论文标题
IMAIR：使用IMU信号的图像表示形式的空气写入识别框架
ImAiR: Airwriting Recognition framework using Image Representation of IMU Signals
论文作者
论文摘要
空气写作识别的问题集中在识别通过手指在自由空间中移动写的字母。这是一种手势识别，字典对应于特定语言的字母。特别是，使用来自腕上戴腕部设备的传感器数据的空气写入识别可以用作人机交互（HCI）应用程序的用户输入媒介。使用这种腕上戴的设备对空气轨迹的识别受到限制，在文献中构成了当前工作的基础。在本文中，我们首先编码从手腕上的可穿戴惯性测量单元（IMU）获得的时间序列数据来提出一个空中写入识别框架，然后使用基于深度学习的模型来识别书面字母。使用不同技术（例如自我相似性矩阵（SSM），Gramian Angular Field（GAF）和Markov Transition Transition Field（MTF），将IMU中3轴加速度计和陀螺仪记录的信号编码为图像，以形成两组3个通道图像。然后将它们馈送到两个单独的分类模型中，并根据从两个模型获得的类别条件概率的平均值进行信函预测。已经使用了几种用于图像分类的标准模型架构，例如Resnet，Densenet，Vggnet，Alexnet和Googlenet的变体。在两个公开可用数据集上进行的实验证明了拟议策略的功效。我们实施的代码将在https://github.com/ayushayt/imair上提供。
The problem of Airwriting Recognition is focused on identifying letters written by movement of finger in free space. It is a type of gesture recognition where the dictionary corresponds to letters in a specific language. In particular, airwriting recognition using sensor data from wrist-worn devices can be used as a medium of user input for applications in Human-Computer Interaction (HCI). Recognition of in-air trajectories using such wrist-worn devices is limited in literature and forms the basis of the current work. In this paper, we propose an airwriting recognition framework by first encoding the time-series data obtained from a wearable Inertial Measurement Unit (IMU) on the wrist as images and then utilizing deep learning-based models for identifying the written alphabets. The signals recorded from 3-axis accelerometer and gyroscope in IMU are encoded as images using different techniques such as Self Similarity Matrix (SSM), Gramian Angular Field (GAF) and Markov Transition Field (MTF) to form two sets of 3-channel images. These are then fed to two separate classification models and letter prediction is made based on an average of the class conditional probabilities obtained from the two models. Several standard model architectures for image classification such as variants of ResNet, DenseNet, VGGNet, AlexNet and GoogleNet have been utilized. Experiments performed on two publicly available datasets demonstrate the efficacy of the proposed strategy. The code for our implementation will be made available at https://github.com/ayushayt/ImAiR.
