Design and Implementation of a Wearable IoT System Based on Time of Flight (ToF) Sensor for Personal Safety Support in Commuter Line Transportation

Abstract

The development of Internet of Things (IoT) technology has enabled the implementation of intelligent systems to enhance personal safety in various environments, including public transportation. Commuter Line (KRL) transportation often experiences high passenger density, which may increase the risk of physical collisions and unsafe proximity between passengers. This study aims to design and implement a wearable IoT-based system utilizing a Time of Flight (ToF) sensor for real-time object proximity detection. The proposed system integrates a VL53L0X ToF sensor, an ESP32 microcontroller, a vibration motor, a buzzer, and the Blynk platform for monitoring and notification purposes. A threshold-based detection method combined with time-duration analysis was applied to identify potentially unsafe proximity conditions. Experimental results show that the VL53L0X sensor was able to detect objects within a distance range of 20–60 cm with measurement errors ranging from 0% to 8%, depending on the object characteristics. The system successfully activated warning notifications when objects were detected within the predefined threshold distance, achieving an average response time of 2.02 seconds. False alarm testing demonstrated stable system performance without unintended notification activation when objects were outside the detection threshold. Furthermore, environmental noise testing indicated that sensor performance remained stable under noise levels of 70–90 dB. These results demonstrate that the proposed wearable IoT system can serve as a practical personal safety support solution for users in crowded and dynamic commuter transportation environments.