Field Sobriety Test Digitization With Computer Vision Impairment Scoring

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Solution Overview

Problem

Standardized Field Sobriety Tests (SFSTs) are subject to human error and environmental factors, leading to uncertain and potentially inadmissible results in determining impairment.

Innovation Solution

A system utilizing computer vision and machine learning to track body landmarks during SFSTs, analyze motion and audio data, and generate objective impairment scores through machine learning models trained on various data sources, including medical information and expert feedback.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If standardized field sobriety tests are administered manually by officers, then the tests can be conducted in the field, but the results are subject to human error and subjective interpretation

Engineering Contradiction:

Improvefield administration capabilityVSAvoidtest result accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent replaces the manual mechanical observation and interpretation system with an automated computer vision and machine learning system. Cameras capture video data of the subject performing SFSTs, and machine learning models automatically analyze the motion data to determine impairment indicators, eliminating human subjective interpretation while maintaining field administration capability

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an intermediary automated analysis system between the subject's performance and the impairment determination. The system uses video capture devices, motion tracking algorithms, and machine learning models as intermediaries to objectively measure and interpret test results, removing the direct human observation that causes subjectivity

Inventive Principle:

Principle #24Intermediary (Mediator)

2Device complexity

If manual observation is used for SFST results, then the process is simple, but environmental factors and officer distractions affect reliability

Engineering Contradiction:

Improvetest administration simplicityVSAvoidtest result consistency

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The patent replaces the simple but unreliable manual observation system with an automated video-based measurement system. The system uses cameras to capture test performance, automatically tracks body landmarks, and applies machine learning models to determine impairment, maintaining operational simplicity while significantly improving reliability by eliminating environmental distractions and officer subjectivity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Loss of time

If subjective interpretation is used for SFST results, then the administration process is quick, but the results lack credibility in court

Engineering Contradiction:

Improvetest administration timeVSAvoidresult objectivity

Core Design Contradiction:

Loss of timeVSMeasurement precision

Solution Approach 1:

The patent replaces subjective human interpretation with automated machine learning-based analysis. The system quickly processes video data through computer vision algorithms and machine learning models to generate objective impairment determinations, maintaining rapid administration while providing court-admissible objective evidence through automated measurement and analysis

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250226110A1Systems and methods for field sobriety test digitizing and analyzing

Publication Date: 2025.07.10 MUIR SEAN

AI summary

Standard field sobriety tests (SFST) are administered by a computing system that includes hardware and software configured to track body movement by receiving motion data and tracking one or more body landmarks including head, eyes, pupils, hands, feet, center of mass, and others to determine SFST clues associated with impairment. Machine learning techniques are trained to determine the captured motion presents SFST clues and determine a confidence level that the subject is impaired. The system can also record and store data associated with the SFST for later analysis or playback.