IEEE Trans Neural Syst Rehabil Eng. 2019 Apr;27(4):733-742. doi: 10.1109/TNSRE.2019.2904477. Epub 2019 Mar 12.
This paper proposes a novel approach for online, individualized gait analysis, based on an adaptive periodic model of any gait signal. The proposed method learns a model of the gait cycle during online measurement, using a continuous representation that can adapt to inter- and intra-personal variability by creating an individualized model. Once the algorithm has converged to the input signal, key gait events can be identified based on the estimated gait phase and amplitude. The approach is implemented and tested on retirement home resident 6 min walk (6MW) data using wearable accelerometers at the ankle. The proposed approach converges within approximately four gait cycles and achieves 3% error in detecting initial swing events.11 An early version of this work was presented in [1]. A more extensive description of related work and an extended method, including optimization of learning rates, were added to this paper. Further, this paper applies and evaluates the method to a new and much larger gait dataset taken from older adults who each have a variety of medical conditions. Therefore, the experimental protocol was also updated and the results are entirely novel.
本文提出了一种新的在线个体化步态分析方法，基于任何步态信号的自适应周期模型。该方法在在线测量过程中使用连续表示来学习步态周期模型，通过创建个体化模型来适应个体间和个体内的变异性。一旦算法收敛到输入信号，就可以根据估计的步态相位和幅度来识别关键步态事件。该方法使用可穿戴加速度计在脚踝处对退休人员的 6 分钟步行（6MW）数据进行了实现和测试。该方法在大约四个步态周期内收敛，并在检测初始摆动事件方面实现了 3%的误差。[1] 中介绍了该方法的早期版本。本文对相关工作进行了更广泛的描述，并扩展了该方法，包括学习率的优化。此外，本文还将该方法应用于从患有各种疾病的老年人那里采集的新的、更大的步态数据集，并对其进行了评估。因此，实验方案也进行了更新，结果完全是新的。
