A Novel Distribution-Embedded Neural Network for Sensor-Based Activity
Recognition
Abstract
Feature-engineering-based machine learning models and deep learning models have been explored
for wearable-sensor-based human activity recognition. For both types of methods, one crucial
research issue is how to extract proper features
from the partitioned segments of multivariate sensor readings. Existing methods have different
drawbacks: 1) feature-engineering-based methods
are able to extract meaningful features, such as statistical or structural information underlying the segments, but usually require manual designs of features for different applications, which is time consuming, and 2) deep learning models are able to
learn temporal and/or spatial features from the sensor data automatically, but fail to capture statistical information. In this paper, we propose a novel
deep learning model to automatically learn meaningful features including statistical features, temporal features and spatial correlation features for
activity recognition in a unified framework. Extensive experiments are conducted on four datasets
to demonstrate the effectiveness of our proposed
method compared with state-of-the-art baselines.