Unsupervised Embedding Learning for Human Activity Recognition Using Wearable Sensor Data
This addresses the challenge of activity recognition in ubiquitous computing for users of wearable devices, but it is incremental as it builds on existing unsupervised methods.
The paper tackles the problem of recognizing human activities from unlabeled wearable sensor data by proposing an unsupervised embedding learning approach that projects activities into a space where similar ones are close together, and experiments on three benchmark datasets show it improves clustering performance compared to applying techniques directly to the original data.
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.