, IN202041042946 discloses IOT based smart wearable social distancing alert system, CA3082221 discloses social distancing personal warning device, IN202011025365 discloses method and system for managing social distancing.
[008] There exist many drawbacks in the existing system. So, there is a need to develop an IOT and deep learning enabled device for monitoring social distancing.
Objective of the invention
[009] An objective of the present invention is to attempt to overcome the problems of the prior art and provide an IOT and deep learning enabled device for monitoring social distancing.
[0010] In a preferred embodiment, the present invention minimizes physical contact between humans by producing a louder alarm, alerting workers when they are too close to each other.
[0011] It is therefore an object of the invention is to send alerts to employee's personal wearable band in case of violating social distancing.
[0012] It is therefore an object of the invention is to reducing or preventing the spread of the virus will save lives.
[0013] These and other objects and characteristics of the present invention will become apparent from the further disclosure to be made in the detailed description given below.
Summary of the invention:
[0014] Accordingly following invention provides an IOT and deep learning enabled device for monitoring social distancing. The proposed invention invention minimizes physical contact between humans by producing a louder alarm, alerting workers when they are too close to each other. The proposed invention utilizes PIR sensor to monitor human motions nearby another person in working place. The captured data is send to raspberry pi kit for further analysis of finding distance between two persons by using Euclidean distance. The persons who are violating within six feet of each other are predicted by using Euclidean distance. Persons are classified based on social distancing threshold by using deep learning multilayer perceptron neural network adaptive configuration. For security purpose, alarm will intimate with person ID who are violating social distance to supervisor or senior persons and concerned employees. The person will get alert and keep social distance from nearby person for spreading of any diseases.
'0 Brief description of drawing:
[0015] This invention is described by way of example with reference to the following drawing where,
[0016] Figure 1 of sheet 1 illustrates architecture for an IOT and deep learning enabled device for monitoring social distancing. Whereas, 100 denotes elastic band, 101 denotes PIR sensor, 102 denotes buzzer, 103 denotes raspberry pi with deep learning MLPNN configuration, 104 denotes sound alert, 105 denotes manager, 106 denotes supervisor, 107 denotes higher authorities,
108 denotes user module, 109 denotes hardware module, 110 denotes receiver module
[0017] Figure 2 of sheet 2 illustrates flow chart of proposed invention.
[0018] In order that the manner in which the above-cited and other advantages and objects of the invention are obtained, a more particular description of the invention briefly described above will be referred, which are illustrated in the appended drawing. Understanding that these drawing depict only typical embodiment of the invention and therefore not to be considered limiting on its scope, the invention will be described with additional specificity and details through the use of the accompanying drawing.
Detailed description of the invention:
[0019] The present invention relates to an IOT and deep learning enabled device for monitoring social distancing. More particularly proposed invention minimizes physical contact between humans by producing a louder alarm, alerting workers when they are too close to each other. The proposed system comprises of PIR sensor, multilayer perceptron neural network, rasberry Pi, buzzer, battery and WiFi connectivity. In proposed invention the wearable handband made up of elastic band and consist of PIR sensor, Buzzer.
[0020] PIR sensor: Pyroelectric infrared (PIR) sensors are sensing systems to detect human body movement in an indoor environment. It has two slots in it; each slot is made of a special material that is sensitive to IR. When the sensor is idle, both slots detect the equal amount of IR, the ambient amount discharged from the room or walls or outdoors. When heartfelt bodies like human or animal crosses, it first captures one half of the PIR sensor, which roots a positive differential change between the two halves. When the warm body leaves the sensing area, the reverse happens, whereby the sensor makes a negative differential change. These change pulses are what is detected.
[0021] PIR are fundamentally made of a pyroelectric which can sense levels of infrared radiation. Everything produces some low-level radiation, and the warmer something is the more radiation is released. The sensor in a motion detector is actually divided in two halves.
The two halves are bound up so that they stop each other out. If one half sees more or less IR radiation than the other, the output will swipe high or low. PIR sensors is used to sense whether a human has moved in or out of the sensors range.
