Smart Helmet with Cloud GPS GSM Technology for Accident and Alcohol Detection
| Autor: | Ranjitha Shet, Prateeksha Nashipudi, Praveen M. Dhulavvagol, Anand S. Meti, Renuka Ganiger |
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| Rok vydání: | 2018 |
| Předmět: |
050210 logistics & transportation
Event (computing) business.industry Computer science 05 social sciences ComputerApplications_COMPUTERSINOTHERSYSTEMS Cloud computing 02 engineering and technology Computer security computer.software_genre Accident (fallacy) GSM 0502 economics and business 0202 electrical engineering electronic engineering information engineering Global Positioning System 020201 artificial intelligence & image processing business computer Location tracking |
| Zdroj: | Communications in Computer and Information Science ISBN: 9789811090585 |
| DOI: | 10.1007/978-981-10-9059-2_31 |
| Popis: | Today in India and most other countries, accident is considered as an unexpected and unintended event. According to the survey road accidents lead to hospitalization, injuries and disabilities, considering these facts the safety of the riders has become a most crucial and important issue and concern in majority of the countries. The usage of two wheeler’s vehicles is increasing day by day for transport convenience we need to have proper safety and efficient measures to handle accidents. In the proposed paper we discuss smart helmet system using cloud and GPS technology for accident detection and location tracking, GSM is used to send a notification message of accident location to the concerned people to avoid major casualty/life of the person can be saved. MQ3 sensor is also embedded in the helmet to check whether the motorcyclist has consumed alcohol or not. The sensor data captured is stored in the cloud so that the accident information can be fetched anywhere and anytime at a faster rate. Considering the above facts, the prototype of smart helmet is designed and implemented to enhance the safety of the motorcyclist. This new idea will reduce the risk of motorcyclist life. The experimental results confined that the developed prototype model gives 84% accurate result of the accident detection and concerned persons will receive notification message. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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