Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
Department of Otolaryngology, Michigan Medicine, Ann Arbor, MI, 48109, USA.
J Neuroeng Rehabil. 2022 Dec 1;19(1):132. doi: 10.1186/s12984-022-01099-z.
Vestibular 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.
Thirty 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.
Several 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.
These 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.
前庭功能障碍会损害个体维持姿势和/或凝视稳定性的能力。描述前庭功能障碍患者的步态异常有助于识别易跌倒的高风险患者，并为康复计划提供信息。常用的步态评估工具依赖于临床医生对路径偏差的计时和视觉观察等简单措施。这些简单的措施可能无法捕捉到步态运动学的细微变化。因此，我们研究了使用可穿戴惯性测量单元（IMU）和机器学习（ML）方法自动区分前庭功能障碍患者和年龄匹配的对照组的步态模式。本研究的目的是检查 IMU 放置和步态任务选择对自动前庭步态分类器性能的影响。
30 名研究参与者（15 名前庭功能障碍患者和 15 名年龄匹配的对照组）参加了单次步态研究，在此期间，他们穿着全身 IMU 套件完成了七项步态任务。使用随机森林模型对来自每个 IMU 放置位置的每个步态任务的数据进行训练，报告分类性能的指标是接收器操作特征曲线（AUROC）下的面积分数。
几个模型能够比随机分类更好地对前庭步态进行分类（AUROC>0.5），但它们的性能根据 IMU 放置和步态任务选择而有所不同。结果表明，当闭眼行走时，将单个 IMU 放置在左臂上可获得单个 IMU 的最高 AUROC 分数（AUROC=0.88[0.84，0.89]）。特征置换结果表明，与年龄匹配的对照组相比，患有前庭功能障碍的参与者在闭眼行走时减少了手臂摆动。
这些发现强调了闭眼行走时上肢运动学的差异，这些差异是前庭功能障碍的特征，并证明了基于 IMU 的自动筛查前庭功能障碍的区分能力。进一步的研究应该探索驱动前庭人群手臂摆动差异的机制。
