Unsupervised Embedding Learning for Human Activity Recognition Using
  Wearable Sensor Data
Taoran Sheng Manfred Huber
The embedded sensors in widely used smartphones and other wearable devices
make the data of human activities more accessible. However, recognizing
different human activities from the wearable sensor data remains a challenging
research problem in ubiquitous computing. One of the reasons is that the
majority of the acquired data has no labels. In this paper, we present an
unsupervised approach, which is based on the nature of human activity, to
project the human activities into an embedding space in which similar
activities will be located closely together. Using this, subsequent clustering
algorithms can benefit from the embeddings, forming behavior clusters that
represent the distinct activities performed by a person. Results of experiments
on three labeled benchmark datasets demonstrate the effectiveness of the
framework and show that our approach can help the clustering algorithm achieve
improved performance in identifying and categorizing the underlying human
activities compared to unsupervised techniques applied directly to the original
data set.