DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness

TL;DR

DynImp is a novel method for imputing missing data in wearable sensor time-series by leveraging feature relatedness and temporal dynamics, especially effective under extreme missingness conditions.

Contribution

It introduces a combined approach using feature relatedness and temporal modeling with LSTM autoencoders for robust data imputation in wearable sensing.

Findings

Effective at >50% missing data scenarios

Utilizes multi-sensor feature correlations

Improves activity recognition accuracy

Abstract

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.