# Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data

arXiv: https://arxiv.org/abs/2206.02909 · HTML: https://mlsanity.com/paper/2206.02909/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 78 references that are in the map.
- First posted on arXiv 6 Jun 2022.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks](https://mlsanity.com/paper/1706.00527/) (2017)
- [Multi-task Self-Supervised Learning for Human Activity Detection](https://mlsanity.com/paper/1907.11879/) (2019)
- [Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables](https://mlsanity.com/paper/1604.08880/) (2016)
- [Exploring Contrastive Learning in Human Activity Recognition for Healthcare](https://mlsanity.com/paper/2011.11542/) (2020)
- [Towards Best Practice in Explaining Neural Network Decisions with LRP](https://mlsanity.com/paper/1910.09840/) (2019)
- [Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?](https://mlsanity.com/paper/2201.05119/) (2022)
- [Assessing the State of Self-Supervised Human Activity Recognition using Wearables](https://mlsanity.com/paper/2202.12938/) (2022)
- [Contrastive Predictive Coding for Human Activity Recognition](https://mlsanity.com/paper/2012.05333/) (2020)
- [SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data](https://mlsanity.com/paper/2102.06073/) (2021)
- [Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes](https://mlsanity.com/paper/2111.07089/) (2021)
- [Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphones](https://mlsanity.com/paper/2103.09171/) (2021)

## Built on by (most cited first, 30 of 75)

- [PaPaGei: Open Foundation Models for Optical Physiological Signals](https://mlsanity.com/paper/2410.20542/) (2024)
- [Scaling Wearable Foundation Models](https://mlsanity.com/paper/2410.13638/) (2024)
- [SensorLM: Learning the Language of Wearable Sensors](https://mlsanity.com/paper/2506.09108/) (2025)
- [The first step is the hardest: Pitfalls of Representing and Tokenizing Temporal Data for Large Language Models](https://mlsanity.com/paper/2309.06236/) (2023)
- [Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals](https://mlsanity.com/paper/2412.09758/) (2024)
- [UniMTS: Unified Pre-training for Motion Time Series](https://mlsanity.com/paper/2410.19818/) (2024)
- [LSM-2: Learning from Incomplete Wearable Sensor Data](https://mlsanity.com/paper/2506.05321/) (2025)
- [RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data](https://mlsanity.com/paper/2411.18822/) (2024)
- [HiMAE: Hierarchical Masked Autoencoders Discover Resolution-Specific Structure in Wearable Time Series](https://mlsanity.com/paper/2510.25785/) (2025)
- [Masked Video and Body-worn IMU Autoencoder for Egocentric Action Recognition](https://mlsanity.com/paper/2407.06628/) (2024)
- [Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions](https://mlsanity.com/paper/2507.00191/) (2025)
- [Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing Data](https://mlsanity.com/paper/2403.01133/) (2024)
- [Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task](https://mlsanity.com/paper/2411.14452/) (2024)
- [Large Language Models are Zero-Shot Recognizers for Activities of Daily Living](https://mlsanity.com/paper/2407.01238/) (2024)
- [OSF: On Pre-training and Scaling of Sleep Foundation Models](https://mlsanity.com/paper/2603.00190/) (2026)
- [A Survey on Multimodal Wearable Sensor-based Human Action Recognition](https://mlsanity.com/paper/2404.15349/) (2024)
- [PRIMUS: Pretraining IMU Encoders with Multimodal Self-Supervision](https://mlsanity.com/paper/2411.15127/) (2024)
- [On convex decision regions in deep network representations](https://mlsanity.com/paper/2305.17154/) (2023)
- [Towards Generalizable Human Activity Recognition: A Survey](https://mlsanity.com/paper/2508.12213/) (2025)
- [Foundation Models for CPS-IoT: Opportunities and Challenges](https://mlsanity.com/paper/2501.16368/) (2025)
- [Scaling laws in wearable human activity recognition](https://mlsanity.com/paper/2502.03364/) (2025)
- [WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch](https://mlsanity.com/paper/2502.05783/) (2025)
- [A benchmark for computational analysis of animal behavior, using animal-borne tags](https://mlsanity.com/paper/2305.10740/) (2023)
- [ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition](https://mlsanity.com/paper/2305.13541/) (2023)
- [Graph-Based Physics-Guided Urban PM2.5 Air Quality Imputation with Constrained Monitoring Data](https://mlsanity.com/paper/2506.06917/) (2025)
- [High-Performance Self-Supervised Learning by Joint Training of Flow Matching](https://mlsanity.com/paper/2512.19729/) (2025)
- [Inertia-1: An Open Exploration of Wearable Motion Foundation Models](https://mlsanity.com/paper/2607.06617/) (2026)
- [IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications](https://mlsanity.com/paper/2209.00945/) (2022)
- [LEAVES: Learning Views for Time-Series Biobehavioral Data in Contrastive Learning](https://mlsanity.com/paper/2210.07340/) (2022)
- [Human Activity Recognition Using Self-Supervised Representations of Wearable Data](https://mlsanity.com/paper/2304.14912/) (2023)

## Abstract (arXiv)

Advances in deep learning for human activity recognition have been relatively limited due to the lack of large labelled datasets. In this study, we leverage self-supervised learning techniques on the UK-Biobank activity tracker dataset--the largest of its kind to date--containing more than 700,000 person-days of unlabelled wearable sensor data. Our resulting activity recognition model consistently outperformed strong baselines across seven benchmark datasets, with an F1 relative improvement of 2.5%-100% (median 18.4%), the largest improvements occurring in the smaller datasets. In contrast to previous studies, our results generalise across external datasets, devices, and environments. Our open-source model will help researchers and developers to build customisable and generalisable activity classifiers with high performance.