Deep learning advancements have revolutionized scalable classification in many domains including computer vision. However, when it comes to wearable-based classification and domain adaptation, existing computer vision-based deep learning architectures and pretrained models trained on thousands of labeled images for months fall short. This is primarily because wearable sensor data necessitates sensor-specific preprocessing, architectural modification, and extensive data collection. To overcome these challenges, researchers have proposed encoding of wearable temporal sensor data in images using recurrent plots. In this paper, we present a novel modified-recurrent plot-based image representation that seamlessly integrates both temporal and frequency domain information. Our approach incorporates an efficient Fourier transform-based frequency domain angular difference estimation scheme in conjunction with the existing temporal recurrent plot image. Furthermore, we employ mixup image augmentation to enhance the representation. We evaluate the proposed method using accelerometer-based activity recognition data and a pretrained ResNet model, and demonstrate its superior performance compared to existing approaches.

翻译：深度学习进步彻底改变了许多领域（包括计算机视觉）的可扩展分类。然而，在基于可穿戴设备的分类和域适应方面，现有的基于计算机视觉的深度学习架构以及在数千张标注图像上训练数月的预训练模型表现不足。这主要是因为可穿戴传感器数据需要特定于传感器的预处理、架构修改和大量数据收集。为克服这些挑战，研究人员已提出利用递归图将可穿戴时间序列传感器数据编码为图像。本文提出一种新颖的基于改进递归图的图像表示方法，该表示无缝融合了时域和频域信息。我们的方法结合了基于高效傅里叶变换的频域角度差估计方案与现有的时域递归图图像。此外，我们采用混合图像增强来提升表示质量。我们基于加速度计的活动中识别数据及预训练ResNet模型评估所提方法，并证明其性能优于现有方法。