Internet of Things-Based Smart Helmet with Accident Identification and Logistics Monitoring for Delivery Riders

Abstract

The study developed a smart helmet prototype that prioritizes delivery rider safety and facilitates logistical communication for small businesses. This was achieved with a smart helmet, utilizing IoT equipped with crash detection and logistics monitoring functions. Various sensors such as an accelerometer and alcohol sensors were calibrated to improve accuracy and minimize errors. A mobile application was introduced to coordinate delivery logistics and track the location of drivers. The system had 90% accuracy in distinguishing real accidents, and it also had drunk driver detection with an accuracy of 88%. An ATTM336H GPS module was used for geolocation tracking, and a mobile application built with Bubble.io and Firebase was integrated into the helmet to send alerts the shop owners of Roger’s Top Silog House who provided delivery drivers as participants for the study, who gave us positive feedback indicating that our smart helmet performed very well and exceeded expectations.

Keywords

• Bubble.io
• crash detection
• Firebase
• internet of things
• logistics monitoring
• smart helmet