  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.
