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The embedded sensors in widely used smartphones and other wearable devicesmake the data of human activities more accessible. However, recognizingdifferent human activities from the wearable sensor data remains a challengingresearch problem in ubiquitous computing. One of the reasons is that themajority of the acquired data has no labels. In this paper, we present anunsupervised approach, which is based on the nature of human activity, toproject the human activities into an embedding space in which similaractivities will be located closely together. Using this, subsequent clusteringalgorithms can benefit from the embeddings, forming behavior clusters thatrepresent the distinct activities performed by a person. Results of experimentson three labeled benchmark datasets demonstrate the effectiveness of theframework and show that our approach can help the clustering algorithm achieveimproved performance in identifying and categorizing the underlying humanactivities compared to unsupervised techniques applied directly to the originaldata set.

Unsupervised Embedding Learning for Human Activity Recognition Using Wearable Sensor Data