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arXiv · 2608.13054

Tracing Methamphetamine abuse in under-treatment drivers: How biomechanical and oculomotor features help detect at-risk drivers?

Hamed Salmanzadeh·Alireza Mortezapour·Iman Tahbazzadeh Moghaddam·Farshid Ipackchi·Payam Abedinzadeh·Samira Teimoori
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Abstract

While the detrimental impacts of driving under the influence of stimulants such as methamphetamine are well-documented, the driving performance of individuals currently under-treatment has received considerably less attention. This study compared the behavior of individuals with a history of stimulant abuse (across two distinct treatment phases) with a control group of healthy drivers using a driving simulator. Oculomotor and biomechanical data were continuously collected via an eye-tracker and a Kinect sensor, respectively. These parameters were utilized to train a K-Nearest Neighbors (KNN) classification model designed to detect high-risk behavioral patterns in drivers undergoing methamphetamine rehabilitation. Through the evaluation of various feature combinations and neighborhood configurations, the optimized model successfully discriminated between normal drivers and those with a history of abuse with an accuracy of 90%. Detecting at-risk drivers through technologies embedded in Advanced Driver Assistance Systems (ADAS) by continuously monitoring physiological and behavioral parameters, facilitates a proactive safety strategy. Issuing real-time alerts to the driver, passengers, and external monitoring networks can ultimately mitigate the risk of traffic collisions.

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Hamed Salmanzadeh, Alireza Mortezapour, Iman Tahbazzadeh Moghaddam, Farshid Ipackchi, Payam Abedinzadeh, Samira Teimoori. 2026-08-13. Tracing Methamphetamine abuse in under-treatment drivers: How biomechanical and oculomotor features help detect at-risk drivers?. https://arxiv.org/abs/2608.13054

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