Weakly Supervised Multi-Task Representation Learning for Human Activity Analysis Using Wearables

TL;DR

This paper introduces a weakly supervised multi-task learning approach using a siamese network to analyze wearable sensor data, enabling simultaneous recognition of multiple aspects like activity and identity, often outperforming single-task methods.

Contribution

The paper presents a novel multi-output siamese network framework that learns multiple representations for wearable data, allowing multi-aspect analysis and improved performance over traditional single-task models.

Findings

Effective multi-aspect clustering of sensor data.

Outperforms single-task methods in various scenarios.

Scalable to include additional tasks and partial data.

Abstract

Sensor data streams from wearable devices and smart environments are widely studied in areas like human activity recognition (HAR), person identification, or health monitoring. However, most of the previous works in activity and sensor stream analysis have been focusing on one aspect of the data, e.g. only recognizing the type of the activity or only identifying the person who performed the activity. We instead propose an approach that uses a weakly supervised multi-output siamese network that learns to map the data into multiple representation spaces, where each representation space focuses on one aspect of the data. The representation vectors of the data samples are positioned in the space such that the data with the same semantic meaning in that aspect are closely located to each other. Therefore, as demonstrated with a set of experiments, the trained model can provide metrics for clustering data based on multiple aspects, allowing it to address multiple tasks simultaneously and even to outperform single task supervised methods in many situations. In addition, further experiments are presented that in more detail analyze the effect of the architecture and of using multiple tasks within this framework, that investigate the scalability of the model to include additional tasks, and that demonstrate the ability of the framework to combine data for which only partial relationship information with respect to the target tasks is available.