Embedded System Prototype to Fight Covid-19 Pandemic Contamination with Less Cost

Abstract

In the shadow of the coronavirus (Covid-19) pandemic, the sterilization has become a major necessity for humans to avoid exponential Infection. In order to avoid touching contaminated surfaces, we develop an embedded system prototype, which allows the bottle or any other medium to pour the sterilization product without touching it. The human hands are detected using two sensors HC-SR04 and LM35. To overcome the stability problem due to the problem of sensor noise and enhance the system performance a Kalman filter algorithm is implemented to ensure stable hands detection. The efficiency of the prototype mounted on an Arduino board is checked. After the completion of the prototype, a comprehensive cost analysis is conducted.

Keywords: Arduino; Covid-19; Kalman filter; Sterilization bottle.

Fig. 1

a Old sterilization bottle and b smarter sterilization bottle

Fig. 2

Conceptual diagram of the proposed system

Fig. 3

Proposed prototype

Fig. 4

Human hands sensing by a HC-SR04 ultrasonic sensor

Fig. 5

Trigger versus echo

Fig. 6

Driving circuit for DC water motor

Fig. 7

Conceptual diagram of Kalman Filter estimator

Fig. 8

Flowchart of final source code

Fig. 9

Diagram for the acquisition of temperature data via a LM35 sensor

Fig. 10

Measured and estimated temperature values using a LM35 sensor

Fig. 11

Measured and estimated values of the speed of sound waves using a LM35 sensor

Fig. 12

Diagram for the acquisition data of the speed of sound waves via a HC-SR04 ultrasonic sensor

Fig. 13

Measured and estimated values of the speed of sound waves using a HC-SR04 ultrasonic sensor

Fig. 14

Data acquisition diagram for a hybrid system

Fig. 15

Desired and estimated values of distances using a LM35 sensor and a HC-SR04 ultrasonic sensor

Fig. 16

Response time versus covariance value of the process noise

Fig. 17

Estimated error of distance for different covariance value of the process noise

Fig. 18

Different distance values estimated using a LM35 sensor and a HC-SR04 ultrasonic sensor

Fig. 19

First model of SSB: a Front view and b rear view

Fig. 20

Essential parts of our prototype

Fig. 21

A few results registered

Fig. 22

Basic source code used for the proposed prototype

Fig. 23

Arduino nano and LM35 sensor connection

Fig. 24

Arduino nano and HC-SR04 ultrasonic sensor connection

Fig. 25

Proposed Hybrid system for our prototype

Fig. 26

Future low-cost prototype [39]