School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
Sensors (Basel). 2022 Jul 28;22(15):5632. doi: 10.3390/s22155632.
Identifying the massage techniques of the masseuse is a prerequisite for guiding robotic massage. It is difficult to recognize multiple consecutive massage maps with a time series for current human action recognition algorithms. To solve the problem, a method combining a convolutional neural network, long-term neural network, and attention mechanism is proposed to identify the massage techniques in this paper. First, the pressure distribution massage map is collected by a massage glove, and the data are enhanced by the conditional variational auto-encoder. Then, the features of the massage map group in the spatial domain and timing domain are extracted through the convolutional neural network and the long- and short-term memory neural network, respectively. The attention mechanism is introduced into the neural network, giving each massage map a different weight value to enhance the network extraction of data features. Finally, the massage haptic dataset is collected by a massage data acquisition system. The experimental results show that a classification accuracy of 100% is achieved. The results demonstrate that the proposed method could identify sequential massage maps, improve the network overfitting phenomenon, and enhance the network generalization ability effectively.
识别按摩师的按摩技术是指导机器人按摩的前提。当前的人体动作识别算法很难识别具有时间序列的多个连续的按摩图谱。为了解决这个问题，本文提出了一种结合卷积神经网络、长短期神经网络和注意力机制的方法来识别按摩技术。首先，通过按摩手套采集压力分布按摩图谱，并通过条件变分自编码器对数据进行增强。然后，通过卷积神经网络和长短时记忆神经网络分别提取按摩图谱组在空间域和时间域的特征。将注意力机制引入神经网络中，为每个按摩图谱赋予不同的权重值，以增强网络对数据特征的提取。最后，通过按摩数据采集系统采集按摩触觉数据集。实验结果表明，分类准确率达到 100%。结果表明，所提出的方法能够识别连续的按摩图谱，改善网络的过拟合现象，有效提高网络的泛化能力。
