Weakly Supervised Multi-Task Representation Learning for Human Activity Analysis Using Wearables
This addresses the need for more efficient and integrated analysis of wearable sensor data in applications such as health monitoring, though it is incremental in combining existing multi-task and representation learning ideas.
The paper tackles the problem of analyzing sensor data from wearables for multiple tasks like activity recognition and person identification by proposing a weakly supervised multi-output siamese network that learns separate representation spaces for each aspect, enabling simultaneous task handling and outperforming single-task methods in many cases.
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.