Video2IMU:逼真的IMU功能和视频信号

Arttu Lämsä, Jaakko Tervonen, Jussi Liikka, Constantino Álvarez Casado, Miguel Bordallo L'opez
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引用次数: 4

摘要

基于可穿戴传感器数据的人类活动识别(HAR)可以识别不受约束环境中的运动或活动。HAR是一个具有挑战性的问题,因为它在不同学科之间表现出很大的差异。获得大量标记数据并不简单,因为可穿戴传感器信号不容易通过简单的人工检查进行标记。在我们的工作中,我们建议使用神经网络来生成真实的信号和使用人类活动单目视频的特征。我们展示了如何利用这些生成的特征和信号来训练HAR模型,该模型可以使用可穿戴传感器获得的信号来识别活动。为了证明我们方法的有效性,我们在为改善工业工作安全而创建的活动识别数据集上进行了实验。我们表明,我们的模型能够真实地生成虚拟传感器信号和特征,可用于训练HAR分类器,其性能与使用真实传感器数据训练的分类器相当。我们的研究结果可以使用可用的标记视频数据来训练HAR模型,以对来自可穿戴传感器的信号进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Video2IMU: Realistic IMU features and signals from videos
Human Activity Recognition (HAR) from wearable sensor data identifies movements or activities in unconstrained environments. HAR is a challenging problem as it presents great variability across subjects. Obtaining large amounts of labelled data is not straightforward, since wearable sensor signals are not easy to label upon simple human inspection. In our work, we propose the use of neural networks for the generation of realistic signals and features using human activity monocular videos. We show how these generated features and signals can be utilized, instead of their real counterparts, to train HAR models that can recognize activities using signals obtained with wearable sensors. To prove the validity of our methods, we perform experiments on an activity recognition dataset created for the improvement of industrial work safety. We show that our model is able to realistically generate virtual sensor signals and features usable to train a HAR classifier with comparable performance as the one trained using real sensor data. Our results enable the use of available, labeled video data for training HAR models to classify signals from wearable sensors.
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