论文标题
Cromosim：基于深度学习的跨模式惯性测量模拟器
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator
论文作者
论文摘要
随着可穿戴设备的流行，惯性测量单元（IMU）数据已用于监测和评估人类活动性识别（HAR）等人类流动性。针对这些任务的培训深神经网络（DNN）模型需要大量标记的数据，这些数据很难在不受控制的环境中获取。为了减轻数据稀缺问题，我们设计了CromoSim，这是一种跨模式传感器模拟器，该模拟器模拟了来自运动捕获系统或单眼RGB摄像机的高保真虚拟IMU传感器数据。它利用皮肤多人线性模型（SMPL）进行3D身体姿势和形状表示，从而从任意的身体位置进行模拟。由于测量噪声，校准误差，遮挡和其他建模伪像到IMU数据，训练了DNN模型，以从3D SMPL体三晶石中学习功能映射，从3D SMPL体三晶石中学习。我们评估了Cromosim模拟数据的保真度及其在各种HAR数据集上的数据增强方面的效用。广泛的实验结果表明，在HAR任务中，提出的模型比基线方法提高了6.7％。
With the prevalence of wearable devices, inertial measurement unit (IMU) data has been utilized in monitoring and assessment of human mobility such as human activity recognition (HAR). Training deep neural network (DNN) models for these tasks require a large amount of labeled data, which are hard to acquire in uncontrolled environments. To mitigate the data scarcity problem, we design CROMOSim, a cross-modality sensor simulator that simulates high fidelity virtual IMU sensor data from motion capture systems or monocular RGB cameras. It utilizes a skinned multi-person linear model (SMPL) for 3D body pose and shape representations, to enable simulation from arbitrary on-body positions. A DNN model is trained to learn the functional mapping from imperfect trajectory estimations in a 3D SMPL body tri-mesh due to measurement noise, calibration errors, occlusion and other modeling artifacts, to IMU data. We evaluate the fidelity of CROMOSim simulated data and its utility in data augmentation on various HAR datasets. Extensive experiment results show that the proposed model achieves a 6.7% improvement over baseline methods in a HAR task.
