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

The reliable identification of impairment relevant to safety-critical activities remains a significant challenge for public safety, motivating the exploration of unobtrusive and widely accessible sensing technologies. This study examines the viability of utilising inertial data acquired from consumer-grade smartphones to characterise gait disturbances associated with simulated visual impairment. The study simulates intoxication-related effects using alcohol-impairment goggles and does not involve the measurement of real alcohol intoxication. Two supervised experimental protocols were conducted in which participants traversed predefined walking routes under normal conditions and while wearing alcohol-impairment simulation goggles representing five manufacturer-declared blood alcohol concentration (BAC)-related goggle conditions plus a no-goggles control condition. An initial indoor trial, conducted in a structured corridor environment, yielded limited discrimination of gait dynamics due to strong spatial and visual stabilisation cues. To address this limitation, a subsequent outdoor experiment was conducted along a 100 m path lacking prominent visual reference points, resulting in motion patterns that more closely reflect unconstrained, real-world locomotion. Tri-axial accelerometer and gyroscope signals were recorded using smartphones, followed by artefact removal, segmentation, and standardisation to ensure inter-trial comparability. The resulting curated dataset comprises 290,919 multi-channel samples derived from 96 walking trials involving 16 participants and is released as an openly accessible resource to support further research in gait analysis and classification of gait alterations associated with simulated impairment. Model evaluation was performed using an 80/20 train-test split conducted within each traversal, with training and test windows originating from the same participant and walking session. Consequently, the reported results reflect within-subject performance instead of subject-independent generalisation. Multiple deep learning architectures combining convolutional feature extraction, bidirectional long short-term memory layers, and self-attention mechanisms were systematically evaluated. Using a subject-dependent evaluation protocol, the best-performing architecture achieved an accuracy of 71.4% and a weighted F1-score of 71.5% in distinguishing gait patterns associated with different levels of simulated visual impairment. The best-performing architectures yielded classification performance consistent with exploratory, low-stakes assessment of gait alterations associated with simulated visual impairment, using accelerometer data alone. These findings illustrate the feasibility of using smartphones as auxiliary tools for exploratory, low-stakes screening or educational applications and contribute a publicly released dataset and benchmark results to facilitate methodological advancement in inertial sensor-based gait impairment analysis.