Advanced Institute of Manufacturing with High-tech Innovations, Center for Innovative Research on Aging Society (CIRAS) and Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan.
Sensors (Basel). 2019 Feb 20;19(4):887. doi: 10.3390/s19040887.
In this paper, a multipath convolutional neural network (MP-CNN) is proposed for rehabilitation exercise recognition using sensor data. It consists of two novel components: a dynamic convolutional neural network (D-CNN) and a state transition probability CNN (S-CNN). In the D-CNN, Gaussian mixture models (GMMs) are exploited to capture the distribution of sensor data for the body movements of the physical rehabilitation exercises. Then, the input signals and the GMMs are screened into different segments. These form multiple paths in the CNN. The S-CNN uses a modified Lempel⁻Ziv⁻Welch (LZW) algorithm to extract the transition probabilities of hidden states as discriminate features of different movements. Then, the D-CNN and the S-CNN are combined to build the MP-CNN. To evaluate the rehabilitation exercise, a special evaluation matrix is proposed along with the deep learning classifier to learn the general feature representation for each class of rehabilitation exercise at different levels. Then, for any rehabilitation exercise, it can be classified by the deep learning model and compared to the learned best features. The distance to the best feature is used as the score for the evaluation. We demonstrate our method with our collected dataset and several activity recognition datasets. The classification results are superior when compared to those obtained using other deep learning models, and the evaluation scores are effective for practical applications.
本文提出了一种使用传感器数据进行康复运动识别的多路径卷积神经网络（MP-CNN）。它由两个新组件组成：动态卷积神经网络（D-CNN）和状态转移概率卷积神经网络（S-CNN）。在 D-CNN 中，利用高斯混合模型（GMM）来捕获物理康复运动的传感器数据的分布。然后，输入信号和 GMM 被筛选到不同的段中。这些在 CNN 中形成了多个路径。S-CNN 使用修改后的莱姆普⁻齐夫⁻韦尔奇（Lempel-Ziv-Welch，LZW）算法提取隐藏状态的转移概率作为不同运动的判别特征。然后，将 D-CNN 和 S-CNN 结合起来构建 MP-CNN。为了评估康复运动，我们提出了一个特殊的评估矩阵，以及深度学习分类器，以学习每个康复运动类别的一般特征表示，以及不同水平的特征表示。然后，对于任何康复运动，都可以通过深度学习模型进行分类，并与学习到的最佳特征进行比较。距离最佳特征的距离用作评估的分数。我们使用我们收集的数据集和几个活动识别数据集来演示我们的方法。与使用其他深度学习模型获得的分类结果相比，我们的方法具有更好的分类效果，并且评估分数对于实际应用是有效的。
