Unsupervised Embedding Learning for Human Activity Recognition Using Wearable Sensor Data

TL;DR

This paper introduces an unsupervised embedding method for human activity recognition from wearable sensor data, enabling better clustering and categorization of activities without labeled data.

Contribution

The paper proposes a novel unsupervised embedding approach that improves activity clustering accuracy compared to traditional methods.

Findings

Enhanced clustering performance on benchmark datasets

Effective separation of different human activities in embedding space

Outperforms existing unsupervised techniques

Abstract

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.