There has been a resurgence of applications focused on Human Activity
Recognition (HAR) in smart homes, especially in the field of ambient
intelligence and assisted living technologies. However, such applications
present numerous significant challenges to any automated analysis system
operating in the real world, such as variability, sparsity, and noise in sensor
measurements. Although state-of-the-art HAR systems have made considerable
strides in addressing some of these challenges, they especially suffer from a
practical limitation: they require successful pre-segmentation of continuous
sensor data streams before automated recognition, i.e., they assume that an
oracle is present during deployment, which is capable of identifying time
windows of interest across discrete sensor events. To overcome this limitation,
we propose a novel graph-guided neural network approach that performs activity
recognition by learning explicit co-firing relationships between sensors. We
accomplish this by learning a more expressive graph structure representing the
sensor network in a smart home, in a data-driven manner. Our approach maps
discrete input sensor measurements to a feature space through the application
of attention mechanisms and hierarchical pooling of node embeddings. We
demonstrate the effectiveness of our proposed approach by conducting several
experiments on CASAS datasets, showing that the resulting graph-guided neural
network outperforms the state-of-the-art method for HAR in smart homes across
multiple datasets and by large margins. These results are promising because
they push HAR for smart homes closer to real-world applications.

通过学习传感器之间的显式共同触发关系，我们提出了一种新颖的图导向神经网络方法，该方法通过将离散的输入传感器测量映射到特征空间，应用注意机制和节点嵌入的分层汇聚，在 CASAS 数据集上进行实验证明了我们提出的方法在智能家居人体活动识别方面优于现有方法，并将其推向更接近真实世界应用的方向。