Applied Electronics Department, Roma Tre University, Italy.
Comput Biol Med. 2011 Mar;41(3):164-72. doi: 10.1016/j.compbiomed.2011.01.007. Epub 2011 Feb 4.
A new real-time implementation of a Dynamic Time Warping (DTW)-based classification scheme is presented here, and its performance evaluated on experimental data. Nine young adults were requested to perform instances of eight different purposeful movements described in the Wolf Motor Function Test, while wearing a three-axis accelerometer sensor placed on the inner forearm. Results include the correct recognition percentage, as compared to a classification scheme based on the traditional DTW measure, and the recognition percentage as a function of the time elapsed from the beginning of the performed movements. The Real-Time DTW basically performs with the same accuracy of the traditional DTW-based classification scheme (91.5% of correct recognition percentage), a figure that increases to 96.5% if the multidimensional scheme is adopted. Moreover, more than 60% of movements are correctly recognized before their end, thus setting the way for applications in rehabilitation and assistive technologies, where a real-time control scheme is able to interact with the user while the movement is being performed.
这里提出了一种新的基于动态时间规整（DTW）的分类方案的实时实现，并在实验数据上评估了其性能。要求九名年轻人在穿戴放置在内前臂上的三轴加速度计传感器的情况下，执行 Wolf 运动功能测试中描述的八种不同目的运动的实例。结果包括与基于传统 DTW 度量的分类方案相比的正确识别百分比，以及作为所执行运动开始后经过的时间的函数的识别百分比。实时 DTW 的性能基本上与传统的基于 DTW 的分类方案（91.5%的正确识别百分比）一样准确，如果采用多维方案，这一数字增加到 96.5%。此外，超过 60%的运动在结束前被正确识别，从而为康复和辅助技术中的应用铺平了道路，在这些应用中，实时控制方案能够在运动进行时与用户进行交互。
