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Ensiyeh Keshtkaran

Overview of thesis

Through an industry engagement PhD with Powerfleet®, this research explores the development of an innovative system for detecting alcohol intoxication in drivers using AI and machine learning applied to facial video data. The study introduces a pioneering dataset, the first to feature RGB video recordings of facial expressions at varying levels of alcohol intoxication during simulated driving. It aims to validate the feasibility of detecting intoxication through facial cues captured by standard cameras with an aim to create a cost-effective, reliable solution for drink driver detection that eliminates the need for complex sensor-based systems, offering practical applications to enhance road safety.

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Research

Research Interests

  • AI and computer vision applications in industry
  • Driver Safety Solutions and Driver Monitoring System
  • AIoT

Other work

  • Jan 2023 – Nov 2024, Peer Advisor for Higher by Research Students (ECU) – Perth, Australia
  • Mar 2021 – Dec 2024, PhD Candidate (AI and Machine Learning) – Perth, Australia
  • Sep 2018 - Mar 2021, Technical Support Manager for fleet management, driver safety and vehicle tracking solutions (Australasia and Middle East) – Perth, Australia
  • May 2015 - Sep 2018, Technical Project Coordinator for fleet management, driver safety and vehicle tracking solutions (Australia) – Perth, Australia
  • Aug 2012 - Apr 2015, Product Support Specialist for in-vehicle monitoring systems (IVMS) focused on driver safety (Middle East), UAE, Dubai
  • Jan 2010 - Aug 2012, Product Support Specialist for Automated and Semi-Automated Medical Devices, UAE, Dubai
  • Jun 2009 - Aug 2009, Biomedical Support Engineer for medical laboratory equipment, UAE, Dubai

Supervisors


Contact

Ensiyeh Keshtkaran
PhD Student
Centre of Artificial Intelligence and Machine Learning
School of Science
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