Lee Kyoung-Soub, Chae Sanghoon, Park Hyung-Soon
IEEE Int Conf Rehabil Robot. 2019 Jun;2019:583-586. doi: 10.1109/ICORR.2019.8779475.
This paper analyses the time-window size required to achieve the highest accuracy of the convolutional neural network (CNN) in classifying periodic upper limb rehabilitation. To classify real-time motions by using CNN-based human activity recognition (HAR), data must be segmented using a time window. In particular, for the repetitive rehabilitation tasks, the relationship between the period of the repetitive tasks and optimal size of the time window must be analyzed. In this study, we constructed a data-collection system composed of a smartwatch and smartphone. Five upper limb rehabilitation motions were measured for various periods to classify the rehabilitation motions for a particular time-window size. 5-fold cross-validation technique was used to compare the performance. The results showed that the size of the time-window that maximizes the performance of CNN-based HAR is affected by the size and period of the sample used.
本文分析了在对周期性上肢康复进行分类时，卷积神经网络（CNN）实现最高准确率所需的时间窗口大小。为了通过基于CNN的人体活动识别（HAR）对实时运动进行分类，必须使用时间窗口对数据进行分割。特别是对于重复性康复任务，必须分析重复任务的周期与时间窗口最佳大小之间的关系。在本研究中，我们构建了一个由智能手表和智能手机组成的数据收集系统。针对不同时间段测量了五种上肢康复动作，以对特定时间窗口大小的康复动作进行分类。使用5折交叉验证技术来比较性能。结果表明，使基于CNN的HAR性能最大化的时间窗口大小受所用样本的大小和周期影响。
