ArticleJournal of neuroengineering and rehabilitation2022
Automatic ML-based vestibular gait classification: examining the effects of IMU placement and gait task selection.
Article in Journal of neuroengineering and rehabilitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 16 citations in OpenAlex.
- Assessment of dynamic stability and identification of key tasks, inertial sensors, and parameters in patients with bilateral and unilateral vestibulopathy: investigation in a semi-standardized environment.Journal of neuroengineering and rehabilitation · 2026Article
- Wearable Sensor-Based Gait Analysis in Benign Paroxysmal Positional Vertigo: Quantitative Assessment of Residual Dizziness Using the φ-Bonacci Framework.Life (Basel, Switzerland) · 2026Article
- Characterization of head movement patterns in patients with bilateral and unilateral vestibulopathy during functional mobility tasks.Frontiers in neuroscience · 2026Article
- Automatic multi-IMU-based deep learning evaluation of intensity during static standing balance training exercises.Journal of neuroengineering and rehabilitation · 2025Article
- A deep learning pipeline for detecting vestibular schwannoma patients with unilateral vestibular loss based on kinematic data.Scientific reports · 2025Article
- Inertial data of daily living tasks in bilateral and unilateral vestibulopathy patients and controls.Scientific data · 2025Article
- Protocol for assessing functional impairments in patients with unilateral and bilateral vestibulopathy: a novel approach to evaluate the impact of vestibular loss in daily life setting.Frontiers in neurology · 2025Article
- Measurement of Functional Use in Upper Extremity Prosthetic Devices Using Wearable Sensors and Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Gait as a vital sign: integrating wearables and AI into vestibular and balance medicine.Frontiers in neurologyReview
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Authors and funding
4 authors at 2 institutions in 1 country.
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Abstract
backgroundVestibular deficits can impair an individual's ability to maintain postural and/or gaze stability. Characterizing gait abnormalities among individuals affected by vestibular deficits could help identify patients at high risk of falling and inform rehabilitation programs. Commonly used gait assessment tools rely on simple measures such as timing and visual observations of path deviations by clinicians. These simple measures may not capture subtle changes in gait kinematics. Therefore, we investigated the use of wearable inertial measurement units (IMUs) and machine learning (ML) approaches to automatically discriminate between gait patterns of individuals with vestibular deficits and age-matched controls. The goal of this study was to examine the effects of IMU placement and gait task selection on the performance of automatic vestibular gait classifiers.
methodsThirty study participants (15 with vestibular deficits and 15 age-matched controls) participated in a single-session gait study during which they performed seven gait tasks while donning a full-body set of IMUs. Classification performance was reported in terms of area under the receiver operating characteristic curve (AUROC) scores for Random Forest models trained on data from each IMU placement for each gait task.
resultsSeveral models were able to classify vestibular gait better than random (AUROC > 0.5), but their performance varied according to IMU placement and gait task selection. Results indicated that a single IMU placed on the left arm when walking with eyes closed resulted in the highest AUROC score for a single IMU (AUROC = 0.88 [0.84, 0.89]). Feature permutation results indicated that participants with vestibular deficits reduced their arm swing compared to age-matched controls while they walked with eyes closed.
conclusionsThese findings highlighted differences in upper extremity kinematics during walking with eyes closed that were characteristic of vestibular deficits and showed evidence of the discriminative ability of IMU-based automated screening for vestibular deficits. Further research should explore the mechanisms driving arm swing differences in the vestibular population.
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