[0022] Multilayer Perceptron Neural Network (MLPNN): In the proposed invention, MLPNN is used for the purpose of classification. MLPNN is a feed-forward multilayer network architecture composed of numerous layers of neurons, an input layer, an output layer, and several hidden layers (Haykin, 2008). The MLPNN used in this invention consists of three layers: an input layer, a hidden layer, and an output layer. The input was the distance between persons. It classifies the persons who are violating social distance and who are not violating. The output was the persons social distances violating or not.
[0023] Rasberry Pi : Raspberry Pi 3 Model B with a 1.2 GHz 64-bit quad core processor, on-board 802.1In WiFi, Bluetooth and USB boot capabilities. The Raspberry Pi 3 Model B+ launched with a faster 1.4 GHz processor and a three-times faster gigabit Ethernet (throughput limited to ca. 300 Mbit/s by the internal USB 2.0 connection) or 2.4 / 5 GHz dual-band 802.11ac Wi-Fi (100 Mbit/s)
[0024] In the present invention the wearable handband made up of elastic band and consist of PIR sensor, Buzzer. PIR sensor collects the human's static and motion and will send to '0 Raspberry Pi here connection establish between hand band and raspberry pi. Microcontroller reads and sends sensor values to PC. Herein Euclidean distance is implemented to find the distance between humans, and a Raspberry Pi with deep learning adaptive configuration enabled to classify the humans who are violating the social distance and producing a louder alarm, alerting workers with ID when they are within six feet of each other.
[0025] The proposed device for monitoring social distancing is hand band for monitoring peoples social distancing in working places such as Industries, Schools, and Colleges using deep learning. Social distancing is a communal health issue that objectives is to prevent sick people from coming in close contact with healthy person in order to reduce chances for disease spread.
[0026] The many features and advantages of the invention are apparent from the detailed specification, and thus, it is intended by the appended claims to cover all such features and advantages of the invention which fall within the true spirit and scope of the invention.
Further, since numerous modifications and variations will readily occur to those skilled in the art, it is not desired to limit the invention to the exact construction and operation illustrated and described, and accordingly, all suitable modifications and equivalents may be resorted to, falling within the scope of the invention.
Claims (5)
1. An JOT and deep learning enabled device for monitoring social distancing, comprises; PIR sensor, multilayer perceptron neural network, rasberry Pi, buzzer, battery and WiFi connectivity.
2. The JOT and deep learning enabled device for monitoring social distancing as claimed in claim 1 wherein PIR sensor collects the human's static and motion and will send to Raspberry Pi
3. The JOT and deep learning enabled device for monitoring social distancing as claimed in claim 1 wherein Euclidean distance is implemented to find the distance between humans.
4. The JOT and deep learning enabled device for monitoring social distancing as claimed in claim 1 wherein a buzzer connected to Hand band and Raspberry Pi to alert persons who are violating social distance and manager or senior persons about violating the social distance with employee ID.
5. The JOT and deep learning enabled device for monitoring social distancing as claimed in claim 1 wherein raspberry Pi with deep learning adaptive configuration enabled to classify the humans who are violating the social distance and producing a louder alarm, alerting workers with ID when they are within six feet of each other.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2020103880A AU2020103880A4 (en) | 2020-12-03 | 2020-12-03 | An iot and deep learning enabled device for monitoring social distancing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2020103880A AU2020103880A4 (en) | 2020-12-03 | 2020-12-03 | An iot and deep learning enabled device for monitoring social distancing |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AU2020103880A4 true AU2020103880A4 (en) | 2021-02-11 |
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2020103880A Ceased AU2020103880A4 (en) | 2020-12-03 | 2020-12-03 | An iot and deep learning enabled device for monitoring social distancing |
Country Status (1)
| Country | Link |
|---|---|
| AU (1) | AU2020103880A4 (en) |
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2020
- 2020-12-03 AU AU2020103880A patent/AU2020103880A4/en not_active Ceased
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