Ayachi Fouaz S, Nguyen Hung P, Lavigne-Pelletier Catherine, Goubault Etienne, Boissy Patrick, Duval Christian
Département des sciences de l'activité physique, Université du Québec à Montréal, Canada.
Physiol Meas. 2016 Mar;37(3):442-61. doi: 10.1088/0967-3334/37/3/442. Epub 2016 Feb 25.
A recent trend in human motion capture is the use of inertial measurement units (IMUs) for monitoring and performance evaluation of mobility in the natural living environment. Although the use of such systems have grown significantly, the development of methods and algorithms to process IMU data for clinical purposes is still limited. The aim of this work is to develop algorithms based on wavelet transform and discrete-time detection of events for the automatic segmentation of tasks related activities of daily living (ADL) from body worn IMUs. Seven healthy older adults (73 ± 4 years old) performed 10 ADL tasks in a simulated apartment during trials of different durations (3, 4, and 5 min). They wore a suit (Synertial UK Ltd IGS-180) comprised of 17 IMUs positioned strategically on body segments to capture full body motion. The proposed method automatically detected the number of template waveforms (representing each movement separately) using discrete wavelet transform (DWT) and discrete-time detection of events based on angular velocity, linear acceleration and 3D orientation data of pertinent IMUs. The sensitivity (Se.) and specificity (Sp.) of detection for the proposed method was established using time stamps of10tasks obtained from visual segmentation of each trial using the video records and the avatar provided by the system's software. At first, we identified six pertinent sensors that were strongly associated to different activities (at most two sensors/task) that allowed detection of tasks with high accuracy. The proposed algorithm exhibited significant global accuracy (N events = 1999, Se. = 97.5%, Sp. = 94%), despite the variation in the occurrences of the performed tasks (free living). The Se. varied from 94% to 100% for all the detected ADL tasks and Sp. ranged from 90% to 100% with the worst Sp. = 85 and 87% for Release_mid (reaching for object held just beyond reach at chest height) and Turning_Left tasks, respectively. This study demonstrated that DWT in conjunction with a nonlinear transform and auto-adaptive thresholding process for decision rules are highly efficient in detecting and segmenting tasks performed during free-living activities. This study also helped to determine the optimal number of sensors, and their location to detect such activities. This work lays the foundation for the automatic assessment of mobility performance within the segmented signals, as well as potentially helps differentiate populations based on their mobility patterns and symptomatology.
人体运动捕捉的一个最新趋势是使用惯性测量单元（IMU）来监测自然生活环境中的活动并评估其表现。尽管此类系统的使用已显著增加，但为临床目的处理IMU数据的方法和算法的开发仍然有限。这项工作的目的是开发基于小波变换和事件离散时间检测的算法，用于从佩戴在身体上的IMU自动分割与日常生活活动（ADL）相关的任务。七名健康的老年人（73 ± 4岁）在不同时长（3、4和5分钟）的试验中，于模拟公寓内执行了10项ADL任务。他们穿着一套由17个IMU组成的套装（英国Synertial有限公司的IGS - 180），这些IMU被战略性地放置在身体各部位以捕捉全身运动。所提出的方法使用离散小波变换（DWT）以及基于相关IMU的角速度、线性加速度和3D方向数据的事件离散时间检测，自动检测模板波形的数量（分别代表每个动作）。使用从每个试验的视频记录和系统软件提供的虚拟模型的视觉分割中获得的10项任务的时间戳，确定了所提出方法的检测灵敏度（Se.）和特异性（Sp.）。首先，我们确定了六个与不同活动密切相关的相关传感器（每个任务最多两个传感器），这使得能够高精度地检测任务。尽管执行任务的发生情况存在差异（自由生活），但所提出的算法仍表现出显著的全局准确性（N个事件 = 1999，Se. = 97.5%，Sp. = 94%）。对于所有检测到的ADL任务，Se.在94%至100%之间变化，Sp.在90%至100%之间，其中Release_mid（伸手去拿刚好够不着的胸部高度的物体）和向左转身任务的最差Sp.分别为85%和87%。这项研究表明，结合非线性变换和用于决策规则的自适应阈值处理的DWT在检测和分割自由生活活动期间执行的任务方面非常高效。这项研究还有助于确定检测此类活动的最佳传感器数量及其位置。这项工作为在分割信号内自动评估运动表现奠定了基础，并有可能有助于根据其运动模式和症状对人群进行区分。
