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Related Experiment Video

Updated: May 28, 2026

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

Published on: March 14, 2017

Recognition of Gait Alterations Induced by Alcohol-Impairment Simulation Goggles Using Smartphone Accelerometer

Paweł Marciniak¹, Mariusz Zubert¹

¹Department of Microelectronics and Computer Science, Lodz University of Technology, 93-005 Lodz, Poland.

Sensors (Basel, Switzerland)

|May 27, 2026

Summary

This study explored using smartphone inertial data to detect gait changes from simulated visual impairment. Findings show smartphones can aid in low-stakes gait analysis for educational or screening purposes.

Keywords:

CNN FVG LSTM alcohol goggles drunk goggles gait classification self-attention smartphone sensing wearable inertial sensors

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

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Last Updated: May 28, 2026

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Published on: March 14, 2017

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Area of Science:

Biomedical Engineering
Human-Computer Interaction
Data Science

Background:

Identifying impairment for safety-critical tasks is challenging.
Unobtrusive sensing technologies like smartphones offer potential solutions.
Gait analysis using inertial data can reveal functional impairments.

Purpose of the Study:

To assess the feasibility of using smartphone inertial data to characterize gait disturbances under simulated visual impairment.
To develop and evaluate deep learning models for classifying gait alterations.
To provide an open-access dataset for advancing gait analysis research.

Main Methods:

Participants walked under normal and simulated visual impairment conditions (alcohol goggles).
Inertial data (accelerometer, gyroscope) were collected using smartphones.
Deep learning models (CNN, BiLSTM, attention) were applied for gait classification.
A subject-dependent evaluation protocol was used.

Main Results:

An outdoor experiment yielded more distinct gait patterns than indoor trials.
The best deep learning model achieved 71.4% accuracy and 71.5% F1-score in distinguishing gait patterns.
Accelerometer data alone showed promising classification performance.
Results indicate feasibility for exploratory, low-stakes assessment.

Conclusions:

Smartphones are viable tools for exploratory gait impairment screening.
The study provides a valuable dataset and benchmark for future research.
Methodological advancements in inertial sensor-based gait analysis are facilitated.