Chiarello Mark, Lee Jeonghwan, Salinas Mandy, Hilsabeck Robin, Lewis-Peacock Jarrod, Sulzer James
Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX 78712 USA.
Department of Kinesiology and Health Education, University of Texas at Austin, Austin, TX 78712 USA.
IEEE Sens J. 2023 Feb;23(3):3079-3089. doi: 10.1109/jsen.2022.3227475. Epub 2022 Dec 12.
Early detection of Alzheimer's Disease and Related Disorders (ADRD) has been a focus of research with the hope that early intervention may improve clinical outcomes. The manifestation of motor impairment in early stages of ADRD has led to the inclusion of gait assessments including spatiotemporal parameters in clinical evaluations. This study aims to determine the effect of adding kinetic and kinematic gait features to classification of different levels of cognitive load in healthy individuals. A dual-task paradigm was used to simulate cognitive impairment in 40 healthy adults, with single-task walking trials representing normal, healthy gait. The Paced Auditory Serial Addition Task was administered at two different inter-stimulus intervals representing two levels of cognitive load in dual-task gait. We predicted that a richer dataset would improve classification accuracy relative to spatiotemporal parameters. Repeated Measures ANOVA showed significant changes in 15 different gait features across all three levels of cognitive load. We used three supervised machine learning algorithms to classify data points using a series of different gait feature sets with performance based on the area under the curve (AUC). Classification yielded 0.778 AUC across all three conditions (0.889 AUC Single vs. Dual) using kinematic and spatiotemporal features compared to 0.724 AUC using spatiotemporal features only (0.792 AUC Single vs. Dual). These data suggest that additional kinematic parameters improve classification performance. However, the benefit of measuring a wider set of parameters compared to their cost needs consideration. Further work will lead to a clinically viable ADRD detection classifier.
阿尔茨海默病及相关疾病（ADRD）的早期检测一直是研究的重点，人们希望早期干预可能会改善临床结果。ADRD早期阶段运动障碍的表现促使在临床评估中纳入包括时空参数在内的步态评估。本研究旨在确定在健康个体中，添加动力学和运动学步态特征对不同认知负荷水平分类的影响。采用双任务范式模拟40名健康成年人的认知障碍，单任务步行试验代表正常、健康的步态。在双任务步态中，以两种不同的刺激间隔进行听觉节拍连续加法任务，代表两种认知负荷水平。我们预测，相对于时空参数，更丰富的数据集将提高分类准确率。重复测量方差分析显示，在所有三个认知负荷水平上，15种不同的步态特征有显著变化。我们使用三种监督机器学习算法，使用一系列不同的步态特征集对数据点进行分类，性能基于曲线下面积（AUC）。与仅使用时空特征时的0.724 AUC（单任务与双任务的AUC为0.792）相比，使用运动学和时空特征时，在所有三种情况下的分类AUC为0.778（单任务与双任务的AUC为0.889）。这些数据表明，额外的运动学参数可提高分类性能。然而，与成本相比，测量更广泛参数集的益处需要考虑。进一步的工作将产生一个临床上可行的ADRD检测分类器。
