A. Al-Jawad, M. R. Adame, M. Romanovas, M. Hobert, W. Maetzler, M. Trächtler, K. Möller, Y. Manoli
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The developed algorithm is based on the Dynamic Time Warping (DTW) for multi-dimensional time series and has been applied with the augmented feature for detection and duration assessment of turn state transitions, while a 1-dimensional DTW is used to detect the sit-to-stand and stand-to-sit phases. The feature set is a 3-dimensional vector which consists of the angular velocity, derived angle and features from Linear Discriminant Analysis (LDA). The algorithm was tested on 10 healthy individuals and 20 patients with PD (10 patients with early and late disease phases respectively). 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引用次数: 23
摘要
TUG (Timed Up and Go)是一种临床测试,广泛用于测量平衡和活动能力,例如帕金森病(PD)。测试包括一系列功能活动,即:坐立、3米步行、180°转身、后退、再转身、坐在椅子上。同时使用秒表对测试进行评分,测量PD患者完成测试所需的时间。在这里,这项工作展示了一个仪器化的拖船,使用一个可穿戴的惯性传感器单元连接在人的下背部。与人工目测和秒表评估相比,该方法实现了评估过程的自动化。该算法基于多维时间序列的动态时间翘曲(DTW),并结合增强特征用于旋转状态转换的检测和持续时间评估,而一维DTW用于检测从坐到站和从站到坐的阶段。特征集是由角速度、导出角度和线性判别分析(LDA)的特征组成的三维向量。该算法在10名健康个体和20名PD患者(分别为10名疾病早期和晚期患者)上进行了测试。试验结果表明,所开发的技术能够成功地提取出TUG试验中坐转站、转弯和站坐转换的时间信息。
Using multi-dimensional dynamic time warping for TUG test instrumentation with inertial sensors
The Timed Up and Go (TUG) is a clinical test used widely to measure balance and mobility, e.g. in Parkinson's disease (PD). The test includes a sequence of functional activities, namely: sit-to-stand, 3-meters walk, 180° turn, walk back, another turn and sit on the chair. Meanwhile the stopwatch is used to score the test by measuring the time which the patients with PD need to perform the test. Here, the work presents an instrumented TUG using a wearable inertial sensor unit attached on the lower back of the person. The approach is used to automate the process of assessment compared with the manual evaluation by using visual observation and a stopwatch. The developed algorithm is based on the Dynamic Time Warping (DTW) for multi-dimensional time series and has been applied with the augmented feature for detection and duration assessment of turn state transitions, while a 1-dimensional DTW is used to detect the sit-to-stand and stand-to-sit phases. The feature set is a 3-dimensional vector which consists of the angular velocity, derived angle and features from Linear Discriminant Analysis (LDA). The algorithm was tested on 10 healthy individuals and 20 patients with PD (10 patients with early and late disease phases respectively). The test demonstrates that the developed technique can successfully extract the time information of the sit-to-stand, both turns and stand-to-sit transitions in the TUG test.