Smart Healthcare Security Device on Medical IoT Using Raspberry Pi

This paper aims to improve the protection of two-wheelers. This study is divided into two parts: a helmet unit and a vehicle unit. The primary unit is the helmet unit, which contains a sensor, and the second part is known as the alcohol sensor, which is used to determine whether or not the driver is wearing the user helmet correctly. This data is then transmitted to the vehicle unit via the RF transmitter. The data is encoded with the aid of an encoder. Suppose the alcohol sensor senses that the driver is intoxicated. In that case, the IoT-based Raspberry Pi micro-controller passes the data to the vehicle unit via the RF transmitter, which immediately stops the vehicle from using the Driver circuit to control the relay. To stop the consumption of alcohol, the vehicles would be tracked daily. If the individual driving the vehicle is under the influence of alcohol while driving, the buzzer will automatically trigger. The vehicle key will be switched off.

Alcohol Sensor Based on Surface Plasmon Resonance of ZnO Nanoflowers/Au Structure

Alcohol detection plays a key role in food processing and monitoring. Therefore, we present a fast, high reproducibility and label-free characteristics alcohol photochemical sensor based on the surface plasmon resonance (SPR) effect. By growing ZnO nanoflowers on Au film, the SPR signal red-shifted in the visible region as the alcohol concentration increased. More interestingly, the sensitivity improved to 127 nm/%, which is attributed to the ZnO nanoflowers/Au structure. The goodness of the linear fit was more than 0.99 at a range from 0 vol% to 95 vol% which ensures detection resolution. Finally, a practical application for distinguishing five kinds of alcoholic drinks has been demonstrated. The excellent sensing characteristics also indicate the potential of the device for applications in the direction of food processing and monitoring, and the simple structure fabrication and economic environmental protection make it more attractive.

Cost-Effective Foam-Based Colorimetric Sensor for Roadside Testing of Alcohol in Undiluted Saliva

A novel foam-based colorimetric alcohol sensor was developed for the detection of alcohol in saliva. Detection was based on the color change of a potassium dichromate-sulfuric acid solution absorbed by melamine foam. In the presence of alcohol, the orange colorimetric sensor changed color to brown, green and, ultimately, blue, depending on the concentration of alcohol in the sample. The response of the proposed sensor toward alcohol was linear from 0.10 to 2.5% v/v. The limit of detection was 0.03% v/v. Alcohol concentration could be determined using the naked eye in the range of 0.00 to 10% v/v. The developed alcohol sensor presented good operational accuracy (RSD = 0.30–1.90%, n = 8) and good stability for 21 days when stored at 25 °C and 75 days when stored at 4 °C. The results of alcohol detection with the developed sensor showed no significant difference from the results of spectrophotometric detection at a 95% confidence level (p > 0.05). The sensor was easy to use, small, inexpensive and portable, enabling drivers to accurately measure their own blood alcohol level and providing convenient speed in forensic applications.

Silver nanoparticle based alcohol sensor manufacturing training for detection of halal drinks

Nowadays, halal drinks are one of the sectors that get the attention of various parties. Alcohol in beverages is important in determining the halalness of a beverage. Halal detection can be done using chemical sensors. In the Pharmacy Department, knowledge of analysis using sensors is rare. Therefore, this community service program was carried out to introduce qualitative methods of quick and simple alcohol analysis. The participants are students of the Pharmacy Department of STIKES Anwar Medika in Sidoarjo City. Participants are communities engaged in the health sector who have an interest in halal and how to make sensors for halal testing. This program is implemented by making a video on how to make a chemical sensor for alcohol detection. The chemical sensor is a paper-immobilized colloidal silver-based chemical sensor that is easy to apply in various conditions. Based on the results of the color change produced on the filter paper, from green to bluish which indicates that the food or beverage sample contains alcohol. Based on the questionnaire, participants expressed satisfaction with the increase in knowledge about additional tools that are easy and fast to detect alcohol.

Accident & Alcohol Detection Surveillance Robot

Nowadays in India many accidents are occurred due drunk and drive. So, we have made a project to resolve and predict these incidents, in which we used Arduino, Alcohol sensor, GSM and GPS modules. Here the Alcohol sensor is placed inside the car which will detect whether the driver of the car has drunk or not. If the driver is drunk then the Alcohol sensor will get triggered and the car engine will not turn ON or it will OFF the engine if it is already in ON state. Here the other two modules which are GSM and GPS. If the car has met with an accident the limit switch which is present inside the car will trigger the Arduino by which an SMS will be sent to the particular contacts which are been saved in the Arduino program. The GPS will be connected to the number of satellites so that when the car met with the accident the live location of the car will be sent to the contact through the GSM module. This entire setup of GPS and GSM will be kept inside the car. This entire project can reduce the accidents which are occurred by the drunk and drive and dead corners and also predict the location of the accidents. It results in increasing the human safety and protection

