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Updated: Jun 19, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Discriminating Between Marijuana and Alcohol Gait Impairments Using Tile CNN With TICA Pooling.

Ruojun Li¹, Samuel Chibuoyim Uche¹, Emmanuel Agu¹

¹Worcester Polytechnic Institute Worcester MA 01609 USA.

IEEE Open Journal of Engineering in Medicine and Biology

|November 12, 2025

Summary

This summary is machine-generated.

Machine learning can identify alcohol or marijuana impairment from smartphone gait data. The novel MariaGait deep learning model accurately distinguishes between these substance impairments using sensor data.

Keywords:

Accelerometer alcohol impairment deep learning gyroscope marijuana impairment

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Area of Science:

Biomedical Engineering
Data Science
Neuroscience

Background:

Gait analysis is a potential biomarker for substance impairment.
Distinguishing between alcohol and marijuana impairment using gait is challenging.

Purpose of the Study:

To develop and validate a machine learning model, MariaGait, for discriminating alcohol from marijuana impairment using smartphone gait data.
To assess the efficacy of deep learning techniques, specifically Convolutional Neural Networks (CNNs), for this classification task.

Main Methods:

Utilized time-series smartphone accelerometer and gyroscope data from impaired gait datasets.
Encoded gait data into Gramian Angular Field (GAF) images.
Employed a tiled CNN with TICA pooling, pre-trained on sober gait samples to handle imbalanced data.

Main Results:

MariaGait achieved 94.61% accuracy, 88.61% F1 score, and 94.33% ROC AUC.
Outperformed baseline models including MLP, LSTM, Random Forest, and SVM.
Demonstrated high performance in classifying alcohol versus marijuana impairment.

Conclusions:

MariaGait offers a practical and non-invasive method for identifying the type of substance impairment.
Smartphone sensor data combined with deep learning can effectively differentiate alcohol and marijuana intoxication based on gait patterns.