Jarchi Delaram, Wong Charence, Kwasnicki Richard Mark, Heller Ben, Tew Garry A, Yang Guang-Zhong
IEEE Trans Biomed Eng. 2014 Apr;61(4):1261-73. doi: 10.1109/TBME.2014.2299772.
This paper presents a new approach to gait analysis and parameter estimation from a single miniaturized ear-worn sensor embedded with a triaxial accelerometer. Singular spectrum analysis combined with the longest common subsequence algorithm has been used as a basis for gait parameter estimation. It incorporates information from all axes of the accelerometer to estimate parameters including swing, stance, and stride times. Rather than only using local features of the raw signals, the periodicity of the signals is also taken into account. The hypotheses tested by this study include: 1) how accurate is the ear-worn sensor in terms of gait parameter extraction compared to the use of an instrumented treadmill; 2) does the ear-worn sensor provide a feasible option for assessment and quantification of gait pattern changes. Key gait events for normal subjects such as heel contact and toe off are validated with a high-speed camera, as well as a force-plate instrumented treadmill. Ten healthy adults walked for 20 min on a treadmill with an increasing incline of 2% every 2 min. The upper and lower limits of the absolute errors using 95% confidence intervals for swing, stance, and stride times were obtained as 35.5 ±3.99 ms, 36.9 ±3.84 ms, and 17.9 ±2.29 ms, respectively.
本文提出了一种基于嵌入三轴加速度计的单个小型化耳戴式传感器进行步态分析和参数估计的新方法。奇异谱分析结合最长公共子序列算法已被用作步态参数估计的基础。它整合了来自加速度计所有轴的信息，以估计包括摆动、站立和步幅时间等参数。该方法不仅利用原始信号的局部特征，还考虑了信号的周期性。本研究检验的假设包括：1）与使用装有仪器的跑步机相比，耳戴式传感器在步态参数提取方面的准确性如何；2）耳戴式传感器是否为评估和量化步态模式变化提供了可行的选择。正常受试者的关键步态事件，如足跟接触和足趾离地，通过高速摄像机以及装有测力板的跑步机进行了验证。十名健康成年人在跑步机上行走20分钟，每2分钟坡度增加2%。摆动、站立和步幅时间的绝对误差在95%置信区间下的上限和下限分别为35.5±3.99毫秒、36.9±3.84毫秒和17.9±2.29毫秒。
