Enhancing Two-Wheeler Rider Safety with Helmet-Based Head Movement Monitoring and IoT Integration Using Safe Angle Formula

  • Rahat Tamzid Department of Artificial Intelligence, University of Verona,37129 Verona VR, Italy
  • Ehfaz Faisal Mahee Department of Computer Science and Engineering, Bangladesh University of Business and Technology (BUBT), Rupnagar, Mirpur-2, Dhaka-1216, Bangladesh
  • Md. Anwar Hussen Wadud Department of Computer Science and Engineering, Sunamganj Science and Technology University, Sunamganj-3000, Bangladesh
  • Anichur Rahman National Institute of Textile Engineering and Research (NITER) https://orcid.org/0000-0002-2691-1748
  • Miraz Ahmed Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Savar, Dhaka-1216, Bangladesh
  • Md Ashikur Rahman Department of Computer Science and Engineering, Bangladesh University of Business and Technology (BUBT), Rupnagar, Mirpur-2, Dhaka-1216, Bangladesh
  • Abu Saleh Musa Miah Department of CSE, Bangladesh Army University of Science and Technology (BAUST), Nilphamari, Bangladesh
  • Fahim Al Farid Centre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya 63100, Selangor, Malaysia
  • Sarina Mansor Centre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya 63100, Selangor, Malaysia
Keywords: Two-Wheeler Safety, Smart Helmet System, IoT-Based Accident Prevention, Helmet Safety System, Rider Awareness

Abstract

Two-wheeler safety is crucial due to riders’ vulnerability, yet existing mechanisms often overlook unintentional head movements that compromise control and awareness. Current Internet of Things-based safety systems focus on collision detection, braking assistance, and rider posture monitoring, but often neglect the risks posed by unintended head movements, which can lead to loss of control and harmful accidents. To address this gap, we proposed a helmet-mounted system that calculates a safe head movement angle using a specialized safe angle formula based on motorcycle speed. Our system employs integrated gyroscopic sensors and Global Positioning System data to continuously monitor head orientation and speed, providing real-time alerts. The bike sensor records speed and Z-axis angles to determine bike tilt, while the helmet sensor captures the riders’ head angle. This data is transmitted to a microcontroller in the bike unit, which calculates the angle difference and sets a speed-based safe threshold. If head movement surpasses this calculated threshold, the system triggers audible and visual alarms. By ensuring that riders maintain a safe head position while riding, this system minimizes distractions that could lead to collisions, particularly at high speeds. The proposed solution enhances two-wheeler safety by preventing accidents caused by sudden or excessive head movements, integrating seamlessly with existing motorcycle safety mechanisms. Real-world testing and validation of our system demonstrate its effectiveness in reducing the likelihood of accidents caused by unintentional head movements. This innovation highlights the importance of maintaining safe head orientation and suggests integration into broader safety strategies, advanced rider training, and protective measures. The introduction of the safe angle formula positions this system as a potential benchmark for future motorcycle safety technologies.
Published
2026-02-23
How to Cite
Rahat Tamzid, Ehfaz Faisal Mahee, Md. Anwar Hussen Wadud, Rahman, A., Miraz Ahmed, Md Ashikur Rahman, Abu Saleh Musa Miah, Fahim Al Farid, & Sarina Mansor. (2026). Enhancing Two-Wheeler Rider Safety with Helmet-Based Head Movement Monitoring and IoT Integration Using Safe Angle Formula. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-2668
Section
Research Articles