deepGesture: Deep learning-based gesture recognition scheme using motion sensors
deepGesture: Deep learning-based gesture recognition scheme using motion sensors
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DOI：
10.1016/j.displa.2018.08.001
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发表时间：
2018-12-01
期刊：
影响因子：
4.3
通讯作者：
Dogra, Debi P.
中科院分区：
文献类型：
--
作者：
Kim, Ji-Hae;Hong, Gwang-Soo;Dogra, Debi P.
最近智能手机和传感器技术的进步促进了手势识别的研究。这使得设计高效的手势界面变得容易。然而，通过手势进行人类活动识别（HAR）并不是微不足道的，因为每个人都可能做出不同的手势。在本文中，我们提出了一种基于陀螺仪和加速度计传感器的深度卷积和递归神经网络的手臂手势识别新方法——deepGesture算法。该方法使用四个深度卷积层对原始传感器数据进行自动特征学习。将卷积层的特征作为门控循环单元（GRU）的输入，该单元基于最先进的循环神经网络（RNN）结构来捕获长期依赖关系并对序列数据进行建模。该算法的输入数据是通过配备陀螺仪和加速度计传感器的腕式智能腕带设备提取运动序列数据获得的。数据最初被分割成固定长度的段。对分割后的数据进行标记并构建数据库。然后将标记的数据用于我们的学习算法中。为了验证该算法的适用性，进行了几个实验来衡量手势分类的准确性。与人类活动识别方法相比，我们的实验结果表明，我们提出的deepGesture算法可以将9种定义的手臂手势识别的平均fl分数提高6%。
Recent advancement in smart phones and sensor technology has promoted research in gesture recognition. This has made designing of efficient gesture interface easy. However, human activity recognition (HAR) through gestures is not trivial since each person may pose the same gesture differently. In this paper, we propose deepGesture algorithm, a new arm gesture recognition method based on gyroscope and accelerometer sensors using deep convolution and recurrent neural networks. This method uses four deep convolution layers to automate feature learning in raw sensor data. The features of the convolution layers are used as input of the gated recurrent unit (GRU) which is based on the state-of-the-art recurrent neural network (RNN) structure to capture long-term dependency and model sequential data. The input data of the proposed algorithm is obtained through motion sequence data extracted using a wrist-type smart band device equipped with gyroscope and accelerometer sensors. The data is initially segmented in fixed length segments. The segmented data is labeled and we construct the database. Then the labeled data is used in our learning algorithm. To verify the applicability of the algorithm, several experiments have been performed to measure the accuracy of gesture classification. Compared to the human activity recognition method, our experimental results show that the proposed deepGesture algorithm can increase the average Fl-score for recognition of nine defined arm gestures by 6%.