Impairment Screening Utilizing Biophysical Measurements and Machine Learning Algorithms.

Area of Science:

• Forensic Science
• Computer Science
• Traffic Safety

Background:

• Drug recognition expert (DRE) officers use Standardized Field Sobriety Tests (SFSTs) like Horizontal Gaze Nystagmus (HGN), Walk and Turn (WAT), and One Leg Stand (OLS) to assess driver impairment.
• Current SFSTs rely on trained officers, but decisions can be subjective and face legal scrutiny.
• Automated impairment detection offers potential for objective, court-admissible evidence.

Purpose of the Study:

• To develop and implement a novel automated system for detecting drug impairment in drivers.
• To enhance the accuracy and objectivity of impairment assessments currently performed by DRE officers.
• To explore the use of data analysis and machine learning for reliable impairment detection.

Main Methods:

• Utilized data analysis and machine learning algorithms.
• Implemented a new method for automated impairment detection.
• Collected data from a comprehensive suite of tests performed on 34 participants.

Main Results:

• Successfully developed and implemented an automated impairment detection method.
• The system leverages machine learning to analyze data from various tests.
• Demonstrated a new approach to objective driver impairment assessment.

Conclusions:

• Automated impairment detection systems can assist officers in making more accurate decisions.
• The proposed method has the potential to reduce subjectivity in sobriety evaluations.
• This technology could provide objective, court-admissible evidence for impaired driving cases.