Abstract : In the Korean construction industry, legal and institutional safety management improvements are continually being pursued. However, there was a 4.5% increase in the number of workers’ deaths at construction sites in 2017 compared to the previous year. Failure to wear safety helmets seems to be one of the major causes of the increase in accidents, and so it is necessary to develop technology to monitor whether or not safety helmets are being used. However, the approaches employed in existing technical studies on this issue have mainly involved the use of chinstrap sensors and have been limited to the problem of whether or not safety helmets are being worn. Meanwhile, improper wearing, such as when the chinstrap and harness fixing of the safety helmet are not properly tightened, has not been monitored. To remedy this shortcoming, a sensing safety helmet with a three-axis accelerometer sensor attached was developed in this study. Experiments were performed in which the sensing data were classified whether the safety helmet was being worn properly, not worn, or worn improperly during construction workers’ activities. The results verified that it is possible to differentiate among wearing status of the proposed safety helmet with a high accuracy of 97.0%.
? This work resolves the shortcomings of existing studies in which sensors were employed and the focus was on whether or not the safety helmet was being worn.
? However, the approaches employed in existing technical studies on this issue have mainly involved the use of chinstrap sensors and have been limited to the problem of whether or not safety helmets are being worn.
? In existing studies on sensor-based activity recognition, activities such as walking, standing, running, riding a bicycle, going up stairs, and going down stairs have been classified.
? In the existing studies on user activity recognition via three-axis accelerometer sensors, the determinations were made using only the size of the data of the three-axis accelerometer sensor.
? These are the three main issues which motivates us for developing this project. The first step is to identify the helmet is wear or not. If helmet is wear then ignition will start otherwise it will remains off till helmet is not wear. For these we use FSR sensor. The second step is alcohol detection.
? The design also has pollution information gathering technology where the sensor records the „ppm? of various greenhouse gases and with respect to the location the information is updated on the cloud.
? Wearing a helmet reduces the risk and increases the chances of survival. A helmet is lined with polystyrene and, on hard impact absorbs the shock.
• The proposed system consists of a three-axis accelerometer sensor module attached to the safety helmet, a smartphone app for the worker wearing the helmet, and an on-site PC-based database platform that stores the data.
• The data are stored in the developed smartphone app, and Bluetooth is used for communication, the proposed system performs transmission by using Serial Port Profile (SPP) based on Bluetooth V 3.0.
• The RandomTree algorithm from WEKA to the proper wearing (On), improper wearing (Off), and non-wearing of the safety helmet proposed in this study according to the activities of the workers.
? A smart helmet is a type of protective headgear used by the rider which makes bike driving safer than before. The main purpose of this smart helmet to provide safety for rider.
? Through this paper we intend to present an improvement in existing bike helmet system with speed indication.
? System is made more efficient with addition of intelligence in term of artificial vision using micro controller techniques to estimate actual vehicle situation.
? To achieve this, the system can transmit the information in real time also system is very clever enough to provide information which bike getting high speed then GSM system send a message to government.
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