# Human Activity Recognition Using Self-Supervised Representations of Wearable Data

arXiv: https://arxiv.org/abs/2304.14912 · HTML: https://mlsanity.com/paper/2304.14912/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 12 of 12)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1906.12340/) (2019)
- [Multi-task Self-Supervised Learning for Human Activity Detection](https://mlsanity.com/paper/1907.11879/) (2019)
- [Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data](https://mlsanity.com/paper/2206.02909/) (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)
- [Protecting Sensory Data against Sensitive Inferences](https://mlsanity.com/paper/1802.07802/) (2018)
- [Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision](https://mlsanity.com/paper/1911.05894/) (2019)
- [Are Accelerometers for Activity Recognition a Dead-end?](https://mlsanity.com/paper/2001.08111/) (2020)

## Built on by (most cited first, 2 of 2)

- [HEARTS: Benchmarking LLM Reasoning on Health Time Series](https://mlsanity.com/paper/2603.06638/) (2026)
- [ActiNet: An Open-Source Tool for Activity Intensity Classification of Wrist-Worn Accelerometry Using Self-Supervised Deep Learning](https://mlsanity.com/paper/2510.01712/) (2025)

## Abstract (arXiv)

Automated and accurate human activity recognition (HAR) using body-worn sensors enables practical and cost efficient remote monitoring of Activity of DailyLiving (ADL), which are shown to provide clinical insights across multiple therapeutic areas. Development of accurate algorithms for human activity recognition(HAR) is hindered by the lack of large real-world labeled datasets. Furthermore, algorithms seldom work beyond the specific sensor on which they are prototyped, prompting debate about whether accelerometer-based HAR is even possible [Tong et al., 2020]. Here we develop a 6-class HAR model with strong performance when evaluated on real-world datasets not seen during training. Our model is based on a frozen self-supervised representation learned on a large unlabeled dataset, combined with a shallow multi-layer perceptron with temporal smoothing. The model obtains in-dataset state-of-the art performance on the Capture24 dataset (kappa= 0.86). Out-of-distribution (OOD) performance is kappa = 0.7, with both the representation and the perceptron models being trained on data from a different sensor. This work represents a key step towards device-agnostic HAR models, which can help contribute to increased standardization of model evaluation in the HAR field.