Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning

• URL: http://arxiv.org/abs/2209.00990v1
• Date: Fri, 26 Aug 2022 22:47:18 GMT
• Title: Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning
• Authors: Setareh Rahimi Taghanaki, Michael Rainbow and Ali Etemad
• Abstract summary: We propose a self-supervised learning solution for human activity recognition with smartphone accelerometer data. We develop a model that learns strong representations from accelerometer signals, while reducing the model's reliance on class labels. We evaluate the performance of the proposed solution on three datasets, namely MotionSense, HAPT, and HHAR.
• Score: 16.457778420360537
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this paper, we propose a self-supervised learning solution for human activity recognition with smartphone accelerometer data. We aim to develop a model that learns strong representations from accelerometer signals, in order to perform robust human activity classification, while reducing the model's reliance on class labels. Specifically, we intend to enable cross-dataset transfer learning such that our network pre-trained on a particular dataset can perform effective activity classification on other datasets (successive to a small amount of fine-tuning). To tackle this problem, we design our solution with the intention of learning as much information from the accelerometer signals as possible. As a result, we design two separate pipelines, one that learns the data in time-frequency domain, and the other in time-domain alone. In order to address the issues mentioned above in regards to cross-dataset transfer learning, we use self-supervised contrastive learning to train each of these streams. Next, each stream is fine-tuned for final classification, and eventually the two are fused to provide the final results. We evaluate the performance of the proposed solution on three datasets, namely MotionSense, HAPT, and HHAR, and demonstrate that our solution outperforms prior works in this field. We further evaluate the performance of the method in learning generalized features, by using MobiAct dataset for pre-training and the remaining three datasets for the downstream classification task, and show that the proposed solution achieves better performance in comparison with other self-supervised methods in cross-dataset transfer learning.

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