Online Segmentation of Human Motion for Automated Rehabilitation Exercise Analysis
Online Segmentation of Human Motion for Automated Rehabilitation Exercise Analysis
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DOI：
10.1109/tnsre.2013.2259640
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发表时间：
2014-01-01
影响因子：
4.9
通讯作者：
Kulic, Dana
中科院分区：
文献类型：
--
作者：
Lin, Jonathan Feng-Shun;Kulic, Dana
为了实现康复运动的自动分析，需要一种用于准确识别和分割运动重复的方法。本文提出了一种方法，用于在线，自动分割和识别的运动段从连续的时间序列数据的人体运动，获得从身体安装的惯性测量单元或从运动捕捉数据。所提出的方法使用两个阶段的识别和识别过程，基于速度特征和随机建模的每个运动被识别。在第一阶段中，基于速度特征（诸如速度峰值和零速度交叉）的特征序列来识别运动段候选。在第二阶段，隐马尔可夫模型被用来准确地识别段位置从所识别的候选人。所提出的方法是能够在线分割和识别，使交互式反馈康复应用。该方法在20名健康受试者和4名进行康复运动的康复患者身上进行了验证，使用用户特定模板的分割准确率为87%，使用用户独立模板的分割准确率为79%-83%。
To enable automated analysis of rehabilitation movements, an approach for accurately identifying and segmenting movement repetitions is required. This paper proposes an approach for online, automated segmentation and identification of movement segments from continuous time-series data of human movement, obtained from body-mounted inertial measurement units or from motion capture data. The proposed approach uses a two-stage identification and recognition process, based on velocity features and stochastic modeling of each motion to be identified. In the first stage, motion segment candidates are identified based on a characteristic sequence of velocity features such as velocity peaks and zero velocity crossings. In the second stage, hidden Markov models are used to accurately identify segment locations from the identified candidates. The proposed approach is capable of online segmentation and identification, enabling interactive feedback in rehabilitation applications. The approach is validated on 20 healthy subjects and four rehabilitation patients performing rehabilitation movements, achieving segmentation accuracy of 87% with user specific templates and 79%-83% accuracy with user-independent templates.