Robust Feature Representation Using Multi-Task Learning for Human Activity Recognition

Author:

Azadi Behrooz1ORCID,Haslgrübler Michael1ORCID,Anzengruber-Tanase Bernhard1,Sopidis Georgios1ORCID,Ferscha Alois2

Affiliation:

1. Pro2Future GmbH, Altenberger Strasse 69, 4040 Linz, Austria

2. Institute of Pervasive Computing, Johannes Kepler University, Altenberger Straße 69, 4040 Linz, Austria

Abstract

Learning underlying patterns from sensory data is crucial in the Human Activity Recognition (HAR) task to avoid poor generalization when coping with unseen data. A key solution to such an issue is representation learning, which becomes essential when input signals contain activities with similar patterns or when patterns generated by different subjects for the same activity vary. To address these issues, we seek a solution to increase generalization by learning the underlying factors of each sensor signal. We develop a novel multi-channel asymmetric auto-encoder to recreate input signals precisely and extract indicative unsupervised futures. Further, we investigate the role of various activation functions in signal reconstruction to ensure the model preserves the patterns of each activity in the output. Our main contribution is that we propose a multi-task learning model to enhance representation learning through shared layers between signal reconstruction and the HAR task to improve the robustness of the model in coping with users not included in the training phase. The proposed model learns shared features between different tasks that are indeed the underlying factors of each input signal. We validate our multi-task learning model using several publicly available HAR datasets, UCI-HAR, MHealth, PAMAP2, and USC-HAD, and an in-house alpine skiing dataset collected in the wild, where our model achieved 99%, 99%, 95%, 88%, and 92% accuracy. Our proposed method shows consistent performance and good generalization on all the datasets compared to the state of the art.

Funder

FFG

Austrian Federal Ministry for Digital and Economic Affairs and of the Provinces of Upper Austria and Styria

Austrian Research Promotion Agency FFG

Publisher

MDPI AG

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