# SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning

arXiv: https://arxiv.org/abs/2304.09530 · HTML: https://mlsanity.com/paper/2304.09530/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 14 of 14)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://mlsanity.com/paper/2202.03555/) (2022)
- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [Multi-task Self-Supervised Learning for Human Activity Detection](https://mlsanity.com/paper/1907.11879/) (2019)
- [Exploring Contrastive Learning in Human Activity Recognition for Healthcare](https://mlsanity.com/paper/2011.11542/) (2020)
- [Assessing the State of Self-Supervised Human Activity Recognition using Wearables](https://mlsanity.com/paper/2202.12938/) (2022)
- [Protecting Sensory Data against Sensitive Inferences](https://mlsanity.com/paper/1802.07802/) (2018)
- [Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks](https://mlsanity.com/paper/1903.12489/) (2019)
- [Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart Homes](https://mlsanity.com/paper/2111.12158/) (2021)
- [Personalized Semi-Supervised Federated Learning for Human Activity Recognition](https://mlsanity.com/paper/2104.08094/) (2021)
- [An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression](https://mlsanity.com/paper/1312.6965/) (2013)
- [Towards Deep Clustering of Human Activities from Wearables](https://mlsanity.com/paper/2008.01659/) (2020)

## Abstract (arXiv)

Supervised Deep Learning (DL) models are currently the leading approach for sensor-based Human Activity Recognition (HAR) on wearable and mobile devices. However, training them requires large amounts of labeled data whose collection is often time-consuming, expensive, and error-prone. At the same time, due to the intra- and inter-variability of activity execution, activity models should be personalized for each user. In this work, we propose SelfAct: a novel framework for HAR combining self-supervised and active learning to mitigate these problems. SelfAct leverages a large pool of unlabeled data collected from many users to pre-train through self-supervision a DL model, with the goal of learning a meaningful and efficient latent representation of sensor data. The resulting pre-trained model can be locally used by new users, which will fine-tune it thanks to a novel unsupervised active learning strategy. Our experiments on two publicly available HAR datasets demonstrate that SelfAct achieves results that are close to or even better than the ones of fully supervised approaches with a small number of active learning queries.