Automatic ML-based vestibular gait classification: examining the effects of IMU placement and gait task selection

Journal of NeuroEngineering and Rehabilitation

Research

Jenna Wiens¹  Kathleen H. Sienko²  Safa Jabri²  Wendy Carender³ 

[1] Department of Electrical Engineering and Computer Science, University of Michigan, 48109, Ann Arbor, MI, USA;Department of Mechanical Engineering, University of Michigan, 48109, Ann Arbor, MI, USA;Department of Otolaryngology, Michigan Medicine, 48109, Ann Arbor, MI, USA;

关键词: Balance;    Gait;    Vestibular disorders;    Wearable sensors;    Machine learning;    Classification;   

DOI  :  10.1186/s12984-022-01099-z

 received in 2022-05-03, accepted in 2022-10-25,  发布年份 2022

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2022

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