  Automated Human Activity Recognition has long been a problem of great
interest in human-centered and ubiquitous computing. In the last years, a
plethora of supervised learning algorithms based on deep neural networks has
been suggested to address this problem using various modalities. While every
modality has its own limitations, there is one common challenge. Namely,
supervised learning requires vast amounts of annotated data which is
practically hard to collect. In this paper, we benefit from the self-supervised
learning paradigm (SSL) that is typically used to learn deep feature
representations from unlabeled data. Moreover, we upgrade a contrastive SSL
framework, namely SimCLR, widely used in various applications by introducing a
temporal feature alignment procedure for Human Activity Recognition.
Specifically, we propose integrating a dynamic time warping (DTW) algorithm in
a latent space to force features to be aligned in a temporal dimension.
Extensive experiments have been conducted for the unimodal scenario with
inertial modality as well as in multimodal settings using inertial and skeleton
data. According to the obtained results, the proposed approach has a great
potential in learning robust feature representations compared to the recent SSL
baselines, and clearly outperforms supervised models in semi-supervised
learning. The code for the unimodal case is available via the following link:
https://github.com/bulatkh/csshar_tfa.
