Transfer Learning for Human Activity Recognition in Logistics

在工业4.0的时代，人工活动仍然在物流部门占据主导地位。有关人类活动的发生和持续时间的详细信息对仓储效率以及整个供应链至关重要。由于人工评估在经济上是不合适的，人类活动识别(HAR)的方法获得了相关性。HAR是一种分类任务，用于从时间序列中识别人体运动，这些时间序列已经在智能家居、康复和健康支持等应用中使用。基于非侵入性和高度可靠的身体上设备的HAR具有特别重要的意义，因为这些设备在具有挑战性的场景中扩展了其潜力。训练分类器需要大量的数据，因为人类的运动高度可变和多样化，特别是在物流部门的多样化环境中。本项目的目标是开发一种方法，以避免为物流中的HAR创建和注释高质量的车载设备数据的巨大工作量。不同的物流场景将在配备了高精度光学运动捕捉(OMoCap)的参考场中复制。在此约束环境中，将同步捕获oMoCap和车身设备数据。所有场景的组合OMocap记录构成参考数据集。转移学习和零激发学习的方法将能够跨场景构成这样的参考数据集。将考虑机器学习的方法，特别是深度学习，用于处理时间序列和基于参考oMoCap数据集训练分类器。该分类器将允许对同步的车载设备数据进行自动注释。此外，还将考虑从原始oMoCap数据创建附加合成数据的方法。对自动注释和合成的车载设备数据进行训练的分类器的性能将通过将其与来自真实仓库的手动注释数据进行比较来检查。

In the age of the Industry 4.0, manual activities remain dominant in the logistics sector. Detailed information on the occurrence and duration of relevant human activities is crucial for warehousing efficiency and thus the entire supply chain. As manual assessment is economically inexpedient, methods of human activity recognition (HAR) gain relevance. HAR is a classification task for recognizing human movements from time-series that is already used in applications such as smart-homes, rehabilitation and health support. HAR based on non-invasive and highly reliable on-body devices is of special relevance, as these devices extends its potential in challenging scenarios. Training a classifier demands a large amount of data, as human movements are highly variable and diverse, in particular in the diverse environments of the logistics sector.The objective of this project is to develop a method for avoiding the tremendous effort for creating and annotating high quality on-body-devices data for HAR in logistics. Different logistics scenarios will be replicated in a reference field that is equipped with a highly accurate, optical-motion capturing (oMoCap). In this constraint environment, oMoCap and on-body device data will be captured synchronously. The combined oMocap recordings of all scenarios constitute a reference dataset. Methods of transfer- and zero-shot learning will enable to constitute such reference dataset across the scenarios. Methods of machine learning, especially, deep learning will be considered for processing time-series and for training a classifier based on the reference oMoCap-dataset. The classifier will allow for an automated annotation of the synchronized on-body devices data. Furthermore, methods for creating additional synthetic data from raw-oMoCap data will be considered. The performance of the classifier that is trained on the automatically annotated and synthetic on-body device data will be examined by comparing it to manually annotated data from a real warehouse.