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Cost-Effective Train Presence Detection and Alerting Using Resource-Constrained Devices.

Dimitrios Zorbas1, Maral Baizhuminova1, Dnislam Urazayev1

  • 1School of Engineering & Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
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This study introduces a low-cost, portable system for early train detection using vibration data and linear regression on microcontrollers. It enables real-time alerts for railway safety in remote areas.

Area of Science:

  • Railway Engineering
  • Embedded Systems
  • Machine Learning

Background:

  • Early train detection is crucial for railway personnel safety, especially in remote areas lacking fixed infrastructure.
  • Existing solutions often require expensive, high-power sensors and compute platforms, limiting their deployment.

Purpose of the Study:

  • To develop a lightweight, low-cost, and portable train detection framework for resource-constrained microcontrollers.
  • To enable almost real-time train arrival prediction using vibration data and machine learning.

Main Methods:

  • Utilized low-cost accelerometers to collect vibration data.
  • Implemented a Linear Regression (LR) model for train arrival prediction.
  • Developed a parallel-processing framework for data collection, ML inference, and wireless communication on microcontrollers with limited RAM.
Keywords:
LoRaMachine Learningedge computinglow-powerrailways

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  • Used LoRa for kilometer-range wireless communication.

    • Main Results:

    • The system achieves decision-making (data preprocessing and ML prediction) in under 10 ms.
    • Field tests demonstrated detection of approaching trains 15 seconds in advance with no false negatives.
    • The system exhibited a small number of explainable false positives.
    • Power analysis indicated over 6 days of operation on a 10 Ah battery, with potential for months using wake-on-vibration.

    Conclusions:

    • The proposed system offers a feasible, low-cost solution for early train detection on embedded systems.
    • It enhances railway safety in remote locations by providing timely alerts.
    • The framework's efficiency and low power consumption make it suitable for long-term, autonomous operation.