The neural-network-based human activity recognition (HAR) technique is being increasingly used for activity recognition in virtual reality (VR) users. The major issue of a such technique is the collection large-scale training datasets which are key for deriving a robust recognition model. However, collecting large-scale data is a costly and time-consuming process. Furthermore, increasing the number of activities to be classified will require a much larger number of training datasets. Since training the model with a sparse dataset can only provide limited features to recognition models, it can cause problems such as overfitting and suboptimal results. In this paper, we present a data augmentation technique named gravity control-based augmentation (GCDA) to alleviate the sparse data problem by generating new training data based on the existing data. The benefits of the symmetrical structure of the data are that it increased the number of data while preserving the properties of the data. The core concept of GCDA is two-fold: (1) decomposing the acceleration data obtained from the inertial measurement unit (IMU) into zero-gravity acceleration and gravitational acceleration, and augmenting them separately, and (2) exploiting gravity as a directional feature and controlling it to augment training datasets. Through the comparative evaluations, we validated that the application of GCDA to training datasets showed a larger improvement in classification accuracy ($96.39\%$) compared to the typical data augmentation methods ($92.29\%$) applied and those that did not apply the augmentation method ($85.21\%$).

中文翻译：

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基于重力控制的数据增强技术，用于改善VR用户活动识别

基于神经网络的人类活动识别（HAR）技术正越来越多地用于虚拟现实（VR）用户的活动识别。这种技术的主要问题是收集大规模训练数据集，这对于得出鲁棒的识别模型至关重要。但是，收集大规模数据是一个昂贵且耗时的过程。此外，增加要分类的活动数量将需要大量的训练数据集。由于使用稀疏数据集训练模型只能为识别模型提供有限的功能，因此会引起诸如过度拟合和结果欠佳的问题。在本文中，我们提出了一种数据增强技术，称为基于重力控制的增强（GCDA），可通过基于现有数据生成新的训练数据来缓解稀疏数据问题。数据对称结构的好处在于，它在保留数据属性的同时增加了数据数量。GCDA的核心概念有两个方面：（1）将从惯性测量单元（IMU）获得的加速度数据分解为零重力加速度和重力加速度，并分别进行增强，以及（2）利用重力作为方向特征。并控制它以增强训练数据集。通过比较评估，我们验证了GCDA在训练数据集上的应用显示出分类准确度的较大提高（（1）将从惯性测量单元（IMU）获得的加速度数据分解为零重力加速度和重力加速度，并分别进行增强，以及（2）利用重力作为方向特征并对其进行控制以增强训练数据集。通过比较评估，我们验证了GCDA在训练数据集上的应用显示出分类准确度的较大提高（（1）将从惯性测量单元（IMU）获得的加速度数据分解为零重力加速度和重力加速度，并分别进行增强，以及（2）利用重力作为方向特征并对其进行控制以增强训练数据集。通过比较评估，我们验证了GCDA在训练数据集上的应用显示出分类准确度的较大提高（$96.39％$）与典型的数据扩充方法（$92.29％$）和未采用增强方法的广告素材（$85.21％$）。