Deep learning methods are successfully used in applications pertaining to
ubiquitous computing, health, and well-being. Specifically, the area of human
activity recognition (HAR) is primarily transformed by the convolutional and
recurrent neural networks, thanks to their ability to learn semantic
representations from raw input. However, to extract generalizable features,
massive amounts of well-curated data are required, which is a notoriously
challenging task; hindered by privacy issues, and annotation costs. Therefore,
unsupervised representation learning is of prime importance to leverage the
vast amount of unlabeled data produced by smart devices. In this work, we
propose a novel self-supervised technique for feature learning from sensory
data that does not require access to any form of semantic labels. We learn a
multi-task temporal convolutional network to recognize transformations applied
on an input signal. By exploiting these transformations, we demonstrate that
simple auxiliary tasks of the binary classification result in a strong
supervisory signal for extracting useful features for the downstream task. We
extensively evaluate the proposed approach on several publicly available
datasets for smartphone-based HAR in unsupervised, semi-supervised, and
transfer learning settings. Our method achieves performance levels superior to
or comparable with fully-supervised networks, and it performs significantly
better than autoencoders. Notably, for the semi-supervised case, the
self-supervised features substantially boost the detection rate by attaining a
kappa score between 0.7-0.8 with only 10 labeled examples per class. We get
similar impressive performance even if the features are transferred from a
different data source. While this paper focuses on HAR as the application
domain, the proposed technique is general and could be applied to a wide
variety of problems in other areas.

本文提出了一种基于自监督技术的特征学习方法，应用于无形标签的传感器数据。该方法可以应用于人类活动识别等领域，可以通过转化识别任务提供监督信号提取有价值的特征，并在智能手机环境下的无人监督、半监督和迁移学习的情况下实现了与完全监督网络相当甚至更好的性能。该技术可被广泛应用于其他领域。