论文标题
Dynimp：通过感官和时间相关性的可穿戴感应数据的动态插补
DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness
论文作者
论文摘要
在可穿戴的传感应用中，不可避免地要进行不规则采样或部分缺失，这对任何下游应用都带来了挑战。可穿戴数据的一个独特方面是它是时间序列数据，并且每个通道都可以与另一个通道相关，例如X，Y，Z Z轴的加速度计。我们认为，传统方法很少使用数据的两个时段动力学以及来自不同传感器的功能的相关性。我们提出了一个称为Dynimp的模型，以处理沿特征轴最近的邻居处理不同的时间点的缺失，然后将数据馈入基于LSTM的DeNoising AutoCododer，该自动编码器可以沿时间轴重建丢失。我们在极端缺失方案（$> 50 \％$丢失率）上实验模型，该模型尚未在可穿戴数据中进行广泛测试。我们在活动识别方面的实验表明，该方法可以利用相关传感器的多模式特征，并从历史序列动力学中学习以在极端缺失下重建数据。
In wearable sensing applications, data is inevitable to be irregularly sampled or partially missing, which pose challenges for any downstream application. An unique aspect of wearable data is that it is time-series data and each channel can be correlated to another one, such as x, y, z axis of accelerometer. We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors. We propose a model, termed as DynImp, to handle different time point's missingness with nearest neighbors along feature axis and then feeding the data into a LSTM-based denoising autoencoder which can reconstruct missingness along the time axis. We experiment the model on the extreme missingness scenario ($>50\%$ missing rate) which has not been widely tested in wearable data. Our experiments on activity recognition show that the method can exploit the multi-modality features from related sensors and also learn from history time-series dynamics to reconstruct the data under extreme missingness.