Cloud based Vehicle Tracking System

Vehicle tracking is beneficial for a variety of purposes, including personal vehicle security, public transit systems, fleet management, and more. Furthermore, the number of automobiles on the road is predicted to increase considerably internationally. As a result, a cloud-based vehicle tracking system based on the Global Positioning System (GPS), Global System for Mobile Communications (GSM) modem, and the Raspberry Pi processor board is being developed with the goal of allowing customers to easily and conveniently find their automobiles. The GPS and GSM modules in the tracking system are used to readily find the user's vehicle. The GPS module is used to track the vehicle's location using information such as latitude and longitude. These values are sent to the user through SMS using a GSM modem. Alcohol consumption is detected with the MQ-3 Alcohol sensor. The user can monitor the sensor data via the thingspeak channel. The latitude and longitude coordinates, as well as a webpage to plot the exact position of the vehicle, will be supplied to the user by e-mail. The Raspberry Pi processor board receives the data and outputs the result. The hardware prototype for a cloud-based vehicle tracking system is shown in this study. The GPS receiver module, GSM module, I2C Protocol, Raspberry Pi board, camera, and MQ-3 alcohol sensor are the system's primary hardware devices.

Smart Helmet

Road accidents are increasing day by day because the riders are not using the helmet and due to consumption of alcohol. In today's world, huge numbers of people are dying on road accidents. By using smart helmet, the accidents can be detected. The main target of the project is designing a smart helmet for accident avoidance and alcohol detection. The features of the Smart Helmet are wearing helmet, detection of alcohol, detection of accident. If the person is not wearing helmet the bike will not start and it displays on the LCD. The alcohol sensor recognizes the alcoholic substance in the rider's breath. If the person is drunk then alcohol sensor detects it and displays on the LCD and the bike will not start. If there is no detection of alcohol then the bike starts. If there is no sign of alcoholic substance present and helmet is used, only then the bike will start. The bike will not start if any of the two conditions is violated. If an accident is occurred then it is detected by Vibration sensor and alarm is activated. If the person is in conscious state, then the person can off the alarm and no message is sent to the saved numbers. If the person is in unconscious state, then message will be sent to the saved numbers in GSM module and sends the location to them by GPS. With the use of Smart Helmet, accident rate can be reduced.

Alcohol Detection to Lock Engine and Incident Reporting

Most of the road accidents are occurring due to drunk-driving. We hear about a lot of accidents these days that are caused by drunk driving. Drunk drivers are not in a stable state, and as a result, rash driving occurs on the roadways, endangering the lives of everyone on the road, including the driver. Thousands of traffic accidents were reported by the Indian Ministry of Statistics in 2016. Our project presents the design and implementation of “Alcohol Detection to Lock Engine and incident reporting” using alcohol sensor, arduino UNO, GSM Module. The system will continuously monitor level of alcohol concentration using MQ-3 alcohol sensor and thus turn off the engine of vehicle if the alcohol concentration is above threshold level. The LCD display is used to display the stat us of the vehicle. This model will send the message of whereabouts of the vehicle through GSM module thus reporting the incident .the project provides an efficient solution to control road accidents due to drunk driving.

An Innovative Prototype to Prevent Accidents using Eye Blink Sensor, Ultrasonic Sensor, Alcohol Sensor, MEMS Accelerometer

The ever increasing number of major traffic accidents around the world can be significantly reduced if modern technology is incorporated within the automobile to assess the physical condition of the driver as well as the outside situation of the automobile at regular intervals during the movement of the vehicle and preventive measures are automatically taken for the safety of all concerned entities, both within the vehicle and outside the vehicle. In this paper, design of a model incorporated with four sensors, namely, Eye Blink Sensor, Ultrasonic Sensor, Alcohol Sensor and MEMS Accelerometer, is presented which can monitor the physical state of the driver and the outside situation of the vehicle at regular intervals during his/her driving and raise an audible alarm within the vehicle to alert the driver. The prototype uses Eye Blink Sensor, Ultrasonic Sensor, Alcohol Sensor and MEMS Accelerometer to assess the physical condition of the driver and the outside situation of the vehicle and raise an alarm to alert the driver if any sensor senses any change in the normal condition. The prototype of the design has been successfully implemented which leads to the conclusion that such a system can help in keeping the driver alert at all times and monitor the outside situation while driving and hence facilitate in avoidance of any traffic accidents involving driver’s alertness.

Smart Assistant for Accident Prevention and Rescue

Travelling is playing a vital role in human life, now it has turned to be dangerous due to accidents. In a survey, Govt declared that more than 1.5 lakhs people are expiring in a year via mishap. More-over in the reported death cases two-third victims die due to late arrival of rescue team. In our project Prevention with alcohol sensor & we are interfacing GSM, GPS, Vibration sensor to know the accident occurrence and place of occurrence and sending message to the rescue team to save the victims as soon as possible. If the route is not visible (due to fog) through Ultrasonic sensor we can drive safely to our destination.