Perancangan Helm Pintar dengan Fitur Keselamatan Deteksi Kantuk Berbasis NodeMCU dan Accelerometer

Abstract

Driving safety is a major focus given the high number of accidents caused by drowsy drivers. This article discusses the design of a smart helmet that detects drowsiness to improve rider safety. The smart helmet integrates technology with drowsiness detection to reduce the risk of accidents and provide a safer driving experience. The system uses NodeMCU and MPU6050 Accelerometer to monitor head movement, activating an alarm if the head moves more than 5 degrees, which indicates drowsiness or loss of focus. It is expected that the risk of accidents due to drowsiness can be significantly reduced with this approach. The test results show that the system is able to effectively detect unusual head movements and provide a quick alarm response, thus improving driving safety as expected. In the context of this measurement, the lower error values of 0.70% and 1.18% indicate that the MPU6050 sensor provides more accurate results in measuring the angle against a given reference angle. The angle measurement results between the reference and the MPU6050 sensor show that the value obtained from the sensor is not much different from the reference angle. Although there is a slight difference, the accuracy of the MPU6050 is still reliable for practical purposes, showing consistent performance and close to the actual value. This indicates that the MPU6050 sensor is capable of providing quite precise results, so it can be used as an effective angle measuring device in various applications. The integration of this sensor into smart helmets enables early detection of signs of drowsiness, which can then activate automatic alerts to improve driver safety. Test results also demonstrated the helmet's ability to monitor and send real-time data to ThingSpeak, providing easy-to-understand visualizations, historical data storage, and automatic notifications when signs of drowsiness are detected.