Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands.
Sensors (Basel). 2018 Sep 6;18(9):2967. doi: 10.3390/s18092967.
Detection of human activities along with the associated context is of key importance for various application areas, including assisted living and well-being. To predict a user's context in the daily-life situation a system needs to learn from multimodal data that are often imbalanced, and noisy with missing values. The model is likely to encounter missing sensors in real-life conditions as well (such as a user not wearing a smartwatch) and it fails to infer the context if any of the modalities used for training are missing. In this paper, we propose a method based on an adversarial autoencoder for handling missing sensory features and synthesizing realistic samples. We empirically demonstrate the capability of our method in comparison with classical approaches for filling in missing values on a large-scale activity recognition dataset collected in-the-wild. We develop a fully-connected classification network by extending an encoder and systematically evaluate its multi-label classification performance when several modalities are missing. Furthermore, we show class-conditional artificial data generation and its visual and quantitative analysis on context classification task; representing a strong generative power of adversarial autoencoders.
人类活动及其相关上下文的检测对于各种应用领域（包括辅助生活和健康）至关重要。为了预测用户在日常生活情况下的上下文，系统需要从通常不平衡、嘈杂且存在缺失值的多模态数据中学习。在现实生活条件下，模型也可能会遇到缺失的传感器（例如，用户未佩戴智能手表），如果用于训练的模态之一缺失，则无法推断上下文。在本文中，我们提出了一种基于对抗自动编码器的方法，用于处理缺失的传感器特征并合成逼真的样本。我们通过在野外收集的大规模活动识别数据集上与经典方法进行比较，实证证明了我们方法的能力。我们通过扩展编码器来开发全连接分类网络，并系统地评估当多个模态缺失时的多标签分类性能。此外，我们展示了上下文分类任务上的条件类人工数据生成及其视觉和定量分析；代表对抗自动编码器具有强大的生成能力。